**4. Recycling of polyimides**

Polymer recycling is (or should be) a vital criterion to be taken into account in the life cycle of a product, especially when it comes to PIs. They represent key, reliable, long-life materials for a wide range of high-tech applications, but their recycling is a major challenge. Several technological and economic hurdles hamper the progress in this field.

On one side, recycling should enable the reintegration of discarded PI materials in the technological cycle or the production of new PI materials from recycled ones (especially since PIs represent materials of high-end value). On the other, it implies the quest for real sustainable solutions for the PI wastes issue, even if they represent a very small percent of the total polymer volume in use/to be recycled. Last but not least, it also means cutting down the dependence on oil-based raw materials, this matter being (at least partially) treated in the previous sections.

As in the case of most materials, mechanical and chemical recycling and combustion are the most common polymers' recycling methods, PIs included.

When it comes to the recycling or reuse of PI-based materials, two options are available, depending on whether you are dealing with a thermoplastic or thermoset. Important constraints are met in both cases.

Only a low percent (between 5 and 8%) of thermoplastic PIs is prone to recycling by reuse. There is no clear, general answer to the dilemma regarding the faith of the more than 92% left.

In the case of PI thermosets, the problem is even more complex, since the threedimensional structure impedes easy changes in the physical state, especially to the molten state. Cross-linked PIs cannot be melted, molded, or extruded and therefore suffer from severe limitations in terms of recycling or require complicated or energy-intensive recycling methods. One easy example would be their reuse as additives in composites. If such, the problem of the matrix-reinforcement interface will arise from the homogeneity of the composite.

### **4.1 Degradation of polyimides**

At this point, a separate discussion must be dedicated to the degradation of PI materials. Degradation (commonly known as aging) usually encompasses any modification of the initial polymeric material's features due to one or several external factors like light, heat, or chemicals. This is a major concern when it comes to the application of PI-based materials. Moreover, degradation is a key process when it comes to the reuse or recycling of a PI-based product to meet green environmental criteria. Its importance is also crucial when considering the final disposal or destruction/decomposition of the envisaged material and its environmental impact.

Assisted degradation of polymeric materials is an intricate procedure due to multifaceted mechanisms involving highly reactive macro- and micro-radicals and a plethora of degradation and decomposition products. These mechanisms are strongly dependent on the environment, more precisely on the presence or absence of oxygen. In the particular case of the highly stable PIs, there are also strongly time-dependent mechanisms [139–141].

The degradation of PI-based materials can be the subject of various pathways that will be briefly approached below.


**2.7. Annexin V-FITC assay**

Apoptosis was also investigated by detection of externalized phosphatidylserine using the Annexin V Kit (BD Pharmingen, USA). The cell preparation was carried out in accordance with the manufacturer's protocol. The prepared cells were finally subjected to flow cytometric analysis using laser emitting excitation light at 488 nm and a BD flow cytometer equipped with an Argon laser (Cyan ADP, DAKO, Glostrup, Denmark). The data were analyzed using the Summit V4.3 software.


*3.1. Bench Test*

The seed breakage rate ranged from 0.10 to 0.20%, which was negligible. The monitoring results of the sowing amount at different rotational speeds are shown in Table 1 and Figure 8. In order to further evaluate the accuracy of the system's monitoring of the missed seeding amount, the amount of cotton seeds in the seed picking area of the hole seeder was artificially reduced at each rotation speed, so as to increase the missed seeding amount. According to the above method, the actual missed seeding amount on the seed bed belt was obtained. It was compared with the monitoring value of the monitoring system to evaluate the accuracy of the system's missed broadcast detection. Table 2 and Figure 9 show the monitoring results of the leakage amount at different rotational speeds.

**Table 1.** Sowing rate monitoring results of the seed meter at different speeds.

**Figure 8.** The sowing rate monitoring curve of the seed meter at different speeds.

**Table 2.** Missed broadcast monitoring results of the seed meter at different speeds.

**Figure 9.** The missed broadcast monitoring curve of the seed meter at different speeds.

During the whole test process, there was no white crash screen and no wireless data transmission failure. It could be seen from Table 1 that with the increase of the rotation speed of the seed metering plate, the accuracy rate of sowing monitoring did not decrease significantly. The monitoring accuracy rate was above 97%. The reason for the slight decrease in accuracy was that the higher seeding speed would cause some smaller cotton seeds not to be fully filled into the hole, so that they could not be monitored by the sensor. However, the overall sowing rate monitoring accuracy of the system met the needs of cotton precision sowing monitoring. It can be seen from Table 2 that with the increase of the rotation speed of the seed metering plate, the monitoring accuracy of the missed seeding quantity decreased. The reason was that the high rotation speed of the seeding disc caused the seed flow to collide with the seeding tooth disc many times. This produced a small number of broken seeds, which caused the sensor to falsely detect. However, the monitoring accuracy of the system could still reach more than 95%. The monitoring accuracy of missed broadcasts met the requirements of the monitoring system. The above results show that the monitoring system could more accurately judge the seeding amount and the degree of missing seeding for the stable working seed meter. This could be used for evaluation and reference of sowing quality.

#### *3.2. Field Test*

The seed breakage rate ranged from 0.10 to 0.20%, which was negligible. The results of field sowing monitoring are shown in Table 3 and Figure 10. The field test results showed the following: The accuracy rate of broadcast monitoring was maintained above 93%. The accuracy rate of missed broadcast monitoring was maintained above 91%. The seeding monitoring system worked stably and reliably in the field under the rotating speed of the seeding disc that met the seeding requirements. Compared with the traditional monitoring method, the system could detect the seeding device of the hole seeder more accurately, and find the missed seed in time. This effectively saved the amount of cotton seeds, greatly reduced the cost of sowing, and met the requirements for monitoring the sowing status of cotton precision hole seeders.

Compared with the results of the bench test, the monitoring accuracy of the overall system was slightly reduced during the field test. The reason was that the cotton seeds used in the field test had not been selected manually. Some broken seeds and debris were included. The machine vibrated during operation. Moreover, a lot of dust in the field adhered to the color fiber optic sensor probe and affected the optical fiber transmittance. This subsequently calls for enhancement of the monitoring performance of the sensor and optimization of its installation position to ensure more stability. A condenser lens or a selfcleaning dust removal device should also be added to maintain a good light transmittance

of the sensor and improve the environmental adaptability and operational reliability of the system. In the future, high-precision Beidou positioning technology and mobile Internet technology can be combined to provide support for the field sowing map, missed sowing state map, and variable reseeding prescription operations. The parameters of the seeder would also be automatically set, making the system function more in line with the actual production situation and suitable for different operating scenarios.

**Table 3.** Field sowing monitoring test results.

**Figure 10.** The field sowing monitoring curve of the seed meter at different speeds: (**a**) sowing rate monitoring and (**b**) missed broadcast monitoring.

#### **4. Conclusions**

In this study, a planting quality monitoring system suitable for cotton precision planters was designed. The bench performance test of sowing quantity monitoring and missing sowing detection was carried out on the monitoring system. A field test was carried out on the 2MBJ-12 cotton precision film laying planter to verify the reliability of the system. The bench test and field test of the sowing quality monitoring system of the cotton precision planter showed that the sowing quality monitoring system worked stably and reliably. In the bench test, the accuracy of sowing quantity monitoring was no less than 97%, while the accuracy of missing sowing monitoring was no less than 94%. In the field test, the accuracy rate of sowing rate monitoring was no less than 93%, while the accuracy of missed sowing monitoring was no less than 91%. This improved the work quality and work efficiency of cotton sowing. It met the requirements for monitoring the sowing quality of cotton precision hole seeders.

**Author Contributions:** Conceptualization, Y.Y. and B.Z.; methodology, S.B. and K.N.; software, S.B. and Z.S.; validation, S.B. and Y.Z.; formal analysis, K.N. and S.B.; investigation, S.A. and Y.M.; resources, Y.Y. and B.Z.; data curation, S.B. and L.W.; writing—original draft preparation, S.B.; writing—review and editing, K.N. and L.L.; visualization, Y.Z. and L.Z.; supervision, Y.Y. and B.Z.; project administration, Y.Y. and K.N.; funding acquisition, K.N. and L.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was mainly supported by the Autonomous Region Regional Collaborative Innovation Special (Science and Technology Aid to Xinjiang Program) Project of China (2021E02055).

**Data Availability Statement:** Data are contained within the article. The data presented in this study can be requested from the authors.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**


**4. MEHs based on OIHPs**

A mechanical energy harvester that can produce electricity from mechanical vibrations is a very promising tool to realize sustainable energy generation in remote/ indoor environs and even through human body movements. MEHs generally operate based on either the piezoelectric effect, triboelectric effect, or electromagnetic induction effects [13]. Amongst them, PENGs that operate based on piezoelectric effect and TENGs that operate based on triboelectric effect have attracted intense attention because of their direct power conversion ability and relatively easier fabrication processes. Intense research efforts have been conducted with regard to material and structural design, functionality, operation mechanism, and performance optimization of PENGs and TENGs [8, 13, 18]. More recently, the unique properties, low-temperature solution processing, and tunability of properties via compositionaltuning of OIHPs have highlighted their superior potential in the field of mechanical energy harvesters.


Preface

Nowadays, critical thinking and decision-making skills are paramount, as people are often confronted with outdated approaches, injustices, and conservatism in addressing many important issues. People need to be able to stand for positive change. The educational process plays a significant role in the formation of personality characteristics. It is in the process of learning that self-esteem, tolerance, independence, responsibility, and the ability to discuss and defend one's beliefs develop. Thus, how we teach is just as important as why we teach. With the help of teaching methods like active learning, one can simulate behavior aimed at advocating for change.

Numerous studies show that teaching children and adults is more effective and more enjoyable when it is active. Democratic education helps students to improve and systematize their knowledge and skills, activate their own experience, and develop thinking, memory, attention, imagination, and a positive attitude about the learning process.

In a person-centered team, teachers constantly make decisions, work with other adults and children to create a developmental environment, respect children's opinions and choices, and enable children to develop the skills that will be the foundation of their future success.

The use of active learning methods is extremely important because they have a great impact on students, their acquisition of knowledge, and the development of personal qualities. Some studies show that adult learners prefer active learning methods as well. Others emphasize that in terms of assessing active learning methods and traditional lectures, the success rates of students' learning are commensurate, but the rates of mastering new skills are much greater than those achieved after a course of regular lectures. Studies have also shown that a significant number of adults with different individual learning styles learn more effectively using active learning methods. For teachers, how much a person knows is important, but so is how much they have learned while learning and how they will use the knowledge gained. Thus, a thoughtful approach to the use of active learning methods includes engaging students and teachers in the practice of self-reflection and understanding how adult learners learn most effectively.

#### **Olena Lutsenko**

Candidate of Biological Sciences, Department of Theory and Methods of Teaching Natural Sciences, Oleksandr Dovzhenko Hlukhiv National Pedagogical University, Hlukhiv, Ukraine

#### **Gregory Lutsenko**

Doctor of Pedagogical Sciences, Professor of the Department of Pedagogy and Education Management, Oleksandr Dovzhenko Hlukhiv National Pedagogical University, Hlukhiv, Ukraine

### **Chapter 1**


*2.3. Synthesis of [Ni(en)3]5[PNb12O40(VO)2]*·*17H2O* (**2**)

Compound **2** was synthesized by a similar procedure to that of **1**, but without adding NaVO3. The pH value of the mixture was adjusted to 10.50 using 2 M NaOH and transferred to a Teflon-lined autoclave (23 mL), kept at 140 ◦C for 72 h, and then slowly cooled to room temperature. Brown block crystals of **2** were isolated in about 21.8% yield (based on Nb). Anal. Calcd (%) for **2**: C, 10.53; H, 4.54; N, 12.28; Ni, 8.58; P, 0.91; V, 2.98; Nb, 32.59. Found: C, 10.46; H, 4.42; N, 11.84; Ni, 8.31; P, 0.64; V, 2.77; Nb, 32.86. FT-IR (cm<sup>−</sup>1): 3425 (s), 2945 (w), 2875 (w), 1593 (s), 1469 (m), 1363 (w), 1318 (w), 1275 (s), 1233 (w), 1106 (w), 1027 (s), 947 (s), 877 (s), 815 (w), 709 (s), 638 (w), 505 (w), 469 (w).


**1. Introduction**

Eukaryotic cells are characterized by the presence of the nuclear envelope (NE), a lipid bilayer membrane that separates the cells into two compartments, i.e., the nucleus and the cytoplasm. The NE contains many nuclear pore complexes (NPCs), which are the sole gateway for the exchange of essential biomolecules between the two compartments. NPCs are large protein complexes, with a molecular mass of ~55–66 MDa [1,2] in yeast and ~ 125 MDa in vertebrates [3]. The NPC is composed of 30 different types of proteins called nucleoporins (Nups) [4,5]. One third of these Nups are intrinsically disordered proteins (IDPs), which are anchored to the inner wall of the NPC and are rich in phenylalanine-glycine (FG) repeats. Inside the NPC, these FG-Nups form a central meshwork that provides a permeability barrier for translocating molecules. Various studies have revealed that the NPC allows rapid transport of small molecules (30 kDa or ~5 nm in diameter), but drastically slows down the translocation of larger molecules from one compartment to the other [6–8]. It also has been found that FG Nups bind to nuclear transport receptors (NTRs) [9,10] by means of

hydrophobic interactions, which facilitates the translocation of NTRs by lowering the permeability barrier [11]. Cargoes of diameter up to 40 nm are known to translocate by this facilitated transport mechanism [12,13]. Therefore, the FG-Nups are considered to be crucial in establishing the selective permeability barrier of the NPC.

Nucleocytoplasmic trafficking can be altered by a change in the surface properties of translocating molecules [14], by the deletion of FG-Nups [6,8], and by the change in cohesiveness of the FG-Nups [15]. For example, mutation of the hydrophobic F residues of Nsp1, a representative yeast FG-Nup, into the hydrophilic Serine S reduces the propensity of Nsp1 to form a hydrogel [16]. These experiments revealed that the hydrogels exclude inert molecules, but allow hydrophobic NTRs to enter, which is explained in terms of a local disruption of the cohesive gel network [16,17]. In a separate study [15], the Nsp1 molecules tethered onto the inner surface of solid state NPC mimics formed a dense phase (over 100 mg/mL) and enabled transport selectivity. Kap95 (a yeast NTR) traversed the pore whereas the translocation of tCherry (an inert molecule of similar size) was inhibited. The F, I, L, V to S mutation of Nsp1 resulted in a remarkably less dense FG-Nup network inside the pore, which led to a loss of selectivity, as both tCherry and Kap95 were able to translocate. Taken together, these studies show that the transition from a dense, hydrophobic phase to a dispersed, hydrophilic phase results in the nanopores losing their selective barrier function.

The hydrophobicity of FG-Nups can also be altered through phosphorylation, one of the most abundant protein modifications inside the cell [18,19]. Phosphorylation is catalyzed by kinases and can be reversed by phosphatases. It has been shown that extracellular signal-regulated kinase (ERK), a phosphorylating agent, can directly interact with FG-Nups [20,21], causing FG-Nups to phosphorylate [22–24]. Several in vitro studies have revealed that specifically Nup62, Nup98, Nup153, Nup214, and Nup358 can undergo phosphorylation [25,26]. Furthermore, there is evidence which confirms that FG-Nups undergo phosphorylation in vivo as well [21,23,27,28]. Transport studies demonstrated that the phosphorylation of nucleoporins results in decreased kinetics of active transport of Kap95 [25,27,29] and Kap-cargo complexes [30,31], and increased kinetics of passive transport [32]. These studies indicate that phosphorylation can modulate the selective permeability of the NPCs. However, the molecular mechanism behind the alteration in nucleocytoplasmic transport due to phosphorylation is not well understood.

Molecular dynamics (MD) simulations have proved to be a powerful tool to study the disordered protein structure inside the NPC and the transport through native and biomimetic nanopores [8,15,33–35]. Therefore, in order to understand the molecular mechanism behind the phosphorylation-induced alteration in transport kinetics, we carried out MD studies using our earlier developed one-bead-per-amino-acid (1BPA) coarse grained (CG) model for FG-Nups [35], extended here for phosphorylated FG-Nups. This 1BPA model has been successfully applied to probe the (doughnut-like) density distribution of the disordered domain of yeast NPCs [35], the facilitated transport of NTRs through yeast [33] and biomimetic [15] NPCs, and the size selectivity for passive transport [6], in good agreement with experiments. Although the transport experiments on phosphorylated NPCs cited above were carried out on mammalian NPCs, we here used the yeast NPC model, which has structural and functional similarities to the vertebrate NPC [36].

In the current study, we extended our 1BPA model to phosphorylated FG-Nups and carried out MD simulations of FG-Nups in isolation, as well as within the NPC. We studied the impact of phosphorylation on the structure of the disordered phase and the transport across the NPC in two scenarios. In the first (referred to as the Phos\_N scenario), we used the NetPhosYeast 1.0 server [37] to obtain the phosphorylated residues of the yeast FG-Nups (yielding phosphorylated serine (S) and threonine (T) residues only), and in the second (referred to as the Phos\_Max scenario), we assumed that all phosphorylatable residues (serine (S), histidine (H), threonine (T), and tyrosine (Y)) were phosphorylated. We investigated the changes in conformation of phosphorylated FG-Nups compared to FG-Nups in their native state by using the Stokes radius (*R*S) as a measure for their size (see Section 2.1). We found that phosphorylation causes FG-Nups to extend by an amount that depends on the fraction of phosphorylatable residues and positively charged residues. In Section 2.2, we present a study on the collective interaction of phosphorylated FG-Nups inside the confined environment of the NPC. We found that phosphorylation drastically reduces the FG-Nup density inside the NPC. Finally, in Section 2.3 we report on simulation results of the phosphorylation-affected transport of inert particles and Kap95, and discuss these results in light of the various contributions to the interaction energy inside the NPC. Our transport simulations are in qualitative agreement with the experimentally-observed increase and decrease in transport rate of the passive and active transport pathways, respectively. Note that the Phos\_N scenario predicts more phosphorylation sites than other phosphorylation databases, such as the fungi phosphorylation database (FPD), which provides a comprehensive list of experimentally validated phosphorylation sites [38]. The prediction from the FPD database is incorporated in the Supplementary Materials (see the section "Sensitivity analysis") to provide a scenario for experimentally validated phosphorylation sites. It is important to note that it is unclear which phosphosites predicted in either scenario are phosphorylated simultaneously in vivo, and hence the predictions provided in this study are not meant to mimic specific biological conditions, but rather to shed light on the fundamental mechanisms underlying the changes in transport kinetics of phosphorylated NPCs.

#### **2. Results**

In order to study the effect of phosphorylation on FG-Nups, we started with our previously developed MD model for intrinsically disordered proteins (IDPs) in their native state, coarse-grained at a resolution of one bead per amino acid (1BPA) [35]. This 1BPA model accounts for non-bonded hydrophobic and electrostatic interactions between the amino acids, including the effect of solvent polarity and ionic screening to mimic the solvent conditions inside the NPC. The model is accurate (within 20% error) in predicting the Stokes radius *R*S [35] for a range of FG-Nups and FG-Nup segments [39]. In the current study, we extended the model for phosphorylation by accounting for the change in hydrophobicity and charge of four amino acids: S, H, T, and Y. We used a weighted average scheme of five predictor programs KOWWIN, ClogP, ChemAxon, ALOGPS, and miLogP [40–43] to predict the change in hydrophobicity due to the change in the chemical structure. For details on the model development for phosphorylated FG-Nups we refer to the Materials and Methods section (Section 4.2). The new parameters for phosphorylated amino acids are summarized in Table 1.

**Table 1.** Parameters in the 1BPA forcefield for phosphorylated amino acids. Note that: *ε*1BPA and *ε*weighted are the normalized hydrophobicity values (between 0 and 1) from the 1BPA model [35] and the weighted average scheme (see Section 4) for the amino acids in their native state, respectively; *ε*p is the hydrophobicity of the phosphorylated amino acid; and *q* and *q*p denote the charge of the amino acids in their native and phosphorylated conditions, respectively.

#### *2.1. Effect of Phosphorylation on Isolated FG Nups*

We used our newly developed parametrization for phosphorylation and performed MD simulations to study the effect of phosphorylation on the conformation of isolated FG-Nup segments [35]. The simulated trajectories were analyzed to determine the time averaged *R*S using the Hydro program [44,45]. The predicted *R*S values for the phosphorylated FG-Nups are compared with that of FG-Nups in their native state (from experiments [39] and simulations [35]) in Figure 1. The error bars for the simulation data represent the standard deviation in time for *R*S (See Table S1 in the supplementary data for the source data).

**Figure 1.** Phosphorylation-induced extension of FG-Nup segments. The Stokes radius *R*S (in Angstrom) is depicted for a range of FG-Nup segments in their native and phosphorylated states. The suffix *lc* denotes low charge, *hc* high charge and *s* refers to the stalk region of the Nup. The grey and black bars represent the data in the native state from experiments [39] and simulations (results reproduced from [35]), respectively, and the prediction for the phosphorylated states are plotted in red for Phos\_N and blue for Phos\_Max. For the simulation data, the error bars represent the standard deviation in time (see Table S1 for the source data).

As a result of phosphorylation, the amino acids become more hydrophilic and negatively charged (see Table 1). Thus, compared to the native state, the phosphorylated FG-Nups exhibit enhanced electrostatic repulsion and reduced hydrophobic attraction leading to an overall decrease in intra-molecular cohesion and thus a more extended configuration (see Figure 1). In Table S2, we have summarized the number of amino acids that can be phosphorylated in each FG-Nup segment. The FG-segments are grouped as low charged (*lc*), high charged (*hc*), and stalk (*s*) domains, following the definition of Yamada et al. [39]. We found that the relative abundance of phosphorylatable residues in all FG-Nup segments ranges from ~15% (for Nup116s) to ~33% (for Nsp1n\_lc) for the maximally phosphorylated (Phos\_Max) condition, whereas for the Phos\_N scenario the range is from ~4% (Nup116s) to ~17% (Nup159\_hc). In order to quantify the change in Stokes radius in terms of the number of residues undergoing phosphorylation, we plot the normalized change in *R*S as a function of the percentage of phosphorylatable residues (*n*) for the low charged, high charged, and stalk groups in blue, red, and green data points, respectively, for the Phos\_Max (Figure 2a) and Phos\_N (Figure 2b) scenarios. The change Δ*R*S is normalized as Δ*R*S/(*N* − 1)*b*, where *N* is the total number of residues of the FG-Nup segment and *b* is the coarse-grained bond length (3.8 Angstrom). We fitted the data points for individual groups to a straight line passing through the origin, represented as colored lines in Figure 2a,b. We note that for both Phos\_Max and Phos\_N, the FG-segments from the *lc* group show the highest normalized change in *R*S (blue line in Figure 2a,b), whereas phosphorylation has a smaller effect on size for the *hc* and *s* groups (red and green lines in Figure 2a,b).

**Figure 2.** The normalized change in *R*S (i.e., Δ*R*S/(*N* − 1)*b*) due to phosphorylation as a function of the fraction of phosphorylatable residues (*n*) for (**a**) the Phos\_Max and (**b**) the Phos\_N scenarios [37]. The expression for the change in *R*S (i.e., Δ*R*S = *R*phos S − *R*native S ), with *R*phos S and *R*native S being the Stokes radii of the FG-Nup segments in the phosphorylated and native states, respectively, is normalized with (*N* − 1)*b* where *N* is the total number of residues of the FG-Nup segment, and *b* (= 3.8 Angstrom) is the coarse-grained bond length between neighboring amino acids [35,46]. The data for the FG-Nups from the high charged (*hc*), low charged (*lc*), and stalk (*s*) segments [35,39] are represented in red, blue, and green data points, respectively. The data points of each group are fitted to a straight line passing through the origin revealing different slopes for different groups. For Phos\_Max we observe slopes of 0.087 for *lc* (*R*2 = 0.94), 0.051 for *hc* (*R*2 = 0.92), and 0.053 for *s* (*R*2 = 0.80), respectively, whereas for Phos\_N the slopes are 0.1 for *lc* (*R*2 = 0.86), 0.052 for *hc* (*R*2 = 0.95) and 0.045 for *s* (*R*2 = 0.61). (**c**) The ratio of normalized change in *R*S to the fraction of phosphorylatable residues (*n*) is plotted as a function of the fraction of positively charged residues (*p*) for all data points (black) from the Phos\_Max and Phos\_N scenarios. These data points are fitted to a linear equation, as shown in the figure (giving *R*2 = 0.68). (**d**) The *R*phos S predicted from the theory in Equation (1) compared to *R*phos S computed from the MD simulations, both in Angstrom, show a good agreement with a fitness measure of *R*2 = 0.97.

In order to investigate the varying response for the three groups, as shown in Figure 2a,b, we analyzed the change in hydrophobicity upon phosphorylation and found that it is roughly similar for the three groups, i.e., for FG-Nups from the *lc*, *hc*, and s groups, the hydrophobicity drops by 13-20%, 16–22%, and 12–21%, respectively, for Phos\_Max (see Table S2). Similarly, for Phos\_N, the reduction in hydrophobicity amounts to 3–10%, 7–10%, and 4–12% for the *lc*, *hc*, and *s* groups, respectively, showing no major difference across the three groups. Clearly, the effect of phosphorylation on hydrophobicity alone cannot account for the different *R*S of the groups. It has been argued that the net proline content in IDPs plays an important role in determining the effective Stokes radius [47], as proline provides additional stiffness to the peptide chain because of its ring structure. This effect of proline is included in our 1BPA model in the form of the bonded potentials [46]. However, all the FG-Nup segments analyzed in this study (Figure 1) have a similar 3–8% proline content, and therefore cannot explain the different

Stokes radii across the three groups (see Table S2). Next, we analyzed the effect of charge. Since the net charge of the three families is quite similar (i.e., 2–3%, 0–3%, and 0–1% for *lc*, *hc*, and *s*, respectively), we investigated the occurrence of positively charged residues R and K in the FG-segments and found that the *lc* group contains only 2–3% of positively charged residues in contrast to the *hc* and *s* groups, which have more positively charged residues (7–16% for *hc* and 13–17% for *s*, see Table S2). Thus, it seems that the larger amount of positive charge in *hc* and *s* is more efficient in screening the effect of the negative charge increase induced from phosphorylation than the small amount of positive charges in the *lc* group (see Table S2). In order to confirm this, we plotted the ratio of Δ*R*S/(*N* − 1)*b* normalized by the fraction of phosphorylatable residues (*n*) as a function of the percentage of positive charge content (*p*) in the FG-segments (see Figure 2c). The data points in Figure 2c can be fitted to a straight line with a slope of -0.41 and *y*-intercept of 0.1, with an R2 value of 0.68. Using this observation, the Stokes radius for a phosphorylated FG-segment can be predicted using the following expression:

$$R\_{\rm S}^{\rm phos} = R\_{\rm S}^{\rm native} + bn \ (N-1)(-0.41p + 0.1) \tag{1}$$

We show the predictive power of this formula in Figure 2d, where the *R*phos S predicted using Equation (1) is plotted against the computed *R*phos S from the MD simulations, showing a very good correlation (with *R*2 = 0.97).


*Article* **BPR0C261, An Analogous of Microtubule Disrupting Agent D-24851 Enhances the Radiosensitivity of Human Non-Small Cell Lung Cancer Cells via p53-Dependent and p53-Independent Pathways**

**Jyh-Der Leu 1,2,†, Shih-Ting Lin 3,†, Chiung-Tong Chen 4 , C.-Allen Chang 3 and Yi-Jang Lee 3,5,\***

**Abstract:** (1) Destabilization of microtubule dynamics is a primary strategy to inhibit fast growing tumor cells. The low cytotoxic derivative of microtubule inhibitor D-24851, named BPR0C261 exhibits antitumor activity via oral administration. In this study, we investigated if BPR0C261 could modulate the radiation response of human non-small cell lung cancer (NSCLC) cells with or without p53 expression. (2) Different doses of BPR0C261 was used to treat human NSCLC A549 (p53+/+) cells and H1299 (p53−/−) cells. The cytotoxicity, radiosensitivity, cell cycle distribution, DNA damage, and protein expression were evaluated using an MTT assay, a colony formation assay, flow cytometry, a comet assay, and an immunoblotting analysis, respectively. (3) BPR0C261 showed a dose-dependent cytotoxicity on A549 cells and H1299 cells with IC50 at 0.38 µM and 0.86 µM, respectively. BPR0C261 also induced maximum G2/M phase arrest and apoptosis in both cell lines after 24 h of treatment with a dose-dependent manner. The colony formation analysis demonstrated that a combination of low concentration of BPR0C261 and X-rays caused a synergistic radiosensitizing effect on NSCLC cells. Additionally, we found that a low concentration of BPR0C261 was sufficient to induce DNA damage in these cells, and it increased the level of DNA damage induced by a fractionation radiation dose (2 Gy) of conventional radiotherapy. Furthermore, the p53 protein level of A549 cell line was upregulated by BPR0C261. On the other hand, the expression of PTEN tumor suppressor was found to be upregulated in H1299 cells but not in A549 cells under the same treatment. Although radiation could not induce PTEN in H1299 cells, a combination of low concentration of BPR0C261 and radiation could reverse this situation. (4) BPR0C261 exhibits specific anticancer effects on NSCLC cells by the enhancement of DNA damage and radiosensitivity with p53-dependent and p53-independent/PTENdependent manners. The combination of radiation and BPR0C261 may provide an important strategy for the improvement of radiotherapeutic treatment.

**Keywords:** NSCLC; BPR0C261; radiosensitivity; DNA damage; microtubule inhibitor; p53; PTEN

#### **1. Introduction**

Human non-small cell lung cancer (NSCLC) is the primary type of lung cancer globally. In the United States, 236,740 new cases and 350 deaths per day are reported for lung cancer, which is still the leading cause of cancer death in 2022 [1]. The good news is that the incidence and the mortality of NSCLC are decreasing because of the increasing knowledge on potential risk factors [2]. According to the stage of NSCLC, the treatments range from

**Citation:** Leu, J.-D.; Lin, S.-T.; Chen, C.-T.; Chang, C.-A.; Lee, Y.-J. BPR0C261, An Analogous of Microtubule Disrupting Agent D-24851 Enhances the Radiosensitivity of Human Non-Small Cell Lung Cancer Cells via p53-Dependent and p53-Independent Pathways. *Int. J. Mol. Sci.* **2022**, *23*, 14083. https:// doi.org/10.3390/ijms232214083

Academic Editor: Laura Paleari

Received: 24 August 2022 Accepted: 23 September 2022 Published: 15 November 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

pure surgery, chemo-radiotherapy, immunotherapy, and targeted drug therapy [3]. The adjuvant chemo-radiotherapy is the most traditional and common method for NSCLC therapy in different stages [4,5]. However, effective therapeutic approaches for NSCLC remain to be developed.

Hundreds of *N*-heterocyclic indolyl glyoxylamides have been synthesized and evaluated for their anticancer activity, including cancer cells of murine leukemia, human gastric, breast, and uterus sources [6]. These compounds are structurally analogous to N-(pyridin-4-yl)-[1-(4-chlorbenzyl)-indol-3-yl]-glyoxyl-amid (D-24851) that functions as a microtubule inhibitor containing oral antitumor activity in vivo, as well as multidrug-resistance tumor cells [7]. D-24851 has been reported to induce apoptosis in malignant glioma cells without active p53, a well-known tumor suppressor protein [8]. Interestingly, two compounds (BPR0C123 and BPR0C259) of *N*-heterocyclic indolyl glyoxylamides exhibited different cytotoxicity on human NSCLC cell lines, but also induced p53-independent apoptosis and radiosensitivity to different levels [9]. BPR0C261 is identified as the most cytotoxic molecules against a broad spectrum of mammalian cancer cells in this series of compounds [6]. The antitumor actions of BPR0C261 include antimitosis and anti-angiogenesis in vivo [10]. Because BPR0C261 can also be administrated orally and extend the lifespan of tumor-bearing mice, it is interesting to be considered in clinical applications [10]. Whether BPR0C261 also induces cytotoxicity and radiosensitivity in NSCLC with or without p53 tumor suppressor gene is still unclear.

The homozygous mutation of the p53 tumor suppressor gene is engaged in 50–60% of human cancers [11]. The biochemical role of the p53 gene is to encode a transcription factor and transactivate downstream genes for apoptosis, DNA repair, senescence, and cell cycle arrest [12]. A recent report focused on the crosstalk between p53 and immunity and demonstrated that mutant p53 could suppress innate immunity to promote tumorigenesis [13]. In addition, p53 can transcriptionally suppress the expression of vascular endothelial growth factor (VEGF) and induce the production of arrestin to inhibit angiogenesis in human tumor [14,15]. Although p53 is known to be important for the induction of apoptosis in cancer cells exposed to genotoxicity, the p53-independent pathways are also frequently reported to mediate drug-induced apoptosis [16–18]. For instance, the PTEN (phosphatase and tensin homolog) tumor suppressor has been reported to be involved in p53-independnet apoptosis [19]. PTEN is a negative regulator of the phosphatidylinositol -3 kinase (PI3K)/Akt/mTOR pathway that phosphorylates phosphatidylinositol (4,5)-trisphosphate (PIP2) to PIP3 to trigger a series of signal transduction and promote cell survival, cell cycle progression, cell growth, and angiogenesis [20,21]. Interestingly, the promoter of the PTEN gene harbors a p53-binding element that can be directly regulated by wild-type p53, although PTEN is constitutively expressed through a p53-independnet element [22]. PTEN mutations are mainly discovered in endometrial carcinomas and glioblastomas and are associated with 13.5% of human cancers [23,24]. PTEN can inhibit the cell cycle progression and promote apoptosis in NSCLC cells [25]. Moreover, a combination of radiation and paclitaxel has been reported to trigger a PTEN–PI3K–Akt–Bax signaling cascade in NSCLC xenograft tumors and suppress tumor growth in the absence of functional p53 [26]. However, the effect of BPR0C261 on the expression of p53 and/or PTEN tumor suppressors in NSCLC cells remains to be investigated.

In this study, we combine BPR0C261 and radiation for the treatment of NSCLC cells and compare the results with the individual effect of both methods. The cell cycle, DNA damage, radiosensitivity, and cell apoptosis are measured and evaluated. We also demonstrate that p53 expression is significant in NSCLC cells containing wild-type p53, while PTEN becomes dominant in those with deleted p53. The association of tumor suppressors with the BPR0C261-enhanced radiosensitivity is discussed.

#### **2. Results**

#### *2.1. Effects of BPR0C261 on NSCLC Cells*

First, NSCLC cells were treated with increased concentrations of BPR0C261 and the drug effects on cells were detected by morphological change and the MTT assay. Starting from 0.01 µM BPR0C261, an increase of round-up cells could be detected in A549 cells and H1299 cells exposed to increased concentrations of the drug for 12 h (Figure 1A). We also measured the IC50 of BPR0C261 by a dose-dependent experiment and showed that A549 cells were more sensitive to the drug than H1299 cells (Figure 1B). The IC50 of BPR0C261 on A549 cells and H1299 cells were 0.38 µM and 0.86 µM, respectively.

#### *2.2. Effects of BPR0C261 on the Redistribution of Cell Cycle*

We next examined the cell cycle of A549 cells and H1299 cells treated with BPR0C261. Through the flow cytometry, the percentage of cells at each phase after BPR0C261 treatment was indicated from 6 h to 24 h. The results showed that BPR0C261 caused significant G2/M phase arrest in both A549 cells and H1299 using individual IC50 (Figure 2A,B). Notably, A549 cells harbored wild-type p53 while H1299 cells contained null-p53, and this was associated with different patterns of cell cycle progression treated with BPR0C261 for 3 h to 6 h. That is, the G2/M phase arrest of H1299 cells happened three hours earlier than that of A549 cells treated with BPR0C261. Nevertheless, the highest G2/M phase arrest was still detected in both cell lines after 12 h of treatment. The percentage of each phase of the cell cycle was also quantified in both cell lines treated with the corresponding IC50 of BPR0C261 (Figure 2C,D).

#### *2.3. Effects of High Concentration of BPR0C261 on Induction of Sub-G1 Population in NSCLC Cells*

We next examined cell cycle redistribution including the percentage of sub-G1 phase of NSCLC cells treated with BPR0C261 at 0.1 M and 1 M that were below and over IC50, respectively. Compared to 1 M BPR0C261, 0.1 M BPR0C261 did not induce significant percentage of sub-G1 phase in A549 cells after 24 h of treatment, although both concentrations still induced a significant G2/M phase arrest after 12 h of treatment (Figure 3A). A similar phenomenon was also detected in H1299 cells treated with BPR0C261, but a low concentration of BPR0C261 appeared to induce significant sub-G1 phase compared to untreated control (Figure 3B). The results of DNA histograms of A549 cells and H1299 cells treated with different concentrations of BPR0C261 were quantified (Figure 3C,D).

#### *2.4. The Combination Treatment of Radiation and BPR0C261 Decreased the Survival Fraction in NSCLC Cells*

The surviving fraction of A549 and H1299 were evaluated after a treatment with BPR0C261 or radiation alone and then the combination of the two treatments using the colony formation assay. The concentrations of BPR0C261 used were 0.1 µM and 1 µM, which are lower or higher than the IC50, respectively. Compared to the radiation treatment alone, BPR0C261 enhanced the radiosensitivity of both A549 cells and H1299 cells (Figure 4 and Supplementary Material S1). Intuitively, the higher the concentration of BPR0C261, the lower the cell survival rate. Using the method of Valeriote and Carpentier (see Materials and Methods), the combination of BPR0C261 and radiation showed a synergistic effect on NSCLC cells compared to the treatment with radiation alone (Tables 1 and 2).

**Figure 1.** Cytotoxic effects of BPR0C261 on NSCLC cells. (**A**) Microscopic visualization of cell mor‐ phological changes by BPR0C261. Magnification: 40×; scale bar: 100 m. (**B**,**C**) The MTT assay for estimation of IC50 of BPR0C261 on A549 cells and H1299 cells after 24 h of treatment, respectively. **Figure 1.** Cytotoxic effects of BPR0C261 on NSCLC cells. (**A**) Microscopic visualization of cell morphological changes by BPR0C261. Magnification: 40×; scale bar: 100 m. (**B**,**C**) The MTT assay for estimation of IC50 of BPR0C261 on A549 cells and H1299 cells after 24 h of treatment, respectively.

 **Figure 2.** Analysis of cell cycle distribution by flow cytometry after NSCLC cells were treated with BPR0C261. (**A**) A549 cells and (**B**) H1299 cells were treated with their corresponding IC50 of BPR0C261 with a time‐dependent cell cycle redistribution. (**C**,**D**) Quantification of percentage of each cell cycle phase in A549 cells and H1299 cells, respectively. Each datum represents the mean of three independent experiments ± S.D. **Figure 2.** Analysis of cell cycle distribution by flow cytometry after NSCLC cells were treated with BPR0C261. (**A**) A549 cells and (**B**) H1299 cells were treated with their corresponding IC50 of BPR0C261 with a time-dependent cell cycle redistribution. (**C**,**D**) Quantification of percentage of each cell cycle phase in A549 cells and H1299 cells, respectively. Each datum represents the mean of three independent experiments ± S.D.

**Figure 3.** Comparison of sub‐G1 phase of NSCLC cells treated with high and low concentration of BPR0C261. The sub‐G1 phase of (**A**) A549 cells and (**B**) H1299 cells treated with 0.1 M and 1 M of BPR0C261 for 12 h and 24 h. (**C**,**D**) Quantification of percentage of sub‐G1 phase in A549 cells and H1299 cells treated with BPR0C261, respectively. Each datum represents the mean of three in‐ dependent experiments ± S.D. **Figure 3.** Comparison of sub-G1 phase of NSCLC cells treated with high and low concentration of BPR0C261. The sub-G1 phase of (**A**) A549 cells and (**B**) H1299 cells treated with 0.1 M and 1 M of BPR0C261 for 12 h and 24 h. (**C**,**D**) Quantification of percentage of sub-G1 phase in A549 cells and H1299 cells treated with BPR0C261, respectively. Each datum represents the mean of three independent experiments ± S.D.

**Figure 4.** Effect of BPR0C261 on radiosensitivity of NSCLC cells. The survival fraction of (**A**) A549 cells and (**B**) H1299 cells exposed to different doses of X‐rays after treatment with BPR0C261. \*: *p* < 0.05 for 0.1 μM BPR0C261 treatment compared to radiation alone. #: *p* < 0.05 for 1 μM BPR0C261 treatment compared to radiation alone. **Figure 4.** Effect of BPR0C261 on radiosensitivity of NSCLC cells. The survival fraction of (**A**) A549 cells and (**B**) H1299 cells exposed to different doses of X-rays after treatment with BPR0C261. \*: *p* < 0.05 for 0.1 µM BPR0C261 treatment compared to radiation alone. #: *p* < 0.05 for 1 µM BPR0C261 treatment compared to radiation alone.

**Table 1.** Effect of BPR0C261 combining different doses of X‐rays on A549 cells. **Table 1.** Effect of BPR0C261 combining different doses of X-rays on A549 cells.

**Table 2.** Effect of BPR0C261 combining different doses of X-rays on H1299 cells.

#### *2.5. Effects of BPR0C261 and Radiation on Induction of DNA Damage in NSCLC Cells*

To determine the DNA damage in NSCLC cells treated with BPR0C261 and/or radiation, single-cell gel electrophoresis (comet assay) was exploited for the analysis. The quantification of DNA damage by the comet assay was accomplished by calculating the tail moment. The radiation doses ranging from 2 Gy to 10 Gy showed a dose-dependent increase of DNA damage in A549 cells and H1299 cells (Figure 5A). Surprisingly, the same effect was detected in cells treated with BPR0C261 from 0.01 M to 1 M for 24 h (Figure 5B). We further treated cells with 0.1 M BPR0C261 for 24 h followed by 2 Gy of X-rays exposure. The results showed that this combination induced higher tail moments than the single treatment with BPR0C261 or X-rays on both NSCLC cell lines (Figure 5C). These data indicated that a low concentration of BPR0C261 enhanced the efficacy of a therapeutic radiation dose (2 Gy) for inducing DNA damage.

#### *2.6. The Expression of p53 and PTEN in NSCLC Cells Treated with BPR0C261 and Radiation*

As BPR0C261 induced cell cycle arrest and DNA damage in NSCLC cells with or without p53, we next examined the expression of p53 and PTEN mentioned above after cells were treated with BPR0C261 and/or radiation. We first irradiated A549 cells (p53+/+) and H1299 cells (p53-null) from 2 Gy to 10 Gy and showed that p53 was significantly upregulated in A549 cells after radiation exposure, and PTEN was also upregulated (Figure 6A). On the other hand, PTEN expression was downregulated and phosphorylated Akt (p-Akt) was upregulated in p53-null H1299 cells after irradiation over 8 Gy (Figure 6A). We next treated NSCLC cells with BPR0C261 from 0.01 M to 5 M for 24 h. The results showed that BPR0C261 could efficiently upregulate p53 but not PTEN, and it could suppress the expression of Akt protein and p-Akt in A549 cells (Figure 6B). In H1299 cells, PTEN could be upregulated by BPR0C261 but Akt and p-Akt were not affected (Figure 6B). The drug treatment or radiation alone did not dramatically reduce the ratios of p-Akt/Akt. Furthermore, a combination of 0.1 M BPR0C261 and incremental doses of X-rays showed that p53 and PTEN were upregulated in A549 cells and H1299 cells, respectively (Figure 6C). The expression of Akt protein was not significantly affected by the cotreatment of BPR0C261 and radiation. Compared to untreated control, however, the p-Akt levels of A549 cells were more affected than those of H1299 cells after cotreatment of BPR0C261 and radiation.

 **Figure 5.** Analysis of DNA damage in NSCLC cells using the comet assay. The images of DNA damage in A549 cells and H1299 cells exposed to (**A**) different doses of X‐rays and (**B**) increased concentrations of BPR0C261. The level of DNA damage was quantified by measuring the tail mo‐ ments. (**C**) Comparison of DNA damage induced by a combination of low concentration of BPR0C261 and 2 Gy X‐rays, and individual treatment alone. The microscopic magnification for vis‐ **Figure 5.** Analysis of DNA damage in NSCLC cells using the comet assay. The images of DNA damage in A549 cells and H1299 cells exposed to (**A**) different doses of X-rays and (**B**) increased concentrations of BPR0C261. The level of DNA damage was quantified by measuring the tail moments. (**C**) Comparison of DNA damage induced by a combination of low concentration of BPR0C261 and 2 Gy X-rays, and individual treatment alone. The microscopic magnification for visualization of comets were 20×. \*: *p* < 0.05 compared with BPR0C261 alone. #: *p* < 0.05 compared with radiation alone.

ualization of comets were 20×. \*: *p* < 0.05 compared with BPR0C261 alone. #: *p* < 0.05 compared with

radiation alone.

**Figure 6.** Expression of p53 and PTEN in NSCLC cells treated with BPR0C261 and radiation. (**A**) Western blot analysis of Akt, p‐Akt, PTEN, and p53 in A549 cells and H1299 cells exposed to differ‐ ent doses of X‐rays. (**B**) Same as (**A**) but treated with different concentrations of BPR0C261. (**C**) A combination of BPR0C261 and X‐rays on the expression of proteins described above. NC is the neg‐ ative control without treatment. The blots of Akt, pAkt, PTEN, and p53 were normalized to that of GAPDH, and then compared to untreated control or NC to determine the ratio of each treatment. The results are from three independent experiments. **Figure 6.** Expression of p53 and PTEN in NSCLC cells treated with BPR0C261 and radiation. (**A**) Western blot analysis of Akt, p-Akt, PTEN, and p53 in A549 cells and H1299 cells exposed to different doses of X-rays. (**B**) Same as (**A**) but treated with different concentrations of BPR0C261. (**C**) A combination of BPR0C261 and X-rays on the expression of proteins described above. NC is the negative control without treatment. The blots of Akt, pAkt, PTEN, and p53 were normalized to that of GAPDH, and then compared to untreated control or NC to determine the ratio of each treatment. The results are from three independent experiments.

#### **3. Discussion**

It has been reported that human NSCLC cells are more resistant to ionizing radiation than small cell lung cancer (SCLC) cells in vitro [27]. Additionally, NSCLC accounts for 80–85% of human lung cancer [28]. Therefore, the development of effective and biocompatible radiosensitizers is still ongoing for the treatment of NSCLC.

In a previous study, we demonstrated that BPR0C123 and BPR0C259 could induce G2/M phase arrest in A549 and H1299 cells. However, the significant enhancement of radiosensitivity by these two compounds was only found in NSCLC cells exposed to 10 Gy -rays [9]. Because 2 Gy is routinely used for fractionation radiotherapy in clinics, it is expected to have a radiosensitizer that can raise the effect at low-dose radiation. In this study, we showed that BPR0C261, a well-studied D-24851 derivative [10], could enhance the radiosensitivity at lower radiation doses, especially the p53-null NSCLC cells. Moreover, a low concentration (0.1 M) and high concentration (1 M) of BPR0C261 induced similar radiosensitive effects. This is important because 0.1 M BPR0C261 induced very low cytotoxicity, which is a required criterion for an ideal radiosensitizer [29]. Notably, according to the pattern of survival curves shown in NSCLC cells treated with BPR0C261, it seemed that H1299 cells exhibited stronger responses to X-rays than A549 cells. Because the intrinsic radiosensitivity of A549 cells is higher than that of H1299 cells, this is most likely due to the presence of wild-type p53 that contributes to the radiosensitivity of lung cancer [30].

BPR0C261 is designed to target microtubules and cause antimitotic effect in cancer cells [10]. Thus, it is no doubt that cells treated with this drug will lead to G2/M phase arrest, although the level may be different. The rationale of BPR0C261-induced radiosensitivity is mainly based on the fact that the G2/M phase is the most sensitive phase to radiation [31]. BPR0C261 induced G2/M arrest in A549 and H1299 cells, but H1299 cells exhibited an earlier accumulation of G2/M phase compared to A549 cells. This phenomenon may also be associated with the p53 gene. When cells are exposed to genotoxic agents, p53-dependent G1 phase arrest will be triggered via the transactivation of the p21 gene, a cyclin-dependent kinase inhibitor (CKI) [32,33]. It is speculated that p53 would be activated by BPR0C261 to induce the G1 phase arrest prior to the G2/M phase arrest in A549 cells. On the other hand, the p53-null H1299 cells entered the G2/M phase at an earlier time point after cells were treated with BPR0C261. Although p53 is also demonstrated to be important for G2/M phase arrest in normal human fibroblasts [34], BPR0C261-induced G2/M phase arrest does not depend on the p53 status. Therefore, it may further explain the p53-independent induction of radiosensitivity by this compound.

It has been reported that fractional DNA content is a characteristic of apoptosis that can be recognized in a DNA histogram, namely a sub-G1 or hypodiploid subpopulation detected by flow cytometry [35,36]. Because the sub-G1 phase is believed to be composed of apoptotic cells and necrotic cells, it would be interesting to use other apoptotic and necrotic specific biomarkers to evaluate the types of cell death induced by BPR0C261 in the future [37–39]. BPR0C261 has been reported to induce apoptosis by observing the increase of DNA fragmentation and positive TUNEL signals in gastric MKN-45 cells [10]. The analysis of the sub-G1 subpopulation in NSCLC cells suggests that current data agree with previous report that BPR0C261 may also induce apoptosis in this cancer type. Although p53 is important for inducing apoptosis [40], BPR0C261 could increase the percentage of the sub-G1 phase in both p53-positive and p53-negative NSCLC cells. Therefore, other cell death mechanisms should also be involved in BPR0C261-mediated cytotoxicity. Notably, the induction of the sub-G1 phase in NSCLC cells was more robust using a high concentration of BPR0C261. However, it could induce similar levels of G2/M phase arrest at low and high concentrations, suggesting that a low concentration of BPR0C261 may be biocompatible for combining with other therapeutic methods.

The synergistic effect is another important criterion to evaluate an ideal radiosensitizer. For instance, tirapazamine conjugated to gold nanoparticles exhibited a synergistic radiosensitizing effect on human hepatoma HepG2 cells [41]. Codrug-loaded nanoparticles

has also been reported to improve the synergistic therapeutic efficacy of chemoradiotherapy [42]. It is interesting to combine this compound with nanoparticles which are usually used for drug carriers and tracking, to passively target tumor in vivo through the enhanced permeability and retention (EPR) effect [43,44]. In the present study, BPR0C261 also performed synergistic radiosensitizing effects on p53-wild-type A549 cells and p53-null H1299 cells based on the method of Valeriote and Carpentier [45,46]. Drug-induced PTEN in p53-null NSCLC cells may be important for this phenomenon as PTEN mutation has recently been reported to decrease radiosensitivity in NSCLC cells [47]. An improvement of the synergistic radiosensitizing effect may be also expected by combining BPR0C261 and nanoparticles for in vivo treatment in the future.

Radiation is known to induce DNA damage, but whether D-24851-related derivatives could influence the integrity of DNA has been little studied. Although a low concentration of BPR0C261 (0.1 M) did not cause significant cytotoxicity, the comet assay showed that this treatment could increase the tail moment in this assay. Actually, the use of 0.01 M BPR0C261 was already sufficient to induce DNA damage. The induction of DNA damage by BPRC261 in both NSCLC cell lines was dose-dependent. Because of this effect, a low concentration of BPR0C261 also increased the clinical dose (2 Gy) radiation-induced DNA damage. The impairment of microtubule dynamics by paclitaxel, a microtubule stabilizer, has been reported to induce DNA damage [48]. To the best of our knowledge, this is the first report showing that BPR0C261, a microtubule inhibitor containing antitumor activity also induces DNA damage. It may explain the role of BPR0C261 as a potent radiosensitizer, and the underlying mechanism would be interesting to be further investigated.

The molecular mechanisms disturbed by BPR0C261 to influence the radiosensitivity and DNA damage have not been fully investigated. We started from two common tumor suppressor genes p53 and PTEN and showed that they could account for the p53-dependent and the p53-independent pathways raised by BPR0C261. However, current data only demonstrated the upregulation of the p53 protein in A549 cells treated with radiation and/or BPR0C261. The activation and function of p53 are mainly determined by posttranslational modifications [49]. It would be important to investigate the mechanisms of p53 activation by BPR0C261-mediated radiation responses in the future. As mentioned, we have demonstrated that another two BPR0C derivatives exhibited p53-independent apoptosis [9]. However, the molecules responsible for this phenomenon remain unknown. In this study, PTEN may represent the tumor suppressor gene in p53-independent pathway after cells were treated by BPR0C261, suggesting that PTEN may also play a role in other BPR0C series compounds. Because somatic PTEN mutation is less found in NSCLC [50,51], the induction of PTEN by BPR0C261 may be an important mechanism for this compound to be applied in various lung cancers. We also found that the level of PTEN in p53-null H1299 cells were suppressed by a high dose of radiation (>8 Gy). Because PTEN is a direct downstream target gene transactivated by p53 [22], it would be interesting to further investigate if the maintenance of the PTEN level at a high dose radiation is associated with p53 status. The counterpart of PTEN, named Akt oncoprotein, was also found to be activated by a high dose of radiation. This is consistent with previous reports that radiation can activate the Akt pathway in different cancer types [52,53]. Although the combination of BPR0C261 and radiation induced, respectively, p53 and PTEN in A549 cells and H1299 cells, the expression and activity of Akt was not induced in both cell types compared to untreated control. However, whether the activity of Akt and related signaling are associated with BPR0C261-modulated radiation responses would be interesting to further investigate in the future.

Taken together, BPR0C261 not only decreased cell viability but also induced G2/M phase arrest and DNA damage with a dose-dependent manner in NSCLC cells. BPR0C261 is regarded as a potent radiosensitizer because it showed a synergistic radiosensitizing effect when combining with conventional dose (2 Gy) for fractionation radiotherapy. The underlying mechanism of BPR0C261-induced radiosensitivity is associated with p53 and PTEN molecules in p53-dependent and p53-independent pathways. Current data suggest that BPR0C261 may modulate the effect of radiation on the treatment of lung cancer cells in vitro. To evaluate the radiosensitizing role of BPR0C261 in potent clinical application, an animal study using a xenograft tumor model should be designed soon to confirm the radiosensitizing efficacy of BPR0C261 in vivo.

#### **4. Materials and Methods**

#### *4.1. Cell Culture*

Human A549 epithelial adenocarcinoma cells and human lymph node invaded H1299 nonsmall cell lung carcinoma cells were cultured in Dulbecco's Modified Eagle Medium (DMEM medium) supplemented with 10% fetal bovine serum (FBS), 1% penicillin–streptomycin solution (P/S), and 1% L-glutamine. Cells were incubated at 37 ◦C (5% CO2 in air) and subcultured by trypsinization every 48 h.

#### *4.2. Reagent*

BPR0C261 with the chemical formula N-heterocyclic indolyl glyoxylamide N-(3-methyl-5-isothiazolyl)-2-1[(3-methyl-5-isoxazolyl-)methyl]-1H-3-indoyl-2-oxoacetamide was synthesized in the institute of Biotechnology and Pharmaceutical Research, National Health and Research Institute, Taiwan [10]. The stock solution (1 mM) of BPR0C261 was dissolved in dimethyl sulfoxide (DMSO). An appropriate volume of DMEM medium was added to dilute the stock solution for specific experiments.

#### *4.3. Radiation Source*

Radiation was delivered by the cabinet digital X-ray machine (RS 2000 Biological Research X-ray Irradiator; Rad Source Technologies, Inc., Suwanee, GA, USA) operating at 160 kVp and 25 mA. Cells were suspended in a T-25 flask and exposed to radiation at a dose rate of 38.37 mGy per second.

#### *4.4. Cell Viability Assay*

For cell viability assays, 4.5 × 103 cells were seeded in each well of a 96-well plate. Different concentrations of BPR0C261 were used to treat cells for 24 h. Subsequently, 0.5 mM 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) dissolved in serum free DMEM was added (100 µL/well) into each well and incubated at 37 ◦C for 3 h. DMSO was then used to dissolve the insoluble purple formazan crystals. The absorbance of this solution was quantified at the wavelength of 570 nm by a Tecan's Sunrise absorbance microplate reader (TECAN Group Ltd., Männedorf, Switzerland). The cell viability was determined by the following formula: (the optical density (OD value) of the experiments group/the OD value of control group) × 100%, and the control group was normalized to 100%. The IC50 was calculated using Prism statistical software (Ver.5, GraphPad Software, San Diego, CA, USA). The change of cell viability was also visualized by a bright field microscope (Leica DM IRB, Wetzlar, Germany). The images were captured by a camera (Powershot A260, Canon U.S.A. Inc., New York, NY, USA).

#### *4.5. Flow Cytometry Analysis*

The cell cycle analysis were executed by following a previous report with slight modification [54]. In brief, A549 cells and H1299 cells (1 × 106 each) were seeded in 10 cm dishes and exposed to radiation or drug. The cells were then rinsed, trypsinized, and collected by centrifugation at 600 rpm for 5 min. To fix the cells, 70% precooled ethanol was added to cell pellets and kept at 4 ◦C overnight. The cells were then collected and treated with RNase A for 30 min at room temperature. After centrifugation, the cells were resuspended in 20 µg/mL of propidium iodide (PI) and sieved through a 37 m mesh. The DNA histogram was analyzed using a Beckman Coulter Cytomic FC500 flow cytometer and its bundled software (Beckman Coulter, Inc., Bream CA, USA). The sub-G1, G1, S, and G2/M phases were individually gated for quantification.

#### *4.6. Colony Formation Assay*

The radiosensitivity was determined by calculating the survival fractions of radiation doses. After radiation exposure as mentioned above, the cells were counted and seeded into 6 cm culture dishes and incubated at 37 ◦C for 14 days. The colonies were stained with 1.25% crystal violet in 100% alcohol. A colony should contain at least 50 cells visualized under a bright field microscope. The formula for the plating efficiency (PE) was: PE = (number of colonies/number of cell seeded) × 100%. Survival fraction (SF) = PE of irradiated cells/PE of unirradiated cells. The survival fractions of drug-combining radiation was measured and assayed according to the method of Valeriote and Carpentier [45,46]. In Tables 1 and 2, SFR and SFD are the survival fractions of cells treated by radiation and BPR0C261, respectively. SFR+D is the survival fraction of cells treated with radiation and BPR0C261 at different concentrations. The value of SFR+D was compared to the corresponding SFR × SFD, and the combination treatment effect was considered synergistic when SFR+D < SFR × SFD.

#### *4.7. Single-Cell Gel Electrophoresis Assay (Comet Assay)*

Irradiated cells (1 × 104 ) were mixed with 75 L 0.75% low melting agarose (LAMDA Biotech Inc., St. Louis, MO, USA). It was loaded onto a slide covered by a spread of 1% normal agarose gel. After solidification, another 1% normal agarose gel was used to spread and cover on the low-melting agarose mixed cells. The prepared slide was immersed in the lysis solution (1% Triton X-100, 2.5 M NaCl, 0.1 M ethylenediaminetetraacetic acid (EDTA) and 10 mM Tris, pH = 10) at 4 ◦C for 1 h. Cells in the slide were subjected to electrophoresis in the alkaline electrophoresis buffer (300 mM NaOH, 1 mM EDTA and the pH value was adjusted to higher than 12) for 40 min. The condition of electrophoresis was 1 V/cm with a constant 300 mA. The gels were washed three times by the neutralization buffer (0.4 mM Tris, pH 7.5). Cells were then stained using 20 µg/mL of ethidium bromide (EtBr) as a nucleic acid staining reagent and 25 µL of 20 µg/mL EtBr was used and visualized under a fluorescence microscope (Leica DM IRB, Wetslar, Germany). The images were acquired using a digital camera (Powershot A260, Canon U.S.A. Inc.) and quantified by CometScore 2.0 software (Ver. 2.0, The TriTek Corporation, Fairfax, VA, USA). More than 50 cells were counted in each group.

#### *4.8. Western Blot Analysis*

The Western blot analysis was performed by following a previous report with slight modification [55]. In brief, 30–50 µg of protein lysates were run on the sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), and electrotransferred to a nitrocellulose membrane for the detection of specific proteins by primary antibodies. The primary antibodies used in this study included anti-p53 antibody (GTX70214; GeneTex, Inc., Irvine, CA, USA), anti-PTEN antibody (Cat#9552; Cell Signaling Technology®, Danvers, MA, USA), anti-Akt antibody (sc-7126), phosphor-specific anti-Akt antibody, (sc-7985, Santa Cruz Biotechnology, Inc., Dallas, TX, USA), and anti-glyceraldehyde-3-phosphate dehydrogenase (GAPDH) antibody (MA5-15738; Thermo Fisher Scientific, Waltham, MA, USA). Enhanced chemiluminescent (ECL) agents (Bio-Rad Laboratories, Hercules, CA, USA) were used for imaging the membrane that was acquired by ImageQuant LAS4000 (GE Healthcare, Buckinghamshire, UK). The blots were quantified using the densitometry involved in ImageJ software (National Institutes of Health, Bethesda, MD, USA).

#### *4.9. Statistical Analysis*

Each datum is presented as means ± S.D. The statistical analysis was determined using a *t*-test. The statistical significance was defined as *p* < 0.05. Tables were drawn using GraphPad Prism statistical software (Ver.5, GraphPad Software, San Diego, CA, USA).

**Supplementary Materials:** The supporting information can be downloaded at: https://www.mdpi. com/article/10.3390/ijms232214083/s1.

**Author Contributions:** J.-D.L., C.-A.C. and Y.-J.L. were responsible for the conceptualization and funding acquisition. S.-T.L. was responsible for conducting the experiments and for the methodology. C.-T.C. provided the resources and acted as a consultant for the drug functions. Y.-J.L. wrote the manuscript and validated and visualized the data. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was funded by grants of Taipei City Hospital, RenAi Branch (TPECH-111- 12), the Ministry of Science and Technology of Taiwan (109-2314-B-010-021-MY3), and the Cancer Progression Research Center, National Yang Ming Chiao Tung University from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan (111W31405 and 111W31101).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available in Supplementary Materials.

**Acknowledgments:** We thank Abigail Lin for editing part of the English writing. We thank Fang-Yu Lee's assistance in data collection. We also thank Ya-Huei Wang for administrative works of this research.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


*2.3. Accuracy of Genomic Prediction in Relation to Marker Sets and Pasmo Severity Datasets*

*2.3. Accuracy of Genomic Prediction in Relation to Marker Sets and Pasmo Severity Datasets*  Genomic prediction models were constructed using RR-BLUP with pairwise combinations of the four marker sets and the six PS datasets. Statistical models for the 24 combinations were generated and evaluated for their accuracy (*r*) and relative efficiency (*RE*) using a five-fold random crossvalidation scheme (Table 3). *RE* represents the relative efficiency of GP over direct phenotypic selection which depends on the heritability of a selective trait. Direct phenotypic selection for a trait was considered to have a baseline efficiency of 1. Thus, *RE* values greater than 1 indicate GP models more efficient than direct phenotypic selection in one selection cycle [25–27]. Analysis of variance (ANOVA) (Table S1) indicated that *r* and *RE* both significantly differed among the four marker sets and the six PS datasets; there was also a significant interaction effect between marker sets and PS datasets (Table S1). Owing to the significant marker × phenotype dataset interaction, multiple comparisons of the 24 combinations were performed. For all marker sets, the PS-mean models significantly outperformed those based on individual year datasets (Table 3). The SNP-500QTL marker set models generated significantly higher *r* and *RE* values than any other marker sets (Figure 3). Interestingly, the SNP-67QTL derived models produced slightly but significantly higher values of *r* and *RE* than SNP-134QTL models. The highest *r* and *RE* values were obtained for models combining the SNP-500QTL and PS-mean datasets (Table 3, Figure 3). Intriguingly, the SNP-52347 models yielded the lowest *r* and *RE* values despite including all QTL markers (Table 3, Figure 3); both BL and Bayesian ridge regression (BRR) corroborated this finding (Figure S1). No significant differences in *r* and *RE* values were observed among the three statistical models: RR-BLUP, BL and BRR (Figure Genomic prediction models were constructed using RR-BLUP with pairwise combinations of the four marker sets and the six PS datasets. Statistical models for the 24 combinations were generated and evaluated for their accuracy (*r*) and relative efficiency (*RE*) using a five-fold random cross-validation scheme (Table 3). *RE* represents the relative efficiency of GP over direct phenotypic selection which depends on the heritability of a selective trait. Direct phenotypic selection for a trait was considered to have a baseline efficiency of 1. Thus, *RE* values greater than 1 indicate GP models more efficient than direct phenotypic selection in one selection cycle [25–27]. Analysis of variance (ANOVA) (Table S1) indicated that *r* and *RE* both significantly differed among the four marker sets and the six PS datasets; there was also a significant interaction effect between marker sets and PS datasets (Table S1). Owing to the significant marker × phenotype dataset interaction, multiple comparisons of the 24 combinations were performed. For all marker sets, the PS-mean models significantly outperformed those based on individual year datasets (Table 3). The SNP-500QTL marker set models generated significantly higher *r* and *RE* values than any other marker sets (Figure 3). Interestingly, the SNP-67QTL derived models produced slightly but significantly higher values of *r* and *RE* than SNP-134QTL models. The highest *r* and *RE* values were obtained for models combining the SNP-500QTL and PS-mean datasets (Table 3, Figure 3). Intriguingly, the SNP-52347 models yielded the lowest *r* and *RE* values despite including all QTL markers (Table 3, Figure 3); both BL and Bayesian ridge regression (BRR) corroborated this finding (Figure S1). No significant differences in *r* and *RE* values were observed among the three statistical models: RR-BLUP, BL and BRR (Figure S1).

#### S1). *2.4. Sample Size of Training Populations versus Genomic Prediction Accuracy*

TP size reached 185 (Figure 4).

*2.4. Sample Size of Training Populations versus Genomic Prediction Accuracy*  To find an optimal size for the TP, the relationship between TP size and prediction accuracy was analysed. TPs of various sizes from 18 to 351, corresponding to 5% to 95% of the total 370 accessions, were used to build models with the SNP-500QTL marker set and the PS-mean phenotypic dataset. The prediction accuracy significantly increased for TP sizes up to 100, followed by smaller accuracy To find an optimal size for the TP, the relationship between TP size and prediction accuracy was analysed. TPs of various sizes from 18 to 351, corresponding to 5% to 95% of the total 370 accessions, were used to build models with the SNP-500QTL marker set and the PS-mean phenotypic dataset. The prediction accuracy significantly increased for TP sizes up to 100, followed by smaller accuracy gains with every additional TP size increments (Figure 4). A GP accuracy >0.9 was obtained once the TP size reached 185 (Figure 4).

gains with every additional TP size increments (Figure 4). A GP accuracy >0.9 was obtained once the

cross-validation.

**Table 3.** Accuracy (*r*) and relative efficiency (*RE*) values of the 24 combinations representing the four marker sets and six pasmo severity (PS) datasets using RR-BLUP obtained using a random five-fold cross-validation. **Marker set PS dataset** *r***(**ഥ **±** *s***) 1** *RE* **(**ഥ **±** *s***) 1** PS-mean 0.92 ± 0.02a 1.84 ± 0.04a

*Int. J. Mol. Sci.* **2018**, *19*, x FOR PEER REVIEW 6 of 18

marker sets and six pasmo severity (PS) datasets using RR-BLUP obtained using a random five-fold

1 Different letters represent multiple test significance among the 24 combinations at the 0.05 probability level. 1 Different letters represent multiple test significance among the 24 combinations at the 0.05 probability level.

**Figure 3.** Accuracy (*r*) (**a**) and relative efficiency (*RE*) (**b**) of RR-BLUP prediction models built with combinations of four marker sets using the five-year average PS dataset (PS-mean) and random fivefold cross-validations. Letters above box plots indicated statistical significance (*p* < 0.05) for *r* and *RE* among marker sets. **Figure 3.** Accuracy (*r*) (**a**) and relative efficiency (*RE*) (**b**) of RR-BLUP prediction models built with combinations of four marker sets using the five-year average PS dataset (PS-mean) and random five-fold cross-validations. Letters above box plots indicated statistical significance (*p* < 0.05) for *r* and *RE* among marker sets.

*Int. J. Mol. Sci.* **2018**, *19*, x FOR PEER REVIEW 7 of 18

**Figure 4.** Relationship between the genomic prediction accuracy (*r*) and the size of the training population based on the SNP-500QTL marker set, the PS-mean dataset and the RR-BLUP models. The dash line represents a prediction accuracy of 0.9. **Figure 4.** Relationship between the genomic prediction accuracy (*r*) and the size of the training population based on the SNP-500QTL marker set, the PS-mean dataset and the RR-BLUP models. The dash line represents a prediction accuracy of 0.9.

#### *2.5. Prediction Models of Pasmo Resistance 2.5. Prediction Models of Pasmo Resistance*

All 370 accessions were used as a training population to build a prediction model using the SNP-500QTL genotypic dataset and the PS-mean phenotypic dataset because this combination outperformed all other models. The model was then employed to predict PS in each year (Table 4). Prediction accuracies (*r*) ranging from 0.71 to 0.81 and *RE* values of 1.42 to 1.62 were obtained when predicting PS for individual years (Table 4). All 370 accessions were used as a training population to build a prediction model using the SNP-500QTL genotypic dataset and the PS-mean phenotypic dataset because this combination outperformed all other models. The model was then employed to predict PS in each year (Table 4). Prediction accuracies (*r*) ranging from 0.71 to 0.81 and *RE* values of 1.42 to 1.62 were obtained when predicting PS for individual years (Table 4).

A prediction accuracy as high as 0.98 and a *RE* value of 1.96 were obtained when the model was used to predict PS-means of the 370 accessions (Table 4). A linear relationship was observed between the observed (*y*) and predicted PS (*x*): *y* = 1.0522*x* − 0.3267 (*R*2 = 0.96) (Figure 5a). Based on this equation, the average prediction interval between the two red dashed lines, representing the 95% confidence interval, was only less than 1 (an average of 0.97) on the PS ratings (Figure 5a). A prediction accuracy as high as 0.98 and a *RE* value of 1.96 were obtained when the model was used to predict PS-means of the 370 accessions (Table 4). A linear relationship was observed between the observed (*y*) and predicted PS (*x*): *y* = 1.0522*x* − 0.3267 (*R* 2 = 0.96) (Figure 5a). Based on this equation, the average prediction interval between the two red dashed lines, representing the 95% confidence interval, was only less than 1 (an average of 0.97) on the PS ratings (Figure 5a).

NPQTL in the 370 accessions for the 500 QTL set was tallied. Significant linear correlation between PS-mean and NPQTL (*r* = 0.86 or *R*2 = 0.73) was observed (Figure 5b). This correlation was less than but close to the accuracy of the GP model with SNP-500QTL and higher than the GP models using other marker sets (Table 3). However, the single linear regression equation (*y* = −0.0262*x* + 11.934) of the observed PS (*y*) to NPQTL (*x*) had a large standard deviation for each prediction value, with an average prediction interval width of 2.70, nearly three times the average prediction interval width of the GP model; that is, the NPQTL model had a higher prediction error than the GP model. NPQTL in the 370 accessions for the 500 QTL set was tallied. Significant linear correlation between PS-mean and NPQTL (*r* = 0.86 or *R* 2 = 0.73) was observed (Figure 5b). This correlation was less than but close to the accuracy of the GP model with SNP-500QTL and higher than the GP models using other marker sets (Table 3). However, the single linear regression equation (*y* = −0.0262*x* + 11.934) of the observed PS (*y*) to NPQTL (*x*) had a large standard deviation for each prediction value, with an average prediction interval width of 2.70, nearly three times the average prediction interval width of the GP model; that is, the NPQTL model had a higher prediction error than the GP model.

**Table 4.** Accuracy (*r*) and relative efficiency (*RE*) of genomic prediction for pasmo severity in different years using the RR-BLUP model built with the SNP-500QTL marker set and the PS-mean phenotypic data using all 370 accessions as training data set. **Table 4.** Accuracy (*r*) and relative efficiency (*RE*) of genomic prediction for pasmo severity in different years using the RR-BLUP model built with the SNP-500QTL marker set and the PS-mean phenotypic data using all 370 accessions as training data set.

PS-2016 0.77 1.55

**Figure 5.** Relationship of observed pasmo severity (PS) with PS predicted by a GP model (**a**,**c**) or with PS predicted by the number of QTL with positive-effect alleles (NPQTL) (**b**,**d**). (**a**) Linear regression of observed PS (*y*) to predicted PS (*x*) using the genomic prediction model built with the PS-mean dataset and the SNP-500QTL marker set of all 370 accessions as training data set. (**b**) Linear regression of observed PS (*y*) to NPQTL (*x*) in the 370 flax accessions. (**c**) Relationship of observed PS of 93 randomly chosen accessions with the PS predicted by the genomic model constructed with the SNP-500QTL marker set and PS-mean dataset when a random subset of 277 accessions was used as training population. (**d**) Relationship of observed PS of 93 randomly chosen accessions with the PS predicted by NPQTL (Figure S2) The red dashed lines represent upper and lower boundaries of the 95% prediction intervals, that is, it is expected that the value of a sample lies within that prediction interval in 95% of the samples. The grey band represents the 95% confidence interval, that is, 95% of those intervals include the true value of the population mean. **Figure 5.** Relationship of observed pasmo severity (PS) with PS predicted by a GP model (**a**,**c**) or with PS predicted by the number of QTL with positive-effect alleles (NPQTL) (**b**,**d**). (**a**) Linear regression of observed PS (*y*) to predicted PS (*x*) using the genomic prediction model built with the PS-mean dataset and the SNP-500QTL marker set of all 370 accessions as training data set. (**b**) Linear regression of observed PS (*y*) to NPQTL (*x*) in the 370 flax accessions. (**c**) Relationship of observed PS of 93 randomly chosen accessions with the PS predicted by the genomic model constructed with the SNP-500QTL marker set and PS-mean dataset when a random subset of 277 accessions was used as training population. (**d**) Relationship of observed PS of 93 randomly chosen accessions with the PS predicted by NPQTL (Figure S2) The red dashed lines represent upper and lower boundaries of the 95% prediction intervals, that is, it is expected that the value of a sample lies within that prediction interval in 95% of the samples. The grey band represents the 95% confidence interval, that is, 95% of those intervals include the true value of the population mean.


**About the Editors**

## **Mohamad Nasir Mohamad Ibrahim**

Mohamad Nasir Mohamad Ibrahim obtained his B.Sc. (1994), M.Sc. (1997) and PhD (1999) from Missouri S&T (formerly known as University of Missouri-Rolla, USA). He is currently served as an Associate Professor in the School of Chemical Sciences, Universiti Sains Malaysia (USM). He has published more than 120 journal articles, 17 book chapters and 5 academic books throughout his twenty-year career in USM including "Graphene: A Versatile Advanced Material". Currently, his Scopus h-index is 35 with 4026 citations and he had been granted with ten patents for his R&D products/processes, where five of them are at an international level. More than thirty research grants (1 international grant and 5 industry grants) were secured and utilized to support his team's research activities. He has received fourteen international awards for his research outputs and currently serves as a Guest Editor for Frontiers in Chemistry. He had supervised more than 20 graduate students. His main research areas are Lignin and Lignocellulosic Materials, Nanoparticles, Graphene and Biomaterials and Petroleum Engineering. Recently, he is busy working in the microbial fuel cells topic especially in developing a novel electrode and has published several papers in well reputed journals such as Chemical Engineering Journal, Journal of Cleaner Production, etc. At the moment, he is the pioneer on MFCs research topic in the School of Chemical Sciences, USM. He enjoys sharing his industrial experience, where he spent two and half years working as a R&D Manager at NSE Resources Corporation Sdn Bhd., in his classes.

#### **Patricia Graciela V´azquez**

Patricia Vazquez obtained her PhD (1984) from University of San Luis (Argentina). She currently ´ serves as an Associate Professor in the Faculty of Sciences Exacts, University of La Plata and Principal Researcher of CONICET (Argentina). She has published more than 110 journal articles, 10 book chapters and 3 academic books throughout in 30-year career in CONICET including IUPAC titular member of Green Chemistry Commission.

Currently, her Scopus h-index is 30. More than thirty research grants (international and national grants) were secured and utilized to support her team's research activities. She has received ten national awards for her research outputs and currently serves as a Guest Editor for BMC Nature, among others. She has supervised more than 15 graduate students and fellowships, nationals and internationals. Her main research areas are Nanoparticles, Biomaterials and Advanced Materials for Catalysts. Recently, she has been busy working in the antimicrobial fabric topic, especially in developing a novel additives. At the moment, she is the pioneer on Circular Economy in Argentina.

#### **Mohd. Hazwan Hussin**

Mohd. Hazwan Hussin received his B.Sc (Hons.) in Chemistry and M.Sc degrees from Universiti Sains Malaysia, respectively. He was awarded double-PhD degree program with USM and Universite de Lorraine, France. His research area involves lignocellulosic materials, lignin, cellulose and corrosion protections (inhibition and coatings). He was listed as Top 2% Scientist in the world (under the category of 2019 and 2020 citation) by Stanford University. He is also a Research Fellow for CEGEOTECH, UNIMAP and has been appointed as Visiting Professor at the Universite de Lorraine, France. Dr. Mohd Hazwan is currently served as the Deputy Director for Research Creativity and Management Office (RCMO) USM, Coordinator for USM-UL Centre and Point of Contact for Malaysia-France University Centre (MFUC).


**4. Quadratus lumborum block muscle**

### **4.1 Anatomy**

The quadratus lumborum muscle is part of the posterior abdominal wall and is located dorsal to the iliopsoas muscle. It has its origin in the posteromedial iliac crest in the iliolumbar ligament, and it inserts on the medial border of the twelfth rib (T12) and in the transverse processes of the first and fourth lumbar vertebrae (L1–L4), in the medial third of the iliac crest. Posterior to the quadratus lumborum muscle is the erector spinae muscle group, which consists of the multifidus, longissimus, and iliocostalis (**Figure 5**) [21–23].

The ventral branches of the spinal nerves (including the subcostal and iliohypogastric nerves) run between the quadratus lumborum muscle and its anterior fascia (**Figure 6**).

#### **Figure 5.**

*Quadratus lumborum block. Anatomical concepts, mechanisms, and techniques. (A) A posterior diagram illustration of the musculature of the posterior abdominal wall. The quadratus lumborum muscle originates from medial border of the twelfth rib and lumbar vertebrae transverse processes and inserts into the posteromedial iliac crest. (B) An anterior schematic illustration of the musculature of the posterior abdominal wall. On the left, the psoas muscle has been removed to reveal the ventral rami of the spinal nerve roots and branches passing anterior to the quadratus lumborum muscle. ES, erector spinae; LD; latissimus dorsi; QL, quadratus lumborum; TP, transverse process (Cleveland Clinic Center for Medical Art & Photography © 2018).*

#### *Ultrasound-Guided Regional Analgesia for Post-Cesarean Pain DOI: http://dx.doi.org/10.5772/intechopen.101465*

#### **Figure 6.**

*A schematic illustration of cross-section at L4 level showing the quadratus lumborum muscle with the different layers of the thoracolumbar fascia. On the left, the two-layer model is depicted, where the purple dashed line represents the anterior layer of the thoracolumbar fascia, and the green dashed line represents the transversalis fascia. On the right, the three-layer model is depicted, where the purple dashed line represents the middle layer of the thoracolumbar fascia, and the green dashed line represents the anterior layer of the thoracolumbar fascia. The blue dashed line represents the posterior thoracolumbar fascia. IL, iliocostalis; LD, latissimus dorsi; Lo, longissimus; Mu, multifidus; PM, psoas major; QL, quadratus lumborum; TLF, thoracolumbar fascia. Reprinted with permission, Cleveland Clinic Center for Medical Art & Photography © 2018. All Rights Reserved. showing the quadratus lumborum muscle with the different layers of the thoracolumbar fascia.*

The quadratus lumborum muscle is surrounded by the thoracolumbar fascia (FTL) that comprises the multilayer fascia and aponeurosis with the two proposed models [21–23]:

Knowledge of the anatomy of the thoracolumbar anterior fascia is important to understand the propagation after administration of the local anesthetic. The transverse fascia is divided into two layers: the inner layer that continues with the endothoracic and allows a cephalic spread of the local anesthetic (thoracic paravertebral space), and the outer layer that mixes with the arcuate ligaments of the diaphragm [21, 23, 24]. The abdominal aorta gives rise to the lumbar arteries from which the abdominal branches emerge, and these run lateral and posterior to the quadratus lumborum muscle.

#### *4.1.1 Neurovasculature*

On the ventral aspect of the quadratus lumborum muscle are the iliohypogastric, ilioinguinal, and subcostal nerves enveloped by the transversalis fascia. The sensory dermatome level involves T12–L2. There may be spread to the lateral femoral cutaneous nerve, the obturator, and the femoral nerve within the psoas (L4 and L5). On the dorsal aspect of the quadratus lumborum plane are the dorsal branches of the spinal nerves

that innervate the erector spinae muscle and the sympathetic nerve fibers that innervate the muscles of the abdomen and innervate the thoracolumbar fascia [21–23].