**4. The past, present, and future**

The discovery of DNA as hereditary material and the helical structure base paring of DNA have opened the new avenue toward the current understanding and use of nucleic acids for the development of oligonucleotide-based therapies [6, 34]. The antisense oligonucleotides bind to RNA through Watson-Crick base pairing theory and modulate the function of the targeted RNA. This leads to the key discovery and direct importance of development of oligonucleotide-based drugs and medicine [6, 34]. The initial discovery of antisense technology is to enhance or improve the protein and small molecule-based technology by targeting RNA instead of protein. As a therapeutic strategy, it effects the RNA processing and modulates protein expression by binding to RNAs encoding difficult-to-target proteins. However, translating this technology into the clinic had some disadvantage such as inadequate target

**7**

*Antisense Therapy: An Overview*

*DOI: http://dx.doi.org/10.5772/intechopen.86867*

arrangement, insufficient biological activity, and off-target toxic effects over two decades, but this strategy significantly develops the numbers and types of targets that can be approached for therapy. Further, this technology has a great advantage to develop drugs for the treatment of both monogenic and polygenic diseases as well as influence the human genetics and genomics [12, 34]. The novel chemical modifications of antisense oligonucleotides have been engaged to address these limitations over the years. Both antisense alterations and their mechanism of their action have not only improved the clinical trial design but also provide the breakthrough toward the translation of antisense oligonucleotide-based strategies into therapies [6, 34]. Additionally, multiple clinical trials have clearly confirmed the clinical uses of antisense pharmacology in humans and demonstrated their safety by using various mechanisms. The current antisense drugs, which are under clinical development phase, target different tissues both systemically and locally. The advanced oligonucleotide chemistry further enhances the antisense drug properties by increasing potency, safety, and broader tissue distribution [6]. The advancement of oligonucleotide designs had improved the antisense mechanism of binding to RNA to inhibit their function with and without out RNA degradation. Recently, several researchers demonstrated that antisense oligonucleotides can also be utilized to overexpress or suppress the protein production [34, 35]. Many nonpathogenic disease conditions lack an effective treatment; the rapid development of new improved nextgeneration antisense oligonucleotide-based drugs bear the potential of intense clinical application and therapeutic impact on the treatment soon, demonstrating that this new class of molecular medicines/drugs has several potential and advantageous applications in the clinic. There was always a lag period from the early discoveries till the therapy enters into the clinical trials and further in the market [34]. RNA-based precision medicine and gene therapy still need the improvement because of their off-target effects. The future applications of antisense technology will solely depend on the performance of novel molecules in the clinical trials. Till date tremendous progress has been made toward the antisense technology, despite the fact this has yet to deliver its full potential [36]. There are still more unanswered questions which need further improvement of the technology. The establishment of first-generation phosphorothioate oligodeoxynucleotides is a great asset and valuable pharmacological tool, which has shown the promise of new therapies to the patient. Further development of new improved second- and third-generation antisense oligonucleotides with novel formulation will result in better therapies for patients. Even though the tremendous progress has been made in antisense technology, there are still more questions that remain unanswered for the technology and opportunities to further improve upon the platform [36]. This technology has provided solutions and confidence to diseases which were earlier considered untreatable. The high-cost factor has prevented this treatment mode inaccessible for the general masses. To fulfill the clinical need, future innovations to this technology might help in finding better and affordable cure to many more diseases which is available to all. Furthermore, the development of CRISPR, an RNA-guided gene-editing technology, and the delivery of mRNA transcribed in vitro are a major development of the RNA therapeutics. The clinical applications and validations of RNA-based antisense drugs for modulation of gene/protein expression and genome editing are currently being investigated both in the laboratory and in the clinic [1]. The CRISPR-Cas genome editing has transformed the field and impacted the biomedical science field which has stimulated the development of RNA-based antisense delivery approaches to facilitate clinical translation of CRISPR-Cas technology [1]. For cancer therapy, the first US-based human trial using CRISPR-Cas9 ex vivo is isolated from the T cells of cancer patients by knocking out the genes encoding PD1 and T-cell receptor alpha/beta [1]. Unquestionably, the field of antisense RNA

#### *Antisense Therapy: An Overview DOI: http://dx.doi.org/10.5772/intechopen.86867*

*Antisense Therapy*

is in clinical trial phase [30, 31].

**4. The past, present, and future**

**3.5 Antisense therapy for infectious diseases**

for targeting other exons, which will broaden the treatment of the patient population. **Myotonic dystrophy type 1 (DM1)** is a multisystemic disease caused by a triplet repeat expansion (CTG) in the 3′ untranslated region of myotonic dystrophy protein kinase (DMPK) gene [26]. IONIS-DMPK-2.5Rx is a chimeric antisense oligonucleotide and is currently under a randomized controlled study trail in DM1 patients. **Transthyretin amyloidosis** is a form of systemic amyloidosis caused by misfolded transthyretin protein (TTR), in multiple tissues, including peripheral nerves, the gastrointestinal tract, and the heart [27]. Three different antisense drugs, IONIS-TTRRx, RNase H–dependent, and patisiran, are currently in development for the treatment of TTR amyloidosis, as well as for both familial amyloid polyneuropathy and cardiomyopathy [28]. **Spinal muscular atrophy (SMA)**, a progressive motor neuron disease, usually occurs in infancy or childhood caused by deletions or mutations in the survival of motor neuron 1 (SMN1) gene [29]. Nusinersen drug is a fully modified oligonucleotide designed to bind to a specific sequence in intron 7 of the SMN1 and 2 pre-mRNAs, enhancing exon 7 inclusion and increasing the production of SMN protein [30, 31] which is under review for market authorization. **Huntington's disease (HD)** is an autosomal dominant neurodegenerative disorder resulting from an expanded CAG repeat in the huntingtin (HTT) gene, which causes a toxic gain of function due to an expanded polyglutamine tract in the resulting protein. Antisense oligonucleotide designed to lower total HTT has been shown to provide a prolonged improvement in HD, and the drug

Various antisense mechanisms can be utilized to inhibit viral replication—for example, by binding to viral mRNA to block the translation of the protein or to degrade the viral RNA through an RNase mechanism or by blocking host microR-NAs that support viral replication [6]. Several antisense therapies are currently undergoing clinical trials for various infectious diseases. MicroRNA-122 (miR-122) is highly abundant in the liver and is essential to the stability and propagation of hepatitis C virus (HCV) [32]. This mRNA binds to a highly conserved 5′ untranslated region of the HCV genome, protecting it from degradation and host innate immune responses [32]. Additionally, miR-122 is also believed to play a major role in inflammatory activity in the liver [33], and RG-101, a GalNAc-conjugated oligonucleotide drug, is designed to inhibit miR-122 and HCV replication. The results from this clinical trial were very encouraging and support continued study

The discovery of DNA as hereditary material and the helical structure base paring of DNA have opened the new avenue toward the current understanding and use of nucleic acids for the development of oligonucleotide-based therapies [6, 34]. The antisense oligonucleotides bind to RNA through Watson-Crick base pairing theory and modulate the function of the targeted RNA. This leads to the key discovery and direct importance of development of oligonucleotide-based drugs and medicine [6, 34]. The initial discovery of antisense technology is to enhance or improve the protein and small molecule-based technology by targeting RNA instead of protein. As a therapeutic strategy, it effects the RNA processing and modulates protein expression by binding to RNAs encoding difficult-to-target proteins. However, translating this technology into the clinic had some disadvantage such as inadequate target

**6**

of the drug.

arrangement, insufficient biological activity, and off-target toxic effects over two decades, but this strategy significantly develops the numbers and types of targets that can be approached for therapy. Further, this technology has a great advantage to develop drugs for the treatment of both monogenic and polygenic diseases as well as influence the human genetics and genomics [12, 34]. The novel chemical modifications of antisense oligonucleotides have been engaged to address these limitations over the years. Both antisense alterations and their mechanism of their action have not only improved the clinical trial design but also provide the breakthrough toward the translation of antisense oligonucleotide-based strategies into therapies [6, 34]. Additionally, multiple clinical trials have clearly confirmed the clinical uses of antisense pharmacology in humans and demonstrated their safety by using various mechanisms. The current antisense drugs, which are under clinical development phase, target different tissues both systemically and locally. The advanced oligonucleotide chemistry further enhances the antisense drug properties by increasing potency, safety, and broader tissue distribution [6]. The advancement of oligonucleotide designs had improved the antisense mechanism of binding to RNA to inhibit their function with and without out RNA degradation. Recently, several researchers demonstrated that antisense oligonucleotides can also be utilized to overexpress or suppress the protein production [34, 35]. Many nonpathogenic disease conditions lack an effective treatment; the rapid development of new improved nextgeneration antisense oligonucleotide-based drugs bear the potential of intense clinical application and therapeutic impact on the treatment soon, demonstrating that this new class of molecular medicines/drugs has several potential and advantageous applications in the clinic. There was always a lag period from the early discoveries till the therapy enters into the clinical trials and further in the market [34]. RNA-based precision medicine and gene therapy still need the improvement because of their off-target effects. The future applications of antisense technology will solely depend on the performance of novel molecules in the clinical trials. Till date tremendous progress has been made toward the antisense technology, despite the fact this has yet to deliver its full potential [36]. There are still more unanswered questions which need further improvement of the technology. The establishment of first-generation phosphorothioate oligodeoxynucleotides is a great asset and valuable pharmacological tool, which has shown the promise of new therapies to the patient. Further development of new improved second- and third-generation antisense oligonucleotides with novel formulation will result in better therapies for patients. Even though the tremendous progress has been made in antisense technology, there are still more questions that remain unanswered for the technology and opportunities to further improve upon the platform [36]. This technology has provided solutions and confidence to diseases which were earlier considered untreatable. The high-cost factor has prevented this treatment mode inaccessible for the general masses. To fulfill the clinical need, future innovations to this technology might help in finding better and affordable cure to many more diseases which is available to all. Furthermore, the development of CRISPR, an RNA-guided gene-editing technology, and the delivery of mRNA transcribed in vitro are a major development of the RNA therapeutics. The clinical applications and validations of RNA-based antisense drugs for modulation of gene/protein expression and genome editing are currently being investigated both in the laboratory and in the clinic [1]. The CRISPR-Cas genome editing has transformed the field and impacted the biomedical science field which has stimulated the development of RNA-based antisense delivery approaches to facilitate clinical translation of CRISPR-Cas technology [1]. For cancer therapy, the first US-based human trial using CRISPR-Cas9 ex vivo is isolated from the T cells of cancer patients by knocking out the genes encoding PD1 and T-cell receptor alpha/beta [1]. Unquestionably, the field of antisense RNA

#### *Antisense Therapy*

therapeutics is presently undertaking foremost development, and the potential for using RNA antisense drugs for personalized medicine and immunotherapy as well as to address genetic, infectious, and chronic diseases will ensure the continued development of antisense RNA therapeutics for years to come [1].


*5.3. Induction of the EAE*

The EAE model was induced as previously described [98]. Animals were anesthetized (isoflurane 1.5–2% in oxygen) and immunized subcutaneously at the base of the tail (s.c.) with 200 μg of myelin oligodendrocyte glycoprotein (MOG35–55) peptide (Proteimax Technology, Sao Paulo, SP, Brazil) emulsified in incomplete Freund's adjuvant (IFA) supplemented with *Mycobacterium tuberculosis* (4 mg/mL, H37Ra, Difco™, Detroit, MI, USA). Intraperitoneal (i.p.) injection of 300 ng of pertussis toxin (Sigma-Aldrich™, St. Louis, MO, USA) was performed immediately after immunization and 48 h later. Control animals were treated with the vehicle (complete Freund's adjuvant—CFA, s.c. and pertussis toxin, i.p.). Animals were daily monitored according to the following clinical scores: 0.0, no symptoms; 0.5, some loss of tail tone; 1.0, loss of tail tone; 1.5, hip weakness upon ambulation; 2.0, hip weakness and partial hind limb paresis; 2.5, partial hind limb paralysis; 3.0, total hind limb paralysis but still moving on the cage; 3.5, complete hind limb paralysis and forelimbs weakness; 4.0, total hind limb paralysis and partial forelimbs paralysis; 4.5, complete hind limb paralysis, partial paralysis of the forelimbs, decreased responsiveness (consider euthanasia); 5, Immobile and unresponsive, moribund (immediate euthanasia). In the occurrence of score 3, food and water were turned available inside the cage. In the occurrence of score 4, animals were evaluated twice a day and if this score persists for three consecutive assessments, euthanasia was applied and the score of 5 was considered to the end of the period of evaluation. Body weight was measured during the course of the disease.


4. Methodology

#### 4.1. Model definitions

In this study, I test three models, namely, CAPM, 3F-FF model and 5F-FF model for BIST. The model definitions are given below:

$$\mathbf{R}\_{\rm it} - \mathbf{R}\_{\rm ft} = \mathbf{a}\_{\rm i} + \left(\mathbf{R}\_{\rm Mt} - \mathbf{R}\_{\rm ft}\right) + \mathbf{c}\_{\rm it-CAPM} \tag{5}$$

$$\mathbf{R}\_{\rm if} - \mathbf{R}\_{\rm f} = a\_{\rm i} + \left(\mathbf{R}\_{\rm Mt} - \mathbf{R}\_{\rm f}\right) + \mathbf{s}\_{i} \mathbf{S} \mathbf{M} B\_{\rm t} + h\_{i} \mathbf{H} \mathbf{M} L\_{\rm t} + e\_{\rm i} (\mathbf{3F} \text{-FF model}) \tag{6}$$

$$\mathbf{R}\_{\overline{t}} - \mathbf{R}\_{\overline{\rho}} = \mathbf{a}\_{\overline{t}} + \left(\mathbf{R}\_{\mathbf{M}\overline{t}} - \mathbf{R}\_{\overline{\rho}}\right) + \mathbf{s}\_{\overline{t}} \mathbf{S} \mathbf{M} \mathbf{B}\_{\overline{t}} + h\_{\overline{t}} \mathbf{H} \mathbf{M} \mathbf{L}\_{\overline{t}} + \mathbf{r}\_{\overline{t}} \mathbf{R} \mathbf{M} \mathbf{W}\_{\overline{t}} + \mathbf{c}\_{\overline{\mathbf{r}}} \mathbf{C} \mathbf{M} \mathbf{A}\_{\overline{t}} + \mathbf{e}\_{\overline{\mathbf{r}}} (\mathbf{S} \mathbf{F} \mathbf{-} \mathbf{F} \mathbf{F} \mathbf{m} \mathbf{o} \mathbf{d} \mathbf{d}) \tag{7}$$

where Rit is the return of portfolio i at time t (portfolio creation procedure is given in the following topic); Rft is the risk-free rate approximated by 3-month TRY Libor rate at time t; Rit�Rft is the excess return of portfolio i at time t; RMt is the monthly market return approximated by natural log difference of BIST-100 Index at time t; SMBt, HMLt, RMWt and CMAt are defined in details in the following topic; ai is the intercept; βi is the coefficient of RMt�Rft for portfolio i; si is the coefficient of SMBt for portfolio i; hi is the coefficient of HMLt for portfolio i; ri is the coefficient of RMWt for portfolio i; and ci is the coefficient of RMWt for portfolio i.

#### 4.2. Construction of Fama-French factors

The next step in the analysis is to create sorted portfolios from which the Fama and French factor return series could be calculated. The factors used in the analysis were constructed in a manner similar to the process described in [19], relying solely on 2 � 3 sorts for creating the factors. Other sorting choices might have been used; however, [19] finds no differences in model performance when testing differing sorting methods.

My first data point is at the 31st of December 1999, and the investment variable is calculated as in Eq. (4); the first available year of accounting data used in the sorting process is at the end of fiscal year 2000. The portfolios were sorted at the end of May each year, and therefore the first available return observation in the final analysis is the return of June 2000, sorted according to accounting data at the end of fiscal year 1999. Thus, the time period for the actual analysis is June 2000 to May 2017 or 204 months of return data.

The sorting process is as follows:

(1) First of all, stocks are sorted according to their market cap to define small-sized and bigsized stocks. Fifty percent of the market cap was used as the breakpoint for size. (2) For all other factors, yearly sample 30th and 70th percentiles were used as breakpoints in the sorting method. (3) After the determination of the breakpoints, the stocks in the sample were independently distributed for every year into six size-B/M (where B/M is book-to-market ratio) portfolios, six size-OP (where OP is operational profits divided by book equity showing profitability) portfolios and six size-Inv (where Inv is yearly increase in total assets) portfolios created from the intersections of the yearly breakpoints. (4) All portfolios are value-weighted according to their market cap. (5) Monthly returns were calculated for each of the 18 portfolios. (6) After calculating the sorted portfolio returns, the actual factor returns were calculated.

There are two size groups and three book-to-market (B/M), three operating profitability (OP) and three investment (Inv) groups. The resulting groups are labelled with two letters. The first letter describes the size group, small (S) or big (B). The second letter describes the B/M group, high (H), neutral (N) or low (L); the OP group, robust (R), neutral (N) or weak (W); or the Inv group, conservative (C), neutral (N) or aggressive (A). Stocks in each component are valueweighted to calculate the component's monthly returns (Figure 1).

Figure 1. Sorted portfolio groups to construct Fama-French factors.

Eqs. (8)–(14) define calculations of the Fama-French factors:

My first data point is at the 31st of December 1999, and the investment variable is calculated as in Eq. (4); the first available year of accounting data used in the sorting process is at the end of fiscal year 2000. The portfolios were sorted at the end of May each year, and therefore the first available return observation in the final analysis is the return of June 2000, sorted according to accounting data at the end of fiscal year 1999. Thus, the time period for the actual analysis is

(1) First of all, stocks are sorted according to their market cap to define small-sized and bigsized stocks. Fifty percent of the market cap was used as the breakpoint for size. (2) For all other factors, yearly sample 30th and 70th percentiles were used as breakpoints in the sorting method. (3) After the determination of the breakpoints, the stocks in the sample were independently distributed for every year into six size-B/M (where B/M is book-to-market ratio) portfolios, six size-OP (where OP is operational profits divided by book equity showing profitability) portfolios and six size-Inv (where Inv is yearly increase in total assets) portfolios created from the intersections of the yearly breakpoints. (4) All portfolios are value-weighted according to their market cap. (5) Monthly returns were calculated for each of the 18 portfolios. (6) After calculating the sorted portfolio returns, the actual factor returns

There are two size groups and three book-to-market (B/M), three operating profitability (OP) and three investment (Inv) groups. The resulting groups are labelled with two letters. The first letter describes the size group, small (S) or big (B). The second letter describes the B/M group, high (H), neutral (N) or low (L); the OP group, robust (R), neutral (N) or weak (W); or the Inv group, conservative (C), neutral (N) or aggressive (A). Stocks in each component are value-

weighted to calculate the component's monthly returns (Figure 1).

Figure 1. Sorted portfolio groups to construct Fama-French factors.

June 2000 to May 2017 or 204 months of return data.

74 Financial Management from an Emerging Market Perspective

The sorting process is as follows:

were calculated.

$$\text{SMB}\_{\text{B/M}} = (\text{SH} + \text{SN} + \text{SL})/\text{3} - (\text{BH} + \text{BN} + \text{BL})/\text{3} \tag{8}$$

$$\text{SMB}\_{\text{OP}} = (\text{SR} + \text{SN} + \text{SW})/\Im - (\text{BR} + \text{BN} + \text{BW})/\Im \tag{9}$$

$$\text{GMB}\_{Inv} = (\text{SC} + \text{SN} + \text{SA})/\mathfrak{3} - (\text{BC} + \text{BN} + \text{BA})/\mathfrak{3} \tag{10}$$

$$\text{SMB} = \left( \text{SMB}\_{\text{B}/M} + \text{SMB}\_{\text{OP}} + \text{SMB}\_{\text{inv}} \right) / \text{3} \tag{11}$$

$$\text{HML} = (\text{SH} + \text{BH})/2 - (\text{SL} + \text{BL})/2 \tag{12}$$

$$RMW = (SR + BR)/2 - (SW + BW)/2\tag{13}$$

$$\text{CMA} = (\text{SC} + \text{BC})/2 - (\text{SA} + \text{BA})/2 \tag{14}$$

#### 4.3. Regression portfolios and other data used in the regression analysis

Regressions were run on three sets of 16 left-hand-side regression portfolios. Monthly returns for the portfolios were calculated in a manner similar to constructing the factor portfolios;

For example, S1BM1 shows average of the monthly excess returns of stocks included in the smallest size and lowest bookto-market portfolio.

Table 1. Construction of dependent variables: each notation of a dependent variable in this table shows the monthly excess return of the corresponding sorted portfolio.

Table 2. Average number of stocks in regression portfolios.

equal-weighted portfolios were created from independent 4 4 sorts with 25th, 50th and 75th yearly sample percentiles as breakpoints for both sorting variables. Table 1 shows the constructed dependent variables.

Panels A through C in Table 2 show the average number of stocks in each of the regression portfolios. It is evident from Panel A that high B/M companies are often smaller companies, while low B/M is tilted towards bigger companies. A similar phenomenon can be observed in Panel B, where low operating profitability is a feature of smaller companies and high operating profitability is more often found in stocks with higher market capitalisations.


**5. Numerical results**

This section evaluates the performance of the proposed algorithms in terms of convergence and solution accuracy under different conditions. Radio base stations are considered to be located at cell centers and the traffic is assumed to be inhomogeneous, with each cell having a different and *a priori* known traffic demand. Following the ideas shown in (Lai, 1996), the initial population is constructed using the available *a priori* information, i.e., the algorithm assigns a valid string of frequencies to all the cells following a simple approach: first, the algorithm attempts to assign a set of valid frequencies to as many base stations as possible. In the event that valid frequencies cannot be located to some of the cells, they are then randomly assigned.

Simulated Quenching Algorithm for Frequency Planning in Cellular Systems 61

**Figure 6.** Cellular geometry for the Philadelphia benchmark problem with *n*=21 cells.

In this section, the convergence properties of the proposed methods are studied. Results shown in Table 2 are average values over 25 trials for each problem. The parameters to be set in the GA are: the number of iterations *ng*, the initial mutation and crossover probabilities, the population size *np*, and the parameters of functions *pc(k)* and *pm(k)*. After several trials that helped to fine tune the parameters ensuring that the computation is

• Number of fitness evaluations: 100 (for problem 14), 25,000 (problems 5−8 and 11), 50,000 (problems 12, 15 and 16), 75,000 (problem 13), 100,000 (problems 1−4), 150,000 (problem 10) and 300,000 (problem 9). If the values corresponding to problems 10, 12, 14 and 16 are compared to those shown in (Ngo, 1998) it can be seen that these values mean a reduction of 75% (in problems 15, 12 and 16) and 62.5% (in problem 10) with respect to the number of iterations needed in (Ngo, 1998); in problem 14 both approaches require 100 iterations. Notice that, since not every offspring needs to be evaluated in each generation, the number of fitness evaluations is a more representative

• Initial crossover probability, *pc(0)*: this parameter is set to 0.35 in problems 5−8 and

• Population size, *np*: 10 individuals, except in problems 1−4, 9 and 10, which required 20. • Simulations show that λCSC=1 and λACC=1.3 lead to faster convergence as compared to λACC=1. This result is in accordance to (Lai, 1996), where λACC-optimal=1.1/2 was

On the other hand, the SQ algorithm has been implemented with a mixture of standard and modified *flip-flops* (described in section 4.2). Problems 5−8, 11−12, 14−16 are solved with a configuration of 50-70% of modified flip-flops, while problem instances 1−4, 9, 10 and 13 used 20-40% of modified flip-flops. The remaining cases, in all problem instances, were implemented with standard flip-flops. To explain this experimental adjustment, just notice that the more complex is the problem instance, the more explorative must be the global

Comparative results are shown in Table 2. The performance is measured using the percentage of convergence to the solutions, defined as the ratio of the total number of

**5.2. Adjustment of parameters and convergence performance**

parameter of the performance than the number of generations.

• Initial mutation probability, *pm(0)*: 0.04 for all the problems.

obtained.

11−16, while instances 1−4, 9 and 10 showed better results with 0.25.

search for solutions in order to avoid convergence to suboptimal local minima.

manageable, the optimal values were found to be:


This promising evidence encouraged us to check if oregano extract could exert antineoplastic effects also against a human melanoma cell line with the aim to lay the basis for future desirable application in translational medicine. As expected, significant antiproliferative activity, with a high selectivity index, together with a relevant cytotoxic effect of HCOE on A375 cells was confirmed.

Taking into account all previous data, the concentration of 10 mg/mL of oregano sample was selected for further experiments, showing the best antiproliferative effect.

Cytofluorimetric and RT-PCR analyses proved that the plant extract blocked cell division in the G2/M phase, acting on the expression of specific key genes implicated in the inception of the mitotic process (i.e., CDK1, CCNB1, P21, and P27) [53,55,56] such as paclitaxel, which is a well-known plant anticancer drug with antimitotic property.

Before proceeding with the other analyses, as the final objective of the current research was the valorization of an oregano hydroalcoholic extract for potential chemotherapeutic applications, the control of the safety for the plant preparation with reference to mutagenic properties was necessary. For this purpose, the Ames test, recommended by the European Food Safety Authority (EFSA) as a proper assay to assess food safety [87], was carried out. It provided the proof that HCOE did not have a non-mutagenic effect [88,89], as expected. Indeed, *O. vulgare* and its derivatives, which are accepted as food ingredients by the U.S. Food and Drug Administration, are listed among the GRAS (Generally Recognized As Safe) substances by the Code of Federal Regulations of the USA and are generally well tolerated by the human body, although gastrointestinal upset and skin allergic reactions have been associated to them [90]. However, the lack of data regarding oregano genotoxicity [91,92] highlights the need for further studies on this topic and valorizes the present preliminary results. The mutagen-protective activity of HCOE was also investigated in order to further analyze the plant extract under a healthy point of view. Unfortunately, at all tested doses, no protective property against known mutagenic compounds was documented.

In the second part of this work, the molecular mechanism underlying the bioactivity of HCOE on melanoma cells was investigated in depth. First of all, the efficiency of the MITF pathway was verified, studying the expression rate of MITF, TYR, and TYRP1 genes. The results revealed an antimelanogenic activity of the oregano extract on A375, as already suggested by the literature [93].

At low levels, reactive species play a key role in cell signaling, but their overproduction can lead to mitochondrial alterations (i.e., DNA mutations, respiratory chain damage, membrane permeability loss), oxidative stress, and an inhibition of specific genes, including those related to melanin synthesis [29,94]. Therefore, according to the previous results, intracellular ROS and RNS levels were measured in A375 cells, demonstrating that HCOE10 had a remarkable pro-oxidant effect, especially after 24 h of incubation. This evidence suggested that oregano antitumor activity could be based on a reactive species-mediated apoptotic process. Literature data about carvacrol bioactivity, one of the most abundant phenolic monoterpenoids of oregano (as also documented in the present research by GC-MS analysis, see Table 2), would support this hypothesis. Indeed, several published works showed that this compound induces apoptosis in different tumor cell lines by increasing the ROS amount and disrupting mitochondrial membrane potential [95–97].

All previous considerations suggested that HCOE10 could trigger cell death in A375. For this reason, to clarify which cell death pathway was induced by oregano extract, a pan-caspase inhibitor, Z-VAD-FMK, and an inhibitor of necroptosis, necrostatin-1 [98], were used in co-treatments with HCOE10 on the melanoma cells. The experiments demonstrated that the decrease of tumor cell proliferation was partially due to both apoptosis and necroptosis induction, as already suggested by Savini et al. [99] and Rubin et al. [100]. Indeed, these two phenomena are strongly correlated to each other because they share the same stimuli (such as TNF-α), ligands, and receptors [101,102]. The mechanism underlying the activation of apoptosis and/or necroptosis is still under investigation. Nevertheless, Annexin V/propidium iodide staining in the presence of necrostatin-1, as well as the analysis of TNF-α level, could be performed in the future to better clarify the role of these two pathways in oregano-induced cell death [103,104].

Since in MTT assay, only HCOE10 + Z-VAD double treatment rescued A375 viability, taking into account that mitochondria are both generators of and targets for reactive species (whose levels were previously observed to be increased in the presence of HCOE), a mitochondria impairment caused by oregano extract was hypothesized. To validate this theory, cytofluorimetric analyses, based on the use of MitoTracker Green and MitoTracker Red CMX-ROS probes, were performed. The data confirmed a loss of mitochondrial potential after HCOE10 treatment, while no significant change in mitochondrial mass was appreciated. By contrast, CCCP-treated cells (the positive control) showed an increase of mitochondrial mass, which was probably due to mitochondrial fragmentation [105,106] associated to an expected membrane depolarization.

As known in the literature, the permeabilization of the mitochondrial outer membrane is an event promoted by the pro-apoptotic protein Bax. Bcl-2, on the other hand, is an anti-apoptotic factor that prevents apoptosis by inhibiting Bax. Therefore, an elevated Bax/Bcl-2 ratio is a feature of apoptotis induction. As a consequence of Bax activation, damaged mitochondria release cytochrome c into the cytoplasm, leading to caspase-3 induction and Parp-1 cleavage [107,108]. In view of this, the amount of apoptosis-related proteins (Bax, Bcl-2, Cycs, Casp-3, and Parp-1) were evaluated in A375 after 48 h of incubation with HCOE10. Oregano extract was able to trigger apotosis in the human melanoma cells by increasing Bax and Cycs concentrations and decreasing Bcl-2 and pro Casp-3 amounts. Moreover, as expected, Parp-1 cleavage was evident in oregano-treated cells. HCOE10 + Z-VAD co-treatment reversed HCOE effect, showing protein levels similar to those detected in the respective negative control. All these results confirmed that the plant extract induced, in A375 cells, a caspase-dependent apoptosis, which was mediated by mitochondrial damage. However, considering that both ROS and RNS play an important role also in autophagy and that mitophagy is strictly involved in mitochondrial turnover [109,110], it would be interesting in the future to investigate if autophagy/mitophagy is also induced by oregano treatment.

Another important key element in the apoptotic process is the P53 gene, whose protein promotes BAX gene expression by direct activation of its promoter and BCL2 downregulation [111–113]. P53 is activated in response to a wide range of genotoxic insults. It is involved in several DNA-repair machineries, such as nucleotide excision repair, for the removal of helix-distorting lesions (typical of UV-damage) and base excision repair (BER) in case of base oxidative modifications [114,115]. As known in the literature, plant compounds, especially polyphenols, can cause oxidative DNA strand breakage, alone or in the presence of transition metal ions. Indeed, among all, the copper (that is the most abundant ion of the cell nucleus, together with zinc), after reduction from Cu(II) to Cu(I) form by the action of plant metabolites, is particularly prone to produce ROS (especially the hydroxyl radical) during its re-oxidation, bind chromatin (particularly guanines), and cause DNA breakages [116,117].

The existence of such types of event prompted us to analyze P53 mRNA and protein levels and evaluate DNA damages (detecting γH2AX and 53BP1 foci by IF) upon HCOE10 exposure. Oregano treatment induced in A375 p53 upregulation, at both the transcriptional and translational level, and DNA breakages. In detail, γH2AX is the phosphorylated form of the histone 2AX (H2AX). Its phosphorylation is an early consequence of double and single-strand breakages [118–121]; therefore, the detection of γH2AX is widely used as a marker of DNA damage. On the other hand, p53 binding protein 1 (53BP1) locates only to DNA double-strand breaks [122]. IF analyses highlighted a great number of γH2AX and 53BP1 foci, which often co-localized in HCOE10-treated cells with respect to untreated controls. This evidence, together with Ames test results, pointed out that oregano hydroalcoholic extract acts as a genotoxic but not mutagenic agent in A375 melanoma cells, causing DNA single and double-strand breaks [123]. Furthermore, taking into account that DNA damage often results from the binding of phytochemical to transition metal ions, such as copper [124–126], co-treatments with HCOE10 and a copper chelator (TETA) were performed. Surprisingly, the sequestration of copper by TETA protected A375 cells from HCOE10-induced DNA damage, confirming that this metal ion plays a fundamental role in oregano bioactivity and relative DNA cleavage reaction.

In cancer masses, the concentration of iron and zinc is lower, whereas the copper concentration is usually higher than in healthy tissues [127–132]. This feature can explain why plant compounds exert selective cytotoxic activity against tumor cells but not toward non-tumor ones [133–139], justifying our data about oregano cytotoxicity on B16–F10 and A375 but not on C2C12.

In conclusion, *O. vulgare* hydroalcoholic extract, due to its peculiar chemical profile and pro-oxidant effect, inhibits melanogenesis and melanoma cell proliferation. Through in-depth molecular analyses, the antineoplastic activity of the oregano extract was associated to its ability to trigger programmed cell death (apoptosis and necroptosis) in A375 human melanoma cells via mitochondria and DNA damage. As this molecular mechanism was correlated to the intracellular/nuclear concentration of copper ions, oregano phytochemicals appeared to be slightly toxic or non-toxic for non-tumor cells. All this evidence represents a robust starting point for further studied focused on the design of new anti-melanoma natural drugs. Indeed, according to the present data, *O. vulgare* phytocomplex, working in synergy, represent an excellent candidate as anticancer agent, being highly selective and effective against human melanoma cells.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2304-8158/9/10/1486/s1, Figure S1: HPLC analysis; Table S1: RT-qPCR primers; Table S2: Ames test for mutagenic analysis; Table S3: Ames test for mutagen-protective analysis.

**Author Contributions:** Conceptualization: V.N., A.G. and G.S.; Methodology: A.G.; Formal analysis: V.N., G.D.M. and G.S.; Writing—Original draft preparation: V.N. and A.G.; Writing—Review and editing: all authors; Resources: A.C., G.S. and A.G.; Supervision: Antonella Canini and A.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors want to thank Marco Sarandrea (SARANDREA MARCO & CO. srl) who performed the sampling of the plant material and monks of MOUNT ATHOS VATOPEDI HOLY MONASTERY (Iερα´ Moνη´ Bατoπεδι´oυ) in Greece who kindly provided us oregano plants. Moreover, we also thank Alessandro Grandini for technical assistance performing the Ames test, Miss Sophie Gart for the revision of the English, and Maria Rosa Ciriolo, Katia Aquilano, Carlo Rodolfo and Daniele Lettieri Barbato for their suggestions on mitochondrial analyses. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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

#### **References**

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

© 2020 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 (http://creativecommons.org/licenses/by/4.0/).


*Methodologies for Achieving 1D ZnO Nanostructures Potential for Solar Cells DOI: http://dx.doi.org/10.5772/intechopen.83618*

*Renewable and Sustainable Composites*

For 1D nanostructures, the great reduction of the melting point of a solid material is very obvious. Because of the special characteristics of nanocrystal materials, the high specific boundary area means large stored interface energy. Consequently, 1D nanostructures can be tailored precisely on the basis of their thermal property, such as synthesizing and annealing temperatures [31–33].

For 1D nanostructures, the grain size and boundaries are the dominated factors effected on the electron mean free path and resistance. There is a trend that many physical properties of 1D nanostructures like optical, magnetic, and electrical properties will be enhanced distinctively when the size or dimension of the material

nanowires, nanorods, etc.) are generally prone to be enriched with many surface defects and oxygen and cation vacancies due to their low formation energy within

Magnetic properties of materials are fundamentally determined by the magnetic couplings at the atomic level. Unlike bulk ferromagnetic materials, which usually form multiple magnetic domains, 1D nanomaterials consist of the simple magnetic domain resulted in obvious difference in several important aspects from the prop-

The confinement of the size has a significant effect on the energy levels of the nanowire determination when its diameter decreases to some critical length (Bohr radius). Results indicate that the nanowire absorption edge of silicon is obviously blue-shifted because the bulk silicon indirect bandgap is only 1.1 eV. The characteristics of the absorption spectra are sharp and discrete along with the photoluminescence (PL) in the relatively strong "band-edge." At the same time, along the longitudinal axes of the nanowires, the emitted light is highly polarized [41–44].

Zinc oxide (ZnO) has been investigated for a long time as it is an amazing material with multiple functions. ZnO is a direct wide bandgap semiconductor material with piezoelectric and photoelectric properties. ZnO has a wide direct bandgap of 3.37 eV which is similar to GaN and a high exciton binding energy of 60 meV at room temperature. The wide bandgap gives good optical transparency to visible light which makes ZnO a suitable candidate for short wavelength photonic applications (UV and blue spectral range). ZnO has a non-central symmetric wurtzite structure, and the relevant hexagonal unit cell (*a* = 3.25 Å, *c* = 5.20 Å) packed O2− closely and stacked Zn2+ layers alternately along the *c*-axis direction. Due to the unique fascinating property in electronics, optics, photonics, and magnetics, ZnO provides an impact on applications in various areas, such as solar cells, supercapacitors, sensors, catalysis, light-emitting, actuators, and biomedical devices. ZnO has equal importance in relation to silicon-based 1D nanostructures in the field of 1D

m) scale. Moreover, such 1D nanostructures (such as

**2.2 Thermal property**

**2.3 Electronic property**

reduces to nanosize (~10−9

nanoscale materials [34, 35].

erty of their bulk counterparts [36–40].

**2.4 Magnetic property**

**2.5 Optical property**

**3. Zinc oxide**

**54**

nanostructures, and it has an increasing influence in developing nanotechnology. To date, various quasi-one-dimensional nanostructures of ZnO have been synthesized, i.e., nanowires, nanobelts, and nanotubes [45–47]. **Figure 1(a**–**c)** expresses the images of ZnO rods taken by SEM synthesized at pH 8. It indicates that the length

of synthesized ZnO nanorods is about 4 μm with the diameter of around 700 nm and ZnO nanorods with the flat top surface, and they stack one by one through polar surfaces. From the crystal structure of the ZnO, the ions of Zn and O are arranged alternatively through *c* axis where the bottom surface is O2− terminated (000-1) and the top surface is Zn2+ terminated (0001). The surfaces of the flat top explored in the nanorods of ZnO are contributed to the polar surface disappearance. In the basis solution with the weak volume, the precipitate of Zn(OH)2 solid exists in the reactant solution. Owing to the dipole interaction, Zn(OH)2 solid is taken as the polar surface that could easily make the positive and negative surfaces of ZnO crystal incorporate efficiently. Therefore, the surface energy of the polar surfaces is relatively high than that of the nonpolar surfaces, disappears at the first when the nonpolar surfaces start to slowly grow, and appears in the last stage of ZnO nanostructure crystal growth [48].


**MANAGEMENT OF CHRONIC LIVER DISEASES - RECENT ADVANCES**

Edited by **Xingshun Qi**

#### **Management of Chronic Liver Diseases - Recent Advances**

http://dx.doi.org/10.5772/intechopen.71913 Edited by Xingshun Qi

#### **Contributors**

Andra-Iulia F. Suceveanu, Laura Mazilu, Roxana Hulea, Andreea Gheorghe, Felix Voinea, Claudia Voinea, Adrian-Paul Suceveanu, Irinel Parepa, Anca Pantea Stoian, Hiroshi Doi, Hiroya Shiomi, Ryoong-Jin Oh, Guang Chen, Chinmaya Bal, Marco Antonio Lopez Hernandez, Hiroteru Kamimura, Tomoyuki Sugano, Ryoko Horigome, Naruhiro Kimura, Masaaki Takamura, Hirokazu Kawai, Satoshi Yamagiwa, Shuji Terai

#### **© The Editor(s) and the Author(s) 2018**

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Individual chapters of this publication are distributed under the terms of the Creative Commons Attribution 3.0 Unported License which permits commercial use, distribution and reproduction of the individual chapters, provided the original author(s) and source publication are appropriately acknowledged. If so indicated, certain images may not be included under the Creative Commons license. In such cases users will need to obtain permission from the license holder to reproduce the material. More details and guidelines concerning content reuse and adaptation can be foundat http://www.intechopen.com/copyright-policy.html.

#### **Notice**

Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book.

First published in London, United Kingdom, 2018 by IntechOpen eBook (PDF) Published by IntechOpen, 2019 IntechOpen is the global imprint of INTECHOPEN LIMITED, registered in England and Wales, registration number: 11086078, The Shard, 25th floor, 32 London Bridge Street London, SE19SG – United Kingdom Printed in Croatia

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**5. Profiling of** *Mycobacterium tuberculosis* **gene expression during infection in genetically different mouse models**

We have carried out a comparative study of *M. tuberculosis* transcriptomes in order to reveal the features of expression profiles that correlate with progressing disease, and also to understand the difference between efficient and defective defence mechanisms at the level of bacterial gene expression. To this end, at different stages of the infection process, we performed a comparative quantitative and qualitative analysis of the sequences transcribed during infection of mice sensitive (inefficient immune response) and resistant (efficient response) to these bacteria (Skvortsov et al., 2010).

We have compared transcriptomes of *M. tuberculosis* H37Rv in infected mice of two lineages, I/StSnEgYCit (I/St) and C57BL/6JCit (B6). These lineages have been earlier described in detail (Kondratieva et al., 2010), and the B6 lineage was shown to be more resistant to the infection by *M. tuberculosis* than I/St. In particular, the infection process in B6 mice was less aggressive, and the infected mice had a longer survival.

Female mice of both lineages were aerogenically infected with *M. tuberculosis* bacteria. In 4 and 6 weeks post infection, the infected mice were killed, and total lung RNA isolated. Samples of total RNA from lung tissues of I/St and B6 mice were used to synthesize cDNA enriched in fragments of bacterial cDNA using coincidence cloning procedure (Azhikina et al., 2010). As a result, three cDNA libraries were obtained, which represented transcriptomes of *M. tuberculosis* from lung tissues of I/St mice on week 6 post infection

*Mycobacterium tuberculosis* Transcriptome *In Vivo* Studies –

*M. tuberculosis*-specific (unique)

*M. tuberculosis*-specific (unique)

The number of *M. tuberculosis* genes

Genes expressed, % of the total number

sequences

libraries

expressed in each sample.

A Key to Understand the Pathogen Adaptation Mechanism 137

Library CC4(RES) CC6(SUS) CC6(RES) Total sequences read 73410 75655 40966

sequences, % of total sequences read 20,4 57,7 83,6

expressed (at least one reading) 1012 1353 1940

of genes in the library 25,2 33,7 48,3

Table 1. Results of sequencing and mapping of the CC4(RES), CC6(RES) and CC6(SUS)

Of 4012 *M. tuberculosis* genes and 7 pseudogenes, in the CC4(RES) sample 1012 (25.2%), in CC4(SUS) - 1353 (33.7%), and in CC6(RES) – 1940 (48.3%) genes were expressed. 1428 (35,5%) genes were not expressed in either of the samples, whereas 469 (11.7%) genes were

The distribution of the expressed genes between functional categories is shown in Fig. 3.

Fig. 3. Assignment to biochemical categories of the *M. tuberculosis* H37Rv genes expressed in samples of lung tissue of B6 mice on weeks 4 and 6 post infection and of I/St mice on week 6

The comparison of *M. tuberculosis* transcriptomes during infection in a genetically stable mouse lineage (СС6(RES) vs CC4(RES)) and in genetically different mice (СС6(RES) vs CC6(SUS)), described above, was aimed at the search of genes whose expression is enhanced in the course of infection, specifically in B6 mice on week 6 as compared with

post infection (CC4(RES), CC6(RES) and CC6(SUS) libraries, respectively)

**5.1 Genes whose expression is enhanced during infection**

Mobile elements (insertion sequences and phages) were excluded from the analysis.

14990 43618 34234

(СС6(SUS)) and from lung tissues of B6 mice on weeks 4 and 6 post infection (СС4(RES) and СС6(RES), respectively). The libraries were analyzed using the 454 pyrosequencing technology. A general scheme of the experiment is shown in Fig. 2, and general characteristics of the libraries analyzed are presented in Table 1. In total, sequences of 190031 cDNA fragments were determined: 73410 from CC4(RES), 75655 from CC4(SUS), and 40966 from CC6(RES).

Fig. 2. Scheme of the transcriptomes comparison experiment. Res – genetically resistant mice, Sus – genetically susceptible mice, CC – library enriched with bacterial cDNA

Using a standalone BLASTn algorithm, the nucleotide sequences obtained were mapped to the genome sequence of *M. tuberculosis* H37Rv (GenBank version: AL123456.2). The mapping revealed that the CC4(RES), CC6(SUS) and CC6(RES) samples contained 14990 (20.42%), 43618 (57.65%) and 34234 (83.57%) *M. tuberculosis* sequences, respectively.

The results obtained demonstrate that the technology used allowed us to considerably enrich the cDNA samples with bacterial sequences.

(СС6(SUS)) and from lung tissues of B6 mice on weeks 4 and 6 post infection (СС4(RES) and СС6(RES), respectively). The libraries were analyzed using the 454 pyrosequencing technology. A general scheme of the experiment is shown in Fig. 2, and general characteristics of the libraries analyzed are presented in Table 1. In total, sequences of 190031 cDNA fragments were determined: 73410 from CC4(RES), 75655 from CC4(SUS), and

Fig. 2. Scheme of the transcriptomes comparison experiment. Res – genetically resistant mice, Sus – genetically susceptible mice, CC – library enriched with bacterial cDNA

(20.42%), 43618 (57.65%) and 34234 (83.57%) *M. tuberculosis* sequences, respectively.

enrich the cDNA samples with bacterial sequences.

Using a standalone BLASTn algorithm, the nucleotide sequences obtained were mapped to the genome sequence of *M. tuberculosis* H37Rv (GenBank version: AL123456.2). The mapping revealed that the CC4(RES), CC6(SUS) and CC6(RES) samples contained 14990

The results obtained demonstrate that the technology used allowed us to considerably

40966 from CC6(RES).

Table 1. Results of sequencing and mapping of the CC4(RES), CC6(RES) and CC6(SUS) libraries

Of 4012 *M. tuberculosis* genes and 7 pseudogenes, in the CC4(RES) sample 1012 (25.2%), in CC4(SUS) - 1353 (33.7%), and in CC6(RES) – 1940 (48.3%) genes were expressed. 1428 (35,5%) genes were not expressed in either of the samples, whereas 469 (11.7%) genes were expressed in each sample.

The distribution of the expressed genes between functional categories is shown in Fig. 3. Mobile elements (insertion sequences and phages) were excluded from the analysis.

Fig. 3. Assignment to biochemical categories of the *M. tuberculosis* H37Rv genes expressed in samples of lung tissue of B6 mice on weeks 4 and 6 post infection and of I/St mice on week 6 post infection (CC4(RES), CC6(RES) and CC6(SUS) libraries, respectively)

#### **5.1 Genes whose expression is enhanced during infection**

The comparison of *M. tuberculosis* transcriptomes during infection in a genetically stable mouse lineage (СС6(RES) vs CC4(RES)) and in genetically different mice (СС6(RES) vs CC6(SUS)), described above, was aimed at the search of genes whose expression is enhanced in the course of infection, specifically in B6 mice on week 6 as compared with

*Mycobacterium tuberculosis* Transcriptome *In Vivo* Studies –

**mechanisms**

http://tuberculist.epfl.ch)

even *M. tuberculosis*.

antigenic variability

state of latent infection.

suggest a forced usage of lipids as the source of energy and carbon.

A Key to Understand the Pathogen Adaptation Mechanism 139

Such a picture is quite predictable as the microenvironment in a resistant host is a hostile habitat which can explain the need in more active repair systems. Increased gene expression of lipolytic enzymes (*lipF*, *lipV*, *plcA*), enzymes of the tricarbonic acid cycle and *aceAb* may

**5.1.2 CUGs – genes needed for** *M. tuberculosis* **adaptation to different host defense**

We have revealed 209 genes upregulated in both comparisons. According to the results of transposon mutagenesis, the products of 44 out of these genes are essential in *M. tuberculosis* (Sassetti et al., 2003), the list of these genes is given in Table 2. *Rv3569c*, *Rv3537*, and *Rv3563*  were earlier shown to be essential for survival in mouse macrophages (TubercuList,

A bit less than one third of the 209 genes belong to the conserved hypothetical (59 genes) and unknown (2 genes) categories. In spite of unknown functions, the genes of these categories may be considered potential therapeutic targets, since their low homology to genes of other microorganisms suggests that they are characteristic just of mycobacteria or

The function of the PE/PPE family proteins is not quite clear, but they are suggested to be needed for antigenic variability in mycobacteria (Karboul et al., 2008). Nevertheless, the *Rv0152c* and *Rv0355c* genes had a high expression level in the CC6(RES) sample, and they were also expressed in the CC4(RES) and CC4(SUS) samples. *Rv3135* encodes a protein essential in *M. tuberculosis* H37Rv that may suggest some additional functions beyond

One more feature of CUGs is an increased activity of amino acid metabolism pathways. It is not clear if the stimulation of this metabolism enzyme expression is due to the absence of available amino acids (and the necessity of their synthesis) or their availability (and the possibility to use them). Poor nutrient conditions of the environment are supported by a high level gene expression of various systems of acquisition and accumulation of nutrients, e.g. such as phosphate (*pstS1*) or iron (*irtA*, *mbtC*, *mbtE*, *mbtF*). A shortage of phosphate is indicated by enhanced expression of the *senX3* gene, a sensor component of the senX3 regX3 two-component system that activates the so called stringent response under phosphorus deficiency. The expression of lipid metabolism genes (fadD, fadE, lipU, *lipJ*) suggests a switch to using lipids as a major source of energy and carbon. Enhanced expression of the *narH* and *narK3* genes implies a switch to anaerobic respiration characteristic of latent infection. Finally, the *secA2* gene is also worth mentioning. This gene codes for translocase SecA2 which is a component of the *M. tuberculosis* secondary transport system Sec that provides for, in particular, secretion of superoxide dismutase SodA and catalase katG. A live vaccine based on an *M. tuberculosis* mutant for the *secA2* gene (Hinchey et al.,2011) showed high efficiency and safety in animal trials. Summarizing, it can be said that CUGs reflect characteristic features of infection in a mouse model. An exception is increased expression of the *atpF* and *atpH* genes, although, according to some reports, their expression decreases in the course of infection as energy demand of the pathogen goes down upon transition to the

other time points. The comparison of СС6(RES) vs CC4(RES) allowed to reveal 226 genes upregulated in infected tissues of B6 mice. A similar comparison of СС6(RES) vs CC6(SUS) revealed 253 genes upregulated in the CC6(RES) sample (Fig. 4). We concentrated our search on *M. tuberculosis* genes:

Fig. 4. Venn diagram illustrating the number of upregulated mycobacterial genes in comparisons CC6(RES) vs CC4(RES) (dotted) and CC6(RES) vs CC6(SUS) (solid)