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**Abstract**

The chapter considers the technology of the crude distillation unit in general. The crude distillation unit is at the front-end of the oil refinery. The desalting process and distillation of crude oil are included in the crude distillation unit (CDU). The desalting process of crude oil is imperative to ensure the good quality of crude oil, that is, to remove impurities before its transfer to refining. This procedure minimizes or eliminates harmful substances such as sulfur, water, salts, and even mechanical impurities, which ensures a long operation of pipelines. However, the desalting process is only part of the distillation unit. The other phase of this unit is as already mentioned above is: distillation. Distillation is a more physical than a chemical process. The distillation process is characterised by mass-thermal transfer of materials, which leads to the obtaining of fractions. The distillation in the crude distillation unit is carried out consecutively in two ways: atmospheric and vacuum. In the Russian Federation, we classify oil refining plants in general as follows: fuel, fuel-oil, fuel-petrochemical, and fuel-oil-petrochemical. Also, regardless of the profile of the refinery, great importance is given to the crude distillation unit. The crude distillation unit, if well modeled and organized, makes it possible to obtain already more light products at this stage; therefore, there is a reduction of heavy residues, the refinement of which requires more expensive processes such as hydrocracking or catalytic cracking. The oil topping column K-1, within this framework, has been adopted in several oil refining plants in addition to the main column commonly referred to as K-2. The principal purpose of column K-1 is the separation of light gasoline and the major part of dissolved gases from crude oil. This first step of distillation at K-1 has the effect of normalizing the amount of gasoline hydrocarbons and stabilizing the operation of the main column K-2 despite possible fluctuations in the composition of crude oil. Besides, the CDU equipped with column K-1 demonstrates an increase in some valuable products such as methane, ethane, naphtha, etc.

**Keywords:** demulsifiers, crude oil, desalting process, desalter, electrostatic desalter, sump, separator, crude distillation units, atmospheric distillation units, vacuum distillation units, K-1 oil topping column, K-2 main atmospheric column

## **1. Introduction**

Many crude oils usually contain besides the basic elements of its chemical composition hydrocarbons such as sulfur, oxygen, nitrogen, and mechanical impurities [1]. Oil also contains the gaseous phase as methane, ethane, and liquid phase. Gas typically dissolves in the liquid, whereas in the balance's case remains strong. It is noticed with the experience that natural gas fields are close to oil fields [2]. In general, there are four or three oil: aromatic, paraffin, naphthenic. But there is also a mixed type [1]. The classification of crude oil is carried out for various reasons. The criteria for classifying crude oil refer more or less to its quality [3]. For a better understanding of classificatory at the quality (quality/price), the Petroleum world has adopted a benchmark called stallion as Brent Blend produced in the North Sea, West Texas Intermediate (WTI) from the USA, Dubai Crude from Dubai... However, there are other possibilities to classify oil by density, sulfur content, and viscosity. Crude oil supplied to refineries must meet requirements [4]. The pretreatment of oil from harmful impurities occupies an important place among the main processes associated with the production, collection, and transportation of oil to refineries or export [5]. The harmful impurities of oil can be divided into two groups: hydrophilic (lipophobic) and hydrophobic (lipophilic). The hydrophobic impurities are soluble in oil. These hydrophobic impurities are organometallic compounds (metalloporphyrin complexes), organic acids, among which the most undesirable organochlorine compounds. The Hydrophilic impurities include impurities that are inherently insoluble in oil, these impurities are water and inorganic salts dissolved in it, as well as solid salts, mechanical impurities (sand, clay), hydrogen sulphide, etc. These hydrophilic impurities are in another phase (water), which are dispersed in the oil in the form of drops of water [6, 7]. Therefore, the degree of pre-treatment oil in production areas is an imperative and significantly affects the efficiency and reliability of transportation by pipeline, tanker, rail, and even road transportation [2]. These requirements should facilitate the transportation and future processing of Petroleum. The requirements are summarized as follows: water content, the content of mechanical impurities, saturated steam pressure, the content of chloride salts, and organochlorine compounds [5]. Usually, the degree of pre-treatment of oil for transportation does not consider the content of chloride salts and the content of organochlorine compounds [8], literally because all salts are large in water and not in oil [9], in connection with the decision of the Russian Federation to join the world trade organization (WTO), the requirements for oil are unified following the requirements for prepared oil according to ASTM D1250-97 [4] and EN 224 [3]. However, countries such as Russia use the GOST R 51858-2002 standard for oil, established by oil and gas companies for transportation by oil pipeline [5] for delivery to oil refining plants in the Russian Federation and export. In Russia, the pipeline transport takes into account the above-mentioned points [3, 10]: salt content (100–900 mg/l), water content from 0.5 to 1.0% of mass, impurity content (not over 0.05% of mass).

appoint the distillation unit: Atmospheric-vacuum distillation unit (AVDU). AVDU is itself subdivided into two other units which are: Atmospheric distillations unit (ADU) and vacuum distillation unit (VDU). As a result, in this chapter, we tried to explain the operation of the atmospheric distillation crude, to model it, and to compare the usual model of the crude distillation unit with the model of the Russian

*P& ID diagram flow process of crude distillation unit without vacuum distillation. We would like to emphasize that reboilers were used in this scheme in this way, just for esthetics. Less flow to the reboilers is done differently.*

An emulsion is a suspension of small droplets of a liquid (water) in a second liquid (oil crude) [11]. The liquids, in a stable emulsion, can only be separated by a treatment process which calls the desalting process [10]. The electrical conductivity of oil emulsions in the literature note that it has an ionic nature. A detailed theory of ionic conductivity of liquid dielectrics. Despite the low dielectric constant, the polar components of crude oil dissociate into ions and create a fairly significant amount of electrical conductivity. The reason of this manifestation is asphalt-resinous sub-

The demulsifier is a chemical additive to break the crude oil emulsion. The emulsifier destroys the emulsion by reducing the mechanical resistance of the protective shells formed on the surface of the water droplets. By maintaining a strict hydrophilic-lipophilic balance [6]. These emulsions are destroyed using synthetic surfactants (demulsifiers) added to the oil emulsion [13]. The criteria for selecting and evaluating the demulsifier in terms of technology, that is, yield, are as follows

• Quality of destruction of emulsions (fullness of water separation);

Federation.

**Figure 1.**

*Crude Distillation Unit (CDU)*

*DOI: http://dx.doi.org/10.5772/intechopen.90394*

[8, 12, 14]:

**107**

*1.2.1 Oil emulsion*

**1.2 Description of the crude oil and demulsifier**

stances in oil [12]. Oil emulsion is illustrated in **Figure 2**.

• The velocity of separation of water from oil;

*1.2.2 The demulsifier (surfactant)*


*Article* **A Possibilistic Kalman Filter for the Reduction of the Final Measurement Uncertainty, in Presence of Unknown Systematic Errors**

**Harsha Vardhana Jetti 1 and Simona Salicone 2,\***

**Abstract:** A Kalman filter is a concept that has been in existence for decades now and it is widely used in numerous areas. It provides a prediction of the system states as well as the uncertainty associated to it. The original Kalman filter can not propagate uncertainty in a correct way when the variables are not distributed normally or when there is a correlation in the measurements or when there is a systematic error in the measurements. For these reasons, there have been numerous variations of the original Kalman filter, most of them mathematically based (like the original one) on the theory of probability. Some of the variations indeed introduce some improvements, but without being completely successful. To deal with these problems, more recently, Kalman filters have also been defined using random-fuzzy variables (RFVs). These filters are capable of also propagating distributions that are not normal and propagating systematic contributions to uncertainty, thus providing the overall measurement uncertainty associated to the state predictions. In this paper, the authors make another step forward, by defining a possibilistic Kalman filter using random-fuzzy variables which not only considers and propagates both random and systematic contributions to uncertainty, but also reduces the overall uncertainty associated to the state predictions by compensating for the unknown residual systematic contributions.

**Keywords:** random-fuzzy variables; Kalman filter; systematic uncertainty contributions; styling


Section 3 Digital Evidence

**69**

**Chapter 5**

*Petra Perner*

a Chapter Part II of this topic.

**1. Introduction**

forensic data analysis.

and interpretation.

data analysis, novelty detection, legal aspects

**Abstract**

Novel Methods for Forensic

Multimedia Data Analysis: Part I

The increased usage of digital media in daily life has resulted in the demand for novel multimedia data analysis techniques that can help to use these data for forensic purposes. Processing of such data for police investigation and as evidence in a court of law, such that data interpretation is reliable, trustworthy, and efficient in terms of human time and other resources required, will help greatly to speed up investigation and make investigation more effective. If such data are to be used as evidence in a court of law, techniques that can confirm origin and integrity are necessary. In this chapter, we are proposing a new concept for new multimedia processing techniques for varied multimedia sources. We describe the background and motivation for our work. The overall system architecture is explained. We present the data to be used. After a review of the state of the art of related work of the multimedia data we consider in this work, we describe the method and techniques we are developing that go beyond the state of the art. The work will be continued in

**Keywords:** multimedia forensic data analysis, standardization of forensic data analysis, video and image enhancement, video analysis, image analysis, speech analysis, case-based reasoning, multimedia feature extraction, handwriting, Twitter

The objective of this work is to provide novel methods and techniques for the analysis of forensic multimedia data. These methods and techniques should form a novel toolkit for automatic forensic multimedia data. The data modalities the proposed work is considering are images and videos, text, handwriting, speech and audio signals, social media data, log data, and genetic data. The integration of methods for all these different data modalities in one tool kit should allow the cross-analysis of these data and the detection of events by interlinking between these data. The proposed methods will face on standard forensic tasks, for example, identification of events, persons, or groups and device recognition. Together with the end users and the police forces, new standard tasks will be worked out during the project and will give a new input to the standardization aspect of

The proposed novel methods and techniques will consider all aspects of multimedia data analysis such as device identification and trustworthiness of the data, signal enhancement, preprocessing, feature extraction, signal and data analysis,


**4.1 Wealth ranking of the respondents**

To establish the socio-economic profile of the respondents a wealth ranking approach was used. Wealth ranking was important in this study to determine whether there is any relationship between biogas technology adoption and wealth of the household into wealth ranks using a set of pre-established criteria (Afonja, 1992). Since its introduction in the 1980's, rapid rural appraisal (RRA) wealth ranking has become an increasingly accepted means of assessing relative socio-economic status in the context of applied research projects and development programs (Chambers, 1994). In this study the members of village governments were involved in wealth ranking for their respective villagers. Criteria for wealth ranking was adopted as perceived locally it included aspects of, food security, livestock, dairy cattle and other assets ownership, land and annual incomes (Table 2). Results from wealth ranking (based on communities perception) show that there were no people who were really *well off* in the sample of households but only the so called *slightly well-off*. Out of the 120 respondents 22% were *slightly well-off* while l*ess poor* and *poor* were 55% and 27% respectively (Table 2).

Table 2. Characteristics of each Wealth rank group


**3.1 Effect urease on protein deposition and embryo development**

Given that urease is the plant's only means of assimilating urea, the next question is the metabolic version of "If you are so smart how come you are not rich?" When applied to urease it would read, "If you're so important how come the plant survives without you?" Indeed, completely urease negative mutant soybean plants develop to maturity and produce a relatively good yield of seeds that germinate at normal frequency to propagate another generation. However, if the role of urease is to recycle urea nitrogen generated from arginine (and possibly ureide) degradation, then a protein-rich plant such as soybean may provide a "suppressing" background for a urease defect. Soybean has indeed been intensively bred for large and protein-rich seeds. However, even in soybean we question the dispensability of urease. Ureasenegative mutant plants [41] and nickel-deprived wild type [35, 36] exhibit necrotic leaf tips, apparently due tourea "burn." Similar observations were made in nickeldeprived tomato [48, 49]. More important, perhaps, than the deleterious effects of leaf burn is the loss of significant quantities of nitrogen in a urea dead-end, lost nitrogen that could have a significant negative impact on seed protein deposition during pod fill. The assessment of the agronomic impact of a urease-negative phenotype on soybean performance requires extensive field testing of isogenic or nearly isogenic paired urease-positive and urease-negative lines, preferably in multiple environments. To obtain these lines we are currently engaged in the long process of backcrossing, generation advance, selection for uniformity in maturity group and plant architecture, etc., and amplification of seed stocks [50]. Our prediction that total seed protein of plant will decrease as suggested by [36], who reported a correlation between seed yield and nickel content per seed. However, there was too much variation in their material to obtain a statistically significant difference. It was observed that at the time of flowering completely urease-negative soybean mutants accumulated approximately 100 dry wt of total leaf (green plus necrotic tip) [41]. Assuming that 10% of the leaf dry weight is protein (16% nitrogen), much of which is destined to provide amino acids during pod fill, accumulated urea (47% nitrogen) represents 18% of the nitrogen in leaf protein. The developing soybean embryo does not generate urea [41]. Thus, other than recycling maternally derived urea, urease appears to play no direct role in embryo metabolism. However, it is possible that in monocots a urease role is more critical in embryo development. It has been reported that nickel is essential for development of viable barley embryos [51]. Post dormancy grains could not be rescued by nickel, whereas developing grains could be rescued by the feeding of nickel to the maternal plant. The role of nickel in barley embryo development is not known and may be unrelated to urease. (Urease is the only nickel metalloenzyme yet identified in plants). However, it is possible that a loss of urease activity under nickel-deprivation conditions leads either to urea poisoning or to nitrogen starvation of the embyro. It would be informative to study barley embryo development *in vitro,* where both nickel availability and nitrogen source can be manipulated.


*2.2. Phylogenetic Analysis*

The phylogenetic relationships between the set of MdAQPs with homologous proteins encoded by *A. thaliana*, poplar, and rubber (*Hevea brasiliensis*) is displayed in Figure 1. The analysis allowed the set of apple AQPs to be each assigned membership of one of the five plant AQP subfamilies, namely the MdPIPs (eleven members), the MdTIPs (thirteen members), the MdNIPs (eleven members), the MdSIPs (five members), and the MdXIPs (two members). The MdPIP members were further classified into the two subgroups, MdPIP1 and MdPIP2, the MdSIPs into the two subgroups, MdSIP1 and MdSIP2, and the MdTIPs into the five subgroups, MdTIP1-MdTIP5. The two MdXIPs belonged into the two subgroups MdXIP1 and MdXIP2, respectively, and the MdNIPs were divided into six subgroups, MdNIP1, 2, 4, 5, 6 and 7. On the basis of sharing a level of >90% similarity at the peptide level, 14 pairs of sequences were recognized, namely MdNIP1;1/MdNIP1;2, MdNIP2;1/MdNIP2;2, MdNIP5;1/MdNIP5;2, MdTIP1;3/MdTIP1;4, MdTIP4;1/MdTIP4;2, MdTIP2;1/MdTIP2;2, MdTIP3;1/MdTIP3;2, MdTIP5;1/MdTIP5;2, MdPIP1;1/MdPIP1;2, MdPIP2;1/MdPIP2;2, MdPIP2;3/MdPIP2;4, MdPIP2;6/MdPIP2;7, MdSIP1;2/MdSIP1;3, and MdSIP2;1/MdSIP2;2.

*Int. J. Mol. Sci.* **2019**, *20*, x FOR PEER REVIEW 4 of 18

**Figure 1.** Phylogenetic tree of AQPs from *Arabidopsis thaliana*, *Populus trichocarpa*, *Hevea brasiliensis,* and *Malus domestica*. The protein sequences were aligned by ClustalX and the phylogenetic tree was constructed by the Neighbor-Joining method (1000 bootstrap replicates) in the MEGA6 software. The subgroups are marked by a colorful background(orange for TIPs, purple for PIPs, red for NIPs, blue for XIPs and gray for SIPs). **Figure 1.** Phylogenetic tree of AQPs from *Arabidopsis thaliana*, *Populus trichocarpa*, *Hevea brasiliensis,* and *Malus domestica*. The protein sequences were aligned by ClustalX and the phylogenetic tree was constructed by the Neighbor-Joining method (1000 bootstrap replicates) in the MEGA6 software. The subgroups are marked by a colorful background (orange for TIPs, purple for PIPs, red for NIPs, blue for XIPs and gray for SIPs).

#### *2.3. Chromosomal Location and Gene Structure 2.3. Chromosomal Location and Gene Structure*

It was possible to map 40 of the 42 *MdAQPs* on 16 of the 17 apple chromosomes, but neither *MdNIP1;2* nor *MdTIP3;2* could be placed (Figure 2). The sequence of each of the eleven *MdPIPs* featured three introns; all but one of the thirteen *MdTIPs* featured two introns (the exception was *MdTIP1;1* in which only one intron was present); eight of the eleven *MdNIP* sequences were interrupted by four introns, with three introns present in both *MdNIP5;1* and *MdNIP5;2*, and five in *MdNIP5;3*; three of the five *MdSIPs* harbored two introns, while neither *MdSIP1;2* nor *MdSIP1;3* featured any introns; finally, *MdXIP1;1* had one intron while *MdXIP2;1* included two introns (Figure 3). It was possible to map 40 of the 42 *MdAQPs* on 16 of the 17 apple chromosomes, but neither *MdNIP1;2* nor *MdTIP3;2* could be placed (Figure 2). The sequence of each of the eleven *MdPIPs* featured three introns; all but one of the thirteen *MdTIPs* featured two introns (the exception was *MdTIP1;1* in which only one intron was present); eight of the eleven *MdNIP* sequences were interrupted by four introns, with three introns present in both *MdNIP5;1* and *MdNIP5;2*, and five in *MdNIP5;3*; three of the five *MdSIPs* harbored two introns, while neither *MdSIP1;2* nor *MdSIP1;3* featured any introns; finally, *MdXIP1;1* had one intron while *MdXIP2;1* included two introns (Figure 3).

*Int. J. Mol. Sci.* **2019**, *20*, x FOR PEER REVIEW 5 of 18

**Figure 2.** Distribution of *AQP* genes in apple chromosomes. Two genes (*MdNIP1;2* and *MdTIP3;2*) could not be localized on any chromosome. The scale is in megabases (Mb). **Figure 2.** Distribution of *AQP* genes in apple chromosomes. Two genes (*MdNIP1;2* and *MdTIP3;2*) could not be localized on any chromosome. The scale is in megabases (Mb).

*Int. J. Mol. Sci.* **2019**, *20*, x FOR PEER REVIEW 6 of 18

**Figure 3.** The exon-intron structure of apple *AQP* genes. Upstream/downstream region, exon, and intron are represented by blue box, yellow box, and grey line using GSDS software, respectively. **Figure 3.** The exon-intron structure of apple *AQP* genes. Upstream/downstream region, exon, and intron are represented by blue box, yellow box, and grey line using GSDS software, respectively.

#### *2.4. Conserved Residues in the Apple AQPs 2.4. Conserved Residues in the Apple AQPs*

The NPA motifs, ar/R filter, and Froger's positions were identified via a multiple alignment between the apple AQPs and other plant AQPs (Table 2). These conserved positions were critical for the substrate selectivity of AQPs. Both NPA domains were conserved in all MdPIP and MdTIP members, but the third residue of the first NPA in MdNIP5;1, MdNIP5;2, and MdNIP5;3 was serine rather than alanine, while in MdNIP5;1, MdNIP5;2, and MdNIP6;1, the third residue of the second NPA was valine rather than alanine; in MdNIP2;3 it was glutamate and in MdNIP5;3 it was isoleucine. The MdSIPs all carried a non-conserved third residue in the first NPA, while in addition, the first residue of the second NPA in MdSIP2;2 was serine rather than asparagine. Both the first The NPA motifs, ar/R filter, and Froger's positions were identified via a multiple alignment between the apple AQPs and other plant AQPs (Table 2). These conserved positions were critical for the substrate selectivity of AQPs. Both NPA domains were conserved in all MdPIP and MdTIP members, but the third residue of the first NPA in MdNIP5;1, MdNIP5;2, and MdNIP5;3 was serine rather than alanine, while in MdNIP5;1, MdNIP5;2, and MdNIP6;1, the third residue of the second NPA was valine rather than alanine; in MdNIP2;3 it was glutamate and in MdNIP5;3 it was isoleucine. The MdSIPs all carried a non-conserved third residue in the first NPA, while in addition, the first residue of the second NPA in MdSIP2;2 was serine rather than asparagine. Both the first and third residues of the first NPA of MdXIP1;1 were non-conserved and the third residues of the first NPA of MdXIP2;1 was valine. The ar/R

glycine-serine-glycine-arginine, alanine-isoleucine-glycine-arginine,

and third residues of the first NPA of MdXIP1;1 were non-conserved and the third residues of the first NPA of MdXIP2;1 was valine. The ar/R filter sequence was well conserved within each filter sequence was well conserved within each subfamily, but varied between the subfamilies. Each of the PIPs carried the conserved sequence phenylalanine-histidine-threonine-arginine. The greatest diversity for this motif was present among the NIPs, where each of the tetrapeptides tryptophan-valine-alanine-arginine, glycine-serine-glycine-arginine, alanine-isoleucine-glycine-arginine, threonine-isoleucine-alanine-arginine, and alanine-valine-glycine-arginine was represented. With respect to the Froger's positions, there was also high conservation within each subfamily, but variability between subfamilies. While the P1 position was the only variable residue within the PIP and XIP subfamily members, both the P1 and P2 positions varied among members of TIP and SIP subfamilies, and the P1, P2, and P5 positions were were all non-conserved for NIP subfamily members.

**Table 2.** NPA motifs, ar/R filter, and Froger's positons of apple AQPs.

NPA, Asparagine-Proline-Alanine; Ar/R, aromatic/arginine; LE, loop E; LB, Loop B; H2, transmembrane helix 2; H5, transmembrane helix 5.

*2.5. The Site of Apple PIP2 Expression*

#### *2.5. The Site of Apple PIP2 Expression* a subgroup of the *M. domestica AQP* gene family, the products of which are likely important regulators of water transport across the plasma membrane. The distribution of these hits among

transmembrane helix 2; H5, transmembrane helix 5.

A search of the set of apple ESTs deposited in GenBank resulted in 685 hits for seven *MdPIP2s*, a subgroup of the *M. domestica AQP* gene family, the products of which are likely important regulators of water transport across the plasma membrane. The distribution of these hits among each *PIP2* gene and the organ are shown in Figure 4. The most well represented gene was *MdPIP2;4* (218 hits) and the least well represented was *MdPIP2;5* (seven hits). The site of transcription of these genes can be inferred from the frequency of their transcripts' occurrence in the 76 cDNA libraries assembled from various organs. In the bud libraries, there were 116 such ESTs out of a total of 54,099 sequences; there were 104 out of 54,120 in the leaf libraries; there were 73 out of 35,380 in the stem libraries; there was 94 out of 12,679 in the root libraries; there were 117 out of 44,772 in the flower libraries; there were 176 out of 104,341 in the fruit libraries; and finally out of libraries constructed from in vitro cultured cells, there were 5 out of 5652. When the abundance of transcripts generated from seven of the *PIP2* genes was evaluated by applying a quantitative real-time PCR (qRT-PCR) assay to RNA extracted from *M. hupehensis* root tissue, *PIP2;1* appeared to be the gene most strongly transcribed (Figure 5). Since both drought and salinity stress are sensed by roots, *PIP2;1* was chosen for a detailed functional analysis. In particular, the copy present in *Malus prunifolia* was selected, as this species provides a source of drought-tolerant rootstocks [22]. each *PIP2* gene and the organ are shown in Figure 4. The most well represented gene was *MdPIP2;4* (218 hits) and the least well represented was *MdPIP2;5* (seven hits). The site of transcription of these genes can be inferred from the frequency of their transcripts' occurrence in the 76 cDNA libraries assembled from various organs. In the bud libraries, there were 116 such ESTs out of a total of 54,099 sequences; there were 104 out of 54,120 in the leaf libraries; there were 73 out of 35,380 in the stem libraries; there was 94 out of 12,679 in the root libraries; there were 117 out of 44,772 in the flower libraries; there were 176 out of 104,341 in the fruit libraries; and finally out of libraries constructed from in vitro cultured cells, there were 5 out of 5652. When the abundance of transcripts generated from seven of the *PIP2* genes was evaluated by applying a quantitative real-time PCR (qRT-PCR) assay to RNA extracted from *M. hupehensis* root tissue, *PIP2;1* appeared to be the gene most strongly transcribed (Figure 5). Since both drought and salinity stress are sensed by roots, *PIP2;1* was chosen for a detailed functional analysis. In particular, the copy present in *Malus prunifolia* was selected, as this species provides a source of drought-tolerant rootstocks [22].

*Int. J. Mol.  The frequency of the various *MdPIP2* ESTs present in cDNA libraries constructed from **Figure 4.** The frequency of the various *MdPIP2* ESTs present in cDNA libraries constructed from RNA extracted from buds, leaves, stems, roots, flowers, fruit and in vitro cultured cells.

RNA extracted from buds, leaves, stems, roots, flowers, fruit and in vitro cultured cells.

of three replicates.

*Int. J. Mol. Sci.* **2019**, *20*, x FOR PEER REVIEW 9 of 18

**Figure 5.** Transcriptional profiling of seven *PIP2* genes in the root of *M. hupehensis*. RNA was extracted from the roots of hydroponically-raised *M. hupehensis* seedlings which had formed 7–8 **Figure 5.** Transcriptional profiling of seven *PIP2* genes in the root of *M. hupehensis*. RNA was extracted from the roots of hydroponically-raised *M. hupehensis* seedlings which had formed 7–8 true leaves. Values show in the form mean ± SD (*n* = 3). **Figure 5.** Transcriptional profiling of seven *PIP2* genes in the root of *M. hupehensis*. RNA was extracted from the roots of hydroponically-raised *M. hupehensis* seedlings which had formed 7–8 true leaves. Values show in the form mean ± SD (*n* = 3).

#### true leaves. Values show in the form mean ± SD (*n* = 3). *2.6. The Abiotic Stress Tolerance of A. thaliana Plants Heterologously Expressing MpPIP2;1 2.6. The Abiotic Stress Tolerance of A. thaliana Plants Heterologously Expressing MpPIP2;1*

of the three transgenic lines was then compared with that of wild type (WT) Col-0 plants.

*2.6. The Abiotic Stress Tolerance of A. thaliana Plants Heterologously Expressing MpPIP2;1*  A set of ten independent *A. thaliana* transgenics heterologously expressing *MpPIP2;1* was obtained, A set of ten independent *A. thaliana* transgenics heterologously expressing *MpPIP2;1* was obtained, and three of these were randomly selected to advance to the T3 generation (Figure 6). The performance of the three transgenic lines was then compared with that of wild type (WT) Col-0 plants. A set of ten independent *A. thaliana* transgenics heterologously expressing *MpPIP2;1* was obtained, and three of these were randomly selected to advance to the T3 generation (Figure 6). The performance

and three of these were randomly selected to advance to the T3 generation (Figure 6). The performance

**Figure 6.** Relative expression of *MpPIP2;1* in three *A. thaliana* transgenic lines heterologously **Figure 6.** Relative expression of *MpPIP2;1* in three *A. thaliana* transgenic lines heterologously expressing *MpPIP2;1* using qRT-PCR with Col-0 as control. The value presented was the mean ± SD **Figure 6.** Relative expression of *MpPIP2;1* in three *A. thaliana* transgenic lines heterologously expressing *MpPIP2;1* using qRT-PCR with Col-0 as control. The value presented was the mean ± SD of three replicates.

expressing *MpPIP2;1* using qRT-PCR with Col-0 as control. The value presented was the mean ± SD of three replicates. The contrasting effect of drought stress on the transgenic and WT plants is illustrated in Figure 7. Under well-watered conditions, the growth of the transgenic lines was indistinguishable from that of WT plants. However, when water was withheld for 30 days, none of the WT plants remained viable as they were unable to maintain a sufficient level of leaf hydration (their relative water content fell to 10.5%); in contrast, many of transgenic plants survived, maintaining a leaf relative water content of about 20%. The post-stress survival rate of these latter plants was between 10.3% and 23.5%. A comparison of the leaf malondialdehyde (MDA) content showed that less of this stress marker accumulated in the transgenic plants than in WT plants; similarly, it was established that the relative electrolyte leakage of leaves sampled from the transgenic plants was lower than that of WT The contrasting effect of drought stress on the transgenic and WT plants is illustrated in Figure 7. Under well-watered conditions, the growth of the transgenic lines was indistinguishable from that of WT plants. However, when water was withheld for 30 days, none of the WT plants remained viable as they were unable to maintain a sufficient level of leaf hydration (their relative water content fell to 10.5%); in contrast, many of transgenic plants survived, maintaining a leaf relative water content of about 20%. The post-stress survival rate of these latter plants was between 10.3% and 23.5%. A comparison of the leaf malondialdehyde (MDA) content showed that less of this stress marker accumulated in the transgenic plants than in WT plants; similarly, it was established that the relative electrolyte leakage of leaves sampled from the transgenic plants was lower than that of WT leaves. The activity of each of the enzymes superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD), as well as the content of glutathione (GSH), were all greater in the transgenic The contrasting effect of drought stress on the transgenic and WT plants is illustrated in Figure 7. Under well-watered conditions, the growth of the transgenic lines was indistinguishable from that of WT plants. However, when water was withheld for 30 days, none of the WT plants remained viable as they were unable to maintain a sufficient level of leaf hydration (their relative water content fell to 10.5%); in contrast, many of transgenic plants survived, maintaining a leaf relative water content of about 20%. The post-stress survival rate of these latter plants was between 10.3% and 23.5%. A comparison of the leaf malondialdehyde (MDA) content showed that less of this stress marker accumulated in the transgenic plants than in WT plants; similarly, it was established that the relative electrolyte leakage of leaves sampled from the transgenic plants was lower than that of WT leaves. The activity of each of the enzymes superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD), as well as the content of glutathione (GSH), were all greater in the transgenic plants than in the WT ones. Consistent with this result, the detached leaves of transgenic lines lost water slower than that of WT leaves. After 5 h of dehydration, the water loss rate for transgenic lines OE1, OE2, and OE3 were 17.6%, 8.8%, and 4.13% lower than that of WT, respectively.

leaves. The activity of each of the enzymes superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD), as well as the content of glutathione (GSH), were all greater in the transgenic plants than in the WT ones. Consistent with this result, the detached leaves of transgenic lines lost

plants than in the WT ones. Consistent with this result, the detached leaves of transgenic lines lost water slower than that of WT leaves. After 5 h of dehydration, the water loss rate for transgenic

lines OE1, OE2, and OE3 were 17.6%, 8.8%, and 4.13% lower than that of WT, respectively.

*Int. J. Mol. Sci.* **2019**, *20*, x FOR PEER REVIEW 10 of 18

**Figure 7.** Heterologous expressing *MpPIP2;1* enhanced drought tolerance in *A. thaliana*. (**a**) Phenotypes of transgenic lines and wild type plants under drought stress; (**b**) survival rate after 30 days withholding of water and 7 days after rewatering; (**c**) relative water content after 30 days withholding of water; (**d**) water loss rate of detached leaves; (**e**) malondialdehyde (MDA) content; (**f**) relative electrolyte leakage; (**g**) superoxide dismutase (SOD) activity; (**h**) catalase (CAT) activity; (**i**) peroxidase (POD) activity; and (**j**) glutathione (GSH) content at 0, 15, and 30 days after water withheld. The value presented was the mean ± SD of three replicates, and the bar with different letter was significantly different between plants at *p* < 0.05. **Figure 7.** Heterologous expressing *MpPIP2;1* enhanced drought tolerance in *A. thaliana*. (**a**) Phenotypes of transgenic lines and wild type plants under drought stress; (**b**) survival rate after 30 days withholding of water and 7 days after rewatering; (**c**) relative water content after 30 days withholding of water; (**d**) water loss rate of detached leaves; (**e**) malondialdehyde (MDA) content; (**f**) relative electrolyte leakage; (**g**) superoxide dismutase (SOD) activity; (**h**) catalase (CAT) activity; (**i**) peroxidase (POD) activity; and (**j**) glutathione (GSH) content at 0, 15, and 30 days after water withheld. The value presented was the mean ± SD of three replicates, and the bar with different letter was significantly different between plants at *p* < 0.05.

A series of experiments were conducted to establish whether the constitutive expression of *MpPIP2;1* in *A. thaliana* had any effect on the level of tolerance to salinity stress, as imposed by exposure to 0.3 M NaCl for 14 days (Figure 8). The transgenic plants maintained a superior leaf hydration status compared to the WT plants: Their respective relative water contents were >80% and 49%. While the survival rate of WT plants was 46.4%, that of the transgenic plants was >90%. Compared to WT leaves, those of the transgenic plants accumulated less MDA, developed a lower relative electrolyte leakage, exhibited a higher activity of SOD, POD, and CAT, and their GSH content was greater. A series of experiments were conducted to establish whether the constitutive expression of *MpPIP2;1* in *A. thaliana* had any effect on the level of tolerance to salinity stress, as imposed by exposure to 0.3 M NaCl for 14 days (Figure 8). The transgenic plants maintained a superior leaf hydration status compared to the WT plants: Their respective relative water contents were >80% and 49%. While the survival rate of WT plants was 46.4%, that of the transgenic plants was >90%. Compared to WT leaves, those of the transgenic plants accumulated less MDA, developed a lower relative electrolyte leakage, exhibited a higher activity of SOD, POD, and CAT, and their GSH content was greater.

**Figure 8.** Heterologous expressing *MpPIP2;1* enhanced salt tolerance in *A. thaliana*. (**a**) Phenotypes of transgenic lines and wild type plants treated with 0.3 M NaCl for 14 days; (**b**) Survival rate and (**c**) relative water content after exposure to NaCl; (**d**) MDA content; (**e**) relative electrolyte leakage; (**f**) SOD activity; (**g**) CAT activity; (**h**) POD activity; and (**i**) GSH content at 0 and 14 days after NaCl treatment. The value presented was the mean ± SD of three replicates, and the bar with different letter was significantly different between plants at *p* < 0.05. **Figure 8.** Heterologous expressing *MpPIP2;1* enhanced salt tolerance in *A. thaliana*. (**a**) Phenotypes of transgenic lines and wild type plants treated with 0.3 M NaCl for 14 days; (**b**) Survival rate and (**c**) relative water content after exposure to NaCl; (**d**) MDA content; (**e**) relative electrolyte leakage; (**f**) SOD activity; (**g**) CAT activity; (**h**) POD activity; and (**i**) GSH content at 0 and 14 days after NaCl treatment. The value presented was the mean ± SD of three replicates, and the bar with different letter was significantly different between plants at *p* < 0.05.

#### *2.7. Germination and Root Elongation of MpPIP2;1 Transgenics Exposed to Either Salinity or Osmotic 2.7. Germination and Root Elongation of MpPIP2;1 Transgenics Exposed to Either Salinity or Osmotic Stress*

*Stress*  An experiment was conducted to establish whether the constitutive expression of *MpPIP2;1* in *A. thaliana* had any effect on germination and/or root elongation in the presence of either salinity or osmotic stress (Figure 9). When the seed was imbibed in the absence of a stress agent (mannitol or NaCl), the rate of germination of both the WT and transgenic seed was high, and there were no significant differences between the germination rates of WT and transgenic seeds. However, in the presence of either 0.25 M mannitol or 0.15 M NaCl, the rate of germination of the WT seeds fell to An experiment was conducted to establish whether the constitutive expression of *MpPIP2;1* in *A. thaliana* had any effect on germination and/or root elongation in the presence of either salinity or osmotic stress (Figure 9). When the seed was imbibed in the absence of a stress agent (mannitol or NaCl), the rate of germination of both the WT and transgenic seed was high, and there were no significant differences between the germination rates of WT and transgenic seeds. However, in the presence of either 0.25 M mannitol or 0.15 M NaCl, the rate of germination of the WT seeds fell to just 10%, while that of the transgenic seeds remained >60%. Similarly, the ability of roots to elongate was

just 10%, while that of the transgenic seeds remained >60%. Similarly, the ability of roots to elongate

the same for the WT and transgenic seedlings under non-stressful conditions, but the extent of its inhibition by the presence of either mannitol or NaCl differed between the transgenic and WT seedlings. was the same for the WT and transgenic seedlings under non-stressful conditions, but the extent of its inhibition by the presence of either mannitol or NaCl differed between the transgenic and WT seedlings.

**Figure 9.** Heterologous expressing *MpPIP2;1* enhanced seeds germination and root elongation in *A. thaliana* either under salinity or osmotic stress. (**a**) The phenotype and (**b**) the statistical analyses of the root lengths of transgenic lines and wild type seedlings growing on MS medium (CK), or MS medium with 0.15 M NaCl or 0.25 M mannitol for 14 days; (**c**) germination rate of transgenic and wild type seeds on different mediums for 7 days. The value presented was the mean ± SD of three replicates, and the bar with different letter was significantly different between plants at *p* < 0.05. **Figure 9.** Heterologous expressing *MpPIP2;1* enhanced seeds germination and root elongation in *A. thaliana* either under salinity or osmotic stress. (**a**) The phenotype and (**b**) the statistical analyses of the root lengths of transgenic lines and wild type seedlings growing on MS medium (CK), or MS medium with 0.15 M NaCl or 0.25 M mannitol for 14 days; (**c**) germination rate of transgenic and wild type seeds on different mediums for 7 days. The value presented was the mean ± SD of three replicates, and the bar with different letter was significantly different between plants at *p* < 0.05.

#### **3. Discussion 3. Discussion**

tolerance [27].

The systematic scanning of the content of AQP-encoding genes in the apple genome reported here resulted in the identification of 42 such genes. AQPs make an important contribution to the way in which plants control their uptake of water, and hence represent a key component of their response to drought and osmotic stress [23]. Thus, gaining a full understanding of how apple plants regulate their water balance and adapt to drought and osmotic stress will likely involve revealing the function of many of this set of genes. There is already some experimental evidence which supports the participation of *AQPs* in the stress response of apple. According to Hu et al. (2003), the transcription of both *MdPIP1a* and *MdPIP1b* (here renamed as, respectively, *MdPIP1;2* and *MdPIP1;1*) are up-regulated by osmotic stress [24]. The *M. zumi* homolog of *MdPIP1;1* has been shown to be inducible by salinity (as well as by low temperature) stress [25]. Meanwhile, the constitutive expression of *MzPIP2;1* (homolog of *MdPIP2;4*) in *A. thaliana* has a positive effect on drought tolerance and a small positive one on salinity tolerance [26], and the expression of *MzPIP1;3* (homolog of *MdPIP1;3*) in tomato has been shown to enhance the plants' drought The systematic scanning of the content of AQP-encoding genes in the apple genome reported here resulted in the identification of 42 such genes. AQPs make an important contribution to the way in which plants control their uptake of water, and hence represent a key component of their response to drought and osmotic stress [23]. Thus, gaining a full understanding of how apple plants regulate their water balance and adapt to drought and osmotic stress will likely involve revealing the function of many of this set of genes. There is already some experimental evidence which supports the participation of *AQPs* in the stress response of apple. According to Hu et al. (2003), the transcription of both *MdPIP1a* and *MdPIP1b* (here renamed as, respectively, *MdPIP1;2* and *MdPIP1;1*) are up-regulated by osmotic stress [24]. The *M. zumi* homolog of *MdPIP1;1* has been shown to be inducible by salinity (as well as by low temperature) stress [25]. Meanwhile, the constitutive expression of *MzPIP2;1* (homolog of *MdPIP2;4*) in *A. thaliana* has a positive effect on drought tolerance and a small positive one on salinity tolerance [26], and the expression of *MzPIP1;3* (homolog of *MdPIP1;3*) in tomato has been shown to enhance the plants' drought tolerance [27].

Both moisture and nutrient stress are initially sensed by a plant's roots. Based on its relatively high transcript abundance (inferred both indirectly from the frequency of its representation in EST libraries and directly through a qRT-PCR analysis), *PIP2;1* is the *PIP2* gene most strongly transcribed in the roots of the apple plant. This same gene has been shown, using a suppression subtractive hybridization method, to be up-regulated in response to moisture deficit [28], while two *MdPIP2;1* ESTs have been identified in cDNA libraries developed from the roots of plants from which water had been withheld for a week (LIBEST\_024527). Thus the evidence points to the conclusion that the product of *PIP2;1* is an important component of the apple plant's response to moisture stress. This evidence has been strengthened by the demonstration here that heterologously expressing the gene in *A. thaliana* had a positive effect on the plant's tolerance of both drought and salinity.

A commonly observed plant response to abiotic stress is to accumulate reactive oxygen species (ROS), which become cytotoxic when present in excess [29]. When *A. thaliana* plants heterologously expressing *MpPIP2;1* were exposed to either drought or salinity stress, both the MDA content and the relative electrolyte leakage of their leaves were below the levels shown by WT leaves; both of these traits are correlated with ROS-mediated cellular damage [30]. The maintenance of a non-damaging level of cellular ROS content is achieved both by the activity of a number of enzymes and the synthesis of antioxidant compounds [31]. Both the activity of the enzymes SOD, CAT, and POD and the content of the antioxidant compound GSH were higher in the transgenic than in the WT *A. thaliana* plants subjected to stress. The conclusion is that the product of *MpPIP2;1* likely contributes to protecting the transgenic plants experiencing drought stress by enhancing their ability to control the accumulation of ROS. Similar conclusions have been drawn with respect to AQPs in a number of plant systems [32], so it is reasonable to propose that the product of *MpPIP2;1*, a gene which is strongly transcribed in the root of *M. prunifolia*, is an important determinant of the drought stress response when expressed in its native context.

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

#### *4.1. Identification of the Set of AQP Genes in the Apple Genome*

Apple AQP sequences were recovered from the NCBI Protein database (www.ncbi.nlm.nih.gov/ protein) by entering as a keyword search "(aquaporin OR MIP) AND Malus". The resulting hits were confirmed as genuine AQPs by submitting them to the NCBI Conserved Domain database (www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi). The sequences of identified apple AQPs were used as queries to search Malus x domestica Whole Genome v1.0 and GDDH13 Whole Genome v1.1 sequence for additional members with an E value less than 0.01. A phylogenetic analysis based on the deduced peptide sequences of AQPs encoded by *A. thaliana*, poplar, and rubber [33] was used assign the apple sequences to the five established AQP subfamilies. Multiple sequence alignments were carried out using ClustalX software [34], and an unrooted phylogenetic tree was constructed using MEGA6 software [35], applying the Neighbor-Joining method and 1000 bootstrap replicates.

#### *4.2. Chromosomal Location, Gene Structure, and Protein Properties of Apple AQPs*

The GDDH13 assembly was used to reveal the chromosomal location for each of the *MdAQPs* and to determine the intron/exon structure of each gene. The latter was visualized using GSDS software (bio.tools/GSDS) [36]. The pI and molecular weight of the deduced AQPs were predicted using the ExPASY program (web.expasy.org/compute\_pi/). Transmembrane regions were detected using TMHMM software (www.cbs.dtu.dk/services/TMHMM/) [37], and subcellular localizations were predicted using WoLF PSOR software (wolfpsort.hgc.jp/) [38]. Sequences representing conserved domains, NPA motifs, the ar/R filter, and the Froger positions were manually identified, based on multiple sequence alignments of apple AQPs with heterologous AQPs.


**5. Cytokine profile in viral infections**

In viral infections, the cytokines are implicated to establish an antiviral state as the unspecific first line of defense and virus-specific response. This process initiates through recognition of viral molecules by PRRs, which can be found as transmembrane receptors or in different intracellular compartment. The receptor undergoes a structural change, activating a route of signalization in the cytoplasm that end with the activation of cytoplasmic transcription factors that translocate into the nuclei to promote the expression of different cytokines. Depending of the virus and the type of cell, the type of cytokine produced may vary [70, 71].

#### **5.1 Pattern recognition receptors versus virus**

Viruses can infect virtually all cells of an organism. Epithelial, endothelial, fibroblasts, neurons, as well as innate and adaptive immune cells can be infected. PRRs are present in both nonhematopoietic origin cell and immune cells. Some PRRs recognize viral proteins, but other can detect viral single or double RNA or DNA. In human, there are 10 TLRs distributed in plasmatic membrane and endosome membranes. Of them, TLR-2 and TLR-4 can detect viral surface glycoprotein before the viral penetration. Others like TLR-3, TLR-8, and TLR-9 sense different types of viral nucleic acids in endosomes during virus entering. TLR-8 senses genomic ssRNA, TLR-3 senses dsRNA, and TLR-9 detects nonmethylated CpG viral DNA [72, 73]. Another type of receptors that sense viral RNA are the RNA helicases receptors like RIG-I and melanoma differentiation-associated gene 5 (MDA5) [71, 74]. These receptors have been demonstrated to detect viral dsRNA. This dsRNA can be genomic or an intermediate form during replication, which is formed, virtually, for all virus of single or double RNA during viral replication. However, there is evidence that some dsRNA replicative intermediators can translocate to endosomes where TLR can sense and trigger the signalization way [75].

#### **5.2 Cytokines produced in viral infections**

There are many cytokines with distinct functions. All of them are molecules with less than 20 KDa and can be pleiotropic or redundant, and also, they can synergize or antagonize each other. However, all of them are produced to ensure the virus elimination through the regulation of the immune response against the virus [76]. The process includes detection of the pathogen, signal to neighbor cells, activation and differentiation of innate immune cells, production of adhesion molecules on endothelial cell for extravasation of immune circulating cell, chemotactic molecules to attract cell to the infection foci, increase of phagocytosis, and

**19**

*and anti-inflammatory actions.*

**Figure 3.**

*Cytokine Profiling Plays a Crucial Role in Activating Immune System to Clear Infectious Pathogens*

activation of adaptive cells to specifically eliminate infected cells and extracellular

Cytokine network against viruses starts with some cytokines produced by virus-infected cells (**Figure 3**). Epithelial cell can produce IFN, IL-8, IL-6, IL-1, GM-CSF [78, 79], TNFα [80], IL-18 [81], IL-12 [82], IL-2 [83], and IL-23 [84, 85]. The role of these cytokines is varied, IFN induces an antiviral state, and IL-8 is a potent inflammatory attracting phagocyte cell to the site of infection. IL-1 can promote apoptosis, and it is proinflammatory and chemotactic to neutrophils. GM-CSF is a hematopoietic grow factor that recruits various immune cells to host defense [76, 86]. Moreover, in the infection course, varies cytokines are also produced by innate and adaptive cell that can also be infected or activated. In filovirus infection, IL-1β, IL-5, IL-8, and IL-18, as well as varies chemokines like MIP-1α and β, monocyte chemoattractant protein 1 (MCP-1), and IFN-γ–inducible protein 10 (IP10) among others are produced [77]. In influenza virus infection, TNF-α, IL-1 α and β, and IL-6 and IL-8 are produced [87], and hepatitis C virus can promote the expression of IL-6, IL-8, MIP-1α, and MIP-1β and IL-1 [88], while rotavirus can induce the production of IFN, IL-8, IL-6, IL-1 [89], TNF-α [80], IL-18 [81], IL-12 [82], IL-2 [83], and IL-23 [84, 85]. Thus, the infected cell can upregulate multiple cytokine genes involved in different process as activation of NK, macrophages, and dendritic cells. Increasing the production of cytokines that serve as bridge between innate and adaptive response. In the inflammation process, virus-infected cells produce and secrete proinflammatory cytokines like IL-1, IL-6, IL-8, TNF [70] and

*Cytokines profile in viral infections. The immune response against viruses initiates through recognition of viral molecules by PRRs. These PRRs can activate a signal system culminating in the activation of transcription factors involved in the establishment of an antiviral state and an inflammation process. Cytokine network against viruses start with some cytokines produced by virus infected cells, such as IFNs, IL-8, IL-6, IL-1, GM-CSF, TNFα, IL-18, IL-12, IL-2 and IL-23, inducing a potent inflammatory response, attracting and activating phagocyte cells (e.g. neutrophils, macrophages, dendritic cells), mast cells and NK cells, to the site of infection. Furthermore, these cytokines are involved in the induction of an immune response type Th1/TCL with the purpose of eliminate infected cells and extracellular virus while cytokines such as IL-4, IL-10, IL-13, IL-37, and TGF-β modulate the immune response to a Th2 and Th17 phenotype, which produce immunomodulatory*

*DOI: http://dx.doi.org/10.5772/intechopen.80843*

virus [77].

*Cytokine Profiling Plays a Crucial Role in Activating Immune System to Clear Infectious Pathogens DOI: http://dx.doi.org/10.5772/intechopen.80843*

activation of adaptive cells to specifically eliminate infected cells and extracellular virus [77].

Cytokine network against viruses starts with some cytokines produced by virus-infected cells (**Figure 3**). Epithelial cell can produce IFN, IL-8, IL-6, IL-1, GM-CSF [78, 79], TNFα [80], IL-18 [81], IL-12 [82], IL-2 [83], and IL-23 [84, 85]. The role of these cytokines is varied, IFN induces an antiviral state, and IL-8 is a potent inflammatory attracting phagocyte cell to the site of infection. IL-1 can promote apoptosis, and it is proinflammatory and chemotactic to neutrophils. GM-CSF is a hematopoietic grow factor that recruits various immune cells to host defense [76, 86]. Moreover, in the infection course, varies cytokines are also produced by innate and adaptive cell that can also be infected or activated. In filovirus infection, IL-1β, IL-5, IL-8, and IL-18, as well as varies chemokines like MIP-1α and β, monocyte chemoattractant protein 1 (MCP-1), and IFN-γ–inducible protein 10 (IP10) among others are produced [77]. In influenza virus infection, TNF-α, IL-1 α and β, and IL-6 and IL-8 are produced [87], and hepatitis C virus can promote the expression of IL-6, IL-8, MIP-1α, and MIP-1β and IL-1 [88], while rotavirus can induce the production of IFN, IL-8, IL-6, IL-1 [89], TNF-α [80], IL-18 [81], IL-12 [82], IL-2 [83], and IL-23 [84, 85]. Thus, the infected cell can upregulate multiple cytokine genes involved in different process as activation of NK, macrophages, and dendritic cells. Increasing the production of cytokines that serve as bridge between innate and adaptive response. In the inflammation process, virus-infected cells produce and secrete proinflammatory cytokines like IL-1, IL-6, IL-8, TNF [70] and

#### **Figure 3.**

*Immune Response Activation and Immunomodulation*

**5. Cytokine profile in viral infections**

of cell, the type of cytokine produced may vary [70, 71].

**5.1 Pattern recognition receptors versus virus**

β-defensin 3 [66, 69].

of naïve CD4+ T cells into Th17 cells and the release of IL-17A, IL-17F and IL-22 in response to infections caused by mucosal fungi [68]. These cytokines in conjunction with IL-23 have various functions in the body from a proinflammatory, antiinflammatory, or regulatory activity, which depends on the type of microorganism, the site of infection, and the immunological status of the host (**Figure 2**). In vivo studies have shown that mice deficient of the IL-17 receptor (IL-17RA−/−) cannot limit systemic candidiasis, as well as oropharyngeal candidiasis, being more susceptible to developing mucocutaneous candidiasis, suggesting that the Th17 lineage strongly acts through IL-17, regulating the expansion, recruitment, and migration of neutrophils, as well as CXC-chemokines and antimicrobial proteins such as

In viral infections, the cytokines are implicated to establish an antiviral state as the unspecific first line of defense and virus-specific response. This process initiates through recognition of viral molecules by PRRs, which can be found as transmembrane receptors or in different intracellular compartment. The receptor undergoes a structural change, activating a route of signalization in the cytoplasm that end with the activation of cytoplasmic transcription factors that translocate into the nuclei to promote the expression of different cytokines. Depending of the virus and the type

Viruses can infect virtually all cells of an organism. Epithelial, endothelial, fibroblasts, neurons, as well as innate and adaptive immune cells can be infected. PRRs are present in both nonhematopoietic origin cell and immune cells. Some PRRs recognize viral proteins, but other can detect viral single or double RNA or DNA. In human, there are 10 TLRs distributed in plasmatic membrane and endosome membranes. Of them, TLR-2 and TLR-4 can detect viral surface glycoprotein before the viral penetration. Others like TLR-3, TLR-8, and TLR-9 sense different types of viral nucleic acids in endosomes during virus entering. TLR-8 senses genomic ssRNA, TLR-3 senses dsRNA, and TLR-9 detects nonmethylated CpG viral DNA [72, 73]. Another type of receptors that sense viral RNA are the RNA helicases receptors like RIG-I and melanoma differentiation-associated gene 5 (MDA5) [71, 74]. These receptors have been demonstrated to detect viral dsRNA. This dsRNA can be genomic or an intermediate form during replication, which is formed, virtually, for all virus of single or double RNA during viral replication. However, there is evidence that some dsRNA replicative intermediators can translocate to endosomes where TLR can sense and trigger the

There are many cytokines with distinct functions. All of them are molecules with less than 20 KDa and can be pleiotropic or redundant, and also, they can synergize or antagonize each other. However, all of them are produced to ensure the virus elimination through the regulation of the immune response against the virus [76]. The process includes detection of the pathogen, signal to neighbor cells, activation and differentiation of innate immune cells, production of adhesion molecules on endothelial cell for extravasation of immune circulating cell, chemotactic molecules to attract cell to the infection foci, increase of phagocytosis, and

**18**

signalization way [75].

**5.2 Cytokines produced in viral infections**

*Cytokines profile in viral infections. The immune response against viruses initiates through recognition of viral molecules by PRRs. These PRRs can activate a signal system culminating in the activation of transcription factors involved in the establishment of an antiviral state and an inflammation process. Cytokine network against viruses start with some cytokines produced by virus infected cells, such as IFNs, IL-8, IL-6, IL-1, GM-CSF, TNFα, IL-18, IL-12, IL-2 and IL-23, inducing a potent inflammatory response, attracting and activating phagocyte cells (e.g. neutrophils, macrophages, dendritic cells), mast cells and NK cells, to the site of infection. Furthermore, these cytokines are involved in the induction of an immune response type Th1/TCL with the purpose of eliminate infected cells and extracellular virus while cytokines such as IL-4, IL-10, IL-13, IL-37, and TGF-β modulate the immune response to a Th2 and Th17 phenotype, which produce immunomodulatory and anti-inflammatory actions.*

IFN. These cytokines can be involved in the early defense of the organism. They can activate cells present in the site of infection, and they can recruit leukocyte cells from circulating system through inflammation process (**Figure 3**).

### **5.3 Cytokines' role in viral infection**

IFN is a pleiotropic cytokine produced by virus infection. Although there are three types of IFN called type I (α/β), type II (γ) and type III (λ). Type I IFN plays an important role in control early viral infections. The role of type I IFN is to interfere with viral replication through activating the expression of antiviral molecules. Once IFN is secreted, it can act in autocrine or paracrine (like other cytokines) way, interacting with interferon receptor to induce the production of an antiviral state in the infected and noninfected neighboring cells, inhibiting different step of viral replication [76]. Also, IFN promotes the production of cytokines like IL-12, IL-6, IFN-γ, and TNF-α in innate cells including NK cells and macrophages [90]. Another function of IFN is to enhance differentiation of dendritic cells [91] and promote the antigen presentation [90] to stimulate T and B cells [92], which is redundant with the function of the IL-12 and IL-18 [93, 94]. NK cells are activated by synergism between type 1 IFNs and IL-12. However, cytokines such as IL-10, IL-6, IL-4, IL-13, and TGF-β suppress the actions of IFN, and these cytokines are known for their immunomodulatory and anti-inflammatory actions [95].

TNF-α is other pleiotropic cytokine produced by also nonhematopoietic infected cells and innate and adaptive immune cells, including macrophages, dendritic cells, natural killer, and T and B lymphocytes after being activated [96]. This cytokine can activate the production of adhesion molecules in endothelial cells and promote the extravasation of neutrophils, monocytes, and others immune cells to be attracted to infection foci. TNF also can participate in apoptosis through activating caspases. TNF-α, together with IFN-γ, acts on macrophages, inducing the production of superoxide anions and oxygen and nitrogen radicals [97]. Macrophages can also produce cytokines such as IL-1, IL-6, IL-23, IL-12, and more TNF-α [95].

IL-1 was the first interleukin to be identified and is a pleiotropic cytokine, and it acts synergically with IL-6 on the central nervous system, inducing fever by activation of the hypothalamus-pituitary-adrenal (HPA) axis [98]. This molecule also activates mast cells and induces histamine production, acting as a vasodilator, thus increases the permeability of the membrane [99]. Also, IL-1 is chemotactic factor that induces the passage of neutrophils to the site of infection. This chemotactic function is redundant with the action of IL-8, also known as chemokine CXCL8 [86] also produced by the infected cell. There are cytokines that antagonize these functions of IL-1 such as IL-10, IL-4, and IL-13 recognized for their anti-inflammatory actions [100].

Another pleiotropic cytokine is IL-18, first described as "interferon-γ-inducing factor" and member of IL-1 family. This interleukin and type I IFN are recognized by dendritic cells and trigger a signaling pathway through TRF6 and induce the expression of CD11b+ in the surface of the cell [94]. These activated cells can express cytokines like IL-12, IL-6, IFN-γ, TNFα, and IFN-α, which also participates in other hematopoietic cells [101, 102]. IL-18 also participates synergistically with interleukin 12 on the activation of NK cells [93], stimulating the expression of CD25 and CD69 molecules, promoting their proliferation and cytotoxic capacity, respectively. Once activated, NK cells can induce apoptosis in virus-infected cells and produce other cytokines such as IL-12, IL-6, IL-10, IFN-γ, and TNF-α. Within the cytokines that block these functions of IL-18 are IL-37, IL-10, and TGF-β [103].

**21**

*Cytokine Profiling Plays a Crucial Role in Activating Immune System to Clear Infectious Pathogens*

IL-6 is a soluble mediator with a pleiotropic effect on inflammation, immune response, and hematopoiesis. IL-6 is an important mediator of fever and of the acute phase response, which is redundant with IL-1 and TFN-α and promotes the differentiation of cytotoxic T lymphocytes, which induce the death of infected cells by osmotic lysis [104]. IL-6 synergistically with IL-23 participates in the differentiation of Th17 [105], through the production of RORγt. Once activated, Th17 induces inflammatory response through the expression of cytokines as IL-17 and IL-22. IL-6 also promotes the proliferation of B cells by binding to a complex of receptors (gp80, CD126, and CD130) [106] and, like IL-21 [107], induces the differentiation of plasma cells stimulating the antibody production [108]. However, there exist antagonist cytokines like IL-10, IL-13, and TGF-β that inhibit all these functions of

IL-12, also known as a T cell-stimulating factor, which together with IFN-γ, promotes differentiation of Th1 cells by activation of T-bet, and these cells can activate macrophages through expression of other cytokines like IFN and TNF, amplifying the produced immune response [109]. Although, there is evidence that viruses may selectively induce IFN production and Th1 differentiation even in the absence of

IL-2 participates in the differentiation and proliferation of Th2 (redundant with IL-4) and Treg cells by the expression of GATA-3 and FOXP3, respectively [111, 112]. Th2 cells can express IL-4, IL-5, IL-9, and IL-13, which also have pleiotropic effects in promoting type 2 effector mechanisms, such as B cells secretion of immunoglobulins, eosinophilia, mastocytosis, and M2 macrophage polarization [113]. Treg cells regulate the immune response, suppressing T-cell activation [114]. Treg and Th2 are known for their immunomodulatory and anti-inflammatory actions [95]. Finally, GM-CSF stimulates the generation of dendritic cells and participates in polarization of macrophages M1 [115]. Moreover, GM-CSF has also been associated with Th2 immunity and therefore M2 polarization. GM-CSF is considered a pleiotropic cytokine with inflammatory and anti-inflammatory functions [116].

In parasitic infections is difficult to generalize about the mechanisms of antiparasitic immunity because there is a great variety of different parasites that have different morphology and reside in different locations of tissues and hosts during their life cycles [117]. In this section of the chapter, we will talk about the immune response against protozoa and helminths, two of the main parasites of medical

Protozoan parasites are much larger and more complex pathogens than viruses or bacteria and have developed additional and sophisticated strategies to escape the immune attack of the host. Currently, 30% of humans suffer parasitic protozoan infections worldwide. Life cycles of protozoans generally involve several stages of specific antigenicity, which facilitates their survival and propagation within different cells, tissues, and hosts. Frequently, the host fails to eliminate protozoan infections, which often results in a chronic disease or inapparent infections, in which the

The immune defense mechanisms against protozoan parasites frequently involve several immune cells such as neutrophils, macrophages, and NK cells that mediate the innate response against extracellular protozoan parasites. NK cell and

**6.1 Immune system activation by parasitic protozoan infections**

host continues to act as a reservoir of parasites [118].

*DOI: http://dx.doi.org/10.5772/intechopen.80843*

**6. Cytokine profile in parasitic infections**

importance for human health.

the IL-6 [103].

IL-12 [110].

*Cytokine Profiling Plays a Crucial Role in Activating Immune System to Clear Infectious Pathogens DOI: http://dx.doi.org/10.5772/intechopen.80843*

IL-6 is a soluble mediator with a pleiotropic effect on inflammation, immune response, and hematopoiesis. IL-6 is an important mediator of fever and of the acute phase response, which is redundant with IL-1 and TFN-α and promotes the differentiation of cytotoxic T lymphocytes, which induce the death of infected cells by osmotic lysis [104]. IL-6 synergistically with IL-23 participates in the differentiation of Th17 [105], through the production of RORγt. Once activated, Th17 induces inflammatory response through the expression of cytokines as IL-17 and IL-22. IL-6 also promotes the proliferation of B cells by binding to a complex of receptors (gp80, CD126, and CD130) [106] and, like IL-21 [107], induces the differentiation of plasma cells stimulating the antibody production [108]. However, there exist antagonist cytokines like IL-10, IL-13, and TGF-β that inhibit all these functions of the IL-6 [103].

IL-12, also known as a T cell-stimulating factor, which together with IFN-γ, promotes differentiation of Th1 cells by activation of T-bet, and these cells can activate macrophages through expression of other cytokines like IFN and TNF, amplifying the produced immune response [109]. Although, there is evidence that viruses may selectively induce IFN production and Th1 differentiation even in the absence of IL-12 [110].

IL-2 participates in the differentiation and proliferation of Th2 (redundant with IL-4) and Treg cells by the expression of GATA-3 and FOXP3, respectively [111, 112]. Th2 cells can express IL-4, IL-5, IL-9, and IL-13, which also have pleiotropic effects in promoting type 2 effector mechanisms, such as B cells secretion of immunoglobulins, eosinophilia, mastocytosis, and M2 macrophage polarization [113]. Treg cells regulate the immune response, suppressing T-cell activation [114]. Treg and Th2 are known for their immunomodulatory and anti-inflammatory actions [95]. Finally, GM-CSF stimulates the generation of dendritic cells and participates in polarization of macrophages M1 [115]. Moreover, GM-CSF has also been associated with Th2 immunity and therefore M2 polarization. GM-CSF is considered a pleiotropic cytokine with inflammatory and anti-inflammatory functions [116].


*2.7. Carotenoids Extraction and HPLC Analysis*

Tomato carotenoids were extracted using the method of Siracusa et al. [31]. An aliquot of the cherry tomato puree sample (0.5 g) was transferred into a vial and 5 mL of a n-hexane/acetone/ethanol (2:1:1) solution were added. The vial was left shaking for 40 min. in the dark at room temperature. Subsequently 1 mL of H2O (HPLC grade) was added and a further 2 min. agitation was applied. The resulting heterogeneous mixtures were left decanting until phases separation. The apolar coloured layers were transferred into an amber vial and analysed.

Quantitative analyses were carried out on an HPLC (Shimadzu USA Manufacturing Company Inc., Class VPLC-10 Dvp, Canby, OR, USA) equipped with a DAD (Shimadzu SPD-M10Avp). The column was a Gemini NX C18 (150 × 4.6 mm; 3 µm particle size; Phenomenex, Italy), fitted with a guard cartridge packed with the same stationary phase. The flow rate was 0.7 mL/min. and the injector volume was 20 µL. Carotenoids were eluted with the following gradient of A (Methanol: H2O 75:25) and B (Ethyl acetate): T0 30% B; T15 82% B; T25 30% B. All reagents used were HPLC purity grade: water, methanol and Ethyl acetate were obtained from Merck. The wavelength range was 220–660 nm, and the chromatograms were monitored at 473 nm for lycopene; at 453 nm for β-carotene; at 348 nm for phytofluene and at 288 nm for phytoene. Carotenoids were identified by splitting the peak of the carotenoids from the tomato-solution sample with a standard of β-carotene and lycopene; (*p* ≥ 95% and *p* ≥ 98%, Sigma-Aldrich, St. Louis, MO, USA) and by comparing retention times and UV spectra with those of standards. Quantification of β-carotene and lycopene was performed using external calibration curves; for phytofluene and phytoene the calibration curve of β-carotene was used. Linearity was checked for β-carotene between 3.36 and 21 mg kg−1 (*R* 2 = 1.00) and lycopene between 2.56 and 40.0 mg kg−1 (*R* 2 = 1.00). All analyses were performer in triplicate, including the extraction procedure, and the results were expressed as mg kg−1 DW.