**1. Introduction**

Non-volatile liquid organic semiconducting materials have attracted a lot of interest as emerging functional materials for organic electronic and optoelectronic devices in recent years, because these fluidic materials present outstanding advantages such as tunable optoelectronic responses, degradation-free characteristics, solvent-free processability, and ultimate mechanical flexibility and uniformity [1–8]. Various electronic and optoelectronic applications, such as photorefractive devices, organic light-emitting diodes, dye-sensitized solar cells, memory devices, and optically pumped lasers, have been already demonstrated using non-volatile liquid organic semiconductors as active materials [5–16]. In particular, it has been reported that fresh liquid organic semiconductors can be continuously injected into the devices through microfluidic channels for preventing performance degradation [15,16]. However, due to the relatively low e fficiency in comparison with conventional solid-state devices, considerable research e fforts have been devoted to the development of novel molecular structures and device architectures to improve device performance, which is still a challenge [1,11,13–17].

Since charge carriers (i.e., electrons and holes) for operating organic electronic and optoelectronic devices should be essentially injected from electrodes, e fficient charge injection into the liquid organic layers is necessarily required to realize high-performance devices based on the non-volatile liquid organic semiconductors [3,17,18]. However, the fluidity of the liquid organic materials causes di fficulties in inducing preferable molecular orientations on the electrodes for facilitating charge injection, and intermolecular distances in liquid phases are intrinsically longer than those in solid phases (i.e., less dense molecular packing in liquid phases). Charge injection and transport processes are thus significantly impeded at the interfaces between the electrodes and the liquid organic materials, resulting in overall lower performance [19–25]. In this context, various methods such as inserting bu ffer layers and adding ionic dopants have been employed to reduce charge injection barriers and to increase charge carrier concentrations [12,26–32]. In addition, the charge carrier injection into the organic liquid materials is expected to be significantly improved if the local electric fields inducing the migration of charge carriers near the interfaces are enhanced [18,26,27,33,34]. The electric fields are spontaneously concentrated at the edges of the field plates (i.e., edge e ffect) [35–38], where the field intensities are locally increased, and thus the field plate structures (i.e., shapes of electrodes) play a decisive role in the spatial distributions of electric fields. This phenomenon is also applicable to interface engineering for improving the charge injection characteristics of organic electronic devices.

Here we successfully demonstrate e fficient charge injection into solvent-free liquid organic semiconductors via cracked metal structures with a large number of edges leading to local electric field enhancement. For this work, silver (Ag) thin films, deposited on deformable fluorinated ethylene propylene (FEP) substrates, were used to generate cracks on the field plates in a controlled manner. The Ag films on the polymer substrates were mechanically gripped and stretched up to fixed ratios, resulting in the formation of the cracks with reproducible patterns [39,40]. Although this simple cracking method can be easily performed to fabricate a large number of edges on metal surfaces without any lithographic processes, heavily cracked metal electrodes normally give a rise to high electrical resistance resulting in performance degradation [41–43]. To avoid increases in electrical resistance originating from the structural deformations, the cracked Ag films were transferred and welded onto other intact Ag films to complete the electrode structures, and then the charge injection properties were investigated using a typical non-volatile liquid organic semiconducting material, (9-2-ethylhexyl)carbazole (EHCz) [1,5–8,10,12–14]. The fluidity of EHCz with a glass transition temperature below 0 ◦C facilitates the penetration of the molecules into the crack structures of the electrodes, clearly showing the e ffects of the engineered interfacial structures in charge injection. It was found that the cracked structures significantly increased the current density at a fixed external bias voltage via the local electric field enhancement, which was strongly supported by field intensity calculation using COMSOL Multiphysics software. These results suggest great potential for the development and further refinement of various organic electronic and optoelectronic devices based on non-volatile liquid organic semiconducting materials.


**1. Introduction**

In recent years, multiple large-scale outbreaks of golden tide caused by *Sargassum* have occurred along the coastal waters of the Pacific and Atlantic Ocean [1–4]. This phenomenon may be closely correlated with marine ecological deterioration, including seawater eutrophication and warming [5,6]. Along the northwest Pacific coast, *Sargassum horneri* is a predominant golden-tide-forming seaweed that originally forms extensive underwater forests [7,8]. Sessile populations of *S. horneri* have continuously decreased in this region [9], whereas a prominent drifting biomass has been frequently detected in the coastal regions of China in the last decade [3,10]. Golden tides have caused considerable disruption to coastal ecosystems, including the death of aquatic organisms by hypoxia, resource competition with native species, and even shifts in biological community structures [11,12]. Furthermore, it has brought considerable damage to local economic industries such as tourism, traditional fisheries, and mariculture [13–15].

Positive and negative interactions among plants are widespread in both terrestrial and aquatic ecosystems, and have complicated and changeable effects on population dynamics, community structure, and biodiversity patterns [16–18]. In coastal regions, a common interspecific interaction pattern is for marine plants with overlapping niches to compete for diverse limited environmental resources [19–21]. Epiphytic *Ulva lactuca* has been proven to severely interfere with mariculture of *Neoporphyra haitanensis*, as they

**Citation:** Song, H.; Liu, Y.; Li, J.; Gong, Q.; Gao, X. Interactions between Cultivated *Gracilariopsis lemaneiformis* and Floating *Sargassum horneri* under Controlled Laboratory Conditions. *Water* **2022**, *14*, 2664. https://doi.org/10.3390/w14172664

Academic Editors: Xiangli Tian and Li Li

Received: 20 July 2022 Accepted: 26 August 2022 Published: 28 August 2022

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

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

compete for carbon and nitrogen resources [22]. Additionally, the growth of *Neopyropia yezoensis* was apparently restrained by *Ulva prolifera* proliferation because of competition for space, nutrients, and light for spore attachments [23,24]. Allelopathy is another pattern of interspecific interaction among marine algae, which can stimulate or inhibit each other through the release of biochemical metabolites [25–27]. Yuan et al. [28] showed that aqueous extract from *Sargassum fusiforme* promoted the growth of *Karenia mikimotoi* at 0.2 g L−1, while its growth was inhibited at 1.6 g L−1. Similarly, Patil et al. [29] documented that the growth of *Skeletonema costatum* was not significantly decreased by culture filtrates of *Pyropia haitanensis* at 0.625–10 g FW L−1, while it was greatly inhibited at 15 and 20 g FW L−1. These results imply that interaction through allelopathy is closely associated with the biomass density ratio among species. However, thus far there has been little information produced regarding the influence of the biomass density ratio on the interaction between marine macroalgae.

*Gracilariopsis lemaneiformis* is a common intertidal red seaweed that is naturally distributed across temperate regions worldwide [30–32]. In China, *G. lemaneiformis* is extensively cultivated due to its widespread applications in agar production, the food industry, and abalone aquaculture [33–35]. It is the seaweed species with the third highest production rate in terms of mass, with 368,967 tons of dry weight produced in 2020 [36]. Additionally, the cultivation of this species is beneficial to coastal ecosystems in many ways, including alleviation of harmful microalgal blooms, increasing the dissolved oxygen in the seawater, and maintaining coastal ecological balance [37]. In recent years, the deterioration of the marine environment caused by climate change and human activities has posed a severe threat to the mariculture production of seaweeds [38–40]. Due to the economic and ecological importance of *G. lemaneiformis*, a considerable number of studies about the impacts of diverse abiotic factors on its physiological and biochemical performance have been conducted [41–44]. Nevertheless, the effects of biotic stresses such as macroalgal blooms on *G. lemaneiformis* have rarely been reported.

In the current study, we performed a short-term culture experiment to examine the interactive influences between cultivated *G. lemaneiformis* and floating *S. horneri*. Changes in growth, photosynthesis, NO3-N uptake, and biochemical compositions were estimated. Our results provide significant information to improve the mariculture management of this valuable species and to address marine ecological hazards.

#### **2. Materials and Methods**

#### *2.1. Sampling and Maintenance*

*G. lemaneiformis* thalli were sampled from farmed populations on Lidao Island (36◦26 N, 122◦56 E), China, in June 2020. Meanwhile, *S. horneri* thalli were sampled from floating populations in the cultivation area of *G. lemaneiformis*. Healthy samples were chosen and completely rinsed using sterilized seawater to remove epiphytes and detritus. Algal fragments of *G. lemaneiformis* (6 cm in length) and *S. horneri* (3 cm in length) were respectively excised from the apical position of branches for the experiments. Then, they were incubated in tanks containing 6 L of 25% PESI medium [45]. These fragments were kept for three days at 20 ◦C, 90 μmol photons m−2 s−1, and a 12:12 L:D cycle in order to minimize the impacts of excision.

#### *2.2. Culture Experiment*

A twelve-day culture experiment was performed under mono-culture conditions of *G. lemaneiformis* and *S. horneri* and co-culture at different biomass density ratios (BDRs). For the mono-culture, the initial biomass density was set at 6 g for both species. For the co-culture, the initial BDR was set at 2:1 (4 g *G. lemaneiformis* cultured with 2 g *S. horneri*), 1:1 (3 g *G. lemaneiformis* cultured with 3 g *S. horneri*), and 1:2 (2 g *G. lemaneiformis* cultured with 4 g *S. horneri*). A total of five treatments were set up, and each treatment was conducted in three replicates. In this experiment, 20 ◦C, 90 μmol photons m−2 s−1, and a 12:12 L:D cycle were maintained. This experiment used fifteen tanks, with each tank containing 6 L

of sterilized seawater enriched with 25% PESI medium. Gentle aeration was conducted during this experiment. The medium was changed every three days. The fresh weights (FWs) of all fragments before and after incubation were determined after each fragment was blotted dry. The relative growth rate (RGR; % day−1) was calculated by Equation (1):

$$\text{RGR} = 100 \times (\text{lnW}\_{\text{t}} - \text{lnW}\_{0}) \; / \; \text{t} \tag{1}$$

where W0 is the initial FW, Wt is the final FW, and t is the culture time in days.

#### *2.3. Measurement of Photosynthesis*

Following the culture experiment, we determined the net photosynthetic rates of *G. lemaneiformis* and *S. horneri* using a manual FireStingO2II oxygen meter (Pyro Science GmbH, Aachen, Germany). For each species, 0.33 g (FW) of samples from each treatment were moved to the oxygen electrode cuvette containing 330 mL of 25% PESI medium. Next, the medium was continuously stirred to ensure homogenous oxygen diffusion. The culture conditions were the same as for the experiment above. The oxygen increase in the seawater was regarded as the net photosynthetic rate after an increase in the light density. Before measurements, these samples were placed into the cuvette for 5 min acclimation. The oxygen concentration in the medium was documented every 1 min for 15 min.


**6. Reasons for co-occurrence of ASD with other developmental disorders**

Several theories have been put forward to explain the shared symptoms of the various de‐ velopmental conditions – in other words why specific learning and language and social dis‐ orders are not specific. It is likely that all the explanations below play a part in cooccurrence; the causality of co-morbidity is most probably due to a complex web of interacting factors.


**3. Models for decision support: a case study**

By establishing a conversation with clinicians and embedding the technology within the hospital, it is possible that complicated cases requiring additional insight are brought to the attention of the engineer for the creation of a model, which could be either a computer simulation or a 3D-printed model, or both. The model is created specifically to appreciate features that have clinical implications. An interesting example is represented by a patient (19 year-old male) who presented a diagnosis of aortic coarctation, previously stented, and also had an aberrant left subclavian artery originating from the stent site. The discussion revolved around the possibility of inserting a Cheatham Platinum (CP) covered stent within the existing device to prevent formation of a small developing aortic aneurysm, without occluding the aberrant subclavian artery. Insight into the relative position of all these components was therefore desirable, and a model was reconstructed from CT data, including the stent. The aorta was printed in transparent rigid resin, while the stent was printed using a white stereolithography (SLA), therefore being easily identifiable within the anatomy. Furthermore, a cut was performed alongside the thoracic/descending aorta such that the model could be easily opened and, if necessary, to gather additional measurements. The model (**Figure 4**) contributed to assess the feasibility of the intervention highlighting the spatial relationship between stent, aneurysm and left subclavian artery. Additional computational analyses (including the same 3D domain) were performed to simulate the implantation of the CPcovered stent and the local fluid dynamics post implantation [16]. The intervention was performed successfully.

novel device was developed. The latter was designed with the intention of fitting more dilated RVOTs. In this case, a patient-specific 3D model resulted extremely valuable for practicing device insertion prior to performing the first implantation of the novel device on compassion‐ ate grounds (i.e. first-in-man procedure). A candidate (42-year-old male) presented with a history of cardiac surgeries, deemed not suitable for additional surgery, and, however, still presenting with severe pulmonary regurgitation. The morphology and dimensions of his RVOT suggested that the second-generation PPVI device would have been suitable. A model was created from his computed tomography (CT) examination and 3D printing. As shown in **Figure 3**, the cardiologist was then able to use the model to practice device insertion and deployment prior to the procedure. Particularly, having access to the model allowed the cardiologist a) to guide device personalisation to assure safe anchoring in the specific anatomy and b) to identify a problem in deploying the device if accessing via the right pulmonary artery —conventional approach—while he could successfully deploy it when inserting the line via the left pulmonary artery. The latter strategy was used (successfully) on the day of the

**Figure 3.** A 3D-printed model was used for testing insertion and deployment of a novel device (second-generation, self-expandable PPVI stent). As a valuable practice tool, testing the procedure in the model is allowed to identify an unfeasible access route. LPA, left pulmonary artery; RPA, right pulmonary artery. . Stent diameter at extremities = 40.7

By establishing a conversation with clinicians and embedding the technology within the hospital, it is possible that complicated cases requiring additional insight are brought to the attention of the engineer for the creation of a model, which could be either a computer simulation or a 3D-printed model, or both. The model is created specifically to appreciate

**3. Models for decision support: a case study**

procedure [15].

126 New Trends in 3D Printing

mm.

**Figure 4.** Patient-specific 3D model of stented coarctation, printed with two different materials to better highlight the position of the device (length = 28 mm), including the possibility of opening the model to better appreciate the relative position of the stent with respect to the rest of the anatomy.


**Abbreviations**

*Maintenance and Asset Life Cycle for Reliability Systems DOI: http://dx.doi.org/10.5772/intechopen.85845*

*Reliability and Maintenance - An Overview of Cases*

same time periods of the corrective activities.

inherent to the useful life of the asset.

disaggregating the cost has been suggested.

CMT critical maintenance tasks DCS distributed control system

availability of the assets.

**4. Conclusions**

In this analysis, an increase of 3.26% is observed. A fundamental part of this increase is due to the optimization of downtime, because with the operational excellence model, preventive critical tasks are programmed and executed in the

During the life cycle of an asset or a production system, different costs are incurred, which span the purchase (initial investment) to the operation and maintenance costs that guarantee productive and financially worthy outputs for investors. The life cycle cost corresponds to the costs of both investment and operations

Development and implementation of management models, applied to the maintenance of equipment, often present results in periods that exceed 1 year. This model, based on the ISO 55000 standard, presented immediate results mainly by, firstly, defining maintenance as a strategic activity for the collective benefit of the organization and, secondly, collecting all necessary information pertaining to defining the critical maintenance needs, in such a way so as to guarantee the high

In this chapter a maintenance strategy model of the asset life cycle is proposed. It has a direct influence on maintenance management regarding decision-making, as well as the planning of preventive tasks and analysis of the equipment's useful life. Positive results are obtained in the overall development of this maintenance model. It is possible to notice a reduction of costs in the global execution, and so the average cost per work order has been reduced too. At the same time, an increase in the execution proportion of preventive tasks has been achieved. These findings may help other to implement the model successfully, even though the tasks performed

The common maintenance budget models only present a general sum of costs; it does not provide enough information for decision-making. These results confirm the association between cost control, technical decisions and physical interventions, which have been and exemplified, and therefore, in this document, a new way of

Frequently the summation is separated monthly to fit in with the scale of the time series. These estimators of central tendency allow the visualization of how data variability alters this tendency according to the behavior of the series. It is also

The result of the implementation has shown an increase of the availability

These preliminary results that cover a 12-month period suggest that in the long term/medium term, the availability may reach the level demanded by the company and may guarantee stable operation with lower maintenance costs. Finally, it is important to highlight that, without the support of the general management of the organizations, the initiatives to achieve operational excellence, or an adequate

and the model itself remain in continuous analysis and improvement.

evident that the median is less susceptible than the average.

indicator and a reduction of the general maintenance costs.

management of assets, may fail, causing loss and discouragement.

CMMS computerized maintenance management system

**22**

**Abbreviations**


**Author details**

*Renewable and Sustainable Composites*

summary of some of these patents.

**6. Conclusion**

led to numerous implant failures and lead to investigations to improve mechanical

ers employing natural fiber for biomedical applications and **Table 1** presents a

There have been quite a number of patents registered over the years of research-

The natural fiber polymer composites have gained a lot of ground in terms of acceptance and applications. For these interests to increase continuously, the materials must be designed to meet certain requirements for their specific applicability. For structural applications, the most important property that is of concern is the mechanical properties as they help to predict the behavior of the materials under stress. As discussed in this chapter, NFPCs are being applied in the building and construction works, automobiles, aerospace, packaging, electronics and biomedical devices. The mechanical strength and toughness require by these various industries are quite different and can vary widely from one application to another. Even though the mechanical requirement varies, the factors that determine the mechanical properties are the same. These factors include type of fibers used, source of the fibers, surface treatment and modification carried out on the fibers, type of polymer matrix used (pristine and blended), type of fiber-matrix bond formed, and the internal arrangement which depends on the type of curing treatment after processing or annealing. In addition to this list are the fiber length, fiber orientation and distribution, fiber loading or volume faction, the type of functionality present and the extent of modification. All these factors can be manipulated to give a combination of the right measure of mechanical strength and stiffness or toughness required for the application it is being designed for. The use of compatibilizers and nanoparticles to modify the composites for specific purpose has been widely reported to improve the mechanical properties as well, but proper integrations must be considered. Furthermore, the use of hybrid fibers has also gained wide acceptance because of the improved stiffness and strength owing to the synergy observed in such fiber combination. The possibilities of NFPCs can be best imagined with the right improvement in their mechanical properties and this chapter has highlighted some of these benefits as presented by numerous research investigations across

The financial assistance of the University of Zululand and the National Research

Foundation, South Africa through the South African Research Chair Initiative (SARChI) is hereby acknowledged. OSJ thanks the National Research Foundation (NRF) for a postdoctoral fellowship and funding under South African Research

properties of biomedical devices for diverse applications (**Figure 2**).

**126**

diverse fields.

**Acknowledgements**

Chair for Nanotechnology.

Owonubi J. Shesan1 \* † , Agwuncha C. Stephen2†, Anusionwu G. Chioma3 , Revaprasadu Neerish1 and Sadiku E. Rotimi4,5

1 Department of Chemistry, University of Zululand, Kwadlangezwa, KwaZulu-Natal, South Africa

2 Department of Chemistry, Ibrahim Badamasi Babangida University, Lapai, Nigeria

3 Department of Applied Chemistry, University of Johannesburg, Johannesburg, South Africa

4 Department of Chemical, Metallurgical and Material Engineering, Tshwane University of Technology, Pretoria, South Africa

5 Institute of Nano Engineering Research (INER), Tshwane University of Technology, Pretoria, South Africa

\*Address all correspondence to<EMAIL_ADDRESS>
† These authors contributed equally.

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**7. Conclusion**

Bone metastases are a common manifestation of distant relapse from many types of solid cancers, a significant source of morbidity, and a major contributor to the deterioration of the quality of life. Prompt diagnosis is essential for optimal treatment, which may consist of controlling pain and other symptoms, preserving and restoring function, minimizing the risk of SREs, stabilizing the skeleton, and enhancing local tumor control. This way, a multidisciplinary approach is essential to achieve the best outcome possible.


**High-Octane Gasoline Production from Catalytic Naphtha Reforming**

Cheng Seong Khor

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.80866

#### Abstract

The global drive for environmental sustainability necessitates continuous adjustment, optimization, and improvement in petroleum refining processes to generate energy and products including automotive fuels such as gasoline. At the same time, refiners need to maximize their asset utilization to maintain competitiveness in the business setting. This chapter presents a process advisory and monitoring application to optimize a catalytic naphtha reforming operation to produce high octane gasoline feedstock. A mathematical model is developed for the process to produce hydrocarbons with high anti-knock ratings. The proposed methodology involves formulating a nonlinear programming optimization model to perform data reconciliation. The model objective minimizes the deviations (or errors) between the measured values and the model-reconciled values to reflect the accuracy and reliability of the measurements. The overall procedure is carried out subject to various real-world operation constraints to ensure sustainable processing of the required products, which include hydrogen gas and aromatics. We present a case study to illustrate an implementation of the resulting model in an online environment to improve process operation at an actual refinery in Canada. The computational results show enhanced product quality of a reformate stream with high octane number and increased yields.

Keywords: catalytic naphtha reforming, petroleum refinery process operation, data reconciliation, process monitoring, mathematical modeling, nonlinear optimization

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### 1. Introduction

#### 1.1. Catalytic reforming process

Catalytic reforming is an important process in the petroleum refining industry. It is developed originally to produce components of automotive fuels specifically gasoline, which meet engine requirement for high antiknock quality. The process objective is to convert petroleum naphtha fractions to high rates of aromatic hydrocarbons as selectively as possible since the latter have excellent antiknock ratings. Naphtha fractions are liquid hydrocarbon mixtures with 6–12 carbon atoms and having boiling points in the range of 320–470 K [1, 2]. Reforming also serves two other main purposes in a refinery: it is the main hydrogen producer for use within a refinery or outside it; it also provides feedstock (mainly consists of benzene, toluene, and xylene) for the subsequent downstream petrochemical production processes [3]. A survey of the recent progress on the reforming process focusing on the reactor modeling is available in Refs. [4].

A commercial reformer unit consists of a reactor section, a recycle gas compression section, and a fractionation section. The reactor section consists of a feed system, a few heaters or furnaces, a series of reactors, and a flash separator. A portion of the flashed hydrogen gas is recycled and mixed with the feed and then increased to the reaction temperature by a heat exchanger combining the feed and reactor effluent followed by the first heater. We send the flashed liquid to the fractionation section comprising a distillation column that acts as product stabilizer. The distillate stream strips light gases from the flashed liquid that produce liquefied petroleum gas (LPG) and off-gas as fuel. The main product is the bottoms stream called reformate that is a feedstock for gasoline blending. We use a few (typically three to five) heater-and-reactor pairs to maintain the reaction temperature within a range of 400–500˜C (700–800 K) and at pressures of 10–35 atm using catalysts mainly to accelerate the reaction [3, 6]. Figure 1 displays a general process flow of a commercial reformer unit.

#### 1.2. Data reconciliation method

Data reconciliation involves estimating process variables by comparing their values from process measurements and process models. The models typically comprise material and energy balances or conservation relations. Data reconciliation involves adjusting or correcting errors in measurement values to be consistent with the mass balances. The procedure is useful in the process and its affiliated industries to conduct monitoring and modeling for control, simulation, and optimization as well as to perform instrument maintenance and equipment analysis [5–9].

Progress has been recorded in the literature on data reconciliation techniques that include calculating measurement errors to estimate the model variable values using linear process models [7], classifying the estimated variables [8], and categorizing the measurement values as redundant or nonredundant [9]. In this work, we adopt the data reconciliation procedure that makes use of information from redundant measurement and conservation laws to correct measurement values by converting them into model values based on reliable, accurate, and thermodynamically valid process knowledge incorporated in a nonlinear steady-state model.

Figure 1. General process flow of a commercial reformer unit.

#### 2. Problem statement

We have the following data for a catalytic reforming process: a set of process units; a set of measurements i with vector of known data on their raw scan values y, model values x, standard deviation σi, and weights wi; and a set of tuning parameters p with known nominal values np, model values tp, and scaling factors sp. The model constraints for the process are the mass balances, energy balances, and equilibrium relationships.

In this work, we want to optimize the operation of a catalytic reforming process by minimizing the deviations of values for selected measurements and tuning parameters from their modelcomputed values (i.e., differences computed between the x and y vectors and the np and tp vectors, respectively) subject to process constraints to obtain reconciled process values (as given by the model values). To achieve this aim, we can adjust a set of reconciled variables such as skew factors on the true boiling point of crude oil fractions, product flow rate bias, Murphree efficiencies of the distillation columns, and reaction kinetic parameters [10–12].

#### 3. Model formulation

This section describes the approach used to develop a reforming process monitoring model using the commercial advanced chemical process optimizer platform called SimSci ROMeo [13]. The model developed involves two technologies: cyclic and semi-regenerative reforming with the overall scope covered by the model shown in Figure 2. Figure 3 presents a detailed schematic of the model for the cyclic reforming operation.

Figure 2. Model scope comprising the semi-regenerative and cyclic reforming units.

The optimality of a catalytic reforming process operation is measured based on the yields of the main product of reformate (a gasoline blending feedstock) and the side products of petrochemicals comprising benzene, toluene, and xylene (BTX). We use material-balanced data of the model-computed values and not raw scan values of the measurements to evaluate such performance measures. Hence, we perform data reconciliation on the measurement values, which are obtained from field instruments measuring the actual process operation.

#### 3.1. Data reconciliation model

We formulate the data reconciliation procedure as a least squares minimization version of an optimization problem [14]. The objective function as given in Eq. (1) minimizes the sum of squares of the weighted differences between model values and raw scan values for selected measurements and tuning parameters:

$$\text{minimize } \sum\_{i} w\_{i} \left(\frac{\delta\_{i}}{\sigma\_{i}}\right)^{2} + w\_{p} \left(\frac{t\_{p} - n\_{p}}{s\_{p}}\right)^{2} \tag{1}$$

where wi is the weight matrix for each measurement i (either 1 or 0 otherwise for those not considered) and δi is the offset as given by difference between model value and raw scan value.

The differences or deviations are also called errors, offsets, or biases of the instruments considered. The offset value is weighted or multiplied with a weight factor (i.e., its value is wi = 1) if we decide to reconcile the process value of the associated measurement or tuning parameter. Such a decision is made based on a measurement's reliability, that is, if it is measured value is accurate to be compared against a model-reconciled value; otherwise, the instrument needs to be calibrated. The model constraints mainly consist of the total mass and component balances, energy balances, and equilibrium relationships as well as appropriate bounds on the variables [7, 8].

High-Octane Gasoline Production from Catalytic Naphtha Reforming 29 http://dx.doi.org/10.5772/intechopen.80866

Figure 3. Schematic representation of the cyclic reforming operation model representation.

#### 3.2. Feed characterization

We perform tuning on the reformer feed composition to match the process data as close as possible by adjusting the factors representing the component mole fractions [10]. It is not necessary to adjust for all the components in the reconciliation procedure especially if regular laboratory data on feed composition is not available in terms of its PONA (paraffin, olefin, naphthene, and aromatic) content analysis. We can reduce the number of components to be tuned for the feed slate by equating the mole fraction factors for components with the same number of carbon atoms on the basis that these components have similar molecular weights and densities. This strategy can result in reducing about one-third of the parameters involved in the tuning procedure.

In the absence of composition data, we consider tightening the reformate yield specification by minimizing its offset and reducing its standard deviation value. The reformate yield measured value is given by the ratio between reformate mass flow rates to that of the feed; therefore, no weight is specified on reformate yield because the actual measured values are the product and feed flows.

In a component adjuster model of the SimSci ROMeo software, three choices of methods are available for handling each component, that is, flow, component, and dependent. Typically, only one component is chosen as a dependent component; all others should be chosen as flow or component. There is no definite guideline on specifying the use of a flow or component method for a component. It is best to use the most abundant component which is the dependent component.

Depending on the situation, we can impose bounds on the adjustment factor to get a feasible solution. If the lower bound of the adjustment factor for a component is active, setting its lower bound to a value of �0.5 results in reducing the component mole fraction by 50% (e.g., from 0.05 to 0.025). Correspondingly, a lower bound of �1 reduces the component mole fraction to zero, that is, the component is removed from the resultant outlet stream of the component adjuster. This effect is undesirable; we do not want a component to be removed completely. Therefore, a suggestion is to use a lower bound other than �1 (e.g., �0.8 or �0.75). Similarly, for a component with an active upper bound, an upper bound of 0.5 increases its mole fraction by 50% (e.g., from 0.05 to 0.075). Therefore, we avoid upper bounding of the adjustment factor on the mole fraction of a component as 1 because it results in a doubling effect.

It is extensive and not necessary to tune the compositions of all components in the lumped feed slate. In particular, there is no need to adjust those of the light components of C5s and lower (i.e., C3s–C5s) because their compositions are very low (or almost zero). A typical bound for the tuning parameters of a feed component is [�0.75, 0.75]; a practical bound depends on the actual data.

#### 3.3. Reactor representation

The application uses a proprietary nonlinear surrogate model to represent a reactor bed that incorporates its catalyst- and kinetic-related information. We use a flash drum operating under adiabatic condition to model the pressure of the reactor bed inlet stream and a similar configuration to model the outlet stream as shown in Figure 4. For each reactor, we include a set of

Figure 4. Schematic representation of a reactor.

equations to equate each of the kinetic parameters (e.g., catalyst activity) to a common variable, thereby facilitating the reaction kinetics tuning to match the plant values. Since each reactor is identical, we first develop an individual reactor with its associated auxiliary units (such as the two flash drums at its inlet and outlet) and then duplicate its representation. This approach can be done by way of using a block diagram (or other similar features) that is available, which can help to improve the layout of the graphical user interface besides facilitating with a failed solution or modeling problem.

#### 3.4. Reactor pressure balance

There are a number of ways to represent a reactor pressure balance for data reconciliation purpose depending on the reliability of measurements available: (1) distribute the total pressure drop across the reactor bed equally to the pressure drop of the inlet flash drum and that of the outlet flash drum; (2) honor the inlet and outlet pressure measurements if they are reliable; and (3) relate the pressure drops for the other reactors to that of a reactor with known reliable pressure measurement.

#### 3.5. Reaction kinetic tuning

A general approach in reforming applications is to maintain most of the built-in reaction kinetic tuning parameters equal for each of the reactors by using some form of mathematical relation facility (such as a customization unit feature available in ROMeo). We use the first mathematical relation with a local variable on each kinetic parameter that has an associated tuning parameter to adjust it. Then, for each reactor, we use the second relation to equate a kinetic parameter to its corresponding variable in the first relation.  We put suitable bounds on the tuning parameters to keep the solved parameter values within reasonable range. The tuning parameters are divided into two sets: for catalyst and for catalyst coke. The catalyst tuning parameters are base-2 logarithm (log2) multipliers that are bounded.

#### 3.6. Reactor switch in cyclic reformer

The application has logic to represent the reactor switch for a cyclic reformer. The switch operation involves a swing reactor besides the on-oil reactors to replace the reactor in regeneration. Thus, the catalyst can be regenerated without shutting the unit down. Note that such logic is not required for a semi-regenerative reformer in which all the reactors are taken offstream or out of service for in situ regeneration of the entire catalyst inventory.

The mentioned logic to model the reactor switching involves the following steps with the explanation aided by Figure 5:

• Turns down the following items for the off-oil reactor and turns them on for the on-oil reactors: inlet pressure measurement, reactor custom model unit, inlet and outlet flash drums, customization units on reaction kinetic tuning, customization unit on reactor pressure balance, delta temperature measurement model, and its associated customization unit

Figure 5. Reactor switching in cyclic reformer.

If the swing reactor is off oil, the logic performs the following steps:

The default setting of the logic is to put weights on all the reactor inlet pressure and temperature difference measurement models to account for them in the objective function computation. But when reactor switch happens, the swing reactor inlet pressure measurement may have a large offset that results in a huge objective value; hence, it may merit resolving the model by removing the weight on this tag.

#### 3.7. Measurement models

#### 3.7.1. Absolute error tolerance

The standard deviation parameter is specified to set the absolute error tolerance for a measurement model. Manipulating this tolerance to obtain a desired solution or to represent the importance of a measurement should be avoided; instead, the tolerance represents a measurement's reliability and accuracy.

We use Microsoft Excel to view and analyze the results to help with obtaining a good representation of the measurements; an example of such a result viewer is shown in Figure 6. A measurement may still be of good quality but a particular instance of its value may be bad, for example, because data has stopped flowing to the data historian. Specifying the tolerance of a measurement depends on its precision. The weighting of a measurement can handle a bad valued measurement instance by way of excluding it from being considered in the objective function computation.

Figure 6. An example of result viewer of the reformer model.

It is recommended to set approximately 1% of the process variable value (PV) for most measurements and 5% of the PV for flow rates. Note that a calculated tag is generally not reconciled, that is, unweighted if the input measurement for which the calculations are based on are available.

#### 3.7.2. Measurement screening logic to handle data quality

Specifying the logic for measurement model screening requires review when handling negative flow rate instances to determine if they are caused by zero flows or bad transmitters. The following gives a list of rules of thumb that can be applied:

#### 3.7.3. Two measurements on one variable

There may be two measurements available on the same process variable. It is acceptable to use two measurement models for a dependent variable, but for an independent variable, doing so results in one measurement model used as a controller and another as an indicator; thus, we may face problems if its scan value becomes bad. If both measurement models are necessary, we can specify a new variable equal to the particular process variable and point the indicator measurement to it, besides specifying a screening criterion to set the objective value contribution to zero when the measurement data quality turns bad.

#### 4. Results and discussion

#### 4.1. Key process variables

The key process variables to match are reformate yield, reformer reactor total endotherm, research octane number (RON) of reformate product, and hydrogen recycle gas purity. The available tuning variables are feed composition and reaction kinetic parameters such as overall catalyst activity, acid site activity, and aromatic selectivity. The main input parameters are catalyst weight and reactor bed coke fraction. Table 1 summarizes the key variables that the application uses for process monitoring and their typical values.

#### 4.2. Tuning strategies

To tune the model to achieve the desired values and results, we balance between tuning the group of parameters for the reactor kinetics and that for the feed compositions. We can reduce the variation in one group by letting parameters in the other group move—the deliberation as to which to allow for more movement/adjustment depends on the feedback from the site or a process specialist on their relative importance. If the deliberation is not to change the feed composition much while letting the reactor tuning parameters to move more, then use scaling factors of 1 for both parameter groups with appropriate lower and upper bounds for the parameters. (The solved parameters should not be at bounds.)

However, if the composition of the synthesized feed is significantly different from the actual plant condition, even allowing for the feed composition tuning parameters to move over a wide range may not help with convergence. Therefore, it is important to have good starting feed composition, which can be achieved by calculating the feed distillation cut points at the same time at which the samples are taken.

Two other tuning strategies involve using calculated variables on the reformate yields and reactor total endotherms.

Table 1. Representative key process variable values to match error tolerance for reformate yields.

#### 4.2.1. Reformate yields

Reformate yield is given by the ratio of the reformate stream flow rate to that of the feed stream (typically on a mass basis). A calculated tag is generally not weighted and thus excluded from the objective function calculation. Instead, the tolerances for the two flow measurements used to calculate the yield (i.e., for the reformate and feed streams) are tightened. However, for the purpose of experimenting to determine ways to improve the model, we put a weight on the reformate yield tag and use a tight tolerance by specifying the standard deviation value to an artificially small number but one in which the model is still solvable in reasonable computational time. The resulting model solution gives information on the candidate independent variables whose bounds can be reasonably relaxed, measurement models whose tolerances (i.e., standard deviations) can be adjusted or relaxed, as well as possibly faulty or problematic instrumentation (due to malfunction or calibration issues). Note that this practice highlights a difference between using a data reconciliation application and using a predictive optimization tool in which the latter attempts to meet the reformate yields and other variables tightly.

#### 4.2.2. Reactor total endotherms

Reactor total endotherms are the sum of the temperature differences (i.e., delta in temperatures) of each of the reforming reactors. This variable represents the temperature change and hence the reaction that takes place in a reactor, which can be the desirable endothermic reactions of dehydrogenation of naphthene to aromatic compounds and/or dehydrocyclization of paraffins to naphthenes or the undesirable (but necessary) exothermic reactions of hydrocracking and/or hydrocyclization. There are two relations available (which hence gives rise to uncertainty) to calculate the temperature difference for a reactor: (1) take the difference between the temperature transmitters at the outlet of a preheater (i.e., upstream furnace) and that at the inlet of a furnace downstream and (2) take the difference between the reactor temperature indicators (i.e., thermocouples at the top skin and bottom skin). It is noted that when facing catalyst issues, the second relation may give a higher value than that theoretically attainable due to heat loss that is actually experienced but not accounted for. Similar to the reformate yield being a calculated variable, it is also generally our practice to unweight (or to turn off) the total endotherm tag because it is an artificial or pseudo-tag which may not physically exist at a site.

#### 5. Concluding remarks

In this work, we discuss a systematic workflow to develop a data reconciliation model for a catalytic reforming process to produce automotive fuels. We present the mathematical model formulation in the form of a nonlinear optimization model developed on a commercial software platform. The model is deployed as an online application to improve process advisory and monitoring at a refinery besides providing a rich data source for a multitude of other associated relevant uses. We show the utility of such an application to contribute toward producing energy in an environmentally sustainable manner through an optimal process operation approach with reduced off-spec fuel products.

#### Author details

Cheng Seong Khor1,2\*

\*Address all correspondence to<EMAIL_ADDRESS>
1 Chemical Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar, Perak Darul Ridzuan, Malaysia

2 Academy of Sciences Malaysia, Kuala Lumpur, Malaysia

## References


**4. Viral orthologs of host miRNAs**

Two classes of virus-encoded miRNAs are grouped: viral specific miRNAs and orthologs of host miRNAs. Similar to some viral regulatory proteins, several viral miRNAs have evolved to mimic host effectors. The "seed" region of a miRNA (~nucleotides 2–8 at the 5′ end) plays a key role in directing RISC to its mRNA targets. Binding with perfect seed complementary to the target transcript takes ~60% of regulation by a particular miRNA [45]. Some of viral miRNAs share seed sequences with host miRNAs and at least three viruses: BLV, MDV-1, and Kaposi's Sarcoma-associated Herpesvirus (KSHV) have been shown to regulate transcripts via the same target docking sites as their counterpart host miRNAs [46–48]. Such viral miRNAs could potentially regulate hundreds of transcripts having target sites for a particular host miRNA. Such regulatory networks could affect specific functions such as inhibiting apoptosis.

KSHV and MDV-1 express two distinct miRNAs, which are functional ortholog of host miR-155, a conserved cellular miRNA that is required for the rapid expansion of B and T cells after antigenic stimulation and highly expressed in activated myeloid and lymphoid cells [31, 46–48]. Interestingly, in both MDV-1 and KSHV-induced tumors, there is downregulation of endogenous levels of miR-155 [38, 47, 49], although the mechanisms for such downregulation is not fully understood. It is intriguing to know why these two viruses chose to express their own miR-155 orthologs, while suppressing the host-encoded miRNA with potentially identical functions. The direct role of miR-155 on oncogenesis [50, 51] and induction of cancer [47, 52] have been shown by a number of studies. Furthermore, it has been shown that upregulation of miR-155 is associated with lymphocyte transformation by reticuloendotheliosis virus strain T (REV-T) [53, 54] and EBV [55]. It is therefore striking that mdv1-miR-M4-5p is highly expressed in MDV-1 transformed cells derived from MDV-1-induced T-cell lymphomas [31, 46]. Moreover, lymphoma induction in infected birds is greatly reduced with seed region mutagenesis or deletion of mdv1-miR-M4-5p, suggesting the importance of mdv1-miR-M4-5p in the induction of tumors [34, 35]. The fact that transformation ability of the miR-M4-deleted MDV-1 virus is partly restored by introduction of chicken miR-155, suggests that viral miRNAs can play a major role in enhancing the oncogenic potential of a herpesvirus *in vivo*. However, the oncogenicity of vvMDV strain GX0101 with mdv1-miR-M4 deletion has significantly decreased but was not totally abolished [35]. This report, coupled with the finding that HVT with mdv1-miR-M4 inserted in the genome failed to induce tumors [32], suggested that other factors are also required for viral transformation. Moreover, our recent studies have shown that mdv1-miR-M4 is not essential for maintaining the proliferation of transformed cell lines suggesting that it has probably a more significant role in the initiation of neoplastic transformation [56].

miR-H14-3p, one of the HVT-encoded miRNAs, showed close sequence identity with perfect match of the 21/23 nucleotides including identical seed sequence to the chicken gga-miR-221, suggesting that it is a virus-encoded ortholog [27]. Indeed, the proposed ortholog status of the two miRNAs are supported by the evidence that HVT-miR-H14-3p also modulates the expression of p27Kip1 (cyclin-dependent kinase inhibitor 1B), a known target of miR-221 [57]. Compared to other known viral miRNA orthologs, where only the seed sequences are identical, the full length mature miRNA sequences of miR-H14-3p and gga-miR-221 are almost identical, strongly suggesting that hvt-miR-H14-3p is most likely to have been acquired from the host genome. This is the first example of virus-encoded miRNA showing such close and extended sequence identity with a host miRNA. Furthermore, partial sequence conservation has been observed between the gga-miR-221 locus on chromosome 1 of the chicken genome and the downstream flanking region of hvt-miR-H14-3p in the HVT genome, suggesting hvt-miR-H14-3p is "pirated" by the virus from the host, despite of the fact that herpesviruses have frequently pirated and subverted host genes to their own purposes. Interestingly, p27Kip1, the target shared by the two miRNAs, is a regulator of the cell cycle G1 to S phase transition. MDV-1-induced tumorigenesis may also involve a similar mechanism as miR-221-mediated repression of p27 in cancer progression [58–60]. In order to support viral genome replication and to promote growth of infected cells for additional viral production, the downregulation of p27 could move the cell cycle to the S phase [32]. The sequence homology is also observed between mdv1-miR-M31 and miR-221 in the seed region [31], although it is limited to the minimal miRNA seed region at nucleotide positions 2–7. Although targeting of p27 by miR-M31-3p has not been experimentally validated, the finding that miR-M31 deleted virus reduced the mortality and gross tumor incidence of infected chickens significantly has implied that miR-M31 may act as potential oncogene by targeting p27 [61].

**37**

*Role of Virus-Encoded microRNAs in Avian Viral Diseases*

gga-miR-29 is known to function both as an oncogene and a tumor suppressor depending on the context [62]. Seed sequence homology has been observed between mdv2-miR-M21 and miR-29 suggesting the potential functional ortholog between the two miRNAs. In fact, blv-miR-B4, a BLV-encoded miRNA, has been shown to be a functional ortholog of host miR-29 [12]. Furthermore, RLCVencoded miR-rL1-6-3p and EBV-encoded miR-BART1-3p also share seed sequence with miR-29. Thus, a picture is emerging wherein several virus-encoded miRNAs

It is estimated that ~8% of avian virus-encoded miRNAs possess identical heptameric seed sequence with host miRNAs, thus could potentially act as functional orthologs [7]. However, based on low abundance, untested biogenesis, and unknown functional relevance, it is not clear, whether all of the currently annotated viral or host miRNAs are bona fide miRNAs, thus some seed matches between host and viral miRNAs could arise by chance. Therefore, any proposed functional orthologs between the viral and host miRNAs require further experimental

Although over 500 virus-encoded miRNAs have been identified, an in depth functional study is lacking for most. However, it is clear that virus-encoded miR-NAs can target both cellular and viral mRNAs and this has been confirmed by several studies analyzing the mRNA targetome of the virus-infected cells [64]. The best characterized viral miRNA functions in supporting viral replication include regulating the latent-lytic switch; evading the immune response; promoting cell survival, proliferation, and/or differentiation. All of these functions should be particularly important during persistent infections. As viral miRNAs and proteins work synergistically to promote a cellular environment favorable to the completion of the viral life cycle, modulation of the host cell environment is achieved by multiple and partly redundant mechanisms. The identified targets of avian herpesvirus-encoded

Compared to identification of cellular targets, identifying viral targets of viral miRNAs is more straightforward as viral genomes encode fewer candidate mRNAs. Known examples of viral targets include transcripts, which are antisense to the viral miRNA precursor and transcripts elsewhere with imperfect matches. Perhaps, the former one is the most straightforward examples of determining viral miRNA function as the perfect matching between the miRNA and its target would be predicted to result in a siRNA-like cleavage of the target mRNA if both are co-expressed. For example, iltv-miR-I5 that is antisense to ICP4 cleaves ICP4 mRNA [65]. ICP4, an immediate early viral transactivator, plays a key role in the induction of lytic replication. The ICP4 targeting by viral miRNAs is thought to mediate lytic/latent switch and render the latent state more robust [66]. Apart from iltv-miR-I5, iltv-miR-I6 also maps antisense to the ICP4 gene. However, reporter assay on inhibition of luciferase activity by iltv-miR-I6 was not significant [65]. It has been proposed that this is due to the blockage of accessibility to the binding region following in silico folding of RNA containing the targets for iltv-miR-I5 and iltv-miR-I6. This is consistent with the previous finding that target RNA folding is essential for the efficacy of designed siRNAs [67, 68]. Sequences antisense to known miRNA stem-loop structures may fold into stem-loop structures themselves at an increased propensity.

**5. Target identification of avian herpesvirus miRNAs**

*DOI: http://dx.doi.org/10.5772/intechopen.89688*

are likely to target host pathways [63].

miRNAs are summarized in **Table 1**.

**5.1 Viral targets of viral miRNAs**

validation.

*Non-Coding RNAs*

KSHV and MDV-1 express two distinct miRNAs, which are functional ortholog of host miR-155, a conserved cellular miRNA that is required for the rapid expansion of B and T cells after antigenic stimulation and highly expressed in activated myeloid and lymphoid cells [31, 46–48]. Interestingly, in both MDV-1 and KSHV-induced tumors, there is downregulation of endogenous levels of miR-155 [38, 47, 49], although the mechanisms for such downregulation is not fully understood. It is intriguing to know why these two viruses chose to express their own miR-155 orthologs, while suppressing the host-encoded miRNA with potentially identical functions. The direct role of miR-155 on oncogenesis [50, 51] and induction of cancer [47, 52] have been shown by a number of studies. Furthermore, it has been shown that upregulation of miR-155 is associated with lymphocyte transformation by reticuloendotheliosis virus strain T (REV-T) [53, 54] and EBV [55]. It is therefore striking that mdv1-miR-M4-5p is highly expressed in MDV-1 transformed cells derived from MDV-1-induced T-cell lymphomas [31, 46]. Moreover, lymphoma induction in infected birds is greatly reduced with seed region mutagenesis or deletion of mdv1-miR-M4-5p, suggesting the importance of mdv1-miR-M4-5p in the induction of tumors [34, 35]. The fact that transformation ability of the miR-M4-deleted MDV-1 virus is partly restored by introduction of chicken miR-155, suggests that viral miRNAs can play a major role in enhancing the oncogenic potential of a herpesvirus *in vivo*. However, the oncogenicity of vvMDV strain GX0101 with mdv1-miR-M4 deletion has significantly decreased but was not totally abolished [35]. This report, coupled with the finding that HVT with mdv1-miR-M4 inserted in the genome failed to induce tumors [32], suggested that other factors are also required for viral transformation. Moreover, our recent studies have shown that mdv1-miR-M4 is not essential for maintaining the proliferation of transformed cell lines suggesting that it has probably a more significant role in the initiation of neoplastic transformation [56]. miR-H14-3p, one of the HVT-encoded miRNAs, showed close sequence identity with perfect match of the 21/23 nucleotides including identical seed sequence to the chicken gga-miR-221, suggesting that it is a virus-encoded ortholog [27]. Indeed, the proposed ortholog status of the two miRNAs are supported by the evidence that HVT-miR-H14-3p also modulates the expression of p27Kip1 (cyclin-dependent kinase inhibitor 1B), a known target of miR-221 [57]. Compared to other known viral miRNA orthologs, where only the seed sequences are identical, the full length mature miRNA sequences of miR-H14-3p and gga-miR-221 are almost identical, strongly suggesting that hvt-miR-H14-3p is most likely to have been acquired from the host genome. This is the first example of virus-encoded miRNA showing such close and extended sequence identity with a host miRNA. Furthermore, partial sequence conservation has been observed between the gga-miR-221 locus on chromosome 1 of the chicken genome and the downstream flanking region of hvt-miR-H14-3p in the HVT genome, suggesting hvt-miR-H14-3p is "pirated" by the virus from the host, despite of the fact that herpesviruses have frequently pirated and subverted host genes to their own purposes. Interestingly, p27Kip1, the target shared by the two miRNAs, is a regulator of the cell cycle G1 to S phase transition. MDV-1-induced tumorigenesis may also involve a similar mechanism as miR-221-mediated repression of p27 in cancer progression [58–60]. In order to support viral genome replication and to promote growth of infected cells for additional viral production, the downregulation of p27 could move the cell cycle to the S phase [32]. The sequence homology is also observed between mdv1-miR-M31 and miR-221 in the seed region [31], although it is limited to the minimal miRNA seed region at nucleotide positions 2–7. Although targeting of p27 by miR-M31-3p has not been experimentally validated, the finding that miR-M31 deleted virus reduced the mortality and gross tumor incidence of infected chickens significantly has implied

**36**

that miR-M31 may act as potential oncogene by targeting p27 [61].

gga-miR-29 is known to function both as an oncogene and a tumor suppressor depending on the context [62]. Seed sequence homology has been observed between mdv2-miR-M21 and miR-29 suggesting the potential functional ortholog between the two miRNAs. In fact, blv-miR-B4, a BLV-encoded miRNA, has been shown to be a functional ortholog of host miR-29 [12]. Furthermore, RLCVencoded miR-rL1-6-3p and EBV-encoded miR-BART1-3p also share seed sequence with miR-29. Thus, a picture is emerging wherein several virus-encoded miRNAs are likely to target host pathways [63].

It is estimated that ~8% of avian virus-encoded miRNAs possess identical heptameric seed sequence with host miRNAs, thus could potentially act as functional orthologs [7]. However, based on low abundance, untested biogenesis, and unknown functional relevance, it is not clear, whether all of the currently annotated viral or host miRNAs are bona fide miRNAs, thus some seed matches between host and viral miRNAs could arise by chance. Therefore, any proposed functional orthologs between the viral and host miRNAs require further experimental validation.

## **5. Target identification of avian herpesvirus miRNAs**

Although over 500 virus-encoded miRNAs have been identified, an in depth functional study is lacking for most. However, it is clear that virus-encoded miR-NAs can target both cellular and viral mRNAs and this has been confirmed by several studies analyzing the mRNA targetome of the virus-infected cells [64]. The best characterized viral miRNA functions in supporting viral replication include regulating the latent-lytic switch; evading the immune response; promoting cell survival, proliferation, and/or differentiation. All of these functions should be particularly important during persistent infections. As viral miRNAs and proteins work synergistically to promote a cellular environment favorable to the completion of the viral life cycle, modulation of the host cell environment is achieved by multiple and partly redundant mechanisms. The identified targets of avian herpesvirus-encoded miRNAs are summarized in **Table 1**.

#### **5.1 Viral targets of viral miRNAs**

Compared to identification of cellular targets, identifying viral targets of viral miRNAs is more straightforward as viral genomes encode fewer candidate mRNAs. Known examples of viral targets include transcripts, which are antisense to the viral miRNA precursor and transcripts elsewhere with imperfect matches. Perhaps, the former one is the most straightforward examples of determining viral miRNA function as the perfect matching between the miRNA and its target would be predicted to result in a siRNA-like cleavage of the target mRNA if both are co-expressed. For example, iltv-miR-I5 that is antisense to ICP4 cleaves ICP4 mRNA [65]. ICP4, an immediate early viral transactivator, plays a key role in the induction of lytic replication. The ICP4 targeting by viral miRNAs is thought to mediate lytic/latent switch and render the latent state more robust [66]. Apart from iltv-miR-I5, iltv-miR-I6 also maps antisense to the ICP4 gene. However, reporter assay on inhibition of luciferase activity by iltv-miR-I6 was not significant [65]. It has been proposed that this is due to the blockage of accessibility to the binding region following in silico folding of RNA containing the targets for iltv-miR-I5 and iltv-miR-I6. This is consistent with the previous finding that target RNA folding is essential for the efficacy of designed siRNAs [67, 68]. Sequences antisense to known miRNA stem-loop structures may fold into stem-loop structures themselves at an increased propensity.

#### **Table 1.**

*Avian virus encoded miRNAs and proposed functions highlighted in this review.*

This strategy could allow a virus to express both the viral gene and encode miRNAs antisense to mRNA transcripts lying on the other strand.

Analysis of miRNA binding clusters on the MDV-1 genome by PAR-CLIP identified very strong binding clusters near the 5′ end of the genome. These clusters essentially are antisense to the MDV-1 miRNAs miR-M2, miR-M3, miR-M4, and miR-M12, which are members of Meq-cluster, adjacent to each other, highly expressed and all located antisense to an MDV-1 mRNA encoding the viral R-LORF8 protein [64]. Analysis of indicator constructs in co-transfected 293 T cells or transduced MSB-1 cells confirmed that R-LORF8 is indeed targeted by all four miRNAs. Interestingly, expression of R-LORF8 gene in a form lacking the viral miRNA binding sites caused a substantial reduction in MSB-1 cell growth, suggesting that dysregulated expression of R-LORF8 can exert a deleterious effect in MDV-1 transformed T cells. The molecular basis for this effect is not clear as the function of R-LORF8 in the viral life cycle is currently unknown. The same pattern is observed in MDV-2, where viral miRNAs (miR-M24, M25, M26, M27, M28, and M29) are transcribed from the DNA strand located antisense to R-LORF2. In addition, there are also some other miRNAs encoded by MDV-1, MDV-2, HVT, and DEV, which are antisense to certain viral transcripts [24–28], the possible regulatory roles of those miRNAs against their antisense mRNA transcripts require to be verified individually.

**39**

tumorigenesis.

during infection [71, 72].

**5.2 Cellular targets of viral miRNAs**

*Role of Virus-Encoded microRNAs in Avian Viral Diseases*

As described above, mdv1-miR-M4-5p is a functional ortholog of cellular miR-155. mdv1-miR-M4-5p is also the first avian herpesvirus miRNA known to target both viral and cellular mRNAs. In addition to the cellular targets shared with miR-155, mdv1-miR-M4-5p and mdv1-miR-M4-3p also target viral UL28 and UL32 proteins, respectively. This provided the first evidence of late viral gene targeting by herpesviral miRNA [69]. Rather than 3′UTR, both target sequences are located in the coding region. UL28 and UL32 homologs, which are required for the cleavage and packaging of virion DNA in human herpesvirus 1 (HHV-1) have been found in all subfamilies of mammalian and avian herpesviruses. However, the role of UL28 and UL32 in MDV-1 packaging has not been demonstrated. As UL28 and UL32 are involved in the later stages of MDV replication, it is possible that mdv1-miR-M4 contributes in maintaining MDV-1 latency by downregulating the production of UL28 and UL32 and impairing late MDV morphogenesis and reactivation.

Following the observation of the increased viral pathogenicity or oncogenicity of infected chickens when mdv1-miR-M11 is deleted from Mid-cluster, the potential mechanisms mediated by miR-M11 has been investigated [61]. Meq gene has been predicated as candidate target gene by bioinformatics approach with RNAhybrid. Indeed, Meq as miR-M11 target has been confirmed by dual luciferase assay and qRT-PCR showing downregulation of Meq transcript level following virus infection. It has been suggested that the viral miRNAs in the Meq- or Mid-clusters cooperate with each other for establishing, maintaining latency, and/or triggering

ICP4 and ICP27, two MDV immediate-early (IE) genes, have been identified as potential targets for mdv1-miR-M7-5p by both bioinformatics prediction and subsequent experimental validation [70]. Indeed, this is reflected by a negative correlation between an increase in ICP27 expression and the decreased expression of mdv1-miR-M7-5p during virus reactivation. This is consistent with the early finding that miR-M7-5p is at extremely low levels in MDV-infected CEF but highly expressed in MSB-1 cells [25]. MDV miRNAs produced from LAT-cluster may contribute to the latency by targeting two IE genes. These findings further support the view that herpesvirus miRNAs play a key role in controlling the lytic/latent switch

Viruses such as herpesviruses with latent infection need to keep the host cells alive long enough. Thus, viral miRNAs can promote virus replication through prolonging cell survival and evading immune recognition. Among avian virusencoded miRNAs, the targets of mdv1-miR-M4-5p are most extensively studied due to its critical role in virus induced oncogenesis. As viral orthologs of miR-155, both kshv-miR-K12-11 and mdv1-miR-M4 potentially target the same group of transcription factors as gga-miR-155 including Pu.1, CEBPβ, HIVEP2, BCL2L13, PDCD6, MAP3K7IP2, GPM6B, RREB1, and c-Myb [46, 69]. Subsequent analysis of >1000 cellular mRNAs targeted by MDV-1 miRNAs in MSB-1 cells using photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP) resulted in the identification of 73 mRNA 3′UTR targets for miR-M4-5p, of which nine (C1orf103, CSNK1A1, LATS2, MAP3K14, MYB, NR1D2, RORA, RPS6KA3, and WEE1) had previously been identified as targets for miR-155 or kshv-miR-K12-11 in EBV-transformed human B cells and KSHV-transformed human B cells, respectively [64]. Indicator analysis of these 3′ UTRs gave data consistent with the hypothesis that these mRNAs are indeed targets for both miR-M4 in MSB1 and miR-155 in human B-cell lymphomas. Four additional mRNA targets (*FCHSD2, JARID2, PBEF1*, and RAP2A) were also conserved between miR-M4 in MSB-1 and

*DOI: http://dx.doi.org/10.5772/intechopen.89688*

#### *Role of Virus-Encoded microRNAs in Avian Viral Diseases DOI: http://dx.doi.org/10.5772/intechopen.89688*

*Non-Coding RNAs*

**38**

**Table 1.**

This strategy could allow a virus to express both the viral gene and encode miRNAs

Analysis of miRNA binding clusters on the MDV-1 genome by PAR-CLIP identified very strong binding clusters near the 5′ end of the genome. These clusters essentially are antisense to the MDV-1 miRNAs miR-M2, miR-M3, miR-M4, and miR-M12, which are members of Meq-cluster, adjacent to each other, highly expressed and all located antisense to an MDV-1 mRNA encoding the viral R-LORF8 protein [64]. Analysis of indicator constructs in co-transfected 293 T cells or transduced MSB-1 cells confirmed that R-LORF8 is indeed targeted by all four miRNAs. Interestingly, expression of R-LORF8 gene in a form lacking the viral miRNA binding sites caused a substantial reduction in MSB-1 cell growth, suggesting that dysregulated expression of R-LORF8 can exert a deleterious effect in MDV-1 transformed T cells. The molecular basis for this effect is not clear as the function of R-LORF8 in the viral life cycle is currently unknown. The same pattern is observed in MDV-2, where viral miRNAs (miR-M24, M25, M26, M27, M28, and M29) are transcribed from the DNA strand located antisense to R-LORF2. In addition, there are also some other miRNAs encoded by MDV-1, MDV-2, HVT, and DEV, which are antisense to certain viral transcripts [24–28], the possible regulatory roles of those miRNAs against their antisense mRNA transcripts require to be verified individually.

antisense to mRNA transcripts lying on the other strand.

*Avian virus encoded miRNAs and proposed functions highlighted in this review.*

As described above, mdv1-miR-M4-5p is a functional ortholog of cellular miR-155. mdv1-miR-M4-5p is also the first avian herpesvirus miRNA known to target both viral and cellular mRNAs. In addition to the cellular targets shared with miR-155, mdv1-miR-M4-5p and mdv1-miR-M4-3p also target viral UL28 and UL32 proteins, respectively. This provided the first evidence of late viral gene targeting by herpesviral miRNA [69]. Rather than 3′UTR, both target sequences are located in the coding region. UL28 and UL32 homologs, which are required for the cleavage and packaging of virion DNA in human herpesvirus 1 (HHV-1) have been found in all subfamilies of mammalian and avian herpesviruses. However, the role of UL28 and UL32 in MDV-1 packaging has not been demonstrated. As UL28 and UL32 are involved in the later stages of MDV replication, it is possible that mdv1-miR-M4 contributes in maintaining MDV-1 latency by downregulating the production of UL28 and UL32 and impairing late MDV morphogenesis and reactivation.

Following the observation of the increased viral pathogenicity or oncogenicity of infected chickens when mdv1-miR-M11 is deleted from Mid-cluster, the potential mechanisms mediated by miR-M11 has been investigated [61]. Meq gene has been predicated as candidate target gene by bioinformatics approach with RNAhybrid. Indeed, Meq as miR-M11 target has been confirmed by dual luciferase assay and qRT-PCR showing downregulation of Meq transcript level following virus infection. It has been suggested that the viral miRNAs in the Meq- or Mid-clusters cooperate with each other for establishing, maintaining latency, and/or triggering tumorigenesis.

ICP4 and ICP27, two MDV immediate-early (IE) genes, have been identified as potential targets for mdv1-miR-M7-5p by both bioinformatics prediction and subsequent experimental validation [70]. Indeed, this is reflected by a negative correlation between an increase in ICP27 expression and the decreased expression of mdv1-miR-M7-5p during virus reactivation. This is consistent with the early finding that miR-M7-5p is at extremely low levels in MDV-infected CEF but highly expressed in MSB-1 cells [25]. MDV miRNAs produced from LAT-cluster may contribute to the latency by targeting two IE genes. These findings further support the view that herpesvirus miRNAs play a key role in controlling the lytic/latent switch during infection [71, 72].

## **5.2 Cellular targets of viral miRNAs**

Viruses such as herpesviruses with latent infection need to keep the host cells alive long enough. Thus, viral miRNAs can promote virus replication through prolonging cell survival and evading immune recognition. Among avian virusencoded miRNAs, the targets of mdv1-miR-M4-5p are most extensively studied due to its critical role in virus induced oncogenesis. As viral orthologs of miR-155, both kshv-miR-K12-11 and mdv1-miR-M4 potentially target the same group of transcription factors as gga-miR-155 including Pu.1, CEBPβ, HIVEP2, BCL2L13, PDCD6, MAP3K7IP2, GPM6B, RREB1, and c-Myb [46, 69]. Subsequent analysis of >1000 cellular mRNAs targeted by MDV-1 miRNAs in MSB-1 cells using photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP) resulted in the identification of 73 mRNA 3′UTR targets for miR-M4-5p, of which nine (C1orf103, CSNK1A1, LATS2, MAP3K14, MYB, NR1D2, RORA, RPS6KA3, and WEE1) had previously been identified as targets for miR-155 or kshv-miR-K12-11 in EBV-transformed human B cells and KSHV-transformed human B cells, respectively [64]. Indicator analysis of these 3′ UTRs gave data consistent with the hypothesis that these mRNAs are indeed targets for both miR-M4 in MSB1 and miR-155 in human B-cell lymphomas. Four additional mRNA targets (*FCHSD2, JARID2, PBEF1*, and RAP2A) were also conserved between miR-M4 in MSB-1 and

miR-155 in EBV-transformed B cells. Among these mRNA targets, JARID2, a cell cycle regulator and part of a histone methyltransferase complex, has been shown to promote apoptosis and decrease cell survival when ectopically expressed [53]. *WEE1* encodes a kinase that blocks cell-cycle progression and has been associated with inflammation and cancer [73]. MYB, a transcription factor, is involved in the regulation of hematopoiesis and tumorigenesis. Another interesting shared gene target for miR-155/miR-M4/miR-K12-11 is large tumor suppressor (LATS), which inhibits cell proliferation and promotes apoptosis by inhibiting YAP transcriptional activity through Hippo pathway. Being evolutionarily conserved from avian to mammal, loss of LATS expression is observed in many human cancers, including acute lymphoblastic leukemia [74]. These observations suggest a potential selective advantage for MDV-1 in inducing downregulation of LATS. Taken together, these observations provide additional evidence for the impact of miR-155 and its orthologs on pathways regulating lymphocyte activation, differentiation, apoptosis, proliferation, immune tolerance, and tumorigenesis [7].

In addition to the candidate mRNA targets described above, latent TGF-β binding protein 1 (*LTBP1*) has also been identified as bona fide host mRNA targets for miR-M4 [75]. Inhibition of *LTBP1* expression by miR-M4-5p induced a significant decrease of TGF-β1 secretion and activation, with suppression of TGF- β signaling and upregulation of c-Myc expression. Interestingly, miR-155 has been shown to suppress TGF-β signaling through targeting SMAD2 and SMAD5 in human diseases [76, 77]. The KSHV-encoded miR-K12-11 also inhibits TGF-β signaling through downregulation of SMAD5 [78]. Thus, these findings indicate that dysregulation of the TGF-β signaling pathway by miR-155 and its viral orthologs may be a common feature shared by oncogenic herpesviruses. Interestingly, MDV1-encoded miRNA miR-M3 also suppress TGF-β signaling through targeting host gene SMAD2, and has been shown to suppress drug-induced apoptosis in cell culture [79], thus adding another layer of confidence to the conclusion that suppression of TGF-β signaling pathway is indeed involved in MDV-induced oncogenesis.

Another identified cellular target of miR-M4 is chicken heterogeneous nuclear ribonucleoprotein AB (hnRNPAB). HnRNPAB, a member of the hnRNP family proteins, plays important roles in both normal biological processes and cancer development. Downregulation of hnRNPAB expression promotes proliferation of both CEF and chicken fibroblast cell line DF-1 [80]. Hence, downregulation of hnRNPAB by miR-M4-5p may be one of the important strategies for MDV-1 to trigger the development of MD lymphomas.

PAR-CLIP identified a list of 1104 cellular mRNAs targeted by MDV-1 miRNAs and a list of 1183 mRNAs targeted by MDV-2 miRNAs in MSB1, of which 419 mRNA targets were shared. Analysis of those cellular mRNAs identified several that were targeted by five or more different viral miRNAs in their 3′ UTRs, suggestive of a possibly important role in restricting innate antiviral immunity. IL-18, a proinflammatory cytokine induced upon infection by several different viruses, is able to stimulate IFN-γ production from T cells [81]. Surprisingly, chicken IL-18 gene contains seven viral miRNA target sites in its 3′ UTR [64]. Indeed, MSB1 cell growth is highly sensitive to inhibition by ectopic IL-18 expression. Whether this is due to induction of chicken IFN-γ expression remains to be determined. Thus, in addition to express viral interleukin-8 (vIL-8), which is required for disease progression and tumor development, this data suggest a second way in which MDV manipulates the host cell immune response.

Taken together, all of the data above suggests that latent/oncogenic viruses may proactively create a cellular environment beneficial to viral latency and oncogenesis through viral miRNA targeting cellular factors involved in antiviral processes including apoptosis. Surely, more targets of miRNAs encoded by avian viruses will

**41**

**7. Conclusions**

*Role of Virus-Encoded microRNAs in Avian Viral Diseases*

be identified with the advances in high-throughput technologies. We should be able to understand the role played by these small and highly effective modulators of gene expression once more targets of viral miRNAs are discovered and an integrated approach of demonstrating the functions and molecular pathways is developed.

Despite, several possible cancer-related genes have been identified as their targets of viral miRNAs, the direct *in vivo* role of those miRNAs such as KSHVencoded miRNAs in neoplastic diseases is difficult to demonstrate in the absence of suitable models. However, excellent disease models of infection by MDV in natural avian host allow examination of the oncogenic potential of virus-encoded miRNAs. As described above, it has been shown that the deletion of the Meq-clustered miR-NAs from the viral genome by BAC mutagenesis abolished the oncogenicity of the virus. miR-M4 is essential for the virus in inducing tumor as the mutant virus with miR-M4 deletion or seed sequence mutation almost lost its oncogenicity, providing evidence for a direct *in vivo* role of a viral miRNA in tumor induction [34]. In a separate study, deletions of the Meq-cluster or miR-M4 alone from the viral genome of vvMDV strain GX0101 strongly decreased its oncogenicity [35]. Subsequent work demonstrated that except for mdv1-miR-M4, most of the other Meq-clustered miRNAs also play critical roles in MDV oncogenesis as both mortality and gross tumor incidence of birds infected with the mutant viruses have reduced significantly for the corresponding virus with single miRNA deletion [82]. These studies provide further information for understanding the molecular determinants that

Having demonstrated the critical roles of Meq-clustered miRNAs in MD pathogenesis and/or tumorigenesis, the role of miRNAs in Mid-cluster, which is transcribed by the same promoter as the Meq-cluster in the latent phase of the infection has been investigated by infection of miRNA deleted virus in animal model [61]. The role of miR-M31-3p acting as oncogene and miR-M11-5p acting as tumor suppressor in MD lymphomagenesis have been proposed based on the decreased mortality and gross tumor incidence by miR-M31 deletion mutant and increased viral pathogenicity or oncogenicity of infected chickens when miR-M11 is deleted.

Recent advances in sequencing technology have led to the identification of a number of miRNAs encoded by avian viruses. Given their ability to target cellular and viral transcripts, and the lack of immune response, miRNAs represent an ideal mechanism of gene regulation during viral infection, latency, and persistence. Similar to host miRNAs, the determination of biologically relevant activities of the reported viral miRNAs is the next step forward. Although the function of most viral miRNAs is to be determined, evidence so far does support the view that viral miRNAs are key regulators in virus biology. By targeting key viral lytic genes directly or modulating cellular regulatory pathways indirectly, the virus-encoded miRNAs could contribute significantly toward switching between lytic and latent infections, thereby regulating viral pathogenesis *in vivo*. Whether a small or large number of critical miRNA-target

interactions have functional significance is a key question to be answered.

It is crucial to define the miRNA targets for both understanding observed phenotypes and providing clues to their functional role. Genome wide biochemical screening are becoming more powerful approaches for the generation of reliable

*DOI: http://dx.doi.org/10.5772/intechopen.89688*

**6. Role of viral miRNAs in pathogenesis**

trigger the development of tumors by oncogenic MDVs.

*Non-Coding RNAs*

miR-155 in EBV-transformed B cells. Among these mRNA targets, JARID2, a cell cycle regulator and part of a histone methyltransferase complex, has been shown to promote apoptosis and decrease cell survival when ectopically expressed [53]. *WEE1* encodes a kinase that blocks cell-cycle progression and has been associated with inflammation and cancer [73]. MYB, a transcription factor, is involved in the regulation of hematopoiesis and tumorigenesis. Another interesting shared gene target for miR-155/miR-M4/miR-K12-11 is large tumor suppressor (LATS), which inhibits cell proliferation and promotes apoptosis by inhibiting YAP transcriptional activity through Hippo pathway. Being evolutionarily conserved from avian to mammal, loss of LATS expression is observed in many human cancers, including acute lymphoblastic leukemia [74]. These observations suggest a potential selective advantage for MDV-1 in inducing downregulation of LATS. Taken together, these observations provide additional evidence for the impact of miR-155 and its orthologs on pathways regulating lymphocyte activation, differentiation, apoptosis,

In addition to the candidate mRNA targets described above, latent TGF-β binding protein 1 (*LTBP1*) has also been identified as bona fide host mRNA targets for miR-M4 [75]. Inhibition of *LTBP1* expression by miR-M4-5p induced a significant decrease of TGF-β1 secretion and activation, with suppression of TGF- β signaling and upregulation of c-Myc expression. Interestingly, miR-155 has been shown to suppress TGF-β signaling through targeting SMAD2 and SMAD5 in human diseases [76, 77]. The KSHV-encoded miR-K12-11 also inhibits TGF-β signaling through downregulation of SMAD5 [78]. Thus, these findings indicate that dysregulation of the TGF-β signaling pathway by miR-155 and its viral orthologs may be a common feature shared by oncogenic herpesviruses. Interestingly, MDV1-encoded miRNA miR-M3 also suppress TGF-β signaling through targeting host gene SMAD2, and has been shown to suppress drug-induced apoptosis in cell culture [79], thus adding another layer of confidence to the conclusion that suppression of TGF-β signaling

Another identified cellular target of miR-M4 is chicken heterogeneous nuclear ribonucleoprotein AB (hnRNPAB). HnRNPAB, a member of the hnRNP family proteins, plays important roles in both normal biological processes and cancer development. Downregulation of hnRNPAB expression promotes proliferation of both CEF and chicken fibroblast cell line DF-1 [80]. Hence, downregulation of hnRNPAB by miR-M4-5p may be one of the important strategies for MDV-1 to trigger the devel-

PAR-CLIP identified a list of 1104 cellular mRNAs targeted by MDV-1 miRNAs and a list of 1183 mRNAs targeted by MDV-2 miRNAs in MSB1, of which 419 mRNA targets were shared. Analysis of those cellular mRNAs identified several that were targeted by five or more different viral miRNAs in their 3′ UTRs, suggestive of a possibly important role in restricting innate antiviral immunity. IL-18, a proinflammatory cytokine induced upon infection by several different viruses, is able to stimulate IFN-γ production from T cells [81]. Surprisingly, chicken IL-18 gene contains seven viral miRNA target sites in its 3′ UTR [64]. Indeed, MSB1 cell growth is highly sensitive to inhibition by ectopic IL-18 expression. Whether this is due to induction of chicken IFN-γ expression remains to be determined. Thus, in addition to express viral interleukin-8 (vIL-8), which is required for disease progression and tumor development, this data suggest a second way in which MDV manipulates the

Taken together, all of the data above suggests that latent/oncogenic viruses may proactively create a cellular environment beneficial to viral latency and oncogenesis through viral miRNA targeting cellular factors involved in antiviral processes including apoptosis. Surely, more targets of miRNAs encoded by avian viruses will

proliferation, immune tolerance, and tumorigenesis [7].

pathway is indeed involved in MDV-induced oncogenesis.

opment of MD lymphomas.

host cell immune response.

**40**

be identified with the advances in high-throughput technologies. We should be able to understand the role played by these small and highly effective modulators of gene expression once more targets of viral miRNAs are discovered and an integrated approach of demonstrating the functions and molecular pathways is developed.

## **6. Role of viral miRNAs in pathogenesis**

Despite, several possible cancer-related genes have been identified as their targets of viral miRNAs, the direct *in vivo* role of those miRNAs such as KSHVencoded miRNAs in neoplastic diseases is difficult to demonstrate in the absence of suitable models. However, excellent disease models of infection by MDV in natural avian host allow examination of the oncogenic potential of virus-encoded miRNAs. As described above, it has been shown that the deletion of the Meq-clustered miR-NAs from the viral genome by BAC mutagenesis abolished the oncogenicity of the virus. miR-M4 is essential for the virus in inducing tumor as the mutant virus with miR-M4 deletion or seed sequence mutation almost lost its oncogenicity, providing evidence for a direct *in vivo* role of a viral miRNA in tumor induction [34]. In a separate study, deletions of the Meq-cluster or miR-M4 alone from the viral genome of vvMDV strain GX0101 strongly decreased its oncogenicity [35]. Subsequent work demonstrated that except for mdv1-miR-M4, most of the other Meq-clustered miRNAs also play critical roles in MDV oncogenesis as both mortality and gross tumor incidence of birds infected with the mutant viruses have reduced significantly for the corresponding virus with single miRNA deletion [82]. These studies provide further information for understanding the molecular determinants that trigger the development of tumors by oncogenic MDVs.

Having demonstrated the critical roles of Meq-clustered miRNAs in MD pathogenesis and/or tumorigenesis, the role of miRNAs in Mid-cluster, which is transcribed by the same promoter as the Meq-cluster in the latent phase of the infection has been investigated by infection of miRNA deleted virus in animal model [61]. The role of miR-M31-3p acting as oncogene and miR-M11-5p acting as tumor suppressor in MD lymphomagenesis have been proposed based on the decreased mortality and gross tumor incidence by miR-M31 deletion mutant and increased viral pathogenicity or oncogenicity of infected chickens when miR-M11 is deleted.

#### **7. Conclusions**

Recent advances in sequencing technology have led to the identification of a number of miRNAs encoded by avian viruses. Given their ability to target cellular and viral transcripts, and the lack of immune response, miRNAs represent an ideal mechanism of gene regulation during viral infection, latency, and persistence. Similar to host miRNAs, the determination of biologically relevant activities of the reported viral miRNAs is the next step forward. Although the function of most viral miRNAs is to be determined, evidence so far does support the view that viral miRNAs are key regulators in virus biology. By targeting key viral lytic genes directly or modulating cellular regulatory pathways indirectly, the virus-encoded miRNAs could contribute significantly toward switching between lytic and latent infections, thereby regulating viral pathogenesis *in vivo*. Whether a small or large number of critical miRNA-target interactions have functional significance is a key question to be answered.

It is crucial to define the miRNA targets for both understanding observed phenotypes and providing clues to their functional role. Genome wide biochemical screening are becoming more powerful approaches for the generation of reliable

and accurate target information of viral miRNAs although target identification using target prediction software provided an initial strategy. Several techniques such as RIP-CHIP (RNA-binding protein immunoprecipitation microarray), HITS-CLIP (high-throughput sequencing cross-linking and immunoprecipitation), PAR-CLIP and proteomics analysis have all contributed large amount of data on potential targets of virus-encoded miRNAs [47, 83–89] although only PAR-CLIP has been used for avian herpesvirus miRNA targetome identification [64]. Cross-linking, ligation and sequencing of hybrids (CLASH) technology, another biochemical screen for miRNA targets, promises to generate the most accurate target information to date, leading the way in the generation of high confidence target datasets which will be invaluable for future studies [90, 91]. These new technologies allow unprecedented and largely unbiased views into miRNAs-mediated regulation of gene expression in virus-infected cells. Undoubtedly, further studies using different approaches and technologies are required toward the clear definition of miRNAs targetome and their functional relevance in viral infection, latency, reactivation, and pathogenesis.