*2.5.9 Acha*

Acha, which is also referred to as "fonio", "findi", "fundi", "pom, and kabug" "hungry rice" and "petit mil", is a small-grained cereal that is native to West Africa, which is generally classified among the millet [74, 75]. It is cultivated in various parts of Nigeria, Sierra Leone, Ghana, Guinea Bissau and Benin Republic on less fertile sandy soils that could not support the growth of other more demanding cereals. There are two known varieties of acha which are *Digitaria exilis Kippis stapf.* and *Digitaria iburua Kippis Stapf.* It is regarded as one of the lost crops in the West Africa sub region. Its production is important to West African Farmers, though hindered by several factors among which are poor agronomic performance because of unimproved seeds and husbandary practices. Its West Africa annual production is about 250,000 tonnes [17]. The annual yields of 3098 metric tonnes, 112,000 metric tonnes and 126,000 metric tonnes of fonio were reported in Nigeria [76]. The economic returns of acha when compared with other crops like rice, sorghum, and cowpea showed that it was profitable to grow the crop on a commercial scale [77]. Fonio are the most nutritious and testiest of all grains [77] and it contains 7%

crude protein that is high in leucine (19.8%), methionie and cystine (7%) and valine (5.8%) [78]. Fonio grains are mostly consumed wholly, they are also milled into flour and constitute a versatile raw material for preparation varieties of food such as gruels, porridges, couscous, bread, beer, and beverages [79]. Starch extracted from fonio possesses good disintegrant and binding properties [80] and it also has good glidant properties [81].

### **2.6 Food starch properties**

The starch granules sizes obtained from different crops vary in properties, because of their sources, extraction methods and cultivars [82]. The purity as well as the granules size can be determined using scanning electron microscopy [83]. The amylose content of starch is one of the most important factors influencing the cooking and textural qualities of whole storage root, and quality of starch-based foods [84]. *Dioscorea* starch granules possess varying shapes, which are spherical, oval and polygonal, depending on the species with granule size varying from 2 to 50 mm [85]. Also the X-ray diffraction pattern of the *Dioscorea* starch granules range from the B to C-type, depending on the *Dioscorea* specie [18]. *Dioscorea* starches contain18 to 30% amylose contents and their gelatinization temperature vary from 70 to 92°C [84].

Starch particle sizes obtained from white and yellow yam varieties showed similar patterns with a single symmetrical distribution centered at approximately 32 and 35 mm respectively. Sources, varietal differences and growing conditions significantly influence size and shape of starch granules. However, due to better mouth feel, small sized starch granules has been suggested as possible lipid substitute in food systems [86]. They are also used as laundry-stiffening agents because they possess good fabric penetration ability, better glossiness and stiffness, which are required in textile industries [87].

Sweet potato starch granules possess spherical, oval, and polygonal shapes, and they are about 2–46 mm in size. The X-ray diffraction patterns of the sweet potato starch granules are of type A and they also exhibit 38% crystallinity [88]. The starch in sweet potato is made of 16.1–24.4% amylose, having a swelling power of 80% at 90°C and gelatinization temperature of 64.6–84.6°C [84]. Cassava starch possesses small spherical granules, having an average granule size of 14.7 mm. Its amylose content ranges from 13.6 to 23.8% [84, 89] and its crystallinity has been reported to be 38% [84], while the gelatinization temperature varies from 59.6 to 87.2°C [84, 89]. The X-ray diffraction patterns of the cassava starch granules depict type A [90]. The cocoyam granules (Taro) are small rounded and ellipsoidaltruncated, with their sizes varying from 0.5 to 5 mm in diameter, making them to be more easily digestible [84]. Taro starch has been used in the preparation of some baby foods and diets of people who are allergic to cereals [91]. The X-ray diffraction patterns of the taro starch granules exhibit the typical A-type pattern, while the starch contains 14.0–19% amylose with the pasting temperatures varying from 81 to 85°C [92]. Interestingly, starch from Tannia (*Xanthosoma sagittifolium*) comprises of small rounded and large truncated ellipsoidal-shaped granules, which possesses granular diameters which range from 2 to 50 mm [91]. The amylose contents of different cultivars range from 21.3 to 25.4% [93]. Tannia starch possesses a type A X-ray diffraction pattern, higher pasting temperatures and lower paste viscosity than those of other starches, such as potato starch [89]. Also, it has higher swelling power and solubility at relatively high temperatures than sweet potato starch granule [84]. The maize starch granules exhibit polyhedral granule shapes and differences in their mean granule size range from 2.3 to 19.5 μm. The starch samples show A-type diffraction pattern with strong reflection at 15.25, 18.11,

**205**

*Utilization of Starch in Food and Allied Industries in Africa: Challenges and Prospects*

depending on both genetic and environmental factors [98].

order to meet the requirements of specific industrial processes [13].

and 23.33° [94]. The gelatinization temperatures of maize starch range from 69.16 to 76.98°C [95] and the amylose content varies from 24.74 to 30.32% [94]. Fonio starch granules are polygonal in shape with diameter ranging from 2.0–14.3 mm. Their X-ray diffraction patterns are of the type-A crystalline form [96], with the amylose contents ranging from 18.7% to 19.6%. The millet starch granules are small, spherical to polygonal in shape, but may vary from specie to another. The granular sizes range from 0.8 to 10 mm. The gelatinization temperatures of millet starch ranged from 75.8 to 84.9°C and the amylose content vary from 16.0 to 27.1% [84]. The starch granules obtained from sorghum are typically 3–27 mm and the gelatinization temperature range 61.1–81.2°C [97]. The starch showed the A-type crystalline diffraction pattern and its amylase content varies from 11.2–28.5%,

Native starches are crude starches that are extracted directly from their sources and they are mainly used as food, but irrespective of their sources they are undesirable for many applications because of their inability to withstand processing conditions. Each starch has unique functional properties, and much of the starch used industrially is modified before use, giving a wider range of useful products [99]. Starch can be readily converted chemically and biologically into many useful and diverse products such as paper, textiles, adhesives, beverages, confectionery, pharmaceuticals, and plastics, so as to improve desirable functional properties in

Starch granules' settling is often prevented by presence of various components like mucilage and latex, which may lead not only to loss of starch, but also reducing the quality of extracted starch. However, microbial growth can also be promoted if the extraction residence time is prolonged than necessary, which may also result into breakdown of starch and resultant loss of starch quality. It also affects the color of the starch limiting its utilization in food and industrial applications. Therefore, optimum recovery of starch having physicochemical and functional qualities coupled with economical extraction of starches from cereals and tubers is important. Extraction of starch with water is the most common form of starch extraction, but this has been improved upon over time. The Central Tuber Crops Research Institute, Trivandrum, India research on various chemicals that could improve the yield of starch from various tubers [100, 101]. It was discovered that ammoniacal solutions gave the best results. However, when aqueous ammonia (0.03 M) was used for starch extraction, the yield, paste viscosity and swelling capacity of the extracted starch improved. Ammonia formed a complex with the mucilagenous material in the slurry, thereby releasing the starch granules and promoting faster settling of starch in less viscous slurry, which prevents microbiological damage of the starch due to short residence time. Moorthy [101] observed that lactic and citric acids improved the yield and color of starch from sweet potato tubers. Interestingly, an enzymatic method for enhancing the recovery (26% increase) of starch from cassava tubers using pectinase and cellulase enzymes [101]. The aforementioned enzymes work by altering the integrity of the pectin-cellulosic matrix of cell membranes and thereby promoting the release of the starch granules. The same technique was used to promote starch recovery from sweet potatoes by 20% without affecting starch properties [101].

*DOI: http://dx.doi.org/10.5772/intechopen.95020*

**2.7 Forms of starch**

*2.7.2 Extraction techniques*

*2.7.1 Native starch*

*Utilization of Starch in Food and Allied Industries in Africa: Challenges and Prospects DOI: http://dx.doi.org/10.5772/intechopen.95020*

and 23.33° [94]. The gelatinization temperatures of maize starch range from 69.16 to 76.98°C [95] and the amylose content varies from 24.74 to 30.32% [94]. Fonio starch granules are polygonal in shape with diameter ranging from 2.0–14.3 mm. Their X-ray diffraction patterns are of the type-A crystalline form [96], with the amylose contents ranging from 18.7% to 19.6%. The millet starch granules are small, spherical to polygonal in shape, but may vary from specie to another. The granular sizes range from 0.8 to 10 mm. The gelatinization temperatures of millet starch ranged from 75.8 to 84.9°C and the amylose content vary from 16.0 to 27.1% [84]. The starch granules obtained from sorghum are typically 3–27 mm and the gelatinization temperature range 61.1–81.2°C [97]. The starch showed the A-type crystalline diffraction pattern and its amylase content varies from 11.2–28.5%, depending on both genetic and environmental factors [98].


**4. Virtual model development of a stepper motor**

Stepper motor is an asynchronous machine, the stator of which contains a control winding. The rotor is either fitted with a permanent magnet or made up of a toothed ferromagnetic magnetic circuit. The stepper motor is an impulse-excited electric machine, the movement of which is not continuous but is done stepwise. Its main advantage consists of motor perform‐ ance without the necessity of any controllers, and when the motors are not overloaded, they can work without feedback. The precise control of position or rotation at a constant speed is done simply by counting steps.

According to the construction, the stepper motors are divided into three groups, [8]:

#### **4.1. Hybrid stepper motor**

The hybrid stepper motor accumulates benefits both of stepper motors with variable reluctance and permanent magnets. It has a very small step and high power per unit of weight. The arrangement of the stator winding is similar to this stepper motor with variable reluctance. It differentiates by the rotor that is made from a cylinder permanent magnet with mounted rotor poles along the circumference having teeth. The number of teeth determinates an angle step. Typically, the motors with 50 teeth are produced, in which one step is equal to the angle of 1.8°. In the stator of the hybrid stepper motor, there are usually two windings having terminals arranged as shown in Figure 12.

**Figure 12.** Twelve ways of arranging the stator windings of a two-phase hybrid stepper motor: (a) 4 wires-only start and end points are led to terminals; (b) 5 wires-also the common middle point is led out to terminals; (c) 6 wires-the middle point of each phase is led out to terminals; and (d) 8 wires-the phases are divided into the halves and the start and end points are led to the terminals.

#### **4.2. Control of stepper motors**

According to the types of the windings, the stepper motors are divided into the unipolar stepper motor and the bipolar one. Similarly, the control of the stepper motors is divided into the following:

#### *4.2.1. Four-tact control magnetizing one phase*

**4. Virtual model development of a stepper motor**

16 E-Learning - Instructional Design, Organizational Strategy and Management

done simply by counting steps.

**c.** Hybrid stepper motors

**4.1. Hybrid stepper motor**

arranged as shown in Figure 12.

and end points are led to the terminals.

**4.2. Control of stepper motors**

the following:

**a.** Stepper motors with variable reluctance **b.** Stepper motors with permanent magnets

Stepper motor is an asynchronous machine, the stator of which contains a control winding. The rotor is either fitted with a permanent magnet or made up of a toothed ferromagnetic magnetic circuit. The stepper motor is an impulse-excited electric machine, the movement of which is not continuous but is done stepwise. Its main advantage consists of motor perform‐ ance without the necessity of any controllers, and when the motors are not overloaded, they can work without feedback. The precise control of position or rotation at a constant speed is

According to the construction, the stepper motors are divided into three groups, [8]:

The hybrid stepper motor accumulates benefits both of stepper motors with variable reluctance and permanent magnets. It has a very small step and high power per unit of weight. The arrangement of the stator winding is similar to this stepper motor with variable reluctance. It differentiates by the rotor that is made from a cylinder permanent magnet with mounted rotor poles along the circumference having teeth. The number of teeth determinates an angle step. Typically, the motors with 50 teeth are produced, in which one step is equal to the angle of 1.8°. In the stator of the hybrid stepper motor, there are usually two windings having terminals

**Figure 12.** Twelve ways of arranging the stator windings of a two-phase hybrid stepper motor: (a) 4 wires-only start and end points are led to terminals; (b) 5 wires-also the common middle point is led out to terminals; (c) 6 wires-the middle point of each phase is led out to terminals; and (d) 8 wires-the phases are divided into the halves and the start

According to the types of the windings, the stepper motors are divided into the unipolar stepper motor and the bipolar one. Similarly, the control of the stepper motors is divided into In a simple drawing (Figure 13), the rotor is replaced by a rotating permanent magnet having the north pole (red) and the south pole (blue). By switching the stator winding phases in the in the opposite coils, the north and the south poles are also excited.

The principle of the control consists of exciting (and magnetizing) one phase only. According to the cyclogram in Figure 13a, the sequence of excitation A1-B1-A2-B2 ensures the rotation of the magnetic field in the positive direction. The change of the direction is done through reverse switching of the motor phases. Here the current flows through one winding only.

In case of bipolar control (Figure 13b), the current flows simultaneously through two opposite coils. This means the current is twice higher in comparison with the unipolar type. The bipolar control differs by the necessity of change the current direction.

**Figure 13.** Cyclogram of four-tact control magnetizing one phase of a hybrid stepper motor.

#### *4.2.2. Four-tact control magnetizing two phases*

When two neighboring coils are excited simultaneously, according to the cyclogram in Figure 14, the motor torque is 2 times higher. The position of the rotor will follow the vector sum of magnetic fluxes of both phases. At this type of control, the current flows through all four windings. This type of control is used the most often.

**Figure 14.** Cyclogram of four-tact control of a hybrid stepper motor.

#### *4.2.3. Eight-tact control*

Combining the previous two control algorithms leads to doubling the number of stable states (cyclogram in Figure 15). Consequently, an increase of positioning accuracy is achieved (called "soften up"), without changing the structural adjustment. The previous commutation (fourtact control) was the symmetrical one, and the asymmetrical eight-tact commutation causes doubling of the number of rotor steps.

**Figure 15.** Eight-tact control of a hybrid stepper motor: (a) unipolar control; (b) bipolar control.

#### *4.2.4. Further modes of the control*

Other ways of controlling hybrid stepper motors exist, but due to simplification, we do not deal with them in detail here (although the modes are also included into the virtual model):

#### **4.3. Simulation model of the hybrid stepper motor**

**Figure 14.** Cyclogram of four-tact control of a hybrid stepper motor.

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Combining the previous two control algorithms leads to doubling the number of stable states (cyclogram in Figure 15). Consequently, an increase of positioning accuracy is achieved (called "soften up"), without changing the structural adjustment. The previous commutation (fourtact control) was the symmetrical one, and the asymmetrical eight-tact commutation causes

**Figure 15.** Eight-tact control of a hybrid stepper motor: (a) unipolar control; (b) bipolar control.

Other ways of controlling hybrid stepper motors exist, but due to simplification, we do not deal with them in detail here (although the modes are also included into the virtual model): **•** Vector control. The commutation consists in simultaneous supplying both phases by different voltages. This enables to rotate the stator magnetic field vector by a softer step. **•** Microstepper. This is done by commutation at supplying the coils by harmonic voltages that are mutually shifted by *π*/2. This causes a smooth, continuous movement of the rotor.

*4.2.3. Eight-tact control*

doubling of the number of rotor steps.

*4.2.4. Further modes of the control*

The model of the motor consists of the electrical part (Figure 16) and mechanical part, described by the dynamic equation.

**Figure 16.** Equivalent circuit diagram of hybrid bipolar stepper motor for phase A; *L*a presents the inductance, *R*a is the resistance of phase A winding, and *U*i is the electromotive force depending on the angle of the rotor.

The mathematical models of all motor subsystems together with corresponding simulation schemes are displayed in Table 3.

**Table 3.** Mathematical and simulation models of subsystems of the hybrid bipolar stepper motor

Figure 17 shows the complete simulation scheme consisting of the schemes of subsystems.

#### **4.4. Simulation results of hybrid bipolar stepper motor at various control modes**

A series of experiments before developing the GUI was done in order to verify correctness of the developed motor simulation model.

The hybrid bipolar stepper motor parameters used for simulation are as follows: *L*a = *L*b = 0.058 H, *R*a = *R*b = 30 Ω, *S*a= 1.8°, *N*r = 50, *K*m = 0.8 Nm/A, *T*d = 0 Nm, *b*m = 8.10-4 Nms/rad (damping coefficient), *M*load = 0.1 Nm, *U* = 12 V, and *J* = 6.10-6 kg m2 .

The motor simulation schemes were completed by schemes of simplified voltage sources, enabling control of the chosen stepper motor by whether the full step, half step, reduced (or shortened) step, or microstep.

Just note that the time courses in Figures 18-22 were obtained from the graphical user interface of the hybrid bipolar stepper motor (explained in detail later, in the subchapter 4.5). The graphs display the motor torque *M*, the angular speed *ω*, and the angle of displacement *θ*, and there are courses of the current and supply voltage at the bottom.

Support for Learning of Dynamic Performance of Electrical Rotating Machines by Virtual Models http://dx.doi.org/10.5772/60723 21

**Figure 17.** Simulation model of a hybrid bipolar stepper motor in Simulink

#### *4.4.1. Motor control at full step and magnetizing one phase only*

The angular displacement starts after the voltage is connected to phase B (Figure 18). The angular step displacement is 1.8° (i.e., 90/50). The load torque influence on the angular displacement is very small.

#### *4.4.2. Motor control at full step and magnetizing two phases*

The step displacement in this control mode control comes to a half in comparison with the previous case 0.9° (Figure 19). Also, the load torque has lesser influence on the angular displacement like in the previous case.

#### *4.4.3. Motor control at the half step*

**Equation**

**Total motor torque**

**motion equation**

**relation angle -speed**

**for Mathematical model (equations) Simulation model in Simulink**

*Mc* =*Ma* + *Mb* + *Md*

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*J* ⋅ (*Mc* −*bm* ⋅*ω* −*Mload* )

**Table 3.** Mathematical and simulation models of subsystems of the hybrid bipolar stepper motor

(damping coefficient), *M*load = 0.1 Nm, *U* = 12 V, and *J* = 6.10-6 kg m2

are courses of the current and supply voltage at the bottom.

Figure 17 shows the complete simulation scheme consisting of the schemes of subsystems.

A series of experiments before developing the GUI was done in order to verify correctness of

The hybrid bipolar stepper motor parameters used for simulation are as follows: *L*a = *L*b = 0.058 H, *R*a = *R*b = 30 Ω, *S*a= 1.8°, *N*r = 50, *K*m = 0.8 Nm/A, *T*d = 0 Nm, *b*m = 8.10-4 Nms/rad

The motor simulation schemes were completed by schemes of simplified voltage sources, enabling control of the chosen stepper motor by whether the full step, half step, reduced (or

Just note that the time courses in Figures 18-22 were obtained from the graphical user interface of the hybrid bipolar stepper motor (explained in detail later, in the subchapter 4.5). The graphs display the motor torque *M*, the angular speed *ω*, and the angle of displacement *θ*, and there

.

**4.4. Simulation results of hybrid bipolar stepper motor at various control modes**

*dθ dt* =*ω*

*dω dt* = 1

the developed motor simulation model.

shortened) step, or microstep.

Interchange of active coils results in a varying torque and angular velocity, as shown in the simulation results of half-step (Figure 20). The angle displacement step is 0.9°.

#### *4.4.4. Vector control of the motor*

In the vector control, two phases are supplied by different voltages (Figure 21). This allows the rotation of the vector of the stator magnetic field. In our case, the nominal value of the voltages *U*1 and the second one has the value that ensures the constant step. In this case, this is *U*2 = 0.4*U*1.

**Figure 18.** Time courses of the hybrid bipolar stepper motor at full step and magnetizing one phase.

**Figure 19.** Time courses at the full step and magnetizing both phases.

Support for Learning of Dynamic Performance of Electrical Rotating Machines by Virtual Models http://dx.doi.org/10.5772/60723 23

**Figure 20.** Time courses for the chosen half step.

**Figure 18.** Time courses of the hybrid bipolar stepper motor at full step and magnetizing one phase.

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**Figure 19.** Time courses at the full step and magnetizing both phases.

**Figure 21.** Time courses for vector control and step equal to 0.45°

### *4.4.5. Microstepper of the motor*

At microstepper, one phase is supplied by the sine voltage and the second phase by the cosine voltage (Figure 22). It is possible to replace the harmonic voltage by a discrete course with a low frequency of 1.5 Hz. The achieved final step is 0.21°.

### **4.5. GUI for the hybrid stepper motors**

Similarly, like in the previous case, GUI application was developed for analyzing both the bipolar and unipolar hybrid stepper motors. The chosen supply mode is selected by a button. The application allows to implement a simple change of parameters to choose the mode of the motor run to load the motor and to visualize results in four basic charts: for the motor torque, angular speed, the angle of the rotation of the rotor and the line voltage, and the currents in the respective phases.

#### *4.5.1. Description of the screen with graphs*

The GUI screen (Figure 23) displays the motor torque *M*, the angular speed ω, the angle of displacement *θ*, and there are courses of the current and supply voltage at the bottom.

The panel *Type of Motor* (upper right) contains two buttons: Bipolar SM and Unipolar SM, where the user selects the mode of basic control of the stepper motor. After selecting the chosen mode, the button turns green.

The panel *Control Mode* (Figure 24a) contains three further subpanels. In the subpanel *Direction*, a direction of rotation is chosen; in the subpanel *Step Modes*, a value of the step is chosen from the choice: full step, half step, reduced or shortened step for the vector control, and, the last choice, microstep. In the last subpanel *Power Supply Phase*, it is possible to choose either the active one or both phases of the motor (this possibility is available only when the full step is chosen).

In the panel *Other Simulation Parameters* (Figure 24b), the user inputs further information required by some modes of control. Here the first panel box is accessible only if a reduced step is chosen in the control mode panel. This gives a possibility to choose a reduced step divided into four parts or into eight. The next panel box is available when the microstep is chosen. The choice gives a possibility to change the frequency of the input sinus-cosinus signals. By the Slider Step Size, it is possible to soften the step size. In the last panel box, it is possible to change the time of simulation *T*s, and the parameter *T*v is related to the microstepper (the text box is available only when the microstepper mode is chosen).

The tools panel and the context menu have similar meaning like in the previous case (see Table 2). At the mentioned step choice, the user can change the direction of rotation of the motor to select other cyclogram of motor supply, to choose the length of each cyclogram (by the parameter *T*v), to change the frequency of a voltage supply at the microstepper, and to choose the step size when the movement of the rotor is continuous.

Support for Learning of Dynamic Performance of Electrical Rotating Machines by Virtual Models http://dx.doi.org/10.5772/60723 25

**Figure 22.** Time courses at the microstepper with the step 0.21°.

*4.4.5. Microstepper of the motor*

the respective phases.

**4.5. GUI for the hybrid stepper motors**

*4.5.1. Description of the screen with graphs*

mode, the button turns green.

full step is chosen).

low frequency of 1.5 Hz. The achieved final step is 0.21°.

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available only when the microstepper mode is chosen).

the step size when the movement of the rotor is continuous.

At microstepper, one phase is supplied by the sine voltage and the second phase by the cosine voltage (Figure 22). It is possible to replace the harmonic voltage by a discrete course with a

Similarly, like in the previous case, GUI application was developed for analyzing both the bipolar and unipolar hybrid stepper motors. The chosen supply mode is selected by a button. The application allows to implement a simple change of parameters to choose the mode of the motor run to load the motor and to visualize results in four basic charts: for the motor torque, angular speed, the angle of the rotation of the rotor and the line voltage, and the currents in

The GUI screen (Figure 23) displays the motor torque *M*, the angular speed ω, the angle of displacement *θ*, and there are courses of the current and supply voltage at the bottom.

The panel *Type of Motor* (upper right) contains two buttons: Bipolar SM and Unipolar SM, where the user selects the mode of basic control of the stepper motor. After selecting the chosen

The panel *Control Mode* (Figure 24a) contains three further subpanels. In the subpanel *Direction*, a direction of rotation is chosen; in the subpanel *Step Modes*, a value of the step is chosen from the choice: full step, half step, reduced or shortened step for the vector control, and, the last choice, microstep. In the last subpanel *Power Supply Phase*, it is possible to choose either the active one or both phases of the motor (this possibility is available only when the

In the panel *Other Simulation Parameters* (Figure 24b), the user inputs further information required by some modes of control. Here the first panel box is accessible only if a reduced step is chosen in the control mode panel. This gives a possibility to choose a reduced step divided into four parts or into eight. The next panel box is available when the microstep is chosen. The choice gives a possibility to change the frequency of the input sinus-cosinus signals. By the Slider Step Size, it is possible to soften the step size. In the last panel box, it is possible to change the time of simulation *T*s, and the parameter *T*v is related to the microstepper (the text box is

The tools panel and the context menu have similar meaning like in the previous case (see Table 2). At the mentioned step choice, the user can change the direction of rotation of the motor to select other cyclogram of motor supply, to choose the length of each cyclogram (by the parameter *T*v), to change the frequency of a voltage supply at the microstepper, and to choose

**Figure 23.** GUI screen for the stepper motor.

**Figure 24.** The panels (a) for selection of the stepper motor control mode and (b) for inputting control and simulation parameters.

#### *4.5.2. The screen for inputting parameters of the bipolar stepper motor*

The GUI screen enables easy change of parameters of a chosen stepper motor. This GUI screen (Figure 25) appears after choice of bipolar motor-the button *Bipolar SM* in the panel *Type of Motor* (see the GUI main screen in Figure 23). The screen also displays differential equations of the mathematical model of the bipolar stepper motor and equivalent diagram of the motor one phase.

**Figure 25.** The GUI screen for inputting parameters for the model of bipolar hybrid stepper motor.

The parameters for any arbitrary bipolar motor are set in the bottom panel *Parameters*. After clicking the model, the Simulink model of the motor is displayed. Pushing the return button causes switch over the main screen.

#### *4.5.3. The screen for inputting parameters of the unipolar stepper motor*

*4.5.2. The screen for inputting parameters of the bipolar stepper motor*

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**Figure 25.** The GUI screen for inputting parameters for the model of bipolar hybrid stepper motor.

one phase.

parameters.

The GUI screen enables easy change of parameters of a chosen stepper motor. This GUI screen (Figure 25) appears after choice of bipolar motor-the button *Bipolar SM* in the panel *Type of Motor* (see the GUI main screen in Figure 23). The screen also displays differential equations of the mathematical model of the bipolar stepper motor and equivalent diagram of the motor

**Figure 24.** The panels (a) for selection of the stepper motor control mode and (b) for inputting control and simulation

After choosing the unipolar stepper motor (the button **Unipolar SM** in the main screen, Figure 22), a screen for inputting unipolar stepper motor parameters is displayed (Figure 26).

**Figure 26.** The GUI screen for inputting parameters for the model unipolar hybrid stepper motor.

#### **4.6. Integration of the realized virtual models into virtual laboratory of electrical machines**

The subject electrical machines taught in the second year of the undergraduate course is devoted to the explanation of the phenomena in the machine, which is supported by various animation models. During lectures, when explaining static and dynamic characteristics of each machine by presentation of demo pictures, animations, videos, and of course using the blackboard for derivation of the dependencies, the virtual models are briefly introduced. The students have a free access to the models through the institutional LAN (the access is also available in the student hostels).

In the laboratory, they measure the characteristics of the motors and compare them with those from virtual models. They also discuss and explain in detail the motor behavior and the influence of motor parameters on the characteristics.

The work with the virtual models saves time at virtual experimentation, but it requires a careful and detailed explanation and analysis of the obtained characteristics by the teacher-how and why is the behavior of the motor corresponding to the form of the characteristics. Otherwise, the students do not fully understand the graphs.

To get feedback from the students and their opinion of introducing the virtual models in the regular teaching, inspired by a questionnaire presented in [9] for Electrical Drives subject we have applied the questionnaire for the Electrical Machines subject and evaluated it (Table 4). Altogether, 36 full-time students of the bachelor study in four groups answered the questions.

**Table 4.** Questionnaire statements and their evaluation

The student ratings on the evaluation of incorporating the virtual models into teaching – both into lectures and prior laboratory work – are generally positive. It was observed that majority of students found it useful, interesting, and contributing to increasing knowledge about the subject.

The students works with the virtual models also in the following years in the subjects like:


**1. Introduction**

The discovery of microwave cooking by Percy Spencer marked the dawn of a new era in microwave heating technology, which has gained huge attention in the scientific areas, especially in synthetic chemistry [1]. Numerous factors enabled the microwave technique to become a breakthrough technology in the complex synthesis process [2]. The significance of microwave heating for the synthesis of high-quality semiconducting nanomaterials, pristine or doped ones, is a subject that needs to be profoundly studied and explored due to its capability to revolutionize the semiconductor industry. The synthesis of high-quality nanocrystals primarily relies on controlled reactions of molecular precursors in a liquid medium at an adequate temperature in the presence of stabilizing agents [3, 4]. Most of the synthetic methods such as wet chemical process [5], emulsion methods, anti-solvent precipitation methods [6], have studied only the impact of the chemical process and parameters, on the properties of as-synthesized nanocrystals. Of late, the effect of additional external stimulations like microwave irradiation [7, 8], ultraviolet/visible light irradiation, ultrasound, etc. is also studied [5]. Microwave heating increases the rate of reaction, thereby considerably decreasing the reaction time without

altering the kinetics and chemical reaction [9–11]. The rate accelerations caused by "specific microwave effect" as well as "non-thermal effects" have to be considered in the microwave heating mechanism. Baghbanzadeh *et al*. propose that microwave dielectric heating can be termed as "specific microwave effects" by which one can achieve rate accelerations that cannot be attained by the conventional methods [12]. In the case of "non-thermal microwave effects", the heating mechanism arises as a result of the direct interaction of microwaves with specific molecules or materials in the reaction medium [2, 12]. Jacob *et al.* report that the enhancement rate of reaction with microwave heating compared to conventional heating is mainly due to the thermal effects which arise due to three significant factors. Firstly, the localized heating effect is a consequence of superheating phenomena due to the abundant ions present in the medium. Secondly, the molecular agitation due to lag of dipole, in following the fast-moving EM wave. Thirdly, increase of diffusion rate of reactants [13].

Earlier, the synthesis of high-quality semiconducting quantum dots was very tough and the process of doping at this length scale makes it even more challenging. Erwin *et al.* reported that the difficulty in doping at nanoscale regime is due to the difference in mechanisms involved in doping at bulk and at the nanoscale, while other reports in literature claim the process of 'self-purification' as the leading cause for de-doping during the growth process [14]. The major daunting challenges arise mostly due to the lack of a comprehensive understanding of all the fundamental mechanisms associated with dopants incorporation and the absence of reliable synthetic procedures where the temperature-dependent dopant impurity atoms diffusion will be minimal [15]. Another challenge involved in doping at the nanoscale is the inherent statistical inhomogeneity of dopants among the nanocrystals. The doped nanomaterials always tend to exhibit a broad range of dopant populations per nanocrystal, which results in effective inhomogeneity in concentration of dopants among nanocrystals. Providing a uniform and instantaneous heating during the reaction process can minimize this problem to a great extent [16]. In this context, microwave heating became the suitable thermal energy source for doping the semiconducting nanocrystals, as it provides rapid and instantaneous heating. Short reaction time, faster reaction rate, uniform volumetric heating, cost-effective and eco-friendly method are the other remarkable features which make microwave heating a prime superior choice over other conventional methods of heating like a hot plate, oil bath, etc. [8]. Moreover, heating by means of conventional methods always results in 'a self-purification' mechanism where the dopants are diffused towards the surface of nanocrystals at the time of growth [14]. By adopting microwave-assisted techniques the aforementioned problems encountered while doping at the nanoscale can be eliminated to a great extent and the synthesized products are found to excel both in quality as well as quantity [17].


*Article* **Comparative Transcriptome Analysis Reveals the Molecular Immunopathogenesis of Chinese Soft-Shelled Turtle (***Trionyx sinensis***) Infected with** *Aeromonas hydrophila*

**Zhao Lv 1 , Yazhou Hu 1, Jin Tan 1, Xiaoqing Wang 1, Xiaoyan Liu 1 and Cong Zeng 2,\***

**Simple Summary:** The Chinese soft-shelled turtle (*Trionyx sinensis*) is an important cultured reptile in East Asia. Hemorrhagic sepsis caused by *Aeromonas hydrophila* infection is the dominant disease in the aquaculture of Chinese soft-shelled turtles, while the molecular pathology is far from clear due to the lag of research on turtle immunology. It has been reported in mammals and fish that the dysfunction of immune responses to pathogen infections causes host tissue hemorrhagic sepsis. In this study, two groups of turtles with different susceptibility to *A. hydrophila* infection are found. A comparative transcriptome strategy is adopted to examine the gene expression profiles in liver and spleen for these two phenotypes of turtles post *A. hydrophila* infection, for the first time revealing the full picture of immune mechanisms against *A. hydrophila*, which provides new insight into the molecular pathology during *A. hydrophila* infection in *T. sinensis*. The findings will promote further investigations on pathogenic mechanisms of hemorrhagic sepsis caused by *A. hydrophila* infection in *T. sinensis*, and also will benefit their culture industry.

**Abstract:** Although hemorrhagic sepsis caused by *Aeromonas hydrophila* infection is the dominant disease in the aquaculture of Chinese soft-shelled turtle, information on its molecular pathology is seriously limited. In this study, ninety turtles intraperitoneally injected with *A. hydrophila* exhibited two different phenotypes based on the pathological symptoms, referred to as active and inactive turtles. Comparative transcriptomes of liver and spleen from these two groups at 6, 24, and 72 h postinjection (hpi) were further analyzed. The results showed that cytokine–cytokine receptor interaction, PRRs mediated signaling pathway, apoptosis, and phagocytosis enriched in active and inactive turtles were significantly different. Pro-inflammatory cytokines, the TLR signaling pathway, NLR signaling pathway, and RLR signaling pathway mediating cytokine expression, and apoptosis-related genes, were significantly up-regulated in inactive turtles at the early stage (6 hpi). The significant up-regulation of phagocytosis-related genes occurred at 24 hpi in inactive turtles and relatively lagged behind those in active turtles. The anti-inflammatory cytokine, IL10, was significantly upregulated during the tested periods (6, 24, and 72 hpi) in active turtles. These findings offer valuable information for the understanding of molecular immunopathogenesis after *A. hydrophila* infection, and facilitate further investigations on strategies against hemorrhagic sepsis in Chinese soft-shelled turtle *T. sinensis*.

**Keywords:** Chinese soft-shelled turtle; *Aeromonas hydrophila*; hemorrhagic sepsis; molecular immunopathogenesis

**Citation:** Lv, Z.; Hu, Y.; Tan, J.; Wang, X.; Liu, X.; Zeng, C. Comparative Transcriptome Analysis Reveals the Molecular Immunopathogenesis of Chinese Soft-Shelled Turtle (*Trionyx sinensis*) Infected with *Aeromonas hydrophila*. *Biology* **2021**, *10*, 1218. https://doi.org/10.3390/ biology10111218

Academic Editor: Patricia Pereiro

Received: 12 October 2021 Accepted: 19 November 2021 Published: 22 November 2021

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**Copyright:** © 2021 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/).

#### **1. Introduction**

Chinese soft-shelled turtle (*Trionyx sinensis*) is an important reptile in East Asia and has been taken as a food resource for a long time in these areas, especially in China and Japan. In ancient Chinese medicine descriptions, consumption of turtles may bring about positive effects for human health, including strengthening immunity, anti-aging, and curing cardiocerebrovascular diseases [1]. The excellent nutriment and medical values mean the turtles have become one of the most critical freshwater aquaculture reptiles, and the annual production is over 325,000 tons in China. However, infectious diseases caused by bacteria and viruses have resulted in severe losses to the aquaculture of turtles [2,3]. It is noteworthy that there are more than 15 kinds of diseases caused by *Aeromonas hydrophila* infection, such as red neck disease, septicemia, furunculosis, etc., accounting for about 60% of the total disease cases in turtles [4,5]. The histopathology of Chinese soft-shelled turtles infected by *A. hydrophila* has been previously described. In general, the pathogenic processes undergo the adhesion of bacteria adhesion factors, and the destruction of host liver, respiratory, and digestive organs by virulence factors such as exotoxin and extracellular enzymes, which finally leads to serious tissue hemorrhagic sepsis and the death of turtles [6,7]. Nevertheless, the molecular pathology of hemorrhagic sepsis caused by *A. hydrophila* infection is far from being elucidated in Chinese soft-shelled turtles.

It has been reported in mammals and fish that the dysfunction of host immune responses to pathogen infections causes tissue hemorrhagic sepsis [8–10]. The immune system generally serves as the security guard for hosts, which holds an immune network composed of many immune cells [11]. The cells of the innate immune system consisting of neutrophils, monocytes, macrophages, dendritic cells, and natural killer cells recognize pathogens, produce cytokines, and engulf pathogens through phagocytosis, which are the first line of host defense against pathogens [12]. These innate immune cells rely on pattern recognition receptors (PRRs) including toll-like receptors (TLRs), nucleotidebinding domain (NOD)-like receptors (NLRs), and retinoic acid-inducible gene I-like receptors (RLRs) to recognize various microbial invaders and produce cytokines that further activate the innate as well as adaptive immune cells [13]. Under normal immune response, immune cells moderately produce and release cytokines, including interleukins (ILs), chemokines, and tumor necrosis factors (TNFs), which can contact more immune cells to participate in the battle between the host and foreign pathogens [14]. When immune cells produce excessive cytokines, especially pro-inflammatory cytokines such as IL1, IL6, IL18, and TNFs ("cytokine storm"), the host immune system is overactivated and attacks self-tissues or cells, causing systemic inflammation, tissue hemorrhagic sepsis, and even death [15,16].

Although the research of turtle immunology lags behind that of mammals and fish, the unique evolutionary status as secondary aquatic reptiles has recently aroused wide concern on the distinct immune response mechanism against pathogens in turtles [17–20]. Fifteen candidate TLR family genes have been identified in *T. sinensis* [21]. After *A. hydrophila* infection, TLR2 and TLR4 are significantly up-regulated in the spleen, indicating the immune response of TLR signaling pathway during bacterial infection in *T. sinensis* [21]. Zhou et al. identified an IL8 homolog from *T. sinensis* and confirmed that IL8 mRNA expression shows significant up-regulation in various tissues, including liver, spleen, kidney, heart, intestine, and blood, after *A. hydrophila* infection [22]. Zhang et al. also investigated the mRNA expression changes of pro-inflammatory cytokines such as IL1β, TNFα, IL6, IL8, and IL12 in *T. sinensis* during acute cold stress, revealing that acute cold stress increases the expression of pro-inflammatory cytokines in the spleen and intestine to withstand *A. hydrophila* infection [23]. These above studies may help us understand the mRNA expression profiles and function of several immune molecules in *T. sinensis*. However, the evidence on the immune response mechanism in *T. sinensis* so far is too scattered and limited, which largely hinders the studying of pathogen–host immunity interaction and the molecular pathology during *A. hydrophila* infection in Chinese softshelled turtles.

RNA-sequencing (RNA-Seq) using next-generation sequencing is one of the most useful methods to survey the character of a transcriptome because it offers the whole data on gene expression. To date, transcriptome profiling using next-generation sequencing technologies has provided new insights into pathogen–host immunity interaction in many aquaculture fish species, such as Nile tilapia (*Oreochromis niloticus*) [24], Gold fish (*Carassius auratus* L.) [25], Ussuri catfish (*Pseudobagrus ussuriensis*) [26], Yellow catfish (*Pelteobagrus fulvidraco*) [27], and Atlantic salmon (*Salmo salar*) [28]. More and more studies have used this high-throughput sequencing approach to identify the expression differences of immune molecules between the resistant and susceptible fish to pathogen infection. For example, Moraleda et al. analyzed the comparative transcriptomes of resistant/susceptible salmons with different immune responses to *Piscirickettsia salmonis* infection and revealed that the gene networks involved in the apoptotic, cytoskeletal reorganization, bacterial invasion, and intracellular trafficking processes are tightly associated with disease resistance/susceptibility [29]. We are originally inspired by the previous research results in mammals and fish that the dysfunction of host immune responses to pathogen infections causes tissue hemorrhagic sepsis. In the present study, therefore, a comparative transcriptome strategy is adopted to reveal the immune gene expression profiles in two phenotypes of Chinese soft-shelled turtles with different susceptibility to *A. hydrophila* infection at different time periods (6, 24, and 72 hpi), and to clarify which immune process abnormalities may be related to the occurrence of hemorrhagic sepsis. The results depict the full picture of immune mechanisms in response to *A. hydrophila* infection, suggesting that the dysfunction of cytokine–cytokine receptor interaction, PRRs mediated signaling pathways, phagocytosis, and apoptosis may cause hemorrhagic sepsis during *A. hydrophila* infection in Chinese soft-shelled turtles. This study, for the first time, reveals the host immunity–pathogen infection interaction and molecular immunopathogenesis during *A. hydrophila* infection by comparative transcriptomes, which may contribute to the development of novel management strategies for disease control and prevention in Chinese soft-shelled turtles.

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

#### *2.1. Experimental Animals and Bacteria Strain*

Experimental turtles (*T. sinensis*) with an average weight of 16.45 ± 1.28 g were obtained from a breeding farm (111◦97 E, 28◦90 N) in Changde City, Hunan Province, China. All the turtles were acclimated in 50 L aquarium with aerated freshwater in a constant temperature laboratory room at 30 ◦C for two weeks before processing and were fed with a commercial diet (Kesheng Feed Stock Co., Ltd., Hangzhou, Zhejiang, China) twice a day. The animal experiments were according to the rules of the Animal Care and Use Committee of Hunan Agricultural University (Changsha, China; Approval Code: 201903297; Approval Date: 11 October 2019).

The bacteria *A. hydrophila*, isolated from the spleen of clinically diseased turtles, was provided by Professor Zhipeng Gao from the College of Animal Science and Technology, Hunan Agricultural University, China. *A. hydrophila* was cultured in Luria Bertani (LB) medium at 30 ◦C with the shaking at 200 rpm. After 18 h culturing, the bacteria were harvested by centrifugation, and suspended with sterile phosphate buffer saline (PBS). The bacteria concentration was adjusted to 1.39 × 109 CFU/mL with PBS, which was previously proved as the medium lethal concentration for turtles during *A. hydrophila* infection.

#### *2.2. Experimental Treatments, Pathological Observation, and Sampling*

For the *A. hydrophila* challenge, 90 turtles were intraperitoneally injected with bacteria suspension (100 μL per turtle, designated as the treatment group). After bacteria injection, the turtles in the treatment group could be divided into two subgroups based on their pathological symptoms and behavior activities. One subgroup of turtles showed weaker ability to feed and move, with significant hemorrhagic symptoms on the body surface and swelling and congestion on viscera after anatomy, which was designated as the inactive

subgroup. The other subgroup of turtles exhibited no obvious pathological symptoms, which was designated as the active subgroup. Ten turtles, injected with 100 μL of PBS, were set up as the control group.

To investigate the expressional profiles of immune-related genes during *A. hydrophila* infection in active and inactive turtles, important immune-related tissues including spleen and liver from the active subgroup turtles (*N* = 3) and inactive subgroup turtles (*N* = 3) were sampled post-injection of *A. hydrophila* at 6, 24, 72 h, respectively. Spleen and liver tissues from the control group turtles (*N* = 3) with PBS injection at 0 h were sampled as the control for the gene expression comparison with those from active subgroup turtles or from inactive subgroup turtles. Each sample was taken three times, and the sampled tissues were stored in liquid nitrogen before RNA extraction.

#### *2.3. RNA Extraction, Library Preparation, and RNA Sequencing (RNA-Seq)*

Total RNA from each sampled tissue was isolated by using RNeasy mini kit (QIAGEN, Germantown, MD, USA) and treated with RNase-free DNase I (QIAGEN, Germantown, MD, USA) at 37 ◦C for 1 h to remove residual genomic DNA. RNA quality and concentration were determined using Agilent Bioanalyser 2100 (Agilent Technologies, Santa Clara, CA, USA) and NanoDrop-1000 (NanoDrop Technologies, Wilmington, DE, USA), respectively. Five micrograms of RNA were used to construct RNA-seq library according to the instruction of Illumina mRNA-Seq Prep Kit (Illumina, San Diego, CA, USA), and the libraries were sequenced by paired-end sequencing on the Illumina HiSeq 2500 sequencing platform (Illumina, San Diego, CA, USA). The quality of RNA-Seq raw reads were assessed with FastQC (version 0.10.1; http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/ accessed on 11 March 2019), and were cleaned by removing adapter sequences, poly-N sequences, and low-quality sequences. The clean reads were then aligned to the published *T. sinensis* genome using HISAT2 [30]. In total, 291.16 GB clean data were produced, and the average of 20.4~36.3 GB clean reads were obtained from samples. About 80% of the clean reads were mapped to the reference genome by using StringTie software (Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA) [31], and the Q30 was >92.86%. The general information of RNA-Seq data is listed in Table S1.

#### *2.4. Identification of the Differentially Expressed Genes (DEGs)*

The relative transcript abundances in tissues (liver and spleen) from active and inactive subgroup turtles at different time periods of *A. hydrophila* infection compared to the control turtles were, respectively, estimated by using StringTie software with expectation maximization method, based on fragments per kilobase of exon per million fragments mapped (FPKM) [31]. Differential expression analysis was performed by using the DESeq R package with default parameters [32]. Benjamini and Hochberg's approach was used to control the false discovery rate (FDR) by resulting *P*-values adjustment [33]. Genes with an adjusted *P*-value (or q-value) < 0.05 found by DEGSeq were assigned as the differentially expressed.

#### *2.5. KEGG Pathway Enrichment Analysis*

Significantly enriched signal transduction pathways represented by DEGs were determined using KEGG pathway enrichment analysis, compared with the whole genome background [34]. The statistical enrichment of DEGs in KEGG pathways was tested using the software KOBAS, with a *P*-value < 0.05 [34]. The significantly enriched KEGG pathways are listed in Tables S2–S5.

#### *2.6. Gene Expression Validation Using Quantitative PCR (qPCR)*

To validate RNA-seq data and gene expression profiles, nine DEGs were randomly selected to perform qPCR. Specific primers were designed using Primer 5 based on the coding sequences of identified genes from the turtle genome; the sequences of primers are listed in Table S6. Primer specificity was ascertained using the following steps: PCR amplification, sequencing of PCR products, and BLAST analysis in the NCBI database.

The RNA samples used for qPCR amplifications were the same as those used to construct the RNA-Seq library mentioned above. The qPCR was performed using the LightCycler®480 Real-Time PCR System (Roche, Basel, Switzerland) with SYBR Green I Master. The reaction mixture (10 μL) comprised 2.5 μL cDNA, 0.5 μL (10 nM) forward primer and 0.5 μL (10 nM) reverse primer, 1.5 μL PCR grade water, and 5 μL Master Mix. Each reaction was performed in triplicate under the following conditions: 95 ◦C for 10 min, 40 cycles of 95 ◦C for 15 s, 55 ◦C for 15 s, and 72 ◦C for 30 s. The relative expression level of each gene was calculated according to the 2−Ct method [35] and normalized to the endogenous control genes of β-actin and GAPDH.

#### **3. Results**

#### *3.1. Symptom Description of the Turtles Challenged with A. hydrophila*

Ninety Chinese soft-shelled turtles were injected with *A. hydrophila* (1.39 × 109 CFU/mL), and the pathological symptoms of turtles were observed. There were no obvious pathological symptoms on the body surface of turtles, and only six turtles showed behavior abnormality with slow-moving action at 6 hpi (post-injection of 6 h). At 24 hpi, the food intake and movement of turtles was overall reduced, and seven turtles died (accounting for about 8%), with the pathological symptom of abdominal congestion. At 72 hpi, 34 turtles (accounting for about 38%) died. The pathological symptoms on the body surface of diseased turtles were obvious, with white spots near the axillae, swelling, and congestion in chest and abdomen (Figure 1A,B). After anatomizing the diseased turtles, pathological symptoms of turtle viscera were easily observed (Figure 1C). The liver and spleen were swelling and congestive, with the tendency to decay, and the color of liver was yellow (Figure 1C). The intestines were filled with no food debris, and the color was white (Figure 1C). There were 13 turtles (accounting for about 14%) without any obvious pathological symptoms at 72 hpi (Figure 1D). During the whole experiment within 72 h, 10 turtles in the control group showed no obvious pathological symptoms and were aggressive and active in feeding and moving (data not shown).

According to pathological symptoms, the Chinese soft-shelled turtles challenged with *A. hydrophila* could be divided into two subgroups, including the active and inactive turtles. The liver and spleen were important immune organs in turtles, and both carried obviously pathological symptoms after *A. hydrophila* infection. Therefore, liver and spleen samples from active and inactive subgroup turtles were taken at 6, 24, and 72 hpi, respectively, and transcriptome sequencing was performed to reveal the differences of gene expression profiles between active and inactive turtles infected by *A. hydrophila*.

Pearson's correlation coefficients were first used to test for biologically repeated correlations between samples (Figure 2A). The generated cluster dendrogram was used to observe the overall correlation of the transcriptomes from the AL group (liver in active turtles), the IL group (liver in inactive turtles), the AS group (spleen in active turtles), and the IS group (spleen in inactive turtles) at different time periods (6, 24, and 72 hpi) (Figure 2A). Three biological replicates of liver and spleen samples from each time period and the transcriptome data both exhibited good correlation (Figure 2A). The similarity test between the three biological replicates required the use of a principal component analysis (PCA) (Figure 2B). Using the first principal component (PC1) and second principal component (PC2), a dimensionality reduction analysis was used to analyze the similarity between each replicate (Figure 2B). Figure 2B showed that biological replicates of samples overall exhibited good similarity. The generated box plot presented the dispersion degree of the gene expression level in a single liver or spleen sample, and intuitively revealed the whole gene expression level difference among all samples (Figure 2C). The results showed that the gene expression level in the spleen was overall higher than that in the liver (Figure 2C).

**Figure 1.** Pathological symptoms of turtles infected by *A. hydrophila*. The pathological symptoms on the body surface including (**A**) white spots near the axillae; (**B**) abdominal congestion in inactive subgroup turtles. (**C**) The pathological symptoms of viscera including liver, spleen, and intestines after anatomy of inactive turtles. (**D**) The active subgroup turtles showed no obvious pathological symptoms and were aggressive and active in feeding and moving. Black arrows indicate the location of pathological symptoms.

Totals of 4092 and 5793 DEGs were obtained in the liver and spleen transcriptomes, respectively (Figure 2D). In the AL group, 321, 401, and 378 genes were significantly upregulated, and 671, 874, and 151 genes were significantly down-regulated at 6, 24, and 72 hpi, respectively (Figure 2D). In the IL group, the numbers of significantly up-regulated genes were 298, 138, and 79; the numbers of significantly down-regulated genes were 268, 381, and 132 at 6, 24, and 72 hpi, respectively (Figure 2D). In the AS group, 219, 176, and 65 genes were significantly up-regulated, and 576, 437, and 206 genes were significantly down-regulated at 6, 24, and 72 hpi, respectively (Figure 2D). In the IS group, the numbers of significantly up-regulated genes were 1193, 203, and 117; the numbers of significantly down-regulated genes were 1996, 401, and 204 at 6, 24, and 72 hpi, respectively (Figure 2D). The results revealed that the number of DEGs in the spleen transcriptomes was more than that in the liver transcriptomes, and the molecular response peaked at 24 hpi in liver, while it peaked at 6 hpi in spleen (Figure 2D).

**Figure 2.** Transcriptional relationship between samples and the overall gene expression profiles. (**A**) Heatmap of correlation value (R square) of 37 libraries from liver or spleen samples. (**B**) Principal component analysis based on all of the expressed genes, showing 14 distinct groups of samples. (**C**) The dispersion degree of the gene expression level in a single liver or spleen sample. (**D**) The significantly up-regulated and down-regulated DEGs identified in livers or spleens from active and inactive turtles at 6, 24, and 72 hpi compared to the control.

#### *3.2. Functional Classification of DEGs in Turtle Liver Transcriptomes by KEGG*

In order to investigate the different molecular response mechanisms against *A. hydrophila* infection in livers from active and inactive turtles, the functional classification of DEGs in AL and IL group transcriptomes at different time periods (6, 24, and 72 hpi) were analyzed by KEGG enrichment analysis, and the results are summarized as follows.

#### 3.2.1. Sequential Changes of KEGG Enrichment in AL Group Turtles

In AL group, the up-regulated DEGs were mainly enriched in immune-related pathways including "cytokine–cytokine receptor interaction", phagocytosis-associated processes including "phagosome", "protein processing in endoplasmic reticulum", and "protein export", and pathogen infection-related pathways, while the down-regulated DEGs

were intensively involved in 17 metabolism pathways and three cell adhesion-related processes at 6 hpi (Figure 3).

**Figure 3.** KEGG enrichment analysis in AL group turtles at different time periods. The top 20 KEGG pathways are presented here in the form of scatterplots to show the up-regulated and down-regulated DEGs enriched in livers from active subgroup turtles at 6, 12, and 72 hpi. The enrichment factor is the ratio between the DEG number and the number of all genes in a certain gene enrichment term. The sizes of the dots on these plots denote the number of DEGs, while colors correspond to the q value range.

At 24 hpi, the majority of up-regulated DEGs were also annotated into immune-related pathways including "cytokine–cytokine receptor interaction", "phagosome", "protein processing in endoplasmic reticulum", "proteasome", and "protein export" (Figure 3). In addition, cytokine expression-mediating pathways including "toll-like receptor signaling pathway" and "NOD-like receptor signaling pathway" were significantly up-regulated (Figure 3). Similar to the response at 6 hpi, the down-regulated DEGs were mainly involved in a series of metabolism and cell adhesion pathways (Figure 3).

At 72 hpi, the up-regulated DEGs could be functionally classified into "toll-like receptor signaling pathway" and "RIG-I-like receptor signaling pathway", pathogen infectionrelated pathways, "apoptosis", and several metabolism-related pathways (Figure 3), while the down-regulated DEGs mainly participated in important metabolism-related pathways such as "insulin signaling pathway" and "adipocytokine signaling pathway" (Figure 3).

#### 3.2.2. Sequential Changes of KEGG Enrichment in IL Group Turtles

Unlike the KEGG enrichment in AL group at 6 hpi, the up-regulated DEGs were mainly enriched in cytokine expression-mediating pathways including "toll-like receptor signaling pathway", "NOD-like receptor signaling pathway", and "RIG-I-like receptor signaling pathway" and "apoptosis", besides "cytokine–cytokine receptor interaction", pathogen infection-related pathways; while phagocytosis-associated processes were not listed in the top 20 up-regulated KEGG pathways in IL group at 6 hpi (Figure 4). The downregulated DEGs were functionally annotated into cell adhesion-related pathways such as "ECM–receptor interaction" and "focal adhesion", and energetic metabolism pathways (Figure 4).

**Figure 4.** KEGG enrichment analysis in IL group turtles at different time periods. The top 20 KEGG pathways are presented here in the form of scatterplots to show the up-regulated and down-regulated DEGs enriched in livers from inactive subgroup turtles at 6, 12, and 72 hpi. The enrichment factor is the ratio between the DEG number and the number of all genes in a certain gene enrichment term. The sizes of the dots on these plots denote the number of DEGs, while colors correspond to the q value range.

At 24 hpi, the term of "cytokine–cytokine receptor interaction" was not listed in the top 20 up-regulated KEGG pathways, while the up-regulated DEGs were mainly involved in phagocytosis-associated processes such as "phagosome", "protein processing in endoplasmic reticulum", "proteasome", and "protein export" (Figure 4). Additionally, "toll-like receptor signaling pathway" was significantly up-regulated (Figure 4). For the down-regulated DEGs, most of them were associated with hormone synthesis and amino acid metabolism (Figure 4).

At 72 hpi, immune-related terms including "toll-like receptor signaling pathway" and "cytosolic DNA-sensing pathway" were listed in the top 20 up-regulated KEGG pathways (Figure 4). The down-regulated DEGs were mainly enriched in energetic metabolism pathways such as "insulin signaling pathway" and "adipocytokine signaling pathway" (Figure 4).

3.2.3. Expression Difference Analysis of Cytokine, Phagocytosis, and Apoptosis-Related Genes between AL and IL Group Turtles

The KEGG pathways related to immune processes including cytokine–cytokine receptor interaction, phagocytosis, and apoptosis enriched in AL and IL group turtles challenged with *A. hydrophila* were quite different (Tables 1–3). Therefore, the fold changes of differentially expressed cytokine, phagocytosis, and apoptosis-related genes were further analyzed. **Table 1.** Fold changes of differentially expressed cytokine and cytokine receptor, and cytokine expression mediating pathway genes in the AL group and the IL group compared to the control.

**Table 1.** *Cont*.

**Note:** This table and the following tables only show the DEGs with the values of log2 (fold change) with FDR < 0.05.

**Table 2.** Fold changes of differentially expressed phagocytosis-related genes in the AL group and the IL group compared to the control.

**Table 3.** Fold changes of up-regulated apoptosis-related genes in the AL group and the IL group compared to the control.

The results showed that the sharpest response of cytokines occurred at 24 hpi in the AL group, with the up-regulation of two cytokine and two cytokine receptor genes (Table 1). Among three cytokine expression-mediating pathways, only the response of tolllike receptor signaling pathway was relatively intense, with five up-regulated genes, also at 24 hpi in the AL group (Table 1), while in IL group, the sharpest response of cytokines occurred at 6 hpi, with the up-regulation of five cytokine and two cytokine receptor genes, and few cytokines were differentially expressed at 24 or 72 hpi (Table 1). Moreover, the up-regulation of differentially expressed cytokine genes at 6 hpi in IL group was overall higher than those in AL group at any time periods (Table 1). Nevertheless, IL10, an antiinflammatory cytokine, was not differentially expressed at any time periods in the IL group, while it was up-regulated at all the time periods in the AL group (Table 1). In addition, the up-regulations of both NOD-like receptor signaling pathway and RIG-I-like receptor signaling pathway genes were intense in the IL group at 6 hpi (Table 1).

Notably, the sharpest response of phagocytosis-related gene occurred at 6 hpi in the AL group, with the up-regulation of 10 DEGs, and lasted until 24 hpi (Table 2). While in the IL group, at 24 hpi, the phagocytosis activity seemed to just start, with five up-regulated phagocytosis-related DEGs (Table 2). In addition, the most intense response of apoptosisrelated genes occurred at 6 hpi in the IL group, with the up-regulation of six DEGs, while in the AL group, the apoptosis seemed to just start at 24 hpi with the up-regulation of five DEGs (Table 3).

#### *3.3. Functional Classification of DEGs in Turtle Spleen Transcriptomes by KEGG*

The functional classification of DEGs in AS and IS transcriptomes at different time periods were also analyzed by KEGG enrichment to further reveal the different immune response mechanisms against *A. hydrophila* infection in spleens between active and inactive turtles. The results are summarized as follows.

#### 3.3.1. Sequential Changes of KEGG Enrichment in AS Group Turtles

In the AS group, the up-regulated DEGs were functionally associated with immune processes including "cytokine–cytokine receptor interaction", "chemokine signaling pathway", "phagosome", "apoptosis", "leukocyte transendothelial migration", "toll-like receptor signaling pathway", "MAPK signaling pathway", and pathogen infection-related pathways at 6 hpi (Figure 5), while the down-regulated DEGs were involved in cell adhesion and metabolism pathways at 6 hpi (Figure 5).

**Figure 5.** KEGG enrichment analysis in AS group turtles at different time periods. The top 20 KEGG pathways are presented here in the form of scatterplots to show the up-regulated and down-regulated DEGs enriched in spleens from active subgroup turtles at 6, 12, and 72 hpi. The enrichment factor is the ratio between the DEG number and the number of all genes in a certain gene enrichment term. The sizes of the dots on these plots denote the number of DEGs, while colors correspond to the q value range.

At 24 hpi, the up-regulated DEGs were mainly annotated into phagocytosis-related pathways such as "phagosome", "protein processing in endoplasmic reticulum", "synaptic vesicle cycle", and "lysosome". In addition, "toll-like receptor signaling pathway" was significantly up-regulated (Figure 5). Unlike the response at 6 hpi, the down-regulated DEGs at 24 hpi could be enriched in pathways including "cytokine–cytokine receptor interaction" and "bacterial invasion of epithelial cells", besides a series of metabolism and cell adhesion pathways (Figure 5).

At 72 hpi, the up-regulated DEGs were functionally classified into apoptosis-related pathways, including "p53 signaling pathway" and "cell cycle", as well as immune defense processes such as "phagosome", "chemokine signaling pathway", and "leukocyte transendothelial migration" (Figure 5), while the down-regulated DEGs mainly participated in important metabolism and cell adhesion pathways (Figure 5).

#### 3.3.2. Sequential Changes of KEGG Enrichment in IS Group Turtles

The strongest immune response of spleen occurred at 6 hpi in the IS group, with 1193 up-regulated and 1996 down-regulated DEGs. Most up-regulated DEGs were enriched in immune-related pathways including "cytokine–cytokine receptor interaction", "toll-like receptor signaling pathway", "NOD-like receptor signaling pathway", and "RIG-I-like receptor signaling pathway", as well as apoptosis-associated processes (Figure 6), while the down-regulated DEGs were functionally annotated into cell adhesion-related pathways

such as "ECM–receptor interaction" and "focal adhesion", and metabolism pathways (Figure 6).

**Figure 6.** KEGG enrichment analysis in IS group turtles at different time periods. The top 20 KEGG pathways are presented here in the form of scatterplots to show the up-regulated and down-regulated DEGs enriched in spleens from inactive subgroup turtles at 6, 12, and 72 hpi. The enrichment factor is the ratio between the DEG number and the number of all genes in a certain gene enrichment term. The sizes of the dots on these plots denote the number of DEGs, while colors correspond to the q value range.

At 24 hpi, the up-regulated DEGs could be related to phagocytosis such as "phagosome" and "lysosome" (Figure 6). Additionally, "toll-like receptor signaling pathway" was listed in the top 20 up-regulated KEGG pathways (Figure 6). For the down-regulated DEGs, most of them were associated with cell adhesion, hormone synthesis, and amino acid metabolism (Figure 6).

Similar to the response at 72 hpi in the AS group, in the IS group, the up-regulated DEGs mainly participated in apoptosis-related pathways including "p53 signaling pathway" and "cell cycle", as well as "phagosome" and "chemokine signaling pathway" at 72 hpi (Figure 6), while the down-regulated DEGs were enriched in a series of hormone synthesis, amino acid metabolism, and cell adhesion pathways (Figure 6).

3.3.3. Expression Difference Analysis of Cytokine, Phagocytosis, and Apoptosis-Related Genes between AS and IS Group Turtles

The fold changes of differentially expressed cytokine, phagocytosis, and apoptosisrelated genes were also analyzed in AS and IS group turtles (Tables 4–6). The results showed that in both AS and IS groups, the sharpest response of cytokines occurred at 6 hpi, while the number of differentially expressed cytokine and cytokine receptor genes in the IS group were overwhelmingly more than that in the AL group (30 up-regulated genes in the IS group vs. seven up-regulated genes in the AS group) (Table 4). Noteworthily, the overall expression of cytokines dramatically decreased at 24 hpi and 72 hpi in the IS group, while gradually decreased in the AS group, and the anti-inflammatory cytokine IL10 was upregulated at all the time periods in the AS group (Table 4). Consistent with the response of cytokines, the expression of toll-like receptor signaling pathway genes were also relatively intense at 6 hpi, but only with three up-regulated genes in the AS group (Table 4), while

in the IS group at 6 hpi, the expressions of three cytokine expression-mediating pathway genes, including toll-like receptor signaling pathway, NOD-like receptor signaling pathway, and RIG-I-like receptor signaling pathway, were overall up-regulated, with 18 up-regulated genes (Table 4).

**Table 4.** Fold changes of differentially expressed cytokine and cytokine receptor, and cytokine expression mediating pathway genes in the AS group and the IS group compared to the control.

**Table 4.** *Cont*.

**Table 4.** *Cont*.

**Table 5.** Fold changes of differentially expressed phagocytosis-related genes in the AS group and the IS group compared to the control.

**Table 5.** *Cont*.

**Table 6.** Fold changes of up-regulated apoptosis-related genes in the AS group and the IS group compared to the control.

The intense response of phagocytosis started at 6 hpi in the AS group, with the upregulation of seven DEGs, and lasted until 24 hpi (six up-regulated DEGs) (Table 5), while in the IS group at 6 hpi, the phagocytosis activity seemed to be inhibited, with 10 downregulated DEGs, and just started at 24 hpi, with 11 up-regulated phagocytosis-related DEGs (Table 5). Nevertheless, in both the AS and IS groups, the sharpest response of apoptosis-related gene expression occurred at 6 hpi, while the number of up-regulated apoptosis-related genes in the IS group (18 DEGs) was significantly more than that in the AL group (four DEGs) (Table 6).

#### *3.4. Validation of DEGs by qPCR*

A total of nine DEGs were randomly selected to perform qPCR to validate RNA-Seq data and gene expression profiles (Figure 7). PCR products with expected sizes were successfully amplified with all the nine specific primer pairs, indicating their availabilities for DEG validation (data not shown). The different amplification efficiencies of the nine DEGs between the experimental and control groups were transformed by log2 (fold change) to compare with the results of RNA-Seq. The results showed that the expression patterns of these genes determined by qPCR were similar to those acquired through RNA-Seq (Figure 7), which confirmed the reliability of the RNA-Seq data. Therefore, the immunerelated genes isolated in this study could be useful references for future studies on the molecular mechanisms of Chinese soft-shelled turtles during *A. hydrophila* infection.

**Figure 7.** Validation of RNA-Seq results by qPCR. Nine DEGs are randomly selected, and the expressions of genes at different time periods are examined relative to the endogenous control genes (β-actin and GAPDH). The relative expression values are transformed into the log2 (fold change) form. The results are shown as the mean ± SEM of liver and spleen tissues derived from 3 individual turtles.

#### **4. Discussion**

For the nutriment and medical values, Chinese soft-shelled turtle *T. sinensis* has been developed into the largest cultured turtle species in East Asia, especially in China and Japan. Serious infectious diseases caused by pathogens including bacteria and viruses is threatening the aquaculture of turtles [3]; in particular, the hemorrhagic sepsis caused by *A. hydrophila*, with more than 15 kinds of diseases, is the most common and troublesome in turtle disease cases [36,37]. Previous studies have reported in mammals and fish that abnormal immune responses to pathogenic infections, such as excessive activation of immune cells and dysfunction of immune responses, can lead the immune system to attack self-uninfected cells, causing systemic inflammation, tissue hemorrhagic sepsis, and even death [38,39]. However, the research on the immune response mechanisms is limited, and the molecular pathology of turtles infected by *A. hydrophila* remains unclear, which greatly hinders the strategy innovations for disease prevention and control in Chinese soft-shelled turtles.

The susceptible and resistant individuals in the natural population have offered excellent materials to study the molecular pathology or molecular basis of resistance for pathogenic diseases in many animal species [40,41]. In livestock and poultry animals, for example, comparative transcriptomes were analyzed to reveal the molecular mechanism differences in response to *Mycoplasma hyopneumoniae* infection in two pig breeds [42]. These two breeds share DEGs that are involved in immune relevant pathways, including cytokine–cytokine receptor interaction pathway, PI3K-Akt signaling pathway, and chemokine signaling pathway [42]. The study demonstrates that more chemokines and interleukins are specifically and significantly up-regulated, which can enhance the immune responses and reduce the susceptibility to *M. hyopneumoniae* infection in resistant pig breed [42]. When cytokine gene expressions are compared between chicken line 6.3 (Marek's disease-resistant chicken) and line 7.2 (Marek's disease-susceptible chicken) in a transcriptome analysis, among the identified 53 cytokines and 96 cytokine receptors, 15 cytokines and 29 cytokine receptors highly expressed in line 6.3 were detected [43]. In aquaculture fish species, critical cytokines including, IL8 and TNFα, are significantly up-regulated in resistant channel catfish (*Ictalurus punctatus*), while susceptible catfish show high expression levels of IL17 in response to *Flavobacterium columnare* infection [44]. The gene networks involved in the apoptotic process are also associated with disease resistance/susceptibility to *Piscirickettsia salmonis* in Atlantic salmon [29]. This evidence indicates that the expression of immune-relevant genes interrelates with disease resistance/susceptibility to pathogenic diseases in animal hosts. In the present study, two phenotypes of Chinese soft-shelled turtles were found after *A. hydrophila* infection. One group of turtles were active in feeding and moving, with no obvious pathological symptoms, which were considered as the resistant turtles, while the other group of turtles showed obviously pathological symptoms, with swelling and congestion in liver and spleen after *A. hydrophila* infection and the reduction of food intake and movement, which are regarded as susceptible turtles. Comparative liver and spleen transcriptomes from these two groups of turtles at different time periods (6, 24, and 72 hpi) were further analyzed to reveal the molecular basis of resistance/susceptibility for turtles infected by *A. hydrophila*. The results indicate that the expression of cytokine, apoptosis-, and phagocytosis-related genes in both liver and spleen of the inactive turtles is significantly distinct from those in the active turtles analyzed by KEGG pathway enrichment. Therefore, we infer that these gene expression differences may be related to the molecular pathology or resistant basis to *A. hydrophila* infection in Chinese soft-shelled turtles.

Cytokines are a class of low-molecular-weight-secreted proteins that can transduce signals between cells and exert immune regulation and effector functions [45]. They play important roles in the immune system by regulating the intensity and duration of immune responses [46]. During pathogen infection, cytokines produced by immune cells trigger an inflammatory response, which is essential for the early elimination of pathogens [47]. However, the lasting or excessive production and release of cytokines may initiate the "cytokine

storm", which often leads to various diseases, including hemorrhagic septicemia and even the failure of key organs or death for animal hosts [48]. It has been reported in many aquaculture fish species that abnormal expression of cytokines is linked to serious hemorrhagic septicemia. For example, hemorrhagic septicemia of mandarin fish (*Siniperca chuatsi*) is mainly caused by *A. hydrophila* infection [49]. Histopathological analysis reveals that inflammation, vacuolization, and extensive necrosis exist in the gill, liver, spleen, and head kidney of the diseased mandarin fish [49]. The mRNA expression levels of proinflammatory cytokines including TNFα, CCL3, and IL8 are sharply up-regulated in spleen and head kidney of mandarin fish post-*A. hydrophila* infection [49]. Coincidentally, Chinese perch infected with *A. hydrophila* also shows significantly high mRNA expression levels of pro-inflammatory cytokines, such as TNFα, and IL1β, compared to healthy fish [50]. In tambaqui (*Colossoma macropomum*), IL1β and complement component 4 are intensely up-regulated post-*A. hydrophila* infection [51]. With transcriptome analysis, the KEGG pathways associated with disease and immune responses, such as the cytokine–cytokine receptor interaction, complement and coagulation cascades, and inflammatory bowel disease, are also enriched in *Leiocassis longirostris* with *A. hydrophila* infection [52]. This evidence has suggested the involvement of pro-inflammatory cytokines in pathogenesis of hemorrhagic septicemia caused by *A. hydrophila* infection in fish. In the present study, the up-regulated number of cytokine and cytokine receptor genes are far more, and their up-regulations are more intense in inactive turtles than those in the active turtles. Especially in the spleen of inactive turtles, the significant up-regulations of 12 pro-inflammatory cytokines, including IL1β, IL6, IL8, CCL20, CXCL10, CXCL11, CXCL13, CXCL14, CX3CL1, TNFSF8, TNFSF15, TNFSF18, and 17 cytokine receptors, were identified at 6 hpi. The excessive expression of pro-inflammatory cytokines and their receptors have been confirmed to bring about uncontrolled inflammation, and lead to the pathological changes in tissues or key organs, and systemic hemorrhagic sepsis [48]. Since the high expression of pro-inflammatory cytokines, it is reasonable that the spleen and liver of inactive turtles exhibits the symptoms of hemorrhagic sepsis after *A. hydrophila* infection. In addition, it is worth noting that in both liver and spleen of the active turtles, IL10 is significantly up-regulated at all the tested time periods (6, 24, and 72 hpi). IL10 is well known as an important anti-inflammatory cytokine, which can prevent excessive tissue damage caused by bacterial and viral infections as well as pro-inflammatory responses [53]. Especially in the late phase of pathogen infection, IL10 serves the role in controlling the development of inflammatory diseases [54,55]. These above results collectively hint that the excessive expression of a large number of pro-inflammatory cytokines ("cytokine storm") triggers an imbalanced immune response, which should partly explain the molecular pathology of hemorrhagic sepsis in the liver and spleen of inactive turtles, while the lasting up-regulation of IL10 may be critical for maintaining the immune homeostasis in the active turtles during *A. hydrophila* infection.

In fact, the expression of cytokines is delicately induced and regulated, where signal pathways mediated by PRRs such as TLRs, NLRs, and RLRs undertake the indispensable roles [56]. The innate immune cells utilize PRRs to recognize the invading microorganisms, and trigger downstream immune-related signal cascades [56]. Although the downstream signaling pathways mediated by TLRs, NLRs, and RLRs are different, they all induce the production of specific cytokines. For example, the activation of TLR signaling pathway usually induces the expression of pro-inflammatory cytokines including TNFs, interleukins such as IL1β, IL6, IL8, and IL12, chemokines including CCL3 and CCL5, and interferon genes [57]. It is well known that the activation of RLR signaling pathway initiates interferon production to resist virus, and it can also induce the expression of TNFs, IL8, IL12, and other pro-inflammatory cytokines depending on NF-κB phosphorylation [58], while the activation of NLR signaling pathway is only associated with the induction of pro-inflammatory cytokines such as TNFs, IL1β, IL6, IL8, IL12, CCL3, and CCL5 [59]. In the present study, we found that in both the active and inactive turtles, the TLR-, NLR-, and RLR-mediated signaling pathways exhibited different degrees of activation along with the up-regulation of cytokines after *A. hydrophila* challenge. The difference is that in liver and

spleen of the active turtles, only the activation of the TLR signaling pathway is relatively intense when the cytokine expression is peaking at 24 hpi, while in liver and spleen of the inactive turtles, all the TLR, NLR, and RLR signaling pathways are significantly activated when the cytokine expression is peaking at 6 hpi. It has been reported in mammals and several fish species that immoderate activation of the PRRs-mediated signaling pathways causes excessive expression of pro-inflammatory cytokines, leading to the dysfunction of immune regulation and inflammatory disease [56,60]. Similarly, extensive activation of PRRs-mediated signaling pathways may be the key reason for the excessive expression of pro-inflammatory cytokines, which can explain the molecular pathology of hemorrhagic sepsis in inactive turtles after *A. hydrophila* infection.

Apoptosis is defined as programmed cell death, involved in many physiological processes including homeostasis maintenance, and developments of tissue and organ [61]. Conceptually, cell death appears to protect against most acute bacterial pathogens that infect hosts and, in many cases, even more successfully restricts nonpathogenic or opportunistic bacteria [62]. Therefore, apoptosis is considered as an intrinsic immune defense mechanism in response to microbial infections, and the apoptosis of infected cells is an effective way to eliminate pathogenic niches and prevent their further spreading [63]. It has been reported that bacterial infection sensed by PRRs induces NF-κB-dependent inflammatory cytokines, including those of the TNFs and ILs, which further promote inflammatory signaling through death receptors and induce apoptosis [64]. In Japanese flounder, cell apoptosis, along with the up-regulation of NLRP3, ASC, caspase-1, IL1β, and IL18, in the macrophages has been observed after *Edwardsiella tarda* infection [65]. In addition, the apoptosis of erythrocytes can be induced by *A. hydrophila* infection in grass carp, along with the up-regulation of CCL4, CCL11, CCL20, IL4, and IL12 [66]. These studies indicate that the inflammation caused by bacterial infection is often accompanied by cell apoptosis in hosts. In the present study, we found that in active turtles, only several apoptosis-related genes were significantly up-regulated at 6 hpi in both liver and spleen, and their expressions gradually decreased at 24 and 48 hpi, while in inactive turtles, the up-regulation of a large number of apoptosis-related genes, as well as inflammatory cytokines, including TNFs, ILs, and chemokines, were observed at 6 hpi, and the up-regulation of apoptosis-related genes can last to 72 hpi in both liver and spleen. These results confirm that the up-regulation of apoptosis-related genes in the inactive turtles is more intense and lasting than that in the active turtles. Since exuberant cell apoptosis often accompanies tissue damage and causes the pathological changes in parenchymal organs [67], excessive apoptosis-related gene expression may also be involved in the molecular pathology of hemorrhagic sepsis in the liver and spleen of turtles after *A. hydrophila* infection.

Phagocytosis constitutes an important immune response of immunocytes as the first line of defense to recognize and engulf foreign particles or self-apoptotic cells, followed by the digestion and clearance [68]. It is a primitive conserved innate immune defense mechanism for all metazoans, including vertebrates and invertebrates [69]. Macrophages, neutrophils, and dendritic cells are professional phagocytes that are able to phagocytose large foreign particles (with the diameter of >0.5 μm) such as bacteria [70]. Effective phagocytosis requires two components: particle internalization and phagosome maturation [71]. After the bacteria are recognized by the phagocytes, they undergo endocytosis to form the phagosome in the phagocytes [71]. The nascent or early phagosome has no killing activity, and they must transform into the mature phagosome to obtain the bactericidal properties [71]. The mature phagosome further fuses with the lysosome to form the phagolysosome where there are various bactericidal substances such as reactive oxygen species, and hydrolytic enzymes, including protease, polysaccharase, nuclease, and lipase, that can kill and digest the invading bacteria [71]. After digestion, most of the bacterial residues are discharged outside the host phagocytes, and part of the bacterial degradation products are presented onto the surface of antigen-presenting cells by MHC molecules, which promotes adaptive immune responses [72]. In the present study, we observed the expression differences of phagocytosis-related genes that are involved in the processes

including internalization and formation of the phagosomes, early/mature phagosome, phagolysosome, activation of NADPH oxidase, and antigen presentation between the active and inactive turtles in response to *A. hydrophila* infection. The results indicate that in active turtles, most of the phagocytosis-related genes are significantly up-regulated at 6 hpi and the up-regulation could last until 24 and 72 hpi in both liver and spleen, while in the inactive turtles, only several phagocytosis-related genes are significantly up-regulated in the liver, and even the majority of phagocytosis-related genes are significantly down-regulated in the spleen at 6 hpi; up to 24 hpi, most of the phagocytosis-related genes are significantly up-regulated in the liver and spleen. At 72 hpi, only a small part of the phagocytosis-related genes is up-regulated. Overall, the activity of phagocytosis in the inactive turtles starts later than that in the active turtles. These results suggest that the lag of phagocytosis can lead to the inability to clear bacteria, which also may be one of the important reasons for the persistent inflammation caused by *A. hydrophila* proliferation in the inactive turtles.

#### **5. Conclusions**

In summary, the molecule immune responses of turtles infected by *A. hydrophila* was analyzed, for the first time, by comparative transcriptomes from two group of turtles with different susceptibility to *A. hydrophila* infection. The gene expression profiles indicate that the dysfunction of immune responses, including excessive activation of pro-inflammatory cytokines, PRRs-mediated signaling pathway, and apoptosis, and insufficient phagocytosis activity may contribute to the molecular pathology of hemorrhagic sepsis in liver and spleen of turtles during *A. hydrophila* infection (Figure 8). Although there was a lack of further functional verification for the suspected genes, the data of comparative transcriptomes in this study will provide useful information for future studies on the molecular immunopathogenesis after *A. hydrophila* infection or genetic improvements against hemorrhagic sepsis in Chinese soft-shelled turtles.

**Figure 8.** The suspected molecular immunopathogenesis of hemorrhagic sepsis caused by *A. hydrophila* infection in Chinese soft-shelled turtles.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/biology10111218/s1. Supplementary Table S1: Details of RNA-Seq data. L, liver; S, spleen; AL group, liver in active turtles; IL group, liver in inactive turtles; AS group, spleen in active turtles; IS group, spleen in inactive turtles. One library was constructed for liver and spleen tissue from the control group, while three libraries were constructed for liver and spleen tissue from AL, IL, AS, and IS groups at 6, 24, and 72 hpi (hours post-injection), except that only two libraries from IL group at 6 hpi because one library was failed in quality. In total, 37 libraries were constructed and sequenced. Supplementary Table S2: The list of up-regulated KEGG pathways in livers from active and inactive turtles compared to that in control group. Supplementary Table S3: The list of down-regulated KEGG pathways in livers from active and inactive turtles compared to that in control group. Supplementary Table S4: The list of up-regulated KEGG pathways in spleens from active and inactive turtles compared to that in control group. Supplementary Table S5: The list of down-regulated KEGG pathways in spleens from active and inactive turtles compared to that in control group. Supplementary Table S6: The sequence information of primers for qPCR in this study.

**Author Contributions:** Conceptualization, C.Z., J.T. and Z.L.; methodology, J.T., X.L. and Z.L.; validation, J.T.; formal analysis, J.T., Y.H. and Z.L.; investigation, J.T. and Z.L.; data curation, Z.L.; writing—original draft preparation, J.T. and Z.L.; writing—review and editing, Z.L.; visualization, J.T. and Z.L.; supervision, C.Z.; funding acquisition, C.Z., Z.L., Y.H. and X.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** The work is supported by grants from the Natural Science Foundation of Hunan Province (S2017JJQNJJ1393), Postdoctoral Research Foundation of China (2019M662783), Key projects of Hunan Provincial Education Department (No.19A222) and National Key Research and Development Project: Blue granary (2018YFD0900200).

**Institutional Review Board Statement:** Animal experiments in this study are approved by the Animal Care and Use Committee of Hunan Agricultural University (Approval Code: 201903297; Approval Date: 11 October 2019).

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The fastq files containing the raw reads obtained from each Illumina library in this study could be obtained from the Sequence Read Archive (SRA) under BioProject PRJNA781380. All the data are also available from the corresponding author upon reasonable request.

**Conflicts of Interest:** The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

#### **Abbreviations**

Akt, related to the A and C kinase; CCL, C-C motif chemokine; CFU, colony forming unit; CXCL, C-X-C motif chemokine; ECM, extracellular matrix; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; IL, interleukin; KEGG, Kyoto Encyclopedia of Genes and Genomes; MHC, major histocompatibility complex; NADPH, nicotinamide adenine dinucleotide phosphate coenzyme II; NCBI, National Center for Biotechnology Information; NF-κB, nuclear factor kappa-B; NLR, nucleotidebinding domain-like receptor; NOD, nucleotide-binding domain; PCR, polymerase chain reaction; PI3K, phosphoinositide-3-kinase; PRR, pattern recognition receptor; RIG-I, retinoic acid-inducible gene I; RLR, retinoic acid-inducible gene I-like receptor; TLR, toll-like receptor; TNF, tumor necrosis factor; TNFSF, tumor necrosis factor ligand superfamily member. This list only addresses the abbreviations in contexts. The abbreviations in Tables 1–6 have been explained separately.

#### **References**


**4. W3C MMIWG standards**

The companion working group, Multi-Modal Interaction Working Group (MMIWG), led by Deborah Dahl was attended by almost the same companies attending VBWG. The goal of MMIWG was to extend the scope of standardization beyond the voice or typed input to embrace a much larger set of modalities, such as touch, gesture, emotions, and haptics both as input and output devices for a system.

The major achievements of the W3C MMIWG were the following standards:

• Ink Markup Language, InkML [40], is designed to represent the input of handwriting by a stylus or a finger. In addition to representing traces, InkML offers a rich set of metadata that preserve the appearance of the original input (i.e., color, width, orientation, timing, etc.).

Another achievement of the W3C MMIWG was the definition of a multimodal architecture [46]. The multimodal architecture provides an event-based protocol for an interaction manager, possibly implemented in SCXML 1.0 [27], to coordinate an ensemble of modality components, each responsible for processing inputs or producing outputs in specific modalities. The protocol consists of a limited set of standard LifeCycle events—NewContext, Prepare, Start, Pause, Resume, Cancel, Done, ClearContext, Status, and Extension. The standard events include a set of standard fields, for example, fields to record the source and destination of the event, as well as a Data field, which can contain the results of processing an input.

A very detailed and up-to-date description of W3C multimodal standards is contained in [47].

As you see, there was close relationship between these two W3C working groups whose aim was to create a set of interoperable and complementary standards to expand capabilities of state-of-the-art applications.