*3.1. Performence of Channel Optical Power Monitoring*

In order to alleviate the influence of the photoelectric conversion characteristics of different PDs and different modulation depths on the accuracy of channel optical power monitoring, we first simulated the curve relationship of the calibration coefficient with the modulation depth in the QPSK/16QAM system. The modulation depth of the QPSK/16QAM system is increased from 2% to 15%. Then, the relationship between the calibration coefficient and the modulation depth is shown in Figure 4.

tion.

*3.1. Performence of Channel Optical Power Monitoring*

According to Table 3, we find that the optical label performance in QPSK system is

In order to alleviate the influence of the photoelectric conversion characteristics of

different PDs and different modulation depths on the accuracy of channel optical power monitoring, we first simulated the curve relationship of the calibration coefficient with the modulation depth in the QPSK/16QAM system. The modulation depth of the QPSK/16QAM system is increased from 2% to 15%. Then, the relationship between the

calibration coefficient and the modulation depth is shown in Figure 4.

better than that in 16QAM system, and the optical label power basically exhibits a linear relationship as the modulation depth increases. Optical label power and modulation depth *m* are basically linear, and the label information could be recovered accurately. In addition, the SNR of optical label under different *m* is sufficient for error-free demodula-

**Figure 4.** The calibration coefficient versus modulation depth. **Figure 4.** The calibration coefficient versus modulation depth.

Figure 3 shows that as the modulation depth increases, the calibration coefficient of the QPSK system is relatively constant with little fluctuation, but it increases first and then gradually becomes constant as the modulation depth increases in the 16QAM system. The main reasons are as follows: For the constant-module QPSK signals, the beat noise generated by PD detection is relatively small, which means that even at a low modulation depth, the noise only accounts for a small part of the entire optical label power and causes Figure 3 shows that as the modulation depth increases, the calibration coefficient of the QPSK system is relatively constant with little fluctuation, but it increases first and then gradually becomes constant as the modulation depth increases in the 16QAM system. The main reasons are as follows: For the constant-module QPSK signals, the beat noise generated by PD detection is relatively small, which means that even at a low modulation depth, the noise only accounts for a small part of the entire optical label power and causes little effect on the optical label power. As the modulation depth increases, the optical label power also increases almost linearly. Therefore, the change of modulation depth hardly brings about the change of calibration coefficient.

little effect on the optical label power. As the modulation depth increases, the optical label power also increases almost linearly. Therefore, the change of modulation depth hardly brings about the change of calibration coefficient. However, for the 16QAM system with non-constant modulus, larger beat noise will be generated after PD detection. This noise accounts for a ratio of the entire optical label power at low modulation depths, even exceeding that of the digital label signal modulated by the pilot tone. This results in the optical label power being greater than the expected power at the corresponding modulation depth. In the case of monitoring the optical power of the same channel, the calibration factor is small compared with QPSK systems. The PD beat noise is almost constant or slightly increases with increasing modulation depth, but However, for the 16QAM system with non-constant modulus, larger beat noise will be generated after PD detection. This noise accounts for a ratio of the entire optical label power at low modulation depths, even exceeding that of the digital label signal modulated by the pilot tone. This results in the optical label power being greater than the expected power at the corresponding modulation depth. In the case of monitoring the optical power of the same channel, the calibration factor is small compared with QPSK systems. The PD beat noise is almost constant or slightly increases with increasing modulation depth, but the power value of the pilot-modulated useful digital label increases linearly, resulting in a reduced effect of PD beat noise accompanied by an increase in calibration coefficients. This process gradually flattens out as the power of the digital label signal increases. When the modulation depth is increased to 15%, the effect of the beat noise is small, reaching a situation similar to that of the QPSK system, so the calibration coefficients of the two systems are almost the same at this time.

the power value of the pilot-modulated useful digital label increases linearly, resulting in a reduced effect of PD beat noise accompanied by an increase in calibration coefficients. This process gradually flattens out as the power of the digital label signal increases. When the modulation depth is increased to 15%, the effect of the beat noise is small, reaching a situation similar to that of the QPSK system, so the calibration coefficients of the two sys-The maximum absolute errors of optical power monitoring at different modulation depths on 16QAM and QPSK systems after 20 spans fiber transmission are shown in Figure 5. It shows that the scheme of optical power monitoring using spectrum integration is feasible, and the maximum channel power monitoring error is less than 0.6 dB. The QPSK and the 16QAM systems exhibit same performance, and the maximum monitoring error is similar at different label modulation depths. Therefore, the monitoring accuracy of two systems can be considered to be roughly the same.

tems are almost the same at this time.

0.0

0.3

0.6

**absolute error (dB)**

**Channel power monitoring**

0.9

1.2

1.5

The maximum absolute errors of optical power monitoring at different modulation

*Photonics* **2022**, *9*, x FOR PEER REVIEW 10 of 16

The maximum absolute errors of optical power monitoring at different modulation

0.6

depths on 16QAM and QPSK systems after 20 spans fiber transmission are shown in Figure 5. It shows that the scheme of optical power monitoring using spectrum integration is feasible, and the maximum channel power monitoring error is less than 0.6 dB. The QPSK and the 16QAM systems exhibit same performance, and the maximum monitoring error is similar at different label modulation depths. Therefore, the monitoring accuracy of two

depths on 16QAM and QPSK systems after 20 spans fiber transmission are shown in Figure 5. It shows that the scheme of optical power monitoring using spectrum integration is feasible, and the maximum channel power monitoring error is less than 0.6 dB. The QPSK and the 16QAM systems exhibit same performance, and the maximum monitoring error

systems can be considered to be roughly the same.

systems can be considered to be roughly the same.

0.6

**Figure 5.** The optical power monitoring results of (**a**) 8-channel QPSK, (**b**) 8-channel 16QAM system. **Figure 5.** The optical power monitoring results of (**a**) 8-channel QPSK, (**b**) 8-channel 16QAM system. In addition, taking a QPSK system as an example, the impact of channel number on

In addition, taking a QPSK system as an example, the impact of channel number on the accuracy of channel power monitoring is also investigated. Here, the optical label modulation depth is fixed to 0.10, while the number of monitoring channels is set from 5 to 64. The monitoring error of channel optical power of different wavelength channel In addition, taking a QPSK system as an example, the impact of channel number on the accuracy of channel power monitoring is also investigated. Here, the optical label modulation depth is fixed to 0.10, while the number of monitoring channels is set from 5 to 64. The monitoring error of channel optical power of different wavelength channel numbers after 20-span transmission is shown in Figure 6. the accuracy of channel power monitoring is also investigated. Here, the optical label modulation depth is fixed to 0.10, while the number of monitoring channels is set from 5 to 64. The monitoring error of channel optical power of different wavelength channel numbers after 20-span transmission is shown in Figure 6.

**Number of Channel Figure 6.** The power monitoring error with different channel numbers. **Figure 6.** The power monitoring error with different channel numbers.

**Figure 6.** The power monitoring error with different channel numbers. It can be seen form Figure 6 that when the number of channels is increased from 5 to 64, the monitoring error also increases gradually. The main reason is that as the number of wavelength channels increases, the launch power increases accordingly, and the fiber Kerr nonlinear effect increases, which deteriorates the signal quality of the optical label. In addition, as the number of wavelength channels increases, the spectrum range increases; thus, the inter-channel crosstalk caused by the SRS effect is more serious, and consequently, it will also lead to deterioration of accuracy of optical power monitoring. Moreover, we can see that the monitoring errors fluctuate in a relatively small range. This is because in each simulation with different channel numbers, the optical label sequence is randomly generated, and the random change of the label sequence characteristics It can be seen form Figure 6 that when the number of channels is increased from 5 to 64, the monitoring error also increases gradually. The main reason is that as the number of wavelength channels increases, the launch power increases accordingly, and the fiber Kerr nonlinear effect increases, which deteriorates the signal quality of the optical label. In addition, as the number of wavelength channels increases, the spectrum range increases; thus, the inter-channel crosstalk caused by the SRS effect is more serious, and consequently, it will also lead to deterioration of accuracy of optical power monitoring. Moreover, we can see that the monitoring errors fluctuate in a relatively small range. This is because in each simulation with different channel numbers, the optical label sequence is randomly generated, and the random change of the label sequence characteristics causes the monitoring results to fluctuate slightly. In general, although there is a rising It can be seen form Figure 6 that when the number of channels is increased from 5 to 64, the monitoring error also increases gradually. The main reason is that as the number of wavelength channels increases, the launch power increases accordingly, and the fiber Kerr nonlinear effect increases, which deteriorates the signal quality of the optical label. In addition, as the number of wavelength channels increases, the spectrum range increases; thus, the inter-channel crosstalk caused by the SRS effect is more serious, and consequently, it will also lead to deterioration of accuracy of optical power monitoring. Moreover, we can see that the monitoring errors fluctuate in a relatively small range. This is because in each simulation with different channel numbers, the optical label sequence is randomly generated, and the random change of the label sequence characteristics causes the monitoring results to fluctuate slightly. In general, although there is a rising trend in the error of channel power monitoring, the maximum monitoring error after 20-span transmission does not exceed 0.9 dB, so it is believed that our proposed scheme is applicable to WDM optical networks.

trend in the error of channel power monitoring, the maximum monitoring error after 20-

causes the monitoring results to fluctuate slightly. In general, although there is a rising trend in the error of channel power monitoring, the maximum monitoring error after 20-

### *3.2. Performence of OSNR Estimation* In order to validate the performance of OSNR estimation practically, the NF value of

span transmission does not exceed 0.9 dB, so it is believed that our proposed scheme is

*Photonics* **2022**, *9*, x FOR PEER REVIEW 11 of 16

applicable to WDM optical networks.

*3.2. Performence of OSNR Estimation*

In order to validate the performance of OSNR estimation practically, the NF value of EDFA in each optical channel is set to 5 dB, typically. Meanwhile, the length of each span is identically set to 100 km in 20-span transmission. The input power and output power of EDFAs are monitored based on optical labels, and the OSNR in each channel is calculated by using the proposed method. Then, 16 G Baud PM-16QAM and PM-QPSK 8, 32 and 64 channels WDM transmission simulations are carried out. The result of OSNR estimation as well as the estimate error after 20-span transmission are shown in Figure 7. EDFA in each optical channel is set to 5 dB, typically. Meanwhile, the length of each span is identically set to 100 km in 20-span transmission. The input power and output power of EDFAs are monitored based on optical labels, and the OSNR in each channel is calculated by using the proposed method. Then, 16 G Baud PM-16QAM and PM-QPSK 8, 32 and 64 channels WDM transmission simulations are carried out. The result of OSNR estimation as well as the estimate error after 20-span transmission are shown in Figure 7.

**Figure 7.** Result of OSNR estimation and corresponding error under 100 km equal-length span in (**a**) 8-channel QPSK, (**b**) 8-channel 16QAM, (**c**) 32-channel QPSK, (**d**) 32-channel 16QAM systems, (**e**) 64-channel QPSK systems, and (**f**) 64-channel 16QAM system. **Figure 7.** Result of OSNR estimation and corresponding error under 100 km equal-length span in (**a**) 8-channel QPSK, (**b**) 8-channel 16QAM, (**c**) 32-channel QPSK, (**d**) 32-channel 16QAM systems, (**e**) 64-channel QPSK systems, and (**f**) 64-channel 16QAM system.

could be estimated accurately in each span both in 8/32/64-channels QPSK and 16QAM

Figure 7 shows that the scheme of OSNR estimation is feasible because that OSNR

Figure 7 shows that the scheme of OSNR estimation is feasible because that OSNR could be estimated accurately in each span both in 8/32/64-channels QPSK and 16QAM systems. It shows that the OSNR monitoring error after 20 span transmissions is less than 0.45 dB in both 8-channel PM-QPSK/16QAM systems, while it is about 0.7 dB in 32-channel PM-QPSK/16QAM systems. When the number of transmission channels increases to 64, the OSNR monitoring error after 20 span transmissions is less than 1 dB both in 64-channel PM-QPSK/16QAM systems. The main monitoring error occurs because of the error that occurred during the previous channel power monitoring. With the increase of transmission spans, the ASE noise induced by EDFA continues to accumulate due to multiple cascade amplification in the transmission link, resulting in an increase of channel power monitoring and thereby causing larger OSNR estimation error. In addition, the nonlinear effects and inter-channel cross talk also become more serious as the number of channels increases, which leads to a larger error in OSNR monitoring. Furthermore, other factors such as chromatic dispersion, polarization dependent impairments and so on will also affect the accuracy of power monitoring, which leads to a situation where the longer the transmission distance is, the greater the monitoring error is. 0.45 dB in both 8-channel PM-QPSK/16QAM systems, while it is about 0.7 dB in 32-channel PM-QPSK/16QAM systems. When the number of transmission channels increases to 64, the OSNR monitoring error after 20 span transmissions is less than 1 dB both in 64 channel PM-QPSK/16QAM systems. The main monitoring error occurs because of the error that occurred during the previous channel power monitoring. With the increase of transmission spans, the ASE noise induced by EDFA continues to accumulate due to multiple cascade amplification in the transmission link, resulting in an increase of channel power monitoring and thereby causing larger OSNR estimation error. In addition, the nonlinear effects and inter-channel cross talk also become more serious as the number of channels increases, which leads to a larger error in OSNR monitoring. Furthermore, other factors such as chromatic dispersion, polarization dependent impairments and so on will also affect the accuracy of power monitoring, which leads to a situation where the longer the transmission distance is, the greater the monitoring error is. Meanwhile, to make the simulation more realistic and reliable, the span lengths of

Meanwhile, to make the simulation more realistic and reliable, the span lengths of the 20-spans transmissions in the 8-channel system are set to 100, 60, 80, 40, 100, 50, 70, 20, 100, 60, 30, 70, 50, 80, 20, 100, 40, 100, 30 and 80 km, respectively. Thus, it is convincing to evaluate practically the OSNR estimation and corresponding errors of the target channel 1 (NF = 4) and channel 3 (NF = 5). Figure 8 shows that the maximum OSNR monitoring error is about 0.3 dB after 20-span transmission in both PM-QPSK/16QAM WDM systems. The scheme still works in the case of different span lengths, and the OSNR estimation error also tends to increase for the same reason. However, due to that the total transmission length here is reduced, resulting in a reduction of channel power monitoring, and it further makes the OSNR estimation error smaller than the results in Figure 7a,b. the 20-spans transmissions in the 8-channel system are set to 100, 60, 80, 40, 100, 50, 70, 20, 100, 60, 30, 70, 50, 80, 20, 100, 40, 100, 30 and 80 km, respectively. Thus, it is convincing to evaluate practically the OSNR estimation and corresponding errors of the target channel 1(NF = 4) and channel 3(NF = 5). Figure 8 shows that the maximum OSNR monitoring error is about 0.3 dB after 20-span transmission in both PM-QPSK/16QAM WDM systems. The scheme still works in the case of different span lengths, and the OSNR estimation error also tends to increase for the same reason. However, due to that the total transmission length here is reduced, resulting in a reduction of channel power monitoring, and it further makes the OSNR estimation error smaller than the results in Figure 7a,b.

**Figure 8.** Result of OSNR estimation and corresponding error under [20–100 km] unequal-length span in (**a**,**b**) QPSK and (**c**,**d**) 16QAM systems. **Figure 8.** Result of OSNR estimation and corresponding error under [20–100 km] unequal-length span in (**a**,**b**) QPSK and (**c**,**d**) 16QAM systems.

optical IQ modulator, and arbitrary waveform generator (AWG) available, only single-

To further verify the actual performance of the proposed scheme, the transmission

**4. Experimental Setup and Performance Analysis**

### **4. Experimental Setup and Performance Analysis** *Photonics* **2022**, *9*, x FOR PEER REVIEW 13 of 16 random bit sequences of service signal are generated and mapped into PM-QPSK/16QAM

To further verify the actual performance of the proposed scheme, the transmission experiment is carried out. Figure 9 shows the experimental setup of optical-label based OPM over PM-QPSK and PM-16QAM systems. Since there is only one set of laser source, optical IQ modulator, and arbitrary waveform generator (AWG) available, only single-channel optical fiber transmission and monitoring verification is carried out in this experiment. In addition, limited by the storage depth and sampling rate of the 8190A instrument (up to 12 GSa/s), the service signal rate in the offline experiment is set to 4 G Baud, and the modulation depth of the optical tag to the service signal is set to 0.10. Firstly, random bit sequences of service signal are generated and mapped into PM-QPSK/16QAM symbols. Then, we modulate the 2 Mbit/s ID information [1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1] onto a 40 MHz pilot tone and load the pilot-tone-carried labels onto the service signals by offline DSP. Next, the discrete signals after the matched filter are sent into AWG (M8190A) working at 400 MSa/s. After 80 km standard single-mode fiber (SSMF) transmission and EDFA amplification, an adjustable optical attenuator is used to change the optical power of the channel within the PD's acceptable working range of [−20–0 dBm]. A spectrometer (AQ6370) is used to observe the optical signal spectrum, while a 200 MHz-bandwidth PD is used to detect the optical labels. Then the detected 16QAM and QPSK signals are sampled by a real-time oscilloscope with 400 MSa/s for offline processing. channel optical fiber transmission and monitoring verification is carried out in this experiment. In addition, limited by the storage depth and sampling rate of the 8190A instrument (up to 12 GSa/s), the service signal rate in the offline experiment is set to 4 G Baud, and the modulation depth of the optical tag to the service signal is set to 0.10. Firstly, random bit sequences of service signal are generated and mapped into PM-QPSK/16QAM symbols. Then, we modulate the 2 Mbit/s ID information [1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1] onto a 40 MHz pilot tone and load the pilot-tone-carried labels onto the service signals by offline DSP. Next, the discrete signals after the matched filter are sent into AWG (M8190A) working at 400 MSa/s. After 80 km standard single-mode fiber (SSMF) transmission and EDFA amplification, an adjustable optical attenuator is used to change the optical power of the channel within the PD's acceptable working range of [−20–0 dBm]. A spectrometer (AQ6370) is used to observe the optical signal spectrum, while a 200 MHzbandwidth PD is used to detect the optical labels. Then the detected 16QAM and QPSK signals are sampled by a real-time oscilloscope with 400 MSa/s for offline processing. symbols. Then, we modulate the 2 Mbit/s ID information [1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1] onto a 40 MHz pilot tone and load the pilot-tone-carried labels onto the service signals by offline DSP. Next, the discrete signals after the matched filter are sent into AWG (M8190A) working at 400 MSa/s. After 80 km standard single-mode fiber (SSMF) transmission and EDFA amplification, an adjustable optical attenuator is used to change the optical power of the channel within the PD's acceptable working range of [−20–0 dBm]. A spectrometer (AQ6370) is used to observe the optical signal spectrum, while a 200 MHzbandwidth PD is used to detect the optical labels. Then the detected 16QAM and QPSK signals are sampled by a real-time oscilloscope with 400 MSa/s for offline processing. **Double Polarization IQ Modulator CW Laszer 193.1THz Service Data and Optical EDFA & SSMF 1:99 Coupler &Attenuator**

*Photonics* **2022**, *9*, x FOR PEER REVIEW 13 of 16

channel optical fiber transmission and monitoring verification is carried out in this experiment. In addition, limited by the storage depth and sampling rate of the 8190A instrument (up to 12 GSa/s), the service signal rate in the offline experiment is set to 4 G Baud, and the modulation depth of the optical tag to the service signal is set to 0.10. Firstly,

**200MHz**

**400MSa/s Oscilloscope**

**Offline DSP**

**Figure 9.** Diagram of experimental platform. **Figure 9.** Diagram of experimental platform. shown in Figure 10. It shows that the transmitted DPSK digital optical labels, i.e., ID in-

The waveform of the optical label after differential demodulation in offline DSP is shown in Figure 10. It shows that the transmitted DPSK digital optical labels, i.e., ID information of a wavelength channel, could be accurately recovered without error by using a low-bandwidth PD and low-sample-rate ADC with the help of DSP processing. Furthermore, we can see that the signal quality of labels in QPSK system is better than that of 16QAM. This is due to the fact that after PD reception, the amount of beat noise of QPSK constant-module signals is smaller than that of 16QAM, thereby making the QPSK system The waveform of the optical label after differential demodulation in offline DSP is shown in Figure 10. It shows that the transmitted DPSK digital optical labels, i.e., ID information of a wavelength channel, could be accurately recovered without error by using a low-bandwidth PD and low-sample-rate ADC with the help of DSP processing. Furthermore, we can see that the signal quality of labels in QPSK system is better than that of 16QAM. This is due to the fact that after PD reception, the amount of beat noise of QPSK constant-module signals is smaller than that of 16QAM, thereby making the QPSK system a better optical label signal-to-noise ratio when demodulating the labels. formation of a wavelength channel, could be accurately recovered without error by using a low-bandwidth PD and low-sample-rate ADC with the help of DSP processing. Furthermore, we can see that the signal quality of labels in QPSK system is better than that of 16QAM. This is due to the fact that after PD reception, the amount of beat noise of QPSK constant-module signals is smaller than that of 16QAM, thereby making the QPSK system a better optical label signal-to-noise ratio when demodulating the labels.

The result of power monitoring in experiments is shown in Figure 11. The result **Figure 10.** The demodulated waveform of optical labels in (**a**) QPSK and (**b**) 16QAM systems. **Figure 10.** The demodulated waveform of optical labels in (**a**) QPSK and (**b**) 16QAM systems.

shows that the optical label power can be calculated more accurately, and then, the accurate channel optical power monitoring result can be obtained. The power monitoring error of the QPSK system is similar to that of the 16QAM system, which are both less than 0.3

The result of power monitoring in experiments is shown in Figure 11. The result

shows that the optical label power can be calculated more accurately, and then, the accu-

of the QPSK system is similar to that of the 16QAM system, which are both less than 0.3 dB under 80 km fiber transmission. It is important to point out that the multi-span transmission cannot be fully experimented with, resulting in better channel power monitoring

The result of power monitoring in experiments is shown in Figure 11. The result shows that the optical label power can be calculated more accurately, and then, the accurate channel optical power monitoring result can be obtained. The power monitoring error of the QPSK system is similar to that of the 16QAM system, which are both less than 0.3 dB under 80 km fiber transmission. It is important to point out that the multi-span transmission cannot be fully experimented with, resulting in better channel power monitoring performance than that of our simulation. Moreover, although the error of power monitoring fluctuates irregularly within a range, the accurate channel optical power monitoring performance is sufficient to make it a practical OPM solution. *Photonics* **2022**, *9*, x FOR PEER REVIEW 14 of 16 performance than that of our simulation. Moreover, although the error of power monitoring fluctuates irregularly within a range, the accurate channel optical power monitoring performance is sufficient to make it a practical OPM solution.

**Figure 11.** Results of channel power monitoring and monitoring error (**a**) in QPSK system, and (**b**) **Figure 11.** Results of channel power monitoring and monitoring error (**a**) in QPSK system, and (**b**) in 16QAM system.

### **5. Conclusions**

read and agreed to the published version of the manuscript.

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

**Informed Consent Statement:** Not applicable.

in 16QAM system.

**5. Conclusions**

(2022RC09).

going study.

In this paper, a low-cost and highly efficient optical labels enabled channel power monitoring and OSNR estimation of WDM system has been proposed and demonstrated. DPSK digital optical labels modulated on pilot tones with frequency of tens of MHz were applied as identity indicators and performance predictors tied up to each wavelength channel. The system can not only monitor the performance of all wavelength channels simultaneously using low-cost PD detection, but it is also able to reliably deliver any specific surveillance information to further sense the working state of WDM channels. In addition, in our proposed DSP processing in OPM modules, the channel optical power is monitored using the method of spectral integration, and the OSNR is further estimated by calculating the ASE noise accumulation form all amplifiers. The simulation results of 8, 32 and 64 channels WDM systems under 20 spans transmission show that the maximum monitoring error of channel optical power and the estimation error of OSNR are both less than 1 dB in 16 G Baud PM-QPSK and PM-16QAM systems, respectively. Furthermore, an offline experiment platform was constructed by using a PD with 300 MHz bandwidth and an ADC with 600 MSa/s sample rate, and the results show that the DPSK digital labels In this paper, a low-cost and highly efficient optical labels enabled channel power monitoring and OSNR estimation of WDM system has been proposed and demonstrated. DPSK digital optical labels modulated on pilot tones with frequency of tens of MHz were applied as identity indicators and performance predictors tied up to each wavelength channel. The system can not only monitor the performance of all wavelength channels simultaneously using low-cost PD detection, but it is also able to reliably deliver any specific surveillance information to further sense the working state of WDM channels. In addition, in our proposed DSP processing in OPM modules, the channel optical power is monitored using the method of spectral integration, and the OSNR is further estimated by calculating the ASE noise accumulation form all amplifiers. The simulation results of 8, 32 and 64 channels WDM systems under 20 spans transmission show that the maximum monitoring error of channel optical power and the estimation error of OSNR are both less than 1 dB in 16 G Baud PM-QPSK and PM-16QAM systems, respectively. Furthermore, an offline experiment platform was constructed by using a PD with 300 MHz bandwidth and an ADC with 600 MSa/s sample rate, and the results show that the DPSK digital labels could be accurately recovered with the help of the proposed DSP processing, while the monitoring error of channel optical power is less than 0.3 dB. Therefore, the advantages of low cost and high efficiency make our scheme more pragmatic and more robust and therefore easier to implement and more practical for WDM system applications.

could be accurately recovered with the help of the proposed DSP processing, while the monitoring error of channel optical power is less than 0.3 dB. Therefore, the advantages of low cost and high efficiency make our scheme more pragmatic and more robust and therefore easier to implement and more practical for WDM system applications. **Author Contributions:** Conceptualization, T.Y. and X.C.; formal analysis, T.Y., K.L. and Z.L.; investigation, T.Y. and S.S.; data curation, T.Y. and X.C.; writing—original draft preparation, T.Y., K.L. and X.W.; writing—review and editing, T.Y., L.W. and X.C.; visualization, T.Y. and K.L.; supervision, T.Y., K.L., Z.L., X.W., S.S., L.W. and X.C.; project administration, T.Y. and X.C. All authors have read and agreed to the published version of the manuscript.

**Author Contributions:** Conceptualization, T.Y. and X.C.; formal analysis, T.Y., K.L. and Z.L.; investigation, T.Y. and S.S.; data curation, T.Y. and X.C.; writing—original draft preparation, T.Y., K.L. and X.W.; writing—review and editing, T.Y., L.W. and X.C.; visualization, T.Y. and K.L.; supervi-**Funding:** This work is partly supported by National Natural Science Foundation of China (62001045), Beijing Municipal Natural Science Foundation (4214059), Fund of State Key Laboratory of IPOC (BUPT) (IPOC2021ZT17), and Fundamental Research Funds for the Central Universities (2022RC09).

(62001045), Beijing Municipal Natural Science Foundation (4214059), Fund of State Key Laboratory of IPOC (BUPT) (IPOC2021ZT17), and Fundamental Research Funds for the Central Universities

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the data also forms part of an on-

sion, T.Y., K.L., Z.L., X.W., S.S., L.W. and X.C.; project administration, T.Y. and X.C. All authors have

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the data also forms part of an ongoing study.

**Acknowledgments:** The authors express their appreciation to reviewers for their valuable suggestions.

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

### **References**


**5.6 Diagnostic potential of HLA-E monospecific mAbs**

Immunolocalization of HLA-E on human melanoma cancer tissues was performed using culture supernatants (s) or ascites (a) of TFL monospecific mAbs (TFL-033, TFL-034, TFL-074, and TFL-216), and staining is compared with commercial anti-HLA-E mAb (MEM-E/02) [35, 98]. Titration of Protein-G purified culture supernatants and ascites concentrates of different anti-HLA-E monospecific mAbs are shown in **Table 8**. As revealed in **Figure 4**, the MEM-02 cross-reacts with several HLA class Ia alleles. Although it stains melanoma tissues, due to the paucity of HLA-E specificity, specific localization of HLA-E was confirmed with monospecific anti-HLA-E mAbs (**Figure 9A**).  DOI: http://dx.doi.org/10.5772/intechopen.95235*

> **Table 7.** *Identifying*

 *HLA-E specific epitope or amino acid sequences: Comparing*

 *the two HLA-E restricted sequences with other HLA-I amino acid sequences at the same position.*

and then exposed to 2 mL of beads. The two different HLA-E-restricted peptides, RSARDTA and SEQKSNDASE were synthesized and purified by GenScript Corporation (Piscataway, NJ). The assay was performed in triplicate. Dosimetric peptide inhibition analysis was performed for mAb TFL-033. Before dosimetric peptide inhibition, the mAb TFL-033 was dosimetrically titrated to assess their strength (MFI), and protein-G purified culture supernatants and ascites compared. Then, concentrated Protein-G purified from ascites is titrated and the protein content is measured. Titrimetric inhibition was done with ascites protein-G concentrate. A summary of the peptide inhibition experiments is presented in **Figure 8**. Results confirm that TFL-003 binding to HLA-E can be inhibited dosimetrically using two HLA-E-restricted

*Identifying HLA-E specific epitope or amino acid sequences: Peptide sequences specific and shared between HLA-E and HLA class Ia alleles: Monospecific (HLA-E restricted) versus polyreactive epitopes.*

**Comparison of the amino acid sequences of HLA-E with other HLA-I antigens HLA alleles**

> **Classical HLA-Ia**

**A B Cw F G**

**Nonclassical HLA-Ib**

**Specificity**

3306 restricted

Multispecific

8201-restricted

**Number of amino acids**

47PRAPWMEQE55 9 10000A\*

59EYWDRETR65 8 5 0 0 0 0 A-restricted **65RSARDTA71** 6 **0 0 0 0 0 E-monospecific** 90AGSHTLQW97 8 1 10 48 0 0 Multispecific 108RFLRGYE123 7 24 0 0 0 0 A-restricted 115QFAYDGKDY123 9 1 104 75 0 0 Multispecific 117AYDGKDY123 7 491 831 271 21 30 Highly

126LNEDLRSWTA135 10 239 219 261 21 30 Multispecific 137DTAAQI142 6 0 824 248 0 30 Multispecific 137DTAAQIS143 7 0 52 4 0 30 Multispecific **143SEQKSNDASE152** 10 **0 0 0 0 0 E-monospecific**

163TCVEWL168 6 282 206 200 0 30 Multispecific 182EPPKTHVT190 8 0 0 19 0 0 C-restricted

157RAYLED162 6 01000 B\*

**HLA-E peptide sequences**

*Monoclonal Antibodies*

**Table 6.**

**58**

epitopes. The level of inhibition differed between the two epitopes.

Immunolocalization of HLA-E on human melanoma cancer tissues was performed using culture supernatants (s) or ascites (a) of TFL monospecific mAbs (TFL-033, TFL-034, TFL-074, and TFL-216), and staining is compared with commercial anti-HLA-E mAb (MEM-E/02) [35, 98]. Titration of Protein-G purified culture supernatants and ascites concentrates of different anti-HLA-E monospecific mAbs are shown in **Table 8**. As revealed in **Figure 4**, the MEM-02 cross-reacts with several HLA class Ia alleles. Although it stains melanoma tissues, due to the paucity of HLA-E specificity, specific localization of HLA-E was confirmed with monospecific anti-HLA-E mAbs (**Figure 9A**). Similarly, immune-localization of HLA-E on human

**5.6 Diagnostic potential of HLA-E monospecific mAbs**

#### **Figure 7.**

*Diagrammatic illustration of the structure of HLA-E, closed (intact trimer) and open conformers and specific (private) and shared (public) epitopes. (A) Illustrates the locations of allele-specific sequence (private epitope) and shared peptide (public epitopes) sequence. HLA-E with β2-microglobulin (in blue) showing (B) the allelespecific amino acid sequences (private epitopes) in α1 & α2 helical groove and (C) shared peptide amino acid sequences (public epitopes).*

included Stages I to IV of metastatic gastric cancer with 5 peritoneal, 3 liver, 27 lymph node metastases. TMA was immunostained with TFL-033 mAbs (culture supernatants and ascites), controls were stained without primary mAbs [98]. The diagnostic potential of HLA-E-monospecific mAb TFL-033 for different kinds and stages of gastric cancer is illustrated in **Figure 4a** in International Journal of Cancer [98]. The observations confirm that specific identification and localization of MHC antigens, stringently require monospecific mAbs. The conclusion is highly reliable compared to the use of polyreactive commercial mAbs (MEM-E/02) [36, 98], presented in **Figure 4**. Importantly, characterizations of monospecificity should include (1) multiantigen coated solid matrix assays, e.g., Luminex multiplex SAB assay; (2) titrimetric inhibition with the private epitope of the antigen. Only such

*Dosimetric inhibition of purified culture supernatants of TFL-033 with two HLA-E-restricted peptides, 65RSARDTA71 and 143SEQKSNDASE152, at concentrations ranging from 4.4 to 0.27 mg/well. Although both peptides showed inhibition, the α2 helical peptide SEQKSNDASE showed better dosimetric inhibition than the other peptide. Peptide concentration and peptide content (μG/well) in parenthesis are shown. Pair-sample or equal-variant t-tests were carried out in this investigation using a graphic website (www.originlab.com). (Source*: U. S. Patent No 10,656,158 B2 (*U.S. patent application No. 13/507,537)* issued on May 19, 2020, to Dr. Mepur H. Ravindranath*) see also Int J cancer. 2014;134(7):1558–70. DOI: 10.1002/ijc.28484.*

*Monospecific and Polyreactive Monoclonal Antibodies against Human Leukocyte Antigen-E…*

*DOI: http://dx.doi.org/10.5772/intechopen.95235*

monospecific mAbs are reliable for diagnosis and therapeutic purposes.

*5.7.1 Potential of polyclonal anti-HLA-E mAbs in immune regulation*

**versus polyreactive mAbs**

**Figure 8.**

**61**

**5.7 Differences in the immunoregulatory potentials of HLA-E monospecific**

Immunoregulatory properties of both monospecific (TFL-033) and polyreactive (TFL-006 & TFL-007) anti-HLA-E mAbs were examined for their ability to suppress or activate CD3/CD4+, CD3/CD8+ T cells, T-regs, and CD3+/CD19/20+ B cells. The results show that the polyreactive anti-HLA-E mAbs (TFL-006/TFL-007) are immunosuppressive comparable to IVIg, used in immunotherapy of several diseases [16, 17]. Indeed the anti-HLA antibody profile of IVIg from different sources showed

gastric diffused carcinoma paraffin tissue sections was observed after staining with the diluted ascites of monospecific mAb TFL-033a and MEM-E/02. The reliability of HLA-E tissue localization with monospecific immunostaining of human gastric adenocarcinoma (A, B) with TFL-033 and MEM-E/02 with that obtained for gastric diffuse carcinoma (C, D) control, stained without primary mAbs. MEM-E/02 failed to stain any cells while TFL-033a showed intense and widely distributed staining indicating the overexpression of intact HLA-E (**Figure 9C**). Immunostaining was performed on human breast ductal adenocarcinoma with TFL monospecific-mAbs and results obtained using monospecific anti-HLA-E mAb TFL-216, generated by immunizing HLA-EG, is presented in **Figure 9D**.

Detailed immunodiagnostic analyses were performed using a tissue microarray (TMA) of normal gastric mucosal and primary gastric cancer tissues [98]. Three tissue microarrays (TMAs; US Biomax, Rockville, MD) were carefully selected. The tissue sections of all TMA were 1.5 mm in diameter and 5 μm thick. In TMA of normal gastric mucosa and of primary gastric cancer, which contained 30 adenocarcinomas, 40 diffuse carcinomas and ten normal gastric mucosae were immunostained. TMA array included: well-differentiated, moderately differentiated, poorly differentiated, and undifferentiated cancer. In addition, TMA also

*Monospecific and Polyreactive Monoclonal Antibodies against Human Leukocyte Antigen-E… DOI: http://dx.doi.org/10.5772/intechopen.95235*

#### **Figure 8.**

gastric diffused carcinoma paraffin tissue sections was observed after staining with the diluted ascites of monospecific mAb TFL-033a and MEM-E/02. The reliability of HLA-E tissue localization with monospecific immunostaining of human gastric adenocarcinoma (A, B) with TFL-033 and MEM-E/02 with that obtained for gastric diffuse carcinoma (C, D) control, stained without primary mAbs. MEM-E/02 failed to stain any cells while TFL-033a showed intense and widely distributed staining indicating the overexpression of intact HLA-E (**Figure 9C**). Immunostaining was performed on human breast ductal adenocarcinoma with TFL monospecific-mAbs and results obtained using monospecific anti-HLA-E mAb TFL-216, generated by

*Diagrammatic illustration of the structure of HLA-E, closed (intact trimer) and open conformers and specific (private) and shared (public) epitopes. (A) Illustrates the locations of allele-specific sequence (private epitope) and shared peptide (public epitopes) sequence. HLA-E with β2-microglobulin (in blue) showing (B) the allelespecific amino acid sequences (private epitopes) in α1 & α2 helical groove and (C) shared peptide amino acid*

Detailed immunodiagnostic analyses were performed using a tissue microarray (TMA) of normal gastric mucosal and primary gastric cancer tissues [98]. Three tissue microarrays (TMAs; US Biomax, Rockville, MD) were carefully selected. The tissue sections of all TMA were 1.5 mm in diameter and 5 μm thick. In TMA of normal gastric mucosa and of primary gastric cancer, which contained 30 adeno-

carcinomas, 40 diffuse carcinomas and ten normal gastric mucosae were

immunostained. TMA array included: well-differentiated, moderately differentiated, poorly differentiated, and undifferentiated cancer. In addition, TMA also

immunizing HLA-EG, is presented in **Figure 9D**.

**Figure 7.**

**60**

*sequences (public epitopes).*

*Monoclonal Antibodies*

*Dosimetric inhibition of purified culture supernatants of TFL-033 with two HLA-E-restricted peptides, 65RSARDTA71 and 143SEQKSNDASE152, at concentrations ranging from 4.4 to 0.27 mg/well. Although both peptides showed inhibition, the α2 helical peptide SEQKSNDASE showed better dosimetric inhibition than the other peptide. Peptide concentration and peptide content (μG/well) in parenthesis are shown. Pair-sample or equal-variant t-tests were carried out in this investigation using a graphic website (www.originlab.com). (Source*: U. S. Patent No 10,656,158 B2 (*U.S. patent application No. 13/507,537)* issued on May 19, 2020, to Dr. Mepur H. Ravindranath*) see also Int J cancer. 2014;134(7):1558–70. DOI: 10.1002/ijc.28484.*

included Stages I to IV of metastatic gastric cancer with 5 peritoneal, 3 liver, 27 lymph node metastases. TMA was immunostained with TFL-033 mAbs (culture supernatants and ascites), controls were stained without primary mAbs [98]. The diagnostic potential of HLA-E-monospecific mAb TFL-033 for different kinds and stages of gastric cancer is illustrated in **Figure 4a** in International Journal of Cancer [98]. The observations confirm that specific identification and localization of MHC antigens, stringently require monospecific mAbs. The conclusion is highly reliable compared to the use of polyreactive commercial mAbs (MEM-E/02) [36, 98], presented in **Figure 4**. Importantly, characterizations of monospecificity should include (1) multiantigen coated solid matrix assays, e.g., Luminex multiplex SAB assay; (2) titrimetric inhibition with the private epitope of the antigen. Only such monospecific mAbs are reliable for diagnosis and therapeutic purposes.

#### **5.7 Differences in the immunoregulatory potentials of HLA-E monospecific versus polyreactive mAbs**

#### *5.7.1 Potential of polyclonal anti-HLA-E mAbs in immune regulation*

Immunoregulatory properties of both monospecific (TFL-033) and polyreactive (TFL-006 & TFL-007) anti-HLA-E mAbs were examined for their ability to suppress or activate CD3/CD4+, CD3/CD8+ T cells, T-regs, and CD3+/CD19/20+ B cells. The results show that the polyreactive anti-HLA-E mAbs (TFL-006/TFL-007) are immunosuppressive comparable to IVIg, used in immunotherapy of several diseases [16, 17]. Indeed the anti-HLA antibody profile of IVIg from different sources showed

#### **Table 8.**

*Titration of protein-G purified culture supernatant and ascites concentrates of different HLA-E monospecific mAbs. These concentrates were used for immunolocalization, peptide inhibition studies as well as for their effects on T-lymphoblasts.*

both HLA-Ia and HLA-Ib reactivities [16, 17]. IVIg preparations were reported to suppress CD4+ T cells [102–113], CD20+ B cells [108–113] and expand CD4 + CD25+ T-regs [114, 115]. The polyreactive anti-HLA-E mAbs performed the major immunoregulatory functions better than IVIg [101, 116–118]. These functions are (1) suppression of CD19+ B lymphocyte blastogenesis, proliferation, and suppression of production of anti-HLA-I and anti-HLA-II IgG Abs, (2) suppression of blastogenesis and proliferation of CD4+ as well as CD8+ T lymphocytes, and (3) expansion of CD4 +. CD25+ and FoxP3+ T-regs. The monospecific mAbs, when used as controls failed to perform these functions. Peptide inhibition analyses revealed that mAbs TFL-006 and TFL-007 bind to shared amino acid sequences of HLA-I molecules ( 117AYDGKDYLT125, 126LNEDLRSWTAV136, and 137DTAAQI142) (**Figure 7C**). Possibly such binding affinity of polyreactive but not monospecific mAbs contributes to the unique immunoregulatory functions mimicking IVIg [101, 118].

### *5.7.2 Therapeutic potential of anti-HLA-E monospecific mAbs*

In contrast to polyreactive anti-HLA-E mAb, monospecific mAbs (TFL-033) recognized HLA-E- specific amino acid sequences (65RSARDT70 and154AESADNSKQES144) on the α1 and α2 helices (**Figure 7B**).

**Figure 9.**

*10.1002/ijc.28484.*

**63**

*Immunolocalization of HLA-E in cancer tissues with culture supernatants (s) or ascites (A) of TFL*

*Monospecific and Polyreactive Monoclonal Antibodies against Human Leukocyte Antigen-E…*

*DOI: http://dx.doi.org/10.5772/intechopen.95235*

*monospecific mAbs compared with staining by MEM-E/02, an HLA-E mAb that shows cross-reactivity to HLA class Ia alleles. (A) Human melanoma paraffin tissue sections stained with the culture supernatants of TFL monospecific MAbs and MEM-E/02. (B) Human gastric cancer (diffused carcinoma) paraffin tissue sections stained with the diluted ascites of monospecific MAb TFL-033a and MEM-E/02. (C). Immunostaining of human gastric adenocarcinoma (A, B) and gastric diffuse carcinoma (C, D) control, stained without primary mAbs. Note the differences in staining between the two antibodies; MEM-E/02 failed to stain any cells while TFL-033a showed intense and widely distributed staining indicative of overexpression of intact HLA-E. (D) Human breast ductal adenocarcinoma stained with monospecific anti-HLA-E mAb TFL-216 generated by immunizing HLA-EG. (source*: U. S. Patent No 10,656,158 B2 (*U.S. patent application No. 13/507,537)* issued on May 19, 2020, to Dr. Mepur H. Ravindranath*) see also Int J cancer. 2014;134(7):1558–70. DOI:*

### *<IP_ADDRESS> Monospecific mAbs promote the proliferation of CD8+ T lymphocytes*

To test whether monospecific anti-HLA-E mAbs suppress proliferation of the CD3+, CD4+, or CD8+ T cells, human T lymphocytes (both CD4+ and CD8+) isolated from whole blood of a normal male donor with Ficol Hypaque (31) were treated either with phytohaemagglutinin (PHA, EY Laboratories, San Mateo, CA) at a final concentration of 2.25 mL/mL or not exposed to PHA (31). The mAbs (monospecific mAbs TFL-033, TFL-034, TFL-073, TFL-074, and TFL-216, polyreactive mAb TFL007, and negative control antibodies) were separately added to cells in culture within 2 hours after adding *Monospecific and Polyreactive Monoclonal Antibodies against Human Leukocyte Antigen-E… DOI: http://dx.doi.org/10.5772/intechopen.95235*

#### **Figure 9.**

both HLA-Ia and HLA-Ib reactivities [16, 17]. IVIg preparations were reported to suppress CD4+ T cells [102–113], CD20+ B cells [108–113] and expand CD4 + CD25+ T-regs [114, 115]. The polyreactive anti-HLA-E mAbs performed the major immunoregulatory functions better than IVIg [101, 116–118]. These functions are (1) suppression of CD19+ B lymphocyte blastogenesis, proliferation, and suppression of production of anti-HLA-I and anti-HLA-II IgG Abs, (2) suppression of blastogenesis and proliferation of CD4+ as well as CD8+ T lymphocytes, and (3) expansion of CD4 +. CD25+ and FoxP3+ T-regs. The monospecific mAbs, when used as controls failed to perform these functions. Peptide inhibition analyses revealed that mAbs TFL-006

*Titration of protein-G purified culture supernatant and ascites concentrates of different HLA-E monospecific mAbs. These concentrates were used for immunolocalization, peptide inhibition studies as well as for their*

*(1:50)* 17898 *(1:100)* 16246 *(1:200)* 14004 *(1:400)* 12520

**Sample Dilution TFL-033 TFL-034 TFL-073 TFL-074** Culture Supernatant *Neat* 11273 11601 7781 8493 Protein-G purified Culture supernatant *(1:10)* 4424 2730 1974 2507

> *(1:10)* 11953 10364 7708 8467 *(1:20)* 9423 8146 6861 7500 *(1:40)* 8167 6347 5324 5883 *(1:80)* 6203 4622 3792 4176 *(1:160)* 4139 1379 2683 2438 *(1:320)* 2862 626 1454 943 *(1:640)* 1434 198 590 474 *(1:1280)* 694 98 275 220

Protein-G purified Culture supernatant

Protein-G purified Ascites Concentrate

Concentrate

*Monoclonal Antibodies*

(Eluate # 2)

*effects on T-lymphoblasts.*

**Table 8.**

117AYDGKDYLT125, 126LNEDLRSWTAV136, and 137DTAAQI142) (**Figure 7C**). Possibly such binding affinity of polyreactive but not monospecific mAbs contributes to

In contrast to polyreactive anti-HLA-E mAb, monospecific mAbs (TFL-033)

To test whether monospecific anti-HLA-E mAbs suppress proliferation of the CD3+, CD4+, or CD8+ T cells, human T lymphocytes (both CD4+ and CD8+) isolated from whole blood of a normal male donor with Ficol Hypaque (31) were treated either with phytohaemagglutinin (PHA, EY Laboratories, San Mateo, CA) at a final concentration of 2.25 mL/mL or not exposed to PHA (31). The mAbs (monospecific mAbs TFL-033, TFL-034, TFL-073, TFL-074, and TFL-216, polyreactive mAb TFL007, and negative control antibodies) were separately added to cells in culture within 2 hours after adding

and TFL-007 bind to shared amino acid sequences of HLA-I molecules

the unique immunoregulatory functions mimicking IVIg [101, 118].

recognized HLA-E- specific amino acid sequences (65RSARDT70 and154AESADNSKQES144) on the α1 and α2 helices (**Figure 7B**).

*<IP_ADDRESS> Monospecific mAbs promote the proliferation of CD8+ T lymphocytes*

*5.7.2 Therapeutic potential of anti-HLA-E monospecific mAbs*

(

**62**

*Immunolocalization of HLA-E in cancer tissues with culture supernatants (s) or ascites (A) of TFL monospecific mAbs compared with staining by MEM-E/02, an HLA-E mAb that shows cross-reactivity to HLA class Ia alleles. (A) Human melanoma paraffin tissue sections stained with the culture supernatants of TFL monospecific MAbs and MEM-E/02. (B) Human gastric cancer (diffused carcinoma) paraffin tissue sections stained with the diluted ascites of monospecific MAb TFL-033a and MEM-E/02. (C). Immunostaining of human gastric adenocarcinoma (A, B) and gastric diffuse carcinoma (C, D) control, stained without primary mAbs. Note the differences in staining between the two antibodies; MEM-E/02 failed to stain any cells while TFL-033a showed intense and widely distributed staining indicative of overexpression of intact HLA-E. (D) Human breast ductal adenocarcinoma stained with monospecific anti-HLA-E mAb TFL-216 generated by immunizing HLA-EG. (source*: U. S. Patent No 10,656,158 B2 (*U.S. patent application No. 13/507,537)* issued on May 19, 2020, to Dr. Mepur H. Ravindranath*) see also Int J cancer. 2014;134(7):1558–70. DOI: 10.1002/ijc.28484.*

**Presence or absence of**

**65**

**CD3+ NAÏVE T-CELLS**

**No PHA**

**CD4+/**

**CD4-/**

**CD4+/**

**CD4-/**

**CD4+/**

**CD4-/**

**CD4+/**

**CD4-/**

**CD4+/**

*CD4-/*

**CD4+/**

**CD4-/**

**CD8-**

3,088

65

*NS*

 *0.018*

 *0.013*

 *NS*

 *NS*

 *NS*

 *0.019*

 *0.006*

 *NS*

 *NS*

 *of CD4+ T*

*lymphoblasts*

 *occurs only after*

*Monospecific and Polyreactive Monoclonal Antibodies against Human Leukocyte Antigen-E*

*…*

 *NS*

 *NS*

 16

 55

 48

 23

*7*

17

 4

 26

*18*

8

 21

*DOI: http://dx.doi.org/10.5772/intechopen.95235*

 667

 1,075

 491

 230

*107*

193

 80

 892

*443*

122

 339

Mean

SD

2-tail p [<]

**Table 9.** *TFL-033 promotes*

*PHA activation.*

*T-lymphoblast*

 *proliferation*

 *of CD8+ naïve T cells and*

*T-Lymphoblasts*

 *in the absence or the presence of PHA. The proliferation*

**CD8+**

**CD8-**

**CD8+**

**CD8-**

**CD8+**

**CD8+**

**CD8-**

**CD8-**

*CD8+*

**CD8+**

**CD8-**

**With PHA**

**No PHA**

**CD3+**

**LYMPHOBLASTS**

**With PHA**

**CD4/CD8**

*Monoclonal Antibodies*

**Table 9.** *TFL-033 promotes T-lymphoblast proliferation of CD8+ naïve T cells and T-Lymphoblasts in the absence or the presence of PHA. The proliferationPHAactivation.*

 *of CD4+ T*

*lymphoblasts*

 *occurs only after*

## *Monospecific and Polyreactive Monoclonal Antibodies against Human Leukocyte Antigen-E… DOI: http://dx.doi.org/10.5772/intechopen.95235*

**Presence or absence of**

**64**

**CD3+ NAÏVE T-CELLS**

**No PHA**

**CD4+/**

**CD4-/**

**CD4+/**

**CD4-/**

**CD4+/**

**CD4-/**

**CD4+/**

**CD4-/**

**CD4+/**

*CD4-/*

**CD4+/**

**CD4-/**

*Monoclonal Antibodies*

**CD8-**

No mAb *[n = 5]*

Mean

SD 2-tail p [<]

**mAb TFL-033** (IgG1) [n = 3]

[1/30]

Mean

SD 2-tail p [<]

[1/150]

Mean

SD 2-tail p [<]

**mAb TFL-007**

[1/10]

Mean

SD 2-tail p [<]

[1/50]

2876

136

*NS*

 *NS*

 *NS*

 *NS*

 *NS*

 *NS*

 *NS*

 *NS*

 *0.027*

 *NS*

 *NS*

 *NS*

 72

 19

 26

 33

*2*

3

 17

 79

*25*

4

 29

 451

 1183

 444

 164

*63*

145

 52

 676

*317*

100

 222

(Polyreactivec

 anti-HLA-E,

 IgG2a) [n = 3]

3238

14

*NS*

 *NS*

 *NS*

*NS*

*0.047*

*0.001*

*0.020*

 *NS*

*0.001*

 *0.003*

*NS*

 *0.001*

 64

 21

 22

 30

*17*

13

 9

 80

*31*

14

 16

 681

 1149

 508

 252

*120*

205

 68

 1266

*572*

157

 412

3185

180

*NS*

 *NS*

 *NS*

*0.009*

*NS*

*0.015*

*NS*

 *0.005*

*0.010*

 *0.016*

*NS*

 *0.014*

 146

 58

 12

 40

*27*

80

 13

 86

*23*

16

 20

 755

 1170

 536

 223

*163*

153

 99

 1129

*505*

152

 412

3063

149

 86

 99

> *<0.0001*

 37

 33

*14*

35

 15

 115 *0.001*

*0*

 *NS*

 *0*

*126*

43

 84

 547

 1249

 475

 197

*65*

141

 52

 867

*325*

128

 289

**CD8+**

**CD8-**

**CD8+**

**CD8-**

**CD8+**

**CD8+**

**CD8-**

**CD8-**

*CD8+*

**CD8+**

**CD8-**

**With PHA**

**No PHA**

**CD3+**

**LYMPHOBLASTS**

**With PHA**

**CD4/CD8**

PHA (final 200 mL) (31). Detailed experimental protocol is described elsewhere (31). The effects of mAbs (monospecific mAb TFL-033 and polyreactive mAb TFL-007) on untreated (no PHA) and PHA-treated T lymphocytes in these categories of T cells: CD4+/CD8-, CD4-/CD8 +, CD4 + /CD8 +, and CD4-/CD8- are presented in **Table 9**. There was a significant increase in numbers of CD4-/CD8+ T lymphoblasts among the PHA-treated T lymphoblasts under the influence of TFL-033 s at 1:30 and 1:150). Numbers of PHA-untreated T lymphoblasts increased for almost all mAbs, TFL-033 s at 1/30 and 1/150, TFL-034 s at 1/10 and 1/50, TFL-073 s at 1/50, TFL-074 s at 1/10 [35]. An increase in PHA-untreated T lymphoblasts clarifies the functional potential of HLA-E monospecific mAbs in augmenting CD4/ CD8+ T lymphoblasts. A significant increase in numbers of PHA-treated CD3+/CD4-/CD8+ lymphoblasts suggests that monospecific monoclonal mAbs, particularly TFL-003 confers the potential to augment cytotoxic T cells. Results prompt investigating humanized version TFL-003 on proliferation cytotoxic T-cells.

cancer cells are correlated with disease progression and poor prognosis [60, 82, 130, 134]. Disease progression is attributed to the suppression of the tumor-killing

*Monospecific and Polyreactive Monoclonal Antibodies against Human Leukocyte Antigen-E…*

Cell surface and soluble HLA-E are capable of binding to the inhibitory receptors CD94 and NKG2A on both CTLs (CD3+/CD8+), NK cells (CD2+, CD7+, CD11b+, CD11c+, CD90+, perforin+, & granzyme A+) and NKT cells (plus CD8+) [25, 27, 135, 136]. These cells are capable of destroying tumor cells. These cells interact with MHC-I ligands (HLA-E) on tumor cells through inhibitory receptors. The binding of above mentioned immune cells to HLA-E overexpressed on tumor cells cell surface may explain why the cancer patients failed to respond to NK cell therapies. Interaction between HLA-E and inhibitory receptors involves the binding of HLA-E specific amino acids located on α1 and α2 helices (**Table 7**) to specific amino acids on CD94 and NKG2A (**Figure 10**) [22, 27, 135, 136]. This specific interaction is attributed to the loss of anti-tumor activity of CD8+ CTLs as well as that of NK or NKT cells [22, 27, 135, 136]. We have used the synthetic peptides of these sequences to ascertain the specific binding affinity of anti-HLA-E mAbs (**Figure 8**). The ability of monospecific anti-HLA-E mAbs to bind at the site of epitopes of CD94 and NKG2A on HLA-E favor the use of the monospecific anti-HLA-E mAbs to mask binding sites of inhibitory receptors on HLA-E. Such blocking of HLA-E may help restore the antitumor efficacy of NK cells and CD8+ T cells that were lost due to the interaction of inhibitory receptors and HLA-E. Possibly humanized monospecific

activity of CD8+ cytotoxic T lymphocytes (CTLs) and NKT cells.

*DOI: http://dx.doi.org/10.5772/intechopen.95235*

anti-HLA-E may be potentially considered for anti-cancer NK therapy.

tool, than those binding to specific but cryptic epitopes.

The anti-HLA-E mAbs TFL- 033, TFL-034, TFL-073, and TFL-074 due to their monospecificity are advantageous than the commercial anti-HLA-E mAbs for specific identification and localization of HLA-E on the surface of human cells, particularly in different cancer types. Our observations stress the need for characterization of monospecificity and epitope specificity of any mAb, after analyzing binding affinity on a multiplex solid matrix assays coated with the desired antigen (in question) and the closely related antigens and inhibition of the binding affinity using peptides sequences specific for the antigen in question. This is an important criterion to be followed for all clinical diagnostic and therapeutic antibodies. If specific epitopes are exposed to antigen located on the cell surface, it would be a more valuable diagnostic

The HLA-E monospecific antibodies (e.g., TFL-033) are capable of augmenting proliferation of non-activated CD8+ T cells and activated CD8+ T-lymphoblasts. TFL-033 binds to a unique epitope of HLA-E, a region that is involved in binding to inhibitory receptors (CD94 and NKG2A) present on CD3+/CD8+ T cells (Cytotoxic T cells) and CD3-/CD8+ NKT cells and NK cells. The binding of HLA-E to inhibitory receptors results in the suppression of anti-tumor cytotoxic functions of these immune cells. *Since TFL-033 can also upregulate anti-tumor cytotoxic T cell lymphoblasts and also capable of blocking the interaction between cancer-associated HLA-E and inhibitory receptors CD94/NKG2A, the mAb can be considered as a double-edged sword to eliminate cancer cells.* Therefore, TFL-033 could be a valuable therapeutic agent for passive immunotherapy of human cancer, provided the mAb is humanized.

In contrast to monospecific mAbs, HLA-I polyreactive anti-HLA-E monoclonal Abs (TFL-006 and TFL-007) mimic not only HLA-I reactivity of IVIg but also performs several critical immunoregulatory functions of IVIg, better than IVIg *per se*. These functions include suppression of blastogenesis and proliferation of CD4+ T cells and CD8+ T cells, effective inhibition of production of anti-HLA-I and

**6. Conclusion**

**67**


**Antimicrobial Resistance Profiles of** *Escherichia coli* **from Diarrheic Weaned Piglets after the Ban on Antibiotic Growth Promoters in Feed**

#### **Do Kyung-Hyo 1 , Byun Jae-Won 2 and Lee Wan-Kyu 1,\***

Received: 3 October 2020; Accepted: 27 October 2020; Published: 29 October 2020

**Abstract:** This study aimed to survey the antimicrobial resistance profiles of 690 pathogenic *Escherichia coli* isolates obtained from Korean pigs with symptoms of enteric colibacillosis between 2007 and 2017, while assessing the change in antimicrobial resistance profiles before and after the ban on antibiotic growth promoters (AGPs). Following the Clinical and Laboratory Standards Institute guidelines, the antimicrobial resistance phenotype was analyzed through the disk diffusion method, and the genotype was analyzed by the polymerase chain reaction. After the ban on AGPs, resistance to gentamicin (from 68.8% to 39.0%), neomycin (from 84.9% to 57.8%), ciprofloxacin (from 49.5% to 39.6%), norfloxacin (from 46.8% to 37.3%), and amoxicillin/clavulanic acid (from 40.8% to 23.5%) decreased compared to before the ban. However, resistance to cephalothin (from 51.4% to 66.5%), cefepime (from 0.0% to 2.4%), and colistin (from 7.3% to 11.0%) had increased. We confirmed a high percentage of multidrug resistance before (95.0%) and after (96.6%) the ban on AGPs. The *AmpC* gene was the most prevalent from 2007 to 2017 (60.0%), followed by the *blaTEM* gene (55.5%). The *blaTEM* was prevalent before (2007–2011, 69.3%) and after (2012–2017, 49.2%) the ban on AGPs. These results provide data that can be used for the prevention and treatment of enteric colibacillosis.

**Keywords:** *Escherichia coli*; antimicrobial resistance; swine; weaned piglet; antibiotic growth promoters

### **1. Introduction**

Weaned piglets are vulnerable to disease for many reasons such as changes in environmental conditions, a decline in maternal antibody titers, and various stresses. Presently, enteric colibacillosis, such as postweaning diarrhea and/or edema disease, is frequent in swine farms [1]. *Escherichia coli* (*E. coli*) in weaned piglets can result in serious economic losses due to diarrhea, growth retardation, and increased mortality [2].

Antimicrobials are used to treat colibacillosis in diarrheic weaned piglets. They play a significant role in the prevention and treatment of diseases and have also been used as a feed additive to promote swine growth [3]. However, the widespread and indiscriminate use of antimicrobials has resulted in the emergence of antimicrobial-resistant bacteria and the appearance of antibiotic residues in meat products [4]. Antimicrobial-resistant bacteria represent a threat to the successful treatment of disease in swine farms. As such, antibiotic growth promoters (AGPs) in feeds were banned in Korea in July 2011 [5].

Due to the emergence of antimicrobial resistance as a global problem, Denmark [6], Japan [7], and Canada [8] have started to formally monitor the state of antibiotic resistance. The antimicrobialresistant profiles of pathogenic *E. coli* are changing based on geographical and temporal variations and

previous exposures to antimicrobial agents [9]. Thus, devising control measures for colibacillosis in piggeries requires data regarding the prevalence of antimicrobial susceptibility [9].

Information on the antimicrobial resistance of pathogenic *E. coli* will be useful in establishing treatment and prevention strategies for colibacillosis in the swine industry. Although there have been many studies on the antimicrobial resistance of *E. coli* [10–12], few studies have examined annual trends in antimicrobial resistance profiles of *E. coli* over a decade. Additionally, there was little data comparing the changes in antimicrobial resistance profiles before and after the ban on AGPs. This study aimed to survey the antimicrobial resistance profiles of 690 pathogenic *E. coli* isolates obtained from Korean pigs with symptoms of enteric colibacillosis between 2007 and 2017, while assessing the change in antimicrobial resistance profiles before and after the ban on AGPs.

#### **2. Results**

#### *2.1. Antimicrobial Susceptibility Test*

The results of the antimicrobial susceptibility tests are shown in Table 1. We confirmed high resistance rates to tetracycline (598 isolates, 86.7%), ampicillin (586 isolates, 84.9%), streptomycin (589 isolates, 85.4%). On the other hand, the strains showed low resistance to cefepime (10 isolates, 1.4%) and colistin (68 isolates, 9.9%).

After the ban on AGPs, resistance to gentamicin (68.8% to 39.0%), neomycin (84.9% to 57.8%), ciprofloxacin (49.5% to 39.6%), norfloxacin (46.8% to 37.3%), and amoxicillin/clavulanic acid (40.8% to 23.5%) decreased with respect to that before the ban. There was a rapid decline in the resistance rates of those antimicrobials between 2012 to 2013 and 2014 to 2015. The resistance rates of gentamicin, ciprofloxacin, and norfloxacin in 2012–2013 were 62.4%, 59.6%, and 58.2%, respectively. However, those were decreased to 35.3%, 29.4%, and 27.1% in 2016–2017, respectively. On the other hand, the resistance rate of cephalothin (51.4% to 69.5%) increased, with an emergence of resistance to cefepime (0.0% to 2.1%) after the ban on AGPs. When we compared the resistance to colistin between 2012 to 2013 and 2014 to 2015 using a Chi-square test, there was a significant increase in resistance to colistin (7.1% to 19.9%, *p* < 0.01).

### *2.2. Multidrug Resistance*

The results of the analysis of multidrug resistance rates are shown in Table 2. Before and after the ban on AGPs, the percentage of isolates resistant to seven subclasses were 22.9% and 21.0%, respectively, which were the most prevalent. Before the ban on AGPs, 1.4%, 6.4%, and 10.6% of isolates showed patterns of resistance to three, four, and eight antimicrobial subclasses, respectively. These rates increased to 4.0%, 9.3%, and 16.1% after the ban. However, rates of resistance to 6 and 10 subclasses decreased from 18.3% to 14.8% and from 11.9% to 7.4%, respectively. In terms of multidrug resistance for those with resistance to 3 or more subclasses of drugs among the 12 subclasses of drugs tested, 207 (95.0%) strains before the ban on AGPs, and 456 (96.6%) strains after the ban on AGPs, respectively, showed multidrug resistance.

### *2.3. Antimicrobial Resistance Genes*

Table 3 shows the prevalence of antimicrobial resistance genes of *E. coli* isolated from diarrheic weaned piglets before and after the ban on AGPs. The *AmpC* gene was the most prevalent from 2007 to 2017 (414 isolates, 60.0%), followed by the *blaTEM* gene (383 isolates, 55.5%). Although the percentage decreased after the ban on AGPs, the *blaTEM* gene was still prevalent in 2007–2011 (69.3%) and 2012–2017 (49.2%). Before the ban on AGPs, only five isolates (2.3%) encoded the *blaSHV* gene. However, after the ban on AGPs, 29 isolates (6.1%) tested *blaSHV* positive. Additionally, there was the emergence of *blaCTX-M* group 2 after the ban on AGPs (0.0% to 1.7%). There was no isolate encoding the *mcr*-2 gene in this study. Rates of the *tetA* gene increased annually. From 2007 to 2011, there were 46 (21.1%) *tetA*-gene-encoding isolates. However, it increased to 32.4% (153 isolates) in 2012–2017.

*Antibiotics* **2020**, *9*, 755

1

 Significant differences between before (2007–2011) and after (2012–2017) the ban on antibiotic growth promoters in feed were expressed as \* (*p* < 0.05) and \*\* (*p* < 0.01); GM: gentamicin, S: streptomycin, N: neomycin, CF: cephalothin, CZ: cefazolin, FEP: cefepime, FOX: cefoxitin, NA: nalidixic acid, CIP: ciprofloxacin, NOR: norfloxacin, AM: ampicillin, AMC: amoxicillin/clavulanic acid, SXT: trimethoprim/sulfamethoxazole, C: chloramphenicol, CL: colistin, TE: tetracycline. 2 Difference of the subtotals before (2007–2011) minus after (2012–2017)

the ban on antibiotic growth promoters in feed. 3 BL/BLI: β-lactam/β-lactamase inhibitor combination. 4 FPI: Folate pathway inhibitors.

1 Significant differences between before (2007–2011) and after (2012–2017) the ban on antibiotic growth promoters in feed were expressed as \*\* (*p* < 0.01) Antimicrobial subclass are defined by the Clinical and Laboratory Standards Institute. 2 Difference of the subtotals before (2007–2011) minus after (2012–2017) the ban on antibiotic growth promoters in feed.

111

subtotals before (2007–2011) minus after (2012–2017) the ban on antibiotic growth promoters in feed.

#### **3. Discussion**

Antibiotics are used in intensive pig production systems to control infectious diseases. This widespread use is suspected to be a major cause of antimicrobial resistance [13]. To treat colibacillosis, antimicrobial agents including broad-spectrum-activity drugs, such as β-lactams and fluoroquinolones, are frequently used in veterinary medicine [14]. Usage of antimicrobial agents could be a cause for increasing antimicrobial resistance [15]. Thus, diseased animals might constitute an important reservoir of antimicrobial resistance [14].

In the previous study, we isolated 690 pathogenic *E. coli* strains from weaned piglets showing signs of enteric colibacillosis from 2007 to 2017, and investigated these isolates for adherence (F4, F5, F6, F18, F41, eae, paa, AIDA-I) and toxin (LT, STa, STb, Stx2e, EAST-I) genes [4]. Further, in this study, we tested antimicrobial resistance phenotypes and genotypes. We sought to provide data on the annual antimicrobial resistance profiles in Korean pig farms.

The frequencies of resistance to antimicrobials (gentamicin: 48.4%, neomycin: 66.4%, nalidixic acid 70.3%, ampicillin 84.9%, trimethoprim/sulfamethoxazole 60.9%, and tetracycline 86.7%) observed in this study are clearly higher than the EU average resistance figures (nalidixic acid 59.8%, ampicillin 58.0%, and tetracycline 47.1%), and the USA average resistance figures (gentamicin: 23.9%, neomycin 33.8%, ampicillin 68.1%, and trimethoprim/sulfamethoxazole 22.0%) [16,17].

Caution must be exercised when comparing such data because of the differences in methodologies used, particularly with the use of Clinical and Laboratory Standards Institute (CLSI) clinical breakpoints in this study compared with the use of epidemiological cut-off values (ECOFF's) in the EFSA (European Food Safety Authority) report. The results of this study are based on multiple isolates from diseased piglets. In contrast, the EFSA data were gathered mainly from the national monitoring program based on the sampling of healthy porcine carcasses at slaughter with all samples derived from distinct epidemiological units. The differences in resistance data across countries are not completely unexpected, as intensive pig production throughout Europe operates to different standards and utilizes distinct management practices.

Additionally, the pathogenic *E. coli* strains isolated in this study exhibited high resistance to ampicillin (84.9%), tetracycline (86.7%), and streptomycin (85.4%). This result is similar to the results of studies published in Denmark [6], Japan [7], and Canada [8]. The comparisons of resistance rates to the antimicrobials tested revealed that the isolates were more frequently resistant to ampicillin, tetracycline, and streptomycin—drugs that have been extensively used in large quantities in Korea [18]. Similar results have been described in other Korean reports. Cho et al. reported that the rate of resistance to tetracycline was the highest (97.8%), followed by ampicillin (89.1%) [19]. Lim et al. also reported a high resistance rate of *E. coli* to tetracycline (76.1%), ampicillin (64.6%), and streptomycin (58.4%) [10]. However, the resistance rates reported in this study were higher than those reported by Lim et al. [10]. This might be due to the differences in the origins of the isolates. We isolated from weaned piglets showing symptoms of colibacillosis, however, Lim et al. isolated from healthy pigs. According to the Korean national antimicrobial resistance monitoring systems, pathogenic bacteria tend to be more resistant to antimicrobials than bacteria isolated from normal livestock [20]. Lim et al. [10] assessed the resistance rates of *E. coli* from normal livestock; whereas in this study, we tested the antimicrobial resistance of pathogenic *E. coli* encoding at least one or more virulence factors isolated from pigs with diarrhea.

After the ban on AGPs, resistance rates to gentamicin had dramatically decreased (68.8% to 39.0%). Additionally, there was the emergence of resistance to cefepime (0.0% to 2.1%) and an increase in resistance rates to cephalothin (51.4% to 69.5%) and colistin (7.3% to 11.0%). Antimicrobial resistance is dependent on the level of antimicrobial usage [4]. The sales for antimicrobial agents that are usually used for growth promotion in Korea decreased from 2010 to 2017. Antimicrobial classes, tetracyclines and aminoglycosides, sold as much as 283,865 kg and 58,975 kg in 2010, respectively. However, this decreased to 254,541 kg and 50,503 kg in 2017, respectively. On the other hand, the sales for cephalosporins and phenicols, which are frequently used to treat enteric diseases in swine, increased

from 2010 (4980 kg, and 63,882 kg, respectively) to 2017 (11,312 kg, and 114,716 kg, respectively) [21]. These changes in sales for antimicrobial agents could affect the resistance of isolates. Due to the rare occurrence of bacteria with resistance to it, as well as a paucity of horizontal transmission of resistance mechanisms, colistin has been regularly used for the treatment of enteric colibacillosis [22]. Additionally, the World Health Organization (WHO) has classified colistin as one of the "Highest Priority Critically Important Antimicrobials" in humans [23]. Recently, the plasmid-mediated colistin resistance gene, *mcr* was reported in Korea [24,25]. The observed increase in resistance rates could be attributed to this *mcr* gene. Increased resistance to colistin could pose serious problems not only in veterinary medicine but also in public health. Restrictions on the use of colistin are required to reduce resistance rates.

We confirmed a high frequency of multidrug resistance before (207 isolates, 95.0%) and after (456 isolates, 96.6%) the ban on AGPs. Due to the different types of antimicrobial tested, it is hard to directly compare multidrug resistance rates in comparison to the multidrug resistance rates (30.9%) of pigs with *E. coli* of US origin [26], the multidrug resistance rates of Korean piglets were very high. Since the regulation of the use of antimicrobials is not as strict in Korea as it is in other developed countries, it is considered that the use of antimicrobials by nonspecialists such as livestock workers and not veterinarians might be the cause of this phenomenon [18].

*E. coli* develop resistance mechanisms by using instructions provided by their DNA. Often, antimicrobial resistance genes are found within plasmids. This means that some bacteria can share their antimicrobial resistance genes and make other bacteria become resistant [3]. Extended spectrum β-lactamases (ESBLs) have been reported worldwide, most frequently in Enterobacteriaceae. ESBLs and *AmpC* are plasmid-encode, which are capable of inactivating a large number of beta-lactam antibiotics [15]. Additionally, colistin has been regularly used for the treatment of enteric colibacillosis such as postweaning diarrhea and edema disease due to the rare existence of resistant bacteria and lacking horizontal transmission mechanisms. However, very recently, the plasmid-mediated colistin resistance gene, *mcr* was found in Korea [5].

The *AmpC* gene encodes cephalosporinases and gives rise to serious therapeutic challenges in veterinary medicine. In this study, 414 isolates (60.0%) were positive for *AmpC* gene. β-lactam resistance in *E. coli* generally occurs as a result of the deregulation of the putative *AmpC* gene or the acquisition of a mobile genetic element containing an *AmpC* gene [15]. Consequently, high rates of ampicillin-resistant isolates are to be expected. In this study, we found that there was a high rate of resistance to ampicillin (84.9%).

Both TEM and SHV enzymes belong to the class A family of β-lactamases and are widely disseminated among the *Enterobacteriaceae* from veterinary sources [27] In this study, *blaTEM* was identified in over half the porcine *E. coli* isolates. However, *blaSHV* was detected in only 4.9% of isolates. There was also a decrease in the frequency of *blaTEM* after the ban on AGPs. TEM enzymes often co-exist with CTX-M enzymes in bacteria of animal origin [28]. However, the number of *blaCTX-M* group-positive isolates in this study was low (group 1: 13 isolates, group 2: 8 isolates, group 9: 18 isolates, total: 39 isolates). These results are of interest considering the current epidemiology of these genes.

We also confirmed that the antimicrobial resistance varied according to regions (Tables S1–S3). Resistance to chloramphenicol, gentamicin, neomycin, nalidixic acid, ciprofloxacin, and trimethoprim/ sulfamethoxazole was higher in the northern farms than middle and southern farms after the ban on AGPs. The antimicrobial resistance phenotypes, and the multidrug resistance rates and prevalence of the *tetA* gene increased after the ban on AGPs in the northern farms, unlike in the middle and southern farms. This result is probably due to the fact that more antimicrobials were used in the northern farms than in the middle and southern farms. From 2001 in Korea, sales of antimicrobials for domestic livestock products and fisheries by use, breed, and antimicrobials were analyzed by a comprehensive management system created by the Korean Animal Health Products Association [20]. However, the analyses were not carried out by region [10]. The WHO/FAO/OIE stresses that overall sales,

livestock, annual, periodic, and regional sales data for the use of antimicrobials that can be compared internationally and shared together should be investigated in a standardized way and the results should be expressed in standardized units [29]. Proper risk assessment, guidelines, and policy decisions for antimicrobial resistance management require data on the use of antimicrobials in each region. Therefore, in order to obtain a more accurate use of antimicrobials, it will be necessary to classify antimicrobials used in livestock production in accordance with the International Standards Classification Act and to develop a surveillance system that can further refine the methods of investigation (such as route of administration, breeding, breeding stage, and disease), to enter all prescribed antimicrobial agents, and to train clinical veterinarians.

In this study, we analyzed and compared the antimicrobial resistance phenotypes and genotypes of *E. coli* isolated from Korean diarrheic weaned piglets before and after the ban on AGPs. The trend in our findings suggests a decrease in resistance to gentamicin, neomycin, ciprofloxacin, norfloxacin, and amoxicillin/clavulanic acid after the ban on AGPs. However, resistance to cephalothin, cefepime, and colistin increased. Additionally, there was still a high frequency of multidrug-resistant isolates. Among the tested antimicrobial resistance genes, *AmpC* and *blaTEM* genes were the most prevalent. After the ban on AGPs, the frequency of the *AmpC* gene increased. On the other hand, the frequency of the *blaTEM* gene decreased. These results provide data that can be used for the prevention and treatment of enteric colibacillosis, as well as important data for assessing the impact of banning AGPs on the antimicrobial resistance profiles of *E. coli* isolates. Further studies are needed to determine the specific association of exposures to antimicrobial agents with antimicrobial resistance profiles.

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

#### *4.1. Escherichia coli Isolates*

Between 2007 and 2017, 690 *E. coli* isolates were obtained from weaned piglets showing symptoms of enteric colibacillosis and/or edema disease. The sampled farms consisted of 150 different pig herds (50 to 100 sows per herd) located in 3 areas: the northern (35 farms encompassing the Gangwon, Gyeonggi, and Incheon provinces), middle (46 farms, Chungbuk and Chungnam provinces), and southern (69 farms, Chonbuk, Chonnam, Gyeongbuk, and Gyeongnam provinces) areas of South Korea. The strains were not collected repeatedly from the same farm. The aseptically collected intestinal contents and feces were inoculated on a MacConkey agar (Becton Dickinson, Sparks, MD, USA) and blood agar (Asan Pharmaceutical, Seoul, Korea). After overnight incubation at 37 ◦C, only pure or nearly pure cultured colonies were selected and transferred to blood agar. Suspected colonies were identified as *E. coli* using the VITEK 2 GN ID card via VITEK II system (bioMéreiux, Marcy l'Etoile, France). The isolates were stored in 20% glycerol at −70 ◦C for further experimentation.

#### *4.2. Antimicrobial Susceptibility Test*

The following 16 antimicrobials were selected following the marketing amounts for animal use in Korea: gentamicin (10 µg), streptomycin (10 µg), neomycin (30 µg), ampicillin (10 µg), amoxicillin/clavulanic acid (20/10 µg), cephalothin (30 µg), cefoxitin (30 µg), cefazolin (30 µg), cefepime (30 µg), nalidixic acid (30 µg), ciprofloxacin (5 µg), norfloxacin (10 µg), sulfamethoxazole/trimethoprim (23.75/1.25 µg), chloramphenicol (30 µg), colistin (10 µg), and tetracycline (30 µg). Each antimicrobial disc was purchased from Becton-Dickinson (Sparks, MD, USA). Antimicrobial susceptibility testing was carried out using the Kirby Bauer disk diffusion method [30]. The isolates were inoculated on Mueller-Hinton agar (Becton-Dickinson). The antimicrobial discs were dropped on the agar and incubated at 37 ◦C for 18 h. *Escherichia coli* ATCC 25922 was used for quality control of the experiment. The interpretation of zone of inhibition was determined according to the CLSI standards *Enterobacteriaceae* breakpoints [31]. Intermediate isolates were grouped with resistant isolates. CLSI classified antimicrobial agents by class including several subclasses [31]. We categorized

antimicrobial agents according to CLSI antimicrobial subclasses. Strains resistant to 3 or more CLSI subclasses of drugs were considered as multidrug-resistant strains.

## *4.3. Antimicrobial Resistance Genes*

The *E. coli* genes for antimicrobial resistance were amplified by polymerase chain reaction (PCR) analysis. Bacterial colonies were suspended in 200 µL of distilled water and boiled for 10 min. After centrifugation at 8000× *g*, the supernatant was used as a template for PCR. We tested colistin-resistant *mcr* genes [5], tetracycline-resistant *tetA* genes [32], ampicillin-resistant *AmpC* genes [33], and extended-spectrum β-lactamase: *blaTEM*, *blaSHV*, *blaOXA*, *blaCTX-M* group 1, group 2, and group 9 [34], according to previously-described protocols (Supplementary Table S4). Bacterial colonies were suspended in 200 µL of distilled water and boiled for 10 min. After centrifugation at 8000× *g*, the supernatant was used as a template. The reaction volume, 20 µL, was composed of 2× EmeraldAmp Master Mix (Takara, Otsu, Japan), 2 µM of each primer, and 2 µL of template DNA. PCR product was electrophoresed on 2% agarose gel using Mupid-exU AD140 (Takara), stained with Ethidium bromide, and visualized on a UV trans-illuminator.

### *4.4. Statistical Analysis*

All statistical analyses were performed using SPSS version 12.0 program (SPSS inc., Chicago, IL, USA). To compare antimicrobial resistance before and after the ban on AGPs, chi-square test was performed.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2079-6382/9/11/755/s1, Table S1. Antimicrobial-resistant pathogenic Escherichia coli isolates (%) from weaned piglets with diarrhea in northern, middle, and southern Korean farms before and after the ban on AGPs in feed, Table S2. Multidrug-resistant pathogenic Escherichia coli isolates (%) from weaned piglets with diarrhea in northern, middle, and southern Korean farms before and after the ban on AGPs in feed, Table S3. Antimicrobial resistance genes (%) of pathogenic Escherichia coli from weaned piglets with diarrhea in northern, middle, and southern Korean farms before and after the ban on AGPs in feed, Table S4. Primers for detection of antimicrobial resistance genes.

**Author Contributions:** Conceptualization: D.K.-H., B.J.-W., and L.W.-K.; methodology: B.J.-W.; validation: D.K.-H.; formal analysis: D.K.-H. and B.J.-W.; investigation: D.K.-H.; resources: B.J.-W. and L.W.-K.; data curation: D.K.-H.; writing/original draft preparation: D.K.-H.; writing, reviewing and editing: D.K.-H., B.J.-W., and L.W.-K; visualization: D.K.-H.; supervision: L.W.-K; project administration: L.W.-K; funding acquisition: L.W.-K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by "Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET) through Agriculture, Food and Rural Affairs Convergence Technologies Program for Educating Creative Global Leader, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) (grant number: 320005-4)".

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

### **References**

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