**Spatio-Temporal Variability of the Habitat Suitability Index for the** *Todarodes pacificus* **(Japanese Common Squid) around South Korea**

#### **Dabin Lee 1, Seung Hyun Son 2, Chung-Il Lee 3, Chang-Keun Kang 4 and Sang Heon Lee 1,\***

Received: 21 October 2019; Accepted: 18 November 2019; Published: 20 November 2019

**Abstract:** The climate-induced changes in marine fishery resources in South Korea have been a big concern over the last decades. The climate regime shift has led to not only a change in the dominant fishery resources, but also a decline in fishery landings in several species. The habitat suitability index (HSI) has been widely used to detect and forecast fishing ground formation. In this study, the catch data of the *Todarodes pacificus* (Japanese Common Squid) and satellite-derived environmental parameters were used to estimate the HSI for the *T. pacificus* around South Korea. More than 80% of the total catch was found in regions with a sea surface temperature (SST) of 14.91–27.26 ◦C, sea surface height anomaly (SSHA) of 0.05–0.20 m, chlorophyll-a of 0.32–1.35 mg m−3, and primary production of 480.41–850.18 mg C m−2 d−1. Based on these results, the HSI model for *T. pacificus* was derived. A strong positive relationship (R2 = 0.9260) was found between the HSI and the fishery landings. The climatological monthly mean HSI from 2002 to 2016 showed several hotspots, coinciding with the spawning and feeding grounds of *T. pacificus*. This outcome implies that our estimated HSI can yield a reliable prediction of the fishing ground for *T. pacificus* around South Korea. Furthermore, the approach with the simple HSI model used in this study can be applied elsewhere, and will help us to understand the spatial and temporal distribution of fishery resources.

**Keywords:** Japanese common squid; *Todarodes pacificus*; habitat suitability index (HSI); the Yellow Sea; the South Sea of South Korea

#### **1. Introduction**

*Todarodes pacificus* (Japanese Common Squid) is a commercially important fish species in the South Korea, and is widely distributed around the Korean Peninsula. The fishery production value of *T. pacificus* accounts for about 10% of total fishery production value in South Korea [1]. The commercial catch of *T. pacificus* is mainly performed by jigging and trawling [1]. *T. pacificus* is known as a migratory species which usually migrates to the Yellow Sea and the East/Japan Sea for feeding and moves back to the East China Sea for spawning [2–5]. The community of *T. pacificus* around the Korean Peninsula can be divided into two groups based on the spawning seasons [2–5]. The groups can be expressed as the 'winter spawners' and 'autumn spawners'. Although the spawning ground of both groups are distributed around the South Sea and the East China Sea, their feeding grounds are distributed differently [2–5]. Many previous studies reported that the feeding grounds of *T. pacificus* in Korean waters are located mainly in the Yellow Sea and the East/Japan Sea for 'winter spawners' and 'autumn

spawners', respectively [2–5]. The main prey of the juvenile *T. pacificus* is the zooplankton. The main food source of adult squid is small pelagic fishes. Moreover, the zooplankton is an important food source for the small pelagic fishes Consequently, chlorophyll-*a* (Chl-*a*) concentration and primary production will have an indirect relationship with the distribution of *T. pacificus*, thus they will be useful indicator of habitat distribution of the *T. pacificus*.

In the 1980s, the dominant fishery resources were saury (*Cololabis saira*), cod (*Gadus macrocephalus*), and walleye pollock (*Gadus chalcogramma*) [6,7]. However, in the 1990s, the dominant fish catch was changed to squid (*Todarodes pacificus*) [6,7]. There could be many reasons for the recent changes in fishery resource such as climate change and overfishing [8–11]. However, the main causes for the recent changes still remain unclear. To examine the recent changes in fishery resources in South Korea, we need to understand the relationship between major environmental parameters and the species' habitat formation.

The habitat suitability index (HSI) has been widely used to investigate the distribution of marine fish population [12–17]. The development of HSI would allow us to obtain a useful database to establish a resource management strategy [12,18,19]. Generally, the species abundance has a strong dependency on environmental conditions, and each species has a different environmental preference [20–22]. The HSI model can estimate the relative abundance of a species through this approach. A general form of the HSI model is composited of a number of suitability indices based on relationships between environmental factors and species' abundance. The composited HSI index is a non-dimensional value ranging from 0 to 1. Since the population dynamics of marine fishes are very complex and the HSI model is defined by a few key factors such as sea surface temperature (SST), Chl-*a*, and salinity [14], selecting the main parameters is a significant process in the HSI model derivation. However, several studies already have reported that the spatial distribution of the marine fish population is largely influenced by several environmental variables such as temperature, salinity, chlorophyll-*a* (Chl-*a*) concentration, and sea surface height anomaly in other regions [14,23–25]. Therefore, the derivation of the HSI model will be possible by considering several environmental conditions including these environmental variables. Moreover, the simplified HSI model with a few environmental variables will allow us to understand the spatial and temporal variations of the habitat distribution of the *T. pacificus*.

The objectives of this study can be divided mainly into three goals: (1) to investigate the preferred environmental conditions of the *T. pacificus* using the satellite dataset, (2) to develop the HSI model for the *T. pacificus* around South Korea, and (3) to investigate seasonal and spatial variations of the HSI in the Korean waters.

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

#### *2.1. Fishery Data*

The commercial fishing records for the *T. pacificus* around the South Sea from 2010 to 2016 were obtained from the Large Purse Seine Fishery Cooperatives of South Korea. The total number of reported catches was 9401, and the dataset contains locations and dates, as well as the amount of catch (metric ton, M/T) (Figure 1). The fishing locations were collected at a spatial resolution of 0.17 × 0.17 degrees (latitude × longitude).

**Figure 1.** Summarized commercial catch data for the *T. pacificus* during 2010 to 2016.

#### *2.2. Satellite Dataset*

We obtained the satellite ocean color data from theModerate Resolution Imaging Spectroradiometer (MODIS) onboard the satellite Aqua platform provided by the Ocean Biology Processing Group at NASA Goddard Space Flight Center (https://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua/Mapped/8-Day/4km/). We used the MODIS Level-3 8-day composite data from July 2002 to December 2016 at 4 km of spatial resolution covering the South Korea and nearby oceans. SST, Chl-*a*, photosynthetically available radiation (PAR), and diffuse attenuation coefficient at 490 nm (Kd(490)) datasets were used in this study [26–29].

The primary production was derived using a regional algorithm based on the Vertically Generalized Productivity Model (VGPM) [30] with satellite ocean color data described as:

$$\rm{PP}\_{\rm eu} = 0.66125 \times \rm{P}\_{\rm opt}^{\rm B} \times \left[ \rm{E}\_{0}/\rm{E}\_{0} + 4.1 \right] \times \rm{Z}\_{\rm eu} \times \rm{Chl-a} \times \rm{DL} \tag{1}$$

where PPeu is the daily primary production integrated from euphotic depth (mg C m−2 d−1), PBopt is the optimal carbon fixation rate (mg C (mg Chl)−1 h−1), E0 is the amount of incident photosynthetically available radiation (PAR) during the day (E m−2 d−1), Zeu is the euphotic depth (m) which is derived by the equation 4.6/Kd(490) that represent 1% penetration depth of 490 nm radiation [31], Chl-*a* is the concentration of chlorophyll-a (mg Chl m−2), and DL is the photoperiod (h) which is computed with latitude and date mathematically.

The PBopt is derived from a multiple regression equation with SST and Chl-*a* [32] as follows:

$$P\_{\rm opt}^B = \frac{0.071 \times \text{SST} - 3.2 \times 10^{-3} \times \text{SST}^2 + 3.0 \times 10^{-5} \times \text{SST}^3}{\text{Chl-a}} + \left[1.0 + 0.17 \times \text{SST} - 2.5 \times 10^{-3} \times \text{SST}^2 - 8.0 \times 10^{-5} \times \text{SST}^3\right] \tag{2}$$

In addition to the ocean color data, the sea surface height anomaly (SSHA) datasets were also used for the physical environmental variables in this study. The satellite altimetry products have been produced by SSALTO/DUACS and distributed by AVISO with support from Centre National d'Etudes Spatiales (CNES). We obtained the SSALTO/DUACS SSHA datasets on a Cartesian grid with spatial resolution of 1/4 × 1/4 degrees.

To investigate the environmental conditions when fishing occurred, we extracted mean values in 3 × 3 pixels of each environmental parameter on every catch point from the satellite dataset. The number of catch data matched with the satellite dataset was 5960 (Table 1). Many parts of the satellite data were unavailable due to cloudiness, and thus, only approximately 63% of the catch data was matched with the satellite data.

**Table 1.** The number of commercial catch data matched with satellite datasets.

#### *2.3. HSI Model*

Many previous studies derived HSI models with several environmental variables for a single species [14,16,17,33]. In this study, we chose SST, SSHA, Chl-*a*, and primary production (PP) to calculate HSI. Primary production is a very important factor for biological productivity, so it can be an important factor in determining habitats of marine fishes.

The SIs for four parameters were calculated based on Chen et al. [14]. Each SI was estimated as a value between 0 and 1. The SIs were estimated by using an equation as follows:

$$\text{SI} = \exp(\mathbf{A} \times (\mathbf{X} + \mathbf{B})^2) \tag{3}$$

where A and B are constants, and X is the values for the parameters. After deriving the SIs for four parameters, the indices were combined into a single index.

When combining the SIs for the four variables, several types of models, such as the Continued Product Model (CPM), Minimum Model (MINM), Arithmetic Mean Model (AMM), or Geometric Mean Model (GMM), could be used [14,30–38]. However, several previous studies already reported that the AMM is the most appropriate model for the HSI of several fish species [14,16,17,33]. Thus, we used the AMM as described below:

$$\text{HSI} = (\text{SI}\_1 \times \text{SI}\_2 \times \text{SI}\_3 \times \text{SI}\_4)/4 \tag{4}$$

#### **3. Results**

#### *3.1. Preferred Environmental Conditions of the Todarodes Pacificus*

Monthly distributions of the number of fishing records and the amount of fishery landings showed a high seasonal dependency. Most of the catches were occurred on January, August, and December (Figure 2).

**Figure 2.** Monthly distribution of total fishery landings (M/T) and the number of fishing records.

The *T. pacificus* around South Korea were distributed in a wide range of environmental conditions. Our match-up results showed that the squid catches were distributed in SST ranging from 5.11 to 31.94 ◦C, SSHA from –0.15 to 0.46 m, Chl-*a* from 0.15 to 25.43 mg m−3, and PP from 258.53 to 1209.75 mg C m−2 d−1 (Figure 3). Based on Kaschner et al. [39], we defined the optimum ranges of the three parameters as the 10th percentile and the 90th percentile of each parameter. The optimum ranges were 14.91–27.26 ◦C, 0.05–0.20 m, 0.32–1.35 mg m−3, and 480.41–850.18 mg C m−2 d−1, for SST, SSHA, Chl-*a*, and PP, respectively. In the optimum conditions of SST, SSHA, Chl-*a*, and PP, the accounted amount of total catches was approximately 80% for each factor.

**Figure 3.** Distribution of the suitability index of (**a**) sea surface temperature (SST) (◦C), (**b**) sea surface height anomaly (SSHA) (m), (**c**) chlorophyll-*a* (Chl-*a*) (mg m−3), and (**d**) primary production (PP) (mg C m−2 d−1) on the fishing locations for the *T. pacificus*. Gray square represents an optimum range for each parameter.

Among these results, double peaks are found in the frequency distributions of SST and SSHA. To derive an appropriate suitability index model, the seasonal frequency distribution was derived from

the results (Figure 4). The distribution of suitability index during winter to spring, and summer to autumn showed a similar SST and SSHA range. Based on these results, the suitability index distributions were analyzed on two periods (winter-spring and summer-autumn; Figure 5). Each period showed remarkably different peaks of SST and SSHA. Accordingly, the suitability index models of SST and SSHA for winter to spring and summer to autumn were derived separately.

**Figure 4.** Seasonal distributions of suitability index for the SST (◦C) and SSHA (m) on the fishing locations for the *T. pacificus*.

**Figure 5.** Seasonality of frequency distributions of SST (◦C) and SSHA (m) on the fishing locations for the *T. pacificus*.

#### *3.2. HSI Model Derivation*

To derive the HSI values for the *T. pacificus*, six empirical models were used (Table 2). The SI models for the environmental parameters were adapted from Chen et al. [14]. As mentioned above, the suitability index models for the SST and the SSHA were divided into the two parts; winter to spring and summer to autumn. The constants of the models were obtained from the least squares fitting to derive optimized models for the study area (Figure 6). In the case of Chl-*a*, we used a natural logarithmic form to fit to the asymmetric distribution of the Chl-*a* concentration.

**Table 2.** SI models derived from three environmental parameters (SST, SSHA, Chl-*a*, and PP).

**Figure 6.** Least squares fitting results of (**a**) SST (◦C), (**b**) SSHA (m), (**c**) Chl-*a* (mg m−3), and (**d**) PP (mg C m−2 d−1) with the number of fishing sets (solid line: habitat suitability index (HSI) model, black dot: in situ fishing data).

The RMSE of the SI models for SSTWS, SSTSA, SSHAWS, SSHASA, Chl-*a*, and PP were 0.0500, 0.0745, 0.0871, 0.0667, 0.0640, and 0.0876, respectively. The coefficients of determination (R2) for the SI models were 0.9654, 0.9461, 0.9063, 0.9468, 0.9165, and 0.9072 for SSTWS, SSTSA, SSHAWS, SSHASA, Chl-*a*, and PP, respectively.

The climatological monthly distribution (July 2002–December 2016) of the HSI by the AMM model was derived by averaging datasets for each month. It showed that a relatively high HSI in the South Sea of South Korea. In addition, the high HSI regions were observed in the Yellow Sea and the East/Japan Sea from early summer to autumn (Figure 7).

**Figure 7.** Climatological monthly distribution of the (HSI) around South Korea from 2002 to 2016.

#### *3.3. HSI Model Evaluation*

To validate our HSI model, we compared the HSI values from our model with fishery catches. The HSI was divided into 10 classes with a step of 0.1. Then, we summed the landed fisheries from the region with the HSI values corresponding to each class. A strong positive linear relationship (R2 =0.9260) was observed between the HSI and the fishery landings (Figure 8). In addition, spatial distributions between the HSI and the fish catches for the *T. pacificus* appeared well matched throughout 2010 to 2016 (Figure 9).

**Figure 8.** Correlation between the HSI and the fishery landings. Fishery landings were summed in each range of the HSI value.

**Figure 9.** Spatial distribution of the eight-day composited HSI and the *T. pacificus* catches (black open circle) at corresponding periods in the South Sea.

#### **4. Discussion**

#### *4.1. Environmental Conditions*

Among the several environmental variables, temperature and salinity are well known for the most important factors affect spatial and temporal distribution of marine fish species [40–42]. Several previous studies revealed the habitat formation is closely related with distribution of the SST, SSHA, Chl-*a*, and sea surface salinity (SSS) [14,16,39]. However, salinity was not considered in this study, since its variation in the East/Japan Sea and the South Sea mostly fell within the reported optimal ranges (from 31.93 to 35.7) [39] for the *T. pacificus*. Also, mixed layer depth (MLD) which can be a good indicator for the habitat formation of several species also not considered. The MLD dataset is hard to use in this study due to its relatively poor spatial. We need spatial resolution of at least 0.17 × 0.17 degrees due to the resolution of the catch data. Thus, the MLD is not considered in this study. Instead, we tried to examine the optimal conditions for the *T. pacificus* with other environmental variables.

The optimal environmental conditions for *T. pacificus* in this study showed similar results with several previous studies [5,39]. Alabia et al. [5] already reported that the environmental ranges of the potential squid habitat in the East/Japan Sea. According to them, the SST and the SSHA ranged from 10 to 30 ◦C and –0.16 to 0.36 cm, respectively, which correspond well with the results from our study. The Species Environmental Envelope (HSPEN) in AquaMaps global distribution model [39] also showed similar environmental range with this study. The species' preferred environmental conditions for the *T. pacificus* in AquaMaps are 9.24–23.54 ◦C and 364–939 mg C m−2 d−1 for temperature and PP, respectively [39].

In this study, we tried to derive the HSI for the *T. pacificus* with SST, SSHA, Chl-*a*, and primary production around the South Korea. The habitat depth of *T. pacificus* is known to be at least 30 m and up to 100 m. It seems to be nonsense that HSI derivation for the *T. pacificus* with SST data, but the seasonal habitat distribution would be reflected in the range of SST. On the other hand, phytoplankton is not a direct prey of *T. pacificus*. However, the intermediate consumers in the food web can largely influenced by the distribution of phytoplankton [43]. Indeed, Lee et al. [44] already reported that the spatial distribution of the common minke whale (*Balaenoptera acutorostrata*) is closely related to the Chl-*a* distribution in the East/Japan Sea [44], although there is not any direct linkage in the food web. Moreover, the HSI derivation for the chub mackerel (*Scomber japonicus*) was also successfully conducted

in the South Sea and the East/Japan Sea using primary production dataset [17]. As mentioned before, the zooplankton is an important food source for the juvenile *T. pacificus* and the small pelagic fishes which are main prey for the adult *T. pacificus*. Although there are only a few studies that have used primary production as an environmental variable for the HSI model, not only Chl-*a* but also primary production can be useful indicator of spatio-temporal distribution of the *T. pacificus*.

However, *T. pacificus* distributed mostly around South Korea and Japan (or northwestern pacific). Thus, the HSI model derived from regional dataset should be applied to global ocean or other regions carefully with understanding of the native distribution range of the target species.

#### *4.2. Seasonal Variation of the HSI*

In this study, seasonal variations of the HSI for the *T. pacificus* were observed which are not seen in map of the native habitat ranges. The optimal environmental conditions for the *T. pacificus* in this study showed high seasonal dependences. The optimum ranges of the SST and the SSHA for Summer–Autumn were significantly higher than those of Winter–Spring. The monthly distribution of the catches suggested that January and August are representative fishing time for each group (Figure 2). The catches occurred in January were mainly distributed in the southern part of the East/Japan Sea and the eastern part of the South Sea, while catches occurred in August mostly distributed in the Yellow Sea and the western part of the South Sea. As mentioned above, *T. pacificus* is well known for their characteristic migration routes around the Korean Peninsula. Also, they consist of two groups, the 'winter spawners' and 'autumn spawners'. Their characteristic migration routes suggest that the caught population in January and August might be composed with different group each other. Consequently, the differences in the optimal environmental conditions between Summer–Autumn and Winter–Spring might be caused by these seasonal population distributions of *T. pacificus* in Korean waters.

#### *4.3. Regions with High Catch Probability*

For the investigation of the hotspots, which are the regions with high catch probability, we defined the hotspot as regions with an HSI over 0.7, because more than half of the total catches occurred in areas when the HSI ranged to 0.7 or more. The results of our hotspot analysis showed a seasonal distribution (Figure 10). One of the major hotspots was observed around the South Sea in winter (January–Febuary) and autumn (October–November). As mentioned above, not only the South Sea is well known for the spawning ground of the *T. pacificus*, they spawn during winter and autumn [2–5]. These characteristic spawning ground distributions of the *T.pacificus* might be reflected in the hotspot located in the South Sea. Another major hotspot located in the Yellow Sea was observed on July and September, while the hotspot located in the East/Japan Sea was observed on September and October. Generally, from late spring to early summer, the 'winter spawners' group begins to migrate mainly to the Yellow Sea which is well known for their feeding grounds, and their feeding activity continues until Autumn [3]. In case of the 'autumn spawners', they begin to migrate to the East/Japan Sea from spring to summer, and their feeding activity continues until late summer to autumn [3]. Consequently, the seasonal distribution of the hotspots was in agreement with the general migration patterns of the *T. pacificus* reported previously around the Korean Peninsula.

**Figure 10.** Climatological monthly distribution of the HSI hotspots (HSI > 0.7) for the *T. pacificus* around South Korea from 2002 to 2016.

#### **5. Summary and Conclusions**

In this study, the HSI of the *T. pacificus* was derived by using commercial catch data and satellite datasets between 2010 and 2016. Four environmental parameters (SST, SSHA, Chl-*a,* and PP) were selected as key variables in the habitat formation of the *T. pacificus*.

The optimum environmental conditions for SST, SSHA, Chl-*a*, and PP were 14.91–27.26 ◦C, 0.05–0.20 m, 0.32–1.35 mg m−3, and 480.41–850.18 mg C m−2 d−1, respectively. More than 80% of the total catch obtained from the region within the optimum ranges. SST and SSHA showed high seasonality in frequency distributions (Figure 4). Based on these results, suitability index models for SST and SSHA were derived separately for winter to spring and summer to autumn (Figure 6). In the derivation of the HSI model, the AMM was used to combine the four SIs (SST, SSHA, Chl-*a*, and PP) since many previous studies reported that the AMM is the most appropriate model for marine fish species.

Based on the results from the HSI model, we found a strong positive relationship (R2 = 0.9260) between the HSI and the fishery landings (Figure 8) and good match for the spatial distributions of the *T. pacificus* (Figure 9). The seasonal variations of the spatial distribution of the HSI for the *T. pacificus* were observed in climatological monthly distribution of the HSI (Figure 7). In addition, the hotspot analysis the hotspot analysis revealed several major hotspots around the South Korea (Figure 10). The hotspot observed in the South Sea is consistent with the reported spawning grounds for *T. pacificus*. The hotspots observed in the Yellow Sea and the East/Japan Sea appear to be similar to the feeding grounds of the 'winter spawners' and 'autumn spawners', respectively. Consequently, the seasonal and spatial variations of the HSI match well with the migration patterns of the *T. pacificus* reported previously in the Korean waters. The HSI model derived from this study could help us to predict fishing grounds with high catch probability of the *T. pacificus* around South Korea. The monitoring of the HSI for the *T. pacificus* at the Yellow Sea and the East/Japan Sea can help us to successful fishery management. Ultimately, long-term analysis in spatial and temporal distributions of the HSI for the *T. pacificus* will allow us to understand the recent changes in fishery resources around South Korea.

**Author Contributions:** Conceptualization, D.L. and S.H.L.; data curation, D.L.; funding acquisition, C.-K.K. and S.H.L.; methodology, D.L.; project administration, C.-K.K. and S.H.L.; supervision, S.H.S. and S.H.L.; validation, D.L.; visualization, D.L.; writing—original draft, D.L.; writing—review and editing, S.H.S., C.-I.L., and S.H.L.

**Funding:** This research was supported by "Development of the integrated data processing system for GOCI-II" and and "Long-term change of structure and function in marine ecosystems of Korea" funded by the Ministry of Ocean and Fisheries, Korea.

**Acknowledgments:** The authors would like to thank the anonymous reviewers and the handling editors who dedicated their time for providing the authors with constructive and valuable recommendations.

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

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© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


**Food Compounds Inhibit** *Staphylococcus Aureus* **Bacteria and the Toxicity of Staphylococcus Enterotoxin A (SEA) Associated with Atopic Dermatitis**

Reuven Rasooly and Mendel Friedman

*Western Regional Research Center, Agricultural Research Service, United States Department of Agriculture, Albany, California USA*

#### **1. Introduction**

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> Enterotoxigenic *S. aureus* is a major bacterial pathogen that develops multi-drug resistance to antibiotics (Pereira et al., 2009; Pu et al., 2011; Rhee & Woo, 2010). The enterotoxigenic bacteria and secreted toxins have been reported to cause clinical infections, and it has also been reported that they contaminate a broad variety of foods, including breaded chicken products (Pepe et al., 2006), canned mushrooms (Anderson et al., 1996), cheeses (Ertas et al., 2010; Ostyn et al., 2010; Rosengren et al., 2010), raw milk (Fusco et al., 2011; Heidinger et al., 2009), pork meat (Wallin-Carlquist et al., 2010), and other foods (Balaban & Rasooly, 2000) as well contaminating handles of shopping carts (Mizumachi et al., 2011) causing many foodborne illnesses in the United States each year (Shinefield & Ruff, 2009). Staphylococcal food poisoning is due to the absorption from the digestive tract into the circulation of the enterotoxins preformed in food A toxin level of <1 µg may induce symptoms of food poisoning. This level is reached with a bacterial population of >105 CFU/g food (Stewart, 2005).

> *S. aureus* produces the virulent staphylococcal enterotoxin A (SEA), a single chain protein that consists of 233 amino acid residues with a molecular weight of 27,078 Da. It has been estimated that staphylococcal enterotoxin A (SEA) secreted by the bacteria is associated with 78% of staphylococcal outbreaks (Vernozy-Rozand et al., 2004). Heat used to eliminate the pathogenic bacteria may not eliminate toxins already formed (Margosch et al., 2005; Pepe et al., 2006).

> Staphylococcus enterotoxins (SEs) exhibit two separate biological activities: they cause gastroenteritis in the gastrointestinal tract and they act as a superantigen on the immune system. Previous research has shown that emetic activities and superantigenic activities of SEs are related (Shinefield & Ruff, 2009; Stewart, 2005).

> Because SEA is present in contaminated foods and exerts adverse effects on the gastrointestinal tract, there is a need to find food-compatible safe conditions to inactivate it. Efforts to inhibit the toxin or its release from *S. aureus* include the use of electrolyzed water (Suzuki et al., 2002), high pressure and heat (Margosch et al., 2005), radiation and pulsed electric fields (Walkling-Ribeiro et al., 2008), condensed tannins (Choi et al., 2007) and other plant extracts (Carlos et al., 2010; Ifesan & Voravuthlkunchai, 2009), peptides (Wang et al.,

Food Compounds Inhibit *Staphylococcus Aureus* Bacteria and

**2.4 Prophylactic vaccination of mice with** *S. aureus*

**2.5 Staphylococcus enterotoxin (SEA) activity assays**

significant difference.

**3. Results and discussion**

*aureus*

quadruplicate with control values ~100 colony forming units (CFUs).

the Toxicity of Staphylococcus Enterotoxin A (SEA) Associated with Atopic Dermatitis 389

dried (~10-15 min) and then incubated overnight 37 °C. Each dose was sampled in

The following procedure was adapted from Balaban et al. (1998). RAP (regulatory RNAIII activating protein, 10 μg) was injected with CFA (completer Freund's adjuvant) on first injection, followed with ICFA (incomplete Freund's adjuvant) on second and third injections subcutaneously into 4-week old immunocompetent hairless male mice on days 0, 7, and 21. Control mice were either injected with the adjuvant alone or not injected. Vaccinated and control mice were challenged on day 31 with 1.24 x 108 CFU of wild-type Smith diffuse strain (SD) of *S. aureus* subcutaneously together with 1 mg of Cytodex beads to induce local infection. The size of the lesion was measured daily. Fisher's exact probability test was used to compare proportions of mice developing lesions and mice developing RAP antibodies among the RAP vaccinated, CFA controls, and untreated control groups. Among animals that developed lesions after challenge with *S. aureus*, the size of the lesions was compared by single-factor analysis of variance. Post-hoc testing was done by Fisher's protected least

The procedure was adapted from Friedman et al. (2011). Spleen cells were placed in 96-well plates (1 X 106/mL, 0.2 mL) in Russ-10 medium and treated with various concentrations of SEA following incubation at 37 °C in a 5% CO2 incubator. After incubation at various time points, cell proliferation was measured by adding bromodeoxyuridine (5-bromo-2 deoxyuridine, BrdU)-labeled DNA to each well 4 h before fixation as described by the instructions of the manufacturer. Spectroscopic measurements were made at 620 and 450 nm. A second measure of inhibition activity of SEA activity was determined by an enzyme cleavage assay. Briefly, the glycyl-phenylalanyl-aminofluorocoumarin (GF-AFC) substrate (50 μL) was added to the wells. After mixing and incubation for 30 min at 37°C, this substrate enters intact cells. The live cell protease then cleaves GF-AFC, releasing AFC which generates a fluorescent signal. The resulting fluorescence was measured by a fluorescence plate reader (excitation at 355 nm and emission at 523 nm). We used 5 and 200 ng/mL of the toxin because at (a) < 5 ng/mL we did not detect cell proliferation induced by the toxin; and (b) at ~200 ng/mL proliferation of spleen cells reach a maximum. Results are expressed as representative data from triplicate wells from two different methods, BRDU

Here, we briefly describe our studies designed to demonstrate the potential of bioactive food ingredients to inhibit growth of *S. aureus* bacteria and to inactivate SEA produced by these bacteria. The results suggest that it may possible to reduce the toxic potential of these

**3.1 Naturally occurring compounds inactivate antibiotic-resistant** *Staphylococcus*

Antibiotic resistant microorganisms often arise from the administration of sub-therapeutic levels of antibiotics in animal feeds. They are present in the animal waste, often

and the enzyme cleavage GFA-AFC assays which work only with live cells.

toxin-producing organisms with the aid of edible food ingredients.

2008), phenolic compounds (Rúa et al., 2010), licochalcone A (Qiu et al., 2010), essential oils (de Souza et al., 2010; Friedman et al., 2004a; Nuñez et al., 2007; Parsaeimehr et al., 2010; Qiu et al., 2011), and toxin-specific antibodies (Larkin et al., 2010).

The objective of our research effort is to discover food-compatible ways to inhibit or inactivate both the pathogen and the toxin. In this chapter we will briefly summarize our finding in these two areas that are also relevant to atopic dermatitis.


**15. Summary**

*Innovations in Assisted Reproduction Technology*

**chromatin**

abnormalities of head.

**reproductive outcome**

DNA. PXRX4 is necessary for condensation in testes where as GPRX4 is specifically in epididymis. Hydrogen peroxide acts as signalling molecule and causes phosphorylation of tyrosine molecules in the receptor, necessary for proliferation of sperm cells [31]. Abnormal signalling by hydrogen peroxide leads to sperm cancer which can be avoided by reduction of H2O2 by enzymes like SOD and catalase. Totally GPRX4 and

PXRX4 is necessary for nuclear stability, condensation and spermiogenesis.

**13. Factors responsible for condensation and decondensation of**

**14. Assisted reproductive technologies required for success of**

*Percentage of cells with DNA fragmentation using different techniques of assessment.*

ICSI and IVF are the techniques used in assisted reproductive technology, in which ICSI resulted in more percentage of PCC (pre mature condensation of

Zinc is well known for maintenance of membrane stability and found to be present in seminal plasma. It is decreased during penetration of sperm in to the female reproductive tract and decrease of zinc causes premature decondensation of chromatin as it destabilises the sperm plasma membrane. Albumin also binds to zinc and accelerates decondensation. In humans heparin is involved in decondensation of chromatin. Zinc also binds to free thiols and increases stability of chromatin, necessary for reducing disulfide bonds and accelerating the reaction (**Figure 4**). Few of the recent techniques used for measurement of DNA condensation and decondensation include using of TPM motions and energy. Condensation of DNA leads to decrease in length as protamines are organised as toroids. Where as decondensed chromatin is more in length as protamines are replaced by histones. Differences in motions of the DNA attached to the coverslip is captured and ultimately the difference in energy. Positive relation is seen between the persons with premature decondensation in DNA and infertility and DNA damage based on the studies in human subjects. Sperm fluorescent in situ hybridisation is other technique used to assess the relation between sperm DNA [32] condensation and

**54**

**Figure 4.**

Condensation of sperm genome was necessary for maintaining genome integrity by transition of histones by transition proteins which in turn by protamines. These proteins undergo some of PTM namely phosphorylation, Acetylation, poly ADP ribosylation and methylation. Further stability in interaction between DNA and protamine involves addition of disulfide bonds to form interlinking protamines. Dihydrotestosterone acts as hydrogen donor and acceptor in testes and necessary for maturation of sperms. Zinc acts as stabiliser of Genome through its interaction with protamines and toxicity due to metals responsible for male infertility [36] is due to interaction of these metal ions with protamines. So, safe ART is required to maintain genome integrity in offspring.

### **16. Metosartan acts as genotoxic agent of testes**

Metosartan induces endometrial carcinoma in testes and causes changes in chromatin dynamics and causes prematurity in sperms. It causes competitive and non competitive inhibition of RNase present in male Wistar rats. It also decreases the sperm count, motility and induces abnormalities in sperm structure. It also causes apoptosis in both testes and sperm by increasing the permeability of mitochondria.