Ecology

Affiliations

Sustainable Agroecosystems Group, Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
W. J. Blaser, J. Landolt & J. Six

Council for Scientific and Industrial Research – Soil Research Institute, Kwadaso, Kumasi, Ghana
J. Oppong & E. Yeboah

Institute of Integrative Biology, Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
S. P. Hart

Authors
W. J. Blaser

J. Oppong

S. P. Hart

J. Landolt

E. Yeboah

J. Six

Corresponding author
Correspondence to W. J. Blaser. More

Coronaviruses are positive-stranded RNA viruses that may exert severely negative effects on the mortality and morbidity of a broad range of birds and mammals including humans and domestic animals. The strain called SARS-CoV-2 host-switched from bats to humans in Wuhan, China in November 2019 and subsequently gave rise to a devastating global pandemic called CoVID-191,2,3. Susceptibility of human societies appear to be markedly heterogeneous ranging from modest to very high morbidity. Contrary to general expectations, more developed, wealthier communities living under better hygienic conditions appear to be more threatened than others. Thus, Austria is seemingly more severely hit than Hungary, the Czech Republic than Slovakia, and Israel than Palestine or Jordan.
Evidently, the first step to search for factors influencing this pandemic is to identify environmental correlates of different populations’ susceptibility. Sala and Miyakawa4 suggested that the different BCG vaccination policies across countries may partly explain differences in susceptibility to CoVID-19. Indeed, higher morbidity and mortality is observed in societies with no obligatory BCG vaccination. However, vaccination schemes tend to be uniform within countries, thus this hypothesis cannot explain the huge within-country differences that are often observed, such as those between Northern vs. Southern Italy. Zhu et al.5 described a covariation between exposure to air pollution and CoVID-19 infection.
We hypothesize that certain common infections coming together with a less hygienic lifestyle may trigger the human immune system and thus facilitate some protection against CoVID-19, an argument similar to the so-called ‘hygiene hypothesis’6. Toxoplasmosis is a candidate infection for this purpose because of two reasons. First, it is one of the most widespread latent infections of humanity7,8. As it does not transmit from human to human, its prevalence can be interpreted as a generalized index of group hygiene. Second, its causative agent, the eukaryotic protozoan Toxoplasma gondii, is known to exhibit at least some antiviral effects9.
Toxoplasma gondii is an intracellular parasite that infects birds and mammals as intermediate hosts, while the sexual phase of its life cycle can only be completed in feline definitive hosts, most often in domestic cats. It is distributed in human societies mostly by semi-domestic, partly-feral cats that depredate on infected rodents and birds and then eat their prey. Subsequently, the infective spores are released through their faeces and may get into direct contact with humans to cause infections. Alternatively, domestic animals may be infected by these spores and the consumption of their infected meat transmits T. gondii to humans. Thus, humans act like intermediate hosts, although they are not depredated by cats, and thus this is a dead-end for the parasites. ‘Luxury cats’ living on canned pet-food throughout their life may not transmit this infection. Asymptomatic infections are common in humans, especially among those living in the proximity of semi-feral domestic cats10.
Toxoplasma gondii excretes Dense Granule Protein-7 (GRA-7) into the host cell that inhibits viral replication. Its effect has been proven both in vitro and in vivo against indiana vesiculovirus, influenza A virus, Coxsackie virus, and herpes simplex virus. Overall, GRA-7 exhibits immune-stimulatory and a broad spectrum of antiviral activities via type I interferons signaling9. Moreover, in response to T. gondii infection, laboratory mice highly upregulate Immune Responsive Gene 1 in their lungs11. This is an interferon-stimulated gene that mediates antiviral effects against RNA viruses like the West Nile and Zika viruses through its product named itaconate12. It has been established that GRA-7 could be serve as alternative to treat tuberculosis13.
We need to emphasize, however, that the antiviral activities of Toxoplasma gondii are limited to the first, short and virulent phase of the infection, and not known to operate through the subsequent latent period that may last through the whole life of the host. Therefore, even in societies where a large proportion of the population carries latent toxoplasmosis, the proportion of infections actually expressing antiviral activities is very low. Thus we only claim that Toxoplasma gondii expresses at least some antiviral adaptations. Moreover, the apicoplast proteins of Toxoplasma are known to have immunogenic potential14.
Finally, we chose toxoplasmosis out of the candidate human infections partly because the availability of prevalence data from as many countries as possible. Unfortunately, as in the case of all other human infections, the methodologies of gathering and evaluating epidemiological data can be quite heterogeneous across countries. Below we set out to test whether there is a negative co-variation between levels of toxoplasmosis and CoVID-19 pandemic at a global scale. More

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Materials
BioSim2 version 2 (Information Technology Department of Norwich University, Las Vegas, NE, USA), Maxent version 3.3.3 (AT&T Labs–Research, Florham Park, NJ, USA; https://www.cs.princeton.edu/~schapire/maxent/), and ArcGIS version 10.5 (ESRI Inc., California, USA; https://www.esri.com/) software were respectively used for screening the environmental variables, predicting the A. membranaceus var. mongholicus habitat, and mapping the suitable distribution area of A. membranaceus var. mongholicus in Inner Mongolia. The geographic coordinate information of A. membranaceus var. mongholicus samples was obtained by Handheld GPS (Jarmin Rino530HCx, Nanjing Tiandi Precision Drawing Instrument Equipment Co. Ltd., Nanjing).
The extraction of active components (Astragaloside IV and calycosin-7-glucoside) from A. membranaceus var. mongholicus samples was carried out by experimental equipment, namely pulverizer (FLBP-200, Zhejiang Yili Industry and Trade Co., Ltd., Zhejiang), electronic analytical balance (ME204, Mettle Toledo, Shanghai), temperature-controlled electric heating jacket (KDM, Heze Jingke Instrument Co., Ltd., Heze), rotary evaporator (IKA RV 10, Shanghai Hanpei Electromechanical Equipment Co. Ltd., Shanghai), ultrasonic cleaner (KQ-500DE, Sonxi Ultrasonic, Kunshan), the Soxhlet extractor, reflux extractor, volumetric bottle, and other glass instruments produced by Shuniu Glass Instrument Co., Ltd. (Sichuan). High-performance liquid chromatography-ultraviolet (Ultimate3000, Thermo Fisher Scientific, Waltham, MA, USA), evaporative light scattering detector (ELSD 2000ES, Alltech (Shanghai in China) Co., Ltd.), air generator (XWK-III, Tianjin Jinmin Analytical Instrument Manufacturing Co., Ltd., Tianjin), and Astragaloside IV (CAS: 148321) and calycosin-7-glucoside (CAS: 150326) reference substances of  > 95% purity purchased from Chengdu Pufeide Biotech Co., Ltd (Chengdu) were used for the quantitative analyses of the samples. A laboratory water purifier (XGB-40-B, Shenyang Xinjie Technology Co., Ltd., Shenyang), chromatographic methanol, n-butanol, and phosphoric acid were purchased from Tianjin Fuchen Chemical Reagent Factory, chromatographic acetonitrile was purchased from Tianjin Comeo Chemical Reagent Co., Ltd., and ammonia water was purchased from Tianjin Fengchuan Chemical Reagent Technology Co., Ltd (Tianjin) for the entire High-performance liquid chromatography-ultraviolet analysis.
Acquisition of ecological factor data
In this study, we considered the influence of 74 ecological factors, including climate, soil, topography, vegetation type, and meteorological factors, on the distribution of A. membranaceus var. mongholicus. Climatic data included 59 ecological factors, e.g., monthly mean precipitation (mm), temperature (°C × 10), and sunshine duration (h × 10) from January to December (mm), mean precipitation (mm), temperature (Tmean4-10, °C × 10), and sunshine duration (Smean4-10, h × 10) in the growing season, mean annual sunshine duration (SunshineAnnu, h × 10), and 19 comprehensive climatic factors. These data were based on spatial interpolation of meteorological observation data from 752 surface and automatic meteorological stations in China, collected from 1951 to 2000, with a resolution of 1 km.
The soil data comprised eight ecological factors, which were determined according to a 1:100,000 soil map of the People’s Republic of China (compiled in 1995) provided by the Second National Land Survey. These factors were soil pH, cation exchange capacity (cmol kg−1), sand content (SoilSand, %), clay content (%), soil type (SoilType from FAO-90), soil available water content level (SoilWater), soil texture (SoilTexture, USDA), and organic carbon content (SoilCarbon, %).
Topographic data included three ecological factors, namely altitude (m), slope (°), and aspect with a resolution of 1 km. Vegetation type data included an ecological factor based on vegetation subtype data from a vegetation map of the People’s Republic of China (1:100,000) published by the Institute of Botany, Chinese Academy of Sciences. The comprehensive meteorological data comprised three ecological factors, namely the warmth and coldness indexes (°C) derived from Kira’s thermal index and the humidity index (mm∙°C−1) derived from Xu’s modified version of Kira’s humidity index42,43.
The abovementioned data of ecological factors for studying the ecological suitability and quality regionalization of A. membranaceus var. mongholicus were taken from the “Traditional Chinese Medicine Resources Spatial Information Grid Database” (https://www.tcm-resources.com/) provided by the National Resource Center for Chinese Materia Medica of the China Academy of Chinese Medical Sciences (Beijing, China). The relevant information for each ecological factor, including the category, name and type, is presented in Appendix 1 in Supplementary Information 1.
Collection of A. membranaceus var. mongholicus samples
The cultivation area of A. membranaceus var. mongholicus was approximately 6,666.67 ha (66.67 km2) in Inner Mongolia in 2016 and mainly covered Urad Front Banner, Guyang County, Tumd Right Banner, Wuchuan County, and Harqin Banner44. Based on a full understanding of the regional characteristics of Inner Mongolia, we adopted traditional route and quadrat surveys, taking the village or gacha (level with administrative village) as the smallest sampling unit, to conduct a field survey of A. membranaceus var. mongholicus in the eastern, central, and western regions of Inner Mongolia in 2016. To ensure the uniformity and representativeness of the sample data, we sampled different production areas with significant differences in ecological factors, such as terrain, soil, and vegetation type, according to these two routes. Three to five sampling points were set in each sampling area with a distance of 1 km, and a total of 63 samples of A. membranaceus var. mongholicus were collected in Inner Mongolia. The geographic coordinates of the sampling points were recorded using the handheld GPS. To diminish the influence of different harvesting periods or growth years on the contents of active components in A. membranaceus var. mongholicus, the samples were biennial medicinal materials and were collected in October 2016. Figure 10 shows the survey route and location of the sampling points. The geographical coordinates (latitude and longitude data) of each sampling point are listed in Appendix 4 in Supplementary Information 1.
Figure 10

Survey routes of A. membranaceus var. mongholicus and geographical location of its sampling points. Generated using the ArcMap version 10.5 software (ESRI Inc., California, USA. https://www.esri.com/).

Full size image

Preliminary screening of ecological factors
To diminish the influence of high correlations between ecological factors at the time of MaxEnt modeling, we used Biosim2 to calculate the correlation coefficients between all the ecological factor values extracted from the longitude and latitude of 63 sampling points. According to the correlation coefficient tree diagram calculated using Biosim2, we excluded any ecological factor that had a low correlation with A. membranaceus var. mongholicus growth, based on their potential biological relevance to this species, for each set of highly correlated ecological factors with correlation coefficients  > 0.845. We loaded the retained ecological factors into Biosim2 again and repeated the above-mentioned operation until the correlation coefficient between all the retained ecological factors was ≤ 0.8. The ecological factors finally screened are depicted in Fig. 3.
Calculation and accuracy testing of MaxEnt model
The point locality data of A. membranaceus var. mongholicus and the retained ecological factor data were imported into the MaxEnt model for the calculation. The model parameters were set as follows: The model was run 10 times, the maximum number of iterations was 1,000,000, the convergence threshold was 0.0005, the random test percentage was set to 10, namely, 90% of the point locality data were randomly selected as training data, and the remaining 10% of data points were the test data. Cross validation (the data set was divided into ten parts, of which 9 were used as training data and 1 as test data in turn for the experiment) was used as the replicated run type, and the max number of background points and the remaining parameters were set as default.
In this study, the receiver operating characteristic curve analysis of the distribution of A. membranaceus var. mongholicus in the model was used to evaluate the accuracy of MaxEnt. The area under the receiver operating characteristic (AUC) was not affected by the threshold, and its value ranged from 0 to 1. The larger the value, the higher was the accuracy of the model. When the AUC was in the range 0.5–0.8, the accuracy of the prediction made by the model was inferior; however, the prediction accuracy was reasonable when the AUC was in the range 0.8–0.9. Finally, when the AUC was greater than 0.9, the model produced reliable and accurate prediction results, and the potential distribution of the species could be accurately predicted46,47. The results of 10 training and test sample data repeatedly calculated using MaxEnt are presented in Table 1, and the mean AUC and standard deviation value of the test samples are depicted in Fig. 4. The average growth suitability image of A. membranaceus var. mongholicus obtained through the model was used as the probability layer file of potential A. membranaceus var. mongholicus distribution for studying the ecological suitability regionalization of A. membranaceus var. mongholicus.
Ecological suitability regionalization of A. membranaceus var. mongholicus in Inner Mongolia
The point locality data of A. membranaceus var. mongholicus and its average growth suitability image (distribution probability layer of this species) were simultaneously loaded into ArcGIS. The distribution data of the A. membranaceus var. mongholicus sampling points were used to extract the ecological suitability values in the distribution probability layer, which was rasterized according to the maximum and minimum values of the suitability value. This was done to remove the data outside the range of the sampling points and obtain the region suitable for the cultivation of A. membranaceus var. mongholicus. The natural breaks method in ArcGIS was used to divide the ecological suitability distribution area of the species into four levels: unsuitable (0.00–0.02), secondarily suitable (0.02–0.18), suitable (0.18–0.42), and optimum (0.42–0.90); we used an appropriate color ramp in ArcGIS to indicate the aforementioned levels. Finally, a legend, north arrow, and scale bar were added to complete the map of the ecological suitability of A. membranaceus var. mongholicus at the city level in Inner Mongolia (Fig. 5). To accurately obtain the potential distribution area of A. membranaceus var. mongholicus, based on the ecological suitability of this species in Inner Mongolia, we added county-level administrative data to ArcGIS. Next, we extracted the distribution probability layer of this species by mask to obtain a map of the ecological suitability of A. membranaceus var. mongholicus at the county level. Subsequently, the areas of suitable habitat in the Leagues or Cities in Inner Mongolia were statistically analyzed (Table 2).
Main ecological factors affecting A. membranaceus var. mongholicus growth
The ecological factors screened by Biosim2 were inputted as environmental variables into the Maxent model for model calculation, and the contribution rate of each factor to the growth of A. membranaceus var. mongholicus was determined. To determine the first estimate, in each iteration of the training algorithm the increase in regularized gain was added to the contribution of the corresponding variable, or subtracted from it if the change to the absolute value of λ was negative. For the second estimate, for each environmental variable, the values of that variable on training presence and background data were randomly permuted. Finally, the model was reevaluated on the permuted data and the contribution of each factor was obtained (Fig. 6). Ecological factors with a contribution rate of  > 0% were selected as the main factors to analyze the response curves (Fig. 8A–Q). Those contributing to the growth of the species were used as the main ecological factors for studying the suitability regionalization of high-quality A. membranaceus var. mongholicus in Inner Mongolia.
Content of index components and relationships with main ecological factors
Saponins and flavonoids are the primary active components of Radix Astragali, and they are valuable indicators for evaluating the quality of Radix Astragali in the Chinese, British, and European pharmacopoeia3,48,49. The contents of the saponin astragaloside IV and flavonoid calycosin-7-glucoside in 63 Radix Astragali samples were determined via high performance liquid chromatography according to the Chinese Pharmacopoeia (2015 Edition) (Appendix 3 in Supplementary Information 1). In addition, we used SPSS17.0 statistical analysis software to analyze differences in astragaloside IV and calycosin-7-glucoside content in A. membranaceus var. mongholicus from different production areas in Inner Mongolia. The relationships between astragaloside IV, calycosin-7-glucoside, and the main ecological factors were determined using the correlation matrix (Tables 3, 4). The relationship equations between these index components and the main ecological factors were obtained by stepwise linear regression analysis.
Suitability regionalization of high-quality A. membranaceus var. mongholicus in Inner Mongolia
The relationship equations were respectively inputted into ArcGIS’s grid calculator to obtain the quantitative distribution layers of astragaloside IV and calycosin-7-glucoside in A. membranaceus var. mongholicus. Using the spatial calculation function of ArcGIS, the two abovementioned layers were overlain on the ecological suitability distribution layer of A. membranaceus var. mongholicus, and the spatial suitability distribution regions of astragaloside IV and calycosin-7-glucoside in A. membranaceus var. mongholicus in Inner Mongolia were finally obtained. According to the content limits of these index components in 63 A. membranaceus var. mongholicus samples, the spatial suitability distribution regions of the index components were divided into five grades in ArcGIS, represented by a color ramp from blue to red. A map of the spatial distribution of astragaloside IV and calycosin-7-glucoside in A. membranaceus var. mongholicus in the study area was plotted in ArcGIS (Fig. 9A,B). To determine the regions in Inner Mongolia that were suitable for cultivating high-quality A. membranaceus var. mongholicus, we overlaid the spatial distribution layer of the two active ingredients and the administrative distribution data at the county level to find out where these contents are both maximized (Fig. 9C). The administrative areas under various suitability levels of astragaloside IV and calycosin-7-glucoside distribution are presented in the Table 5. More

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