Description of Environmental Parameters That Indicate the Presence of Coccidioides Immitis, the Causative Agent of Valley Fever

Description of environmental parameters that indicate the presence

of Coccidioides immitis, the causative agent of valley fever in the

Northwestern Mojave Desert

By Prabhjeet Kaur

A Thesis Submitted to the Department of Biology California State University Bakersfield In Partial Fulfillment for the Degree of Masters of Science, Biology

Masters of Science in Biology

Fall 2016

Prabhjeet Kaur

Fall 2016

Description of environmental parameters that indicate the presence of Coccidioides immitis, the causative agent of valley fever in the northwestern Mojave Desert Prabhjeet Kaur Department of Biology California State University, Bakersfield Abstract Coccidioidomycosis, also known as Valley Fever, is a re-emerging infectious disease of the American continent. Coccidioidomycosis is a respiratory disease caused by the inhalation of arthroconidia produced by the dimorphic ascomycete fungi Coccidioides immitis or Coccidioides posadasii known to live in soil environments as a saprophyte. Coccidioides sp. are known to thrive in loamy and highly saline soils. These fungi have the ability to form spores known as arthoconidia, which can survive the dry, hot summers. When soil is disturbed, spores become airborne and can cause infection when inhaled by a host. Recently, the incidence of valley fever has increased significantly in endemic areas of the pathogen such as the Western Mojave Desert and among the general public and has also affected prisoners incarcerated in correctional facilities, such as the one east of California City, our study area. The objective of this research was to determine the soil properties that are essential for the growth of Coccidioides immitis in the Northwestern Mojave Desert near California City. Different physical and chemical properties of soils such as soil pH and soil type are known to determine the distribution of plants, which can therefore indicate certain soil conditions, but these parameters also affect the growth of microorganisms and fungal communities in diverse microhabitats. The hypothesis of this project was that the presence or absence of the pathogen in the soil can be linked to yet to be determined environmental parameters and/or depends on the presence or absence of certain soil microorganisms that could act as antagonists to the pathogen. The physical and chemical soil parameters, as well as plant diversity were determined for each sampling site. In this study, culture independent techniques, such as DNA extraction followed by PCR and DGGE were used to detect the presence of C. immitis, and to determine the diversity of the soil fungal communities in 43 soil samples collected long several transects in 2014 and 2015. Results of this study revealed that the study area is highly endemic for the pathogen, but its presence could not be linked to specific soil parameters. The fungal diversity in the soil was generally low and showed the dominance of members of the Pleosporales and Hypocreales. Members of the fungal genus Ascobolus were only found in soils that were negative of the pathogen and could potentially include antagonists to C.immitis.

Table of Contents Introduction 1 Life Cycle of Coccidioides spp. 2 History of Coccidioidomycosis 3 Symptoms and Treatment of Coccidioidomycosis 5 Environmental Parameters that Determine the Habitat of C. immitis 6 Previous Research 10 Purpose of the Study and Significance 10 Methods 12 Description of the Sampling Sites 12 DNA Extraction 19 PCRs of Fungal rDNA 20 Denaturing Gradient Gel Electrophoresis (DGGE) 24 Phylogenetic Analysis 25 Statistical Analysis 26 Results and Data Analysis 27 Physical and Chemical Soil Parameters 27 PCR Analyses 37 Denaturing Gradient Gel Electrophoresis (DGGE) Analysis 41 Fungal Diversity Along Different Transects 42 Phylogenetic Analysis 52 Discussion 57 Comparison of Nested PCRs 59 DGGE and Fungal Diversity 60 Characterization of Soil Samples 62 Conclusion 64 Literature Cited 66 Appendix 74 1

Description of environmental parameters that indicate the presence of Coccidioides immitis, the causative agent of valley fever in the northwestern Mojave Desert

Prabhjeet Kaur

INTRODUCTION Coccidioidomycosis is a respiratory disease caused by the inhalation of arthroconidia produced by the dimorphic ascomycete fungi Coccidioides immitis or Coccidioides posadasii. The difference in these two species has been distinguished by genetic analyses based on single nucleotide polymorphisms and the size of microsatellites (Fisher et al. 2002). Coccidioides spp. are found in hot, dry regions of the southwestern United States, where winters are relatively mild and the soil is mostly alkaline (Kirkland and Fierer 1996), and were recently also discovered in the deserts of northern China (Pan et al. 2013). The desert-like environment of the Southern San Joaquin Valley and the Mojave Desert in California support the growth of C. immitis only, whereas C. posadasii predominantly occurs in other areas of the southwestern US, Mexico, as well as Central and South America. Greater than 95% of all coccidioidomycosis cases are documented in Arizona and California, but the disease is also found in smaller numbers in New Mexico, Texas, Utah, Nevada (CDC 2015) and China (Wang et al. 2015, Figure 1, showing the endemic areas in New Mexico, Texas, Arizona, and California). It is suspected that the growth of the pathogen in the soil is linked to environmental parameters such as climate, plants, chemical and physical soil parameters, and/or the presence or absence of antagonistic soil microbes (Fisher et al. 2007). The distribution of the pathogen in the soil is sporadic or patchy, and the fungus is generally found down to 30-50 cm beneath the surface (Brown et al. 2013). It appears that the pathogens seem to prefer undisturbed soils characterized by native desert vegetation, such as creosote bushes in California and salt bushes, but can also be detected in other areas, for example grasslands dominated by native and non-native annuals (Fisher et al. 2007). 2

Figure 1. The geographic distribution of coccidioidomycosis based on skin testing. The figure highlights coccidioidomycosis-endemic areas in the United States and predicted endemic areas in Northern Mexico. Dark coloration indicates a heavily disease-endemic area; light coloration indicates a moderately disease-endemic area (Kolivras et al. 2001).

LIFE CYCLE OF COCCIDIOIDES spp. Coccidioides spp. have two growth phases, one as a pathogen in humans and animals and one in the soil. The saprophytic cycle starts in the soil with spores (arthroconidia) that develop into mycelium when moisture and nutrients are available. The mycelium then matures and produces arthroconidia when environmental conditions become unfavorable, e.g. during the dry season. The arthroconidia are then released, and germinate back into mycelia during the rainy season in late fall, winter and spring. The parasitic cycle involves the inhalation of the arthroconidia by animals or humans, which then form spherules filled with endospores in the lungs from which they can disseminate to other parts of the body (Einstein and Johnson 1993, Figure 2). Coccidioides spp. stay dormant in dry, alkaline soils, grow underground when it rains and become airborne when the soil is disturbed by wind, farming, construction and other soil disturbing activities. 3

Figure 2. Life cycle of Coccidioides spp. and growth of the dimorphic fungi pursuing two different life cycles. The conidia are about 3-5 m wide and can be dispersed by either wind or animals (Lewis, Bowers, and Barker 2015).

HISTORY OF COCCIDIOIDOMYCOSIS Coccidioidomycosis was first discovered in 1892 in Argentina, by a medical student, but from soil, it was first isolated in 1932 in Delano, California (Hirschmann 2007). Earlier, Coccidioides spp. was misidentified as a protozoan (Hirschmann 2007). The symptom complex of the disease was so common in the San Joaquin Valley that it became known as “valley fever” or “desert fever”. The lung was emphasized as the major portal of entry of the organism, and in 1927 a filtrate called “coccidioidin” was obtained from the pathogenic form of the fungus to be used for skin testing and to delineate the epidemiology of infection (Hirsch and Benson 1927). The Kern County Department of Public Health performed skin testing with coccidioidin for all cases of valley fever, discovering that most patients with positive skin test results had a history of dust exposure. Out of 104 patients, 94% were Caucasian, whereas 60% of the cases of disseminated disease occurred in those who were not Caucasian (Gifford, Buss and Douds 1937). A study by Smith (1946) concluded that the transmission of the disease from one person to another does not occur and infection starts from the inhalation of arthroconidia. Natural events can increase the risk of infection and have resulted in large outbreaks in the past. For example, the earthquake on January 17, 1994 in Northridge, California (Ventura County) was followed by a major outbreak of 4 coccidioidomycosis, and cases occurred as far north as Sacramento (Jibson and Harp 1998). Ventura County health officials noted a large increase in coccidioidomycosis cases during the first five weeks following the earthquake compared to previous years (Figure 3). The individuals affected by the dust cloud which was generated by the earthquake and which triggerd landslides were three times more likely to be diagonsed with acute coccidioidomycosis compared to those who were not exposed to the dust cloud (CDC 1997; Schneider et al. 1997).

Figure 3. Coccidioidomycosis epidemic curve for Ventura County, California, January 1 through March 15, 1994. The epidemic curve is a histogram showing the temporal distribution of diagnosed coccidioidomycosis cases over time; the peak of the histogram is two weeks following the earthquake (Jibson and Harp 1998).

Construction of new residential buildings during the housing boom in the Antelope Valley in northern Los Angeles County which occurred between 2003-2005 contributed to the increased cases of coccidioidomycosis in this area. An increase from 1,929 cases in 2003 to 4,339 cases was documented by the end of 2005 (Guevara et al. 2015; Figure 4). Soil disturbance from construction of residential and commercial buildings in the area factored with high winds likely increased the number of people becoming infected within this period. The Western Mojave Desert where California City (Kern County) is located, is exposed to daily southwesterly winds. Dust particles are small enough to travel long distances (even up to hundreds of kilometers in one wind event), and can remain in the air for a long time, carrying spores, soil nutrients, and organic matter to areas 5 of deposition and therefore can pose a risk of infection with soil borne pathogens (Joseph 1999; Chin et al. 2007; Fernandes 2010; Figure 4B).

6 A 5

Number of Cases per 100,000 inhabitants 100,000 per Cases of Number 0 2000 2002 2004 2006 2008 2010 2012 2014 Los Angeles County and San Bernardino County

LA County San Bernardino County

300

250

200

150

100 per 100,000 inhabitants 100,000per

50 Cases 0 2000 2005 2010 2015

Number of of Number Antelope Valley (northern Los Angeles County) and Kern County

Antelope Valley Kern County

Figure 4A and B. Annual coccidioidomycosis incidence rates in Antelope Valley, Kern County, Los Angeles County, and San Bernardino County, California, 2000–2015. The data were obtained from the California Department of Public Health and the Center of Disease Control and Prevention (CDC). Figure A shows the number of reported cases in Los Angeles County and San Bernardino County. Figure B shows the number of reported cases in Kern County compared to the cases in Antelope Valley.

SYMPTOMS AND TREATMENT OF COCCIDIOIDOMYCOSIS Valley fever is similar to the flu when it comes to experiencing its symptoms, but it is a very unique fungal infection when studied closely. About 40% of infected individuals develop an 6 illness within 1 to 3 weeks after exposure, which occurs when a person breathes in the airborne arthroconidia from soil (CDC 2016). The symptoms of valley fever are typically characterized by fever, acute cough, headache, malaise, weight loss, chest pain, pneumonia, a rash on the upper body or legs, and muscle aches. In rare cases, infected individuals can develop severe lung disease and lesions on the body; about 1% of the infected individuals develop dissemination to central nervous system (CNS), joints, bones, and skin (Kirkland 1996). While a local fungal infection targets one specific part of the body, a systemic infection disseminates throughout the whole body. African Americans, Filipinos, and persons with immunodeficencies, for example individuals with AIDS, pregnant women in the third trimester, and patients with diabetes, are at increased risk for disease dissemination (Rosenstein et al. 2001). If the infection is mild or local, the symptoms should clear up within a few weeks; however, if the infection is systemic, then the pathogen has to be treated with antifungal drugs, which can lead to many side effects such as kidney damage (CDC 2015). Once in the human body, the conidia change to a yeast form in tissues of the body. As of today, there is no vaccine available to prevent infection from Coccidioides spp. (CDC 2015).

ENVIRONMENTAL PARAMETERS THAT DETERMINE THE HABITAT OF C. immitis Soil microorganisms play a main role in all soil processes and can suppress or promote the growth of plants and soil microorganisms through antagonistic or mutualistic relationships. The diversity of desert soil microbiota and vegetation is adapted to extremely low rainfall, high temperature, high soil salinity, and alkalinity. These environmental parameters influence the activities of soil microorganisms in regard to growth and dispersal (Classen et al. 2015). Fungi are an important group of soil microorganisms found in terrestrial and marine environments. These soil saprophytes generally represent a dominant component of the biomass of the soil important for nutrient recycling. Expansion of the mycelium in the soil can lead to a vast extension of single species. For example, Armillaria solidipes, a member of the Basidiomycota in the order Agaricales has been determined as the world’s largest organism with an expansion of hyphae that extend over several miles (Elsas et al. 2006). Besides functioning as the main decomposers of organic material, fungi can act as mycorrhizal symbionts, and furthermore are being exploited by industries to produce food, antibiotics and immunosuppressants which are needed in modern medicine (Christensen 1989). Coccidioides species are believed to grow as soil saprophytes in arid deserts and are involved in recycling dead organic matter like other fungal species. However, recently, it 7 is being discussed that Coccidioides species should not be termed soil saprophyte like some other fungi commonly found in soils because they are lacking many enzymes needed to biodegrade plant material. Instead, it has been proposed that they have evolved to remain associated with their dead animal hosts in soil and preferentially biodegrade keratin (Sharpton et al. 2016). It has been estimated that Coccidioides spp. diverged 5.1 Mya from Uncinocarpus reesii and have gained 93 genes since then (Sharpton et al. 2016). Culture-independent molecular techniques, consisting of DNA extraction from soil samples followed by PCR and other molecular tools, facilitate the detection and investigation of soil fungal communities (Anderson and Cairney 2004) including soil-borne pathogens. Previous research in the Southern San Joaquin Valley showed that U. reesii and C. immitis which are close relatives in the order Onygenales can coexist at the same sites and another close relative, Gymnoascus reesii which can be found in the same order as well, was indicated as a potential competitor of C. immitis (English 2010). To date, the specific environmental conditions that support the growth of Coccidioides are still not fully understood. A combination of climate conditions such as temperature, precipitation, salinity, and soil parameters such as pH may affect growth and distribution of Coccidioides spp. Biological parameters, such as certain soil microbes or plants that can act as potential stimulators or inhibitors of other soil microbes including the pathogen, should be considered as influencing factors as well.

Vegetation Vegetation, which includes associations of plant species and biological soil crusts (BSCs) should be considered as an environmental factor that might affect the growth and distribution of Coccidioides spp. The Mojave Desert supports high plant species diversity with more than 2,600 species of plants, and BSCs are common in many undisturbed areas (Mackay 2013). Plants have the ability to prevent soil erosion, and thus have a positive effect on reducing fugitive dust emissions. Vegetation cover in the western and northwestern Mojave Desert including the area around California City consists of a variety of different plant species with the Creosote bush (Larrea tridentata), different species of Saltbush (e.g. Atriplex polycarpa) and white bur-sage (Ambrosia dumosa) as the predominant type of bushy vegetation (Sawyer et al. 2009). The Creosote bush (L. tridentata) is a drought tolerant evergreen shrub that is commonly found in warm 8 deserts of the southwestern U.S. together with species such as white bur-sage, another drought deciduous plant (Baldwin and Martens 2002). L. tridentata is often associated with A. dumosa and A. polycarpa in sandy soils of the Mojave Desert. The salt-tolerant A. polycarpa is also associated with playa soils that support only plants that are able to tolerate high salt content (Sawyer et al. 2009) and often have no plant growth at all. The diversity of plant species present on certain sites is determined by soil and climate parameters which influence the presence or absence of certain soil microbes and thus, might be useful as indicators of soils that can support the growth of C. immitis. Titus et al. (2002) pointed out that the presence of perennial shrubs, as well as small mammal burrows strongly influenced microsite soil characteristics and resulted in higher nutrient levels. In arid and semi-arid lands, open spaces are usually covered by BSCs that are formed by living microorganisms and their by-products (Belnap et al. 2001). In most dry regions, BSCs are dominated by cyanobacteria, lichens, mosses, micro-fungi, bacteria, and green algae. BSCs contribute nutrients and organic matters to desert soils and protect the soil from erosion. Soils that have intact BSC tend to be associated with undisturbed soil and not agricultural fields; therefore, these soils are more likely to contain the pathogen.

Soil pH Soil pH is one of the environmental factors controlling the growth and distribution of plants and soil microorganisms across geographic areas. There has not been much research on the influence of soil pH on fungal communities in the Mojave Desert, but research in other geographic areas points towards the importance of pH to fungal communities (Dix, 1995; Fierer et al. 2012). In vitro, many fungal species have a wide pH optimum covering a pH range between 5-9 units without significant inhibition of their growth (Wheeler et al. 1991; Nevarez et al. 2009). C. immitis can grow between a pH range of 3.5 to 9.0 when investigated in vitro, and has been found in clay- rich through sandy soils (Lacy and Swatek 1973; Cordeiro et al. 2006). The endemic areas of C. immitis are generally characterized by the dominance of alkaline soils, and C. immitis is suspected to have a selective advantage over other fungal and bacterial competitors in saline and alkaline soils (Maddy 1957; Fisher 2007).

Climate Although studies performed in Arizona have documented evidence relating outbreaks of valley fever to certain climate conditions, in California, there were problems to support such a correlation. Also, different statistical models were used for the California studies compared to the Arizona studies (Kolivras and Comrie 2003; Zender and Talamantes 2006). The regions endemic for C. immitis in California are characterized by hot summer months with an average of 26-32C, followed by 5-20 inches of rain annually (Swatek 1975). During prolonged periods of hot, dry conditions, the surface of the soil is sterilized which removes many potential competitors of the pathogen. C. immitis arthrospores remain viable below the surface, and when rain falls the pathogen germinates and returns to the surface layer, which then contains fewer competitors (Maddy 1957). Moisture promotes the growth of microbes in general, including C. immitis. After a dry period, the soil can be distributed by winds when disturbed, releasing arthroconidia of C. immitis into the air, exposing humans to infections through inhalation of this dormant form of the pathogen. The majority of infections seem to occur during windy, dusty periods in the dry season following the wet season in late fall and early winter (Maddy 1965).

Elevation Elevation has clearly an influence on vegetation type in the Mojave Desert (e.g. Yeateon and Cody 1976; Thompseon et al. 2005), but investigations on the influence of elevation on the occurrence of the pathogen is non-existent. Coccidioides spp. seem to be adapted to a wide variety of environments including high and low elevations. While valley fever is often linked with lower elevations for example the San Joaquin Valley (Bakersfield elevation 241 ft), it has also been found at higher elevation (e.g. Bear Valley Springs in the Tehachapi Mountains, unpublished data). Furthermore, C. immitis was isolated from soils at 3,200 ft above sea level from a site near Inyokern, California, where anthropology students had acquired coccidioidal infections (Plunkett and Swatek 1957).

Other physical and chemical soil parameters The fungus is thought to be associated with soils containing substantial organic matter, for example, and therefore is thought to be associated with rodent burrows (Egeberg and Ely 1956). In addition to soil pH, other soil parameters might determine if a habitat is supportive of the 10

pathogen, such as the amount of CaCO3 , cation exchange capacity, electrical conductivity, concentration of gypsum, sodium adsorption, available water capacity, type and amount of organic matter, grain size of soil particles, soil surface texture, water content, and saturated hydraulic conductivity (Lauer et al. 2012).

PREVIOUS RESEARCH Previous studies have attempted to correlate the geographic distribution of Coccidioides spp. with factors such as temperature, precipitation, salinity, and pH of the soil. There was a significant connection found between temperature and precipitation and valley fever incidence in Pima County, Arizona (Kolivras and Comrie 2003). In contrast, there was only a weak correlation found between weather variables and valley fever incidence in Kern County (Zender and Talamantes 2006). In an 8-year long study (1955-1962), 5,000 soil samples were collected in the San Joaquin Valley at monthly intervals, and the results showed that a higher concentration of soluble salts (sodium, calcium, magnesium, sulfates, and chlorides) in the soil was significantly correlated with the presence of C. immitis (Elconin et al. 1963). Elconin and colleagues continued searching for antagonists to C. immitis. In the laboratory, C. immitis was grown from soil with other microbial species and the results from this study identified three antagonistic microbial species that showed a clear zone of inhibition (0.5-0.7cm) towards the growth of C. immitis on Sabouraud’s medium. The antagonists were identified as Penicillium janthinellum and two strains of Bacillus subtilis (Egeberg et al. 1964). In previous research, soil parameters that were indicative of the presence of C. immitis in the Southern San Joaquin Valley (Kern County) comprised a pH of 7.5 and above, a clay content of about 30%, a cation exchange capacity of 15-25 milliquivalents/100g, a saturated hydraulic conductivity between 2 and 10 micrometers/s, and a water content (15 bar) between 15 and 20% (Lauer et al. 2012 and 2014). Previous research by English (2010) showed that C. immitis was found in loamy sands and was most likely present in soils with elevated pH.

PURPOSE OF THE STUDY AND SIGNIFICANCE Coccidioidomycosis has been encountered in inmates of California State prisons since 1919, and it has been diagnosed in inmates of various correctional facilities in and around endemic areas (Pappagianis and the Coccidioidomycosis Serology Laboratory 2007). Prisoners of the 11

California City Correctional Facility in California City were concerned about the high number of cases among the prisoners in 2014 (exact numbers not published) and before, which contributed to the development of this project. As of 2016, the facility houses approximately 1,800 prisoners and about 550 administrators, correctional officers and other employees. The ongoing drought, the increase in urbanization and the construction for renewable energy projects in addition to high numbers of agricultural fields that fell fallow in the last few years because of falling water tables, have resulted in an increase in particulate matter 10 (PM10) pollution in the area which had been blamed to contribute to an increase in coccidioidomycosis incidence (Guevara et al. 2015; Lauer et al. 2016).

SIGNIFICANCE The incidence of coccidioidomycosis is increasing progressively as a greater number of cases are being reported recently, particularly in Arizona and California; 10,911 cases were reported in 2014, and 8,109 were reported in 2015 in both counties together (CDC 2015, see Figure 5). Environmental testing for Coccicioides spp. is useful to learn more about the habitat of C. immitis and to determine if the occurrence of the pathogen depends on certain environmental parameters, such as vegetation, fungal diversity in the soil and pH. The data obtained from this research project can be used to educate policy makers, citizens, and land developers about where and when to expect this soil-borne pathogen and thus, ultimately reduce the incidence of this disease that has no cure and for which no vaccine exists (CDC 2016). This project discusses the fungal diversity present in the desert soils that coexist with the pathogen and which might include some species that are able to inhibit its growth.

Figure 5. Number of reported cases of coccidioidomycosis per year in the states where the fungus is endemic. Green indicates the number of cases reported in Arizona which are the highest; blue indicates the number of cases reported in California; and orange indicates the number of cases reported in Nevada, New Mexico, and Utah (CDC 2016). In 2014, the incidence of valley fever increased, 10,911 cases were reported in 2014 (CDC 2015).

We hypothesized that C. immitis is present in soils in the California City area based on the high incidence of coccidioidomycosis among prisoners and the general public. We further hypothesize that the presence or absence of the pathogen in the soil is linked to yet to be determined environmental parameters and/or depends on the presence or absence of certain soil microorganisms that could act as antagonists to the pathogen. This research project investigates if soil pH or other environmental parameters including vegetation present at our sampling sites could serve as an indicator for a suitable habitat of C. immitis. Averaged soil parameter data was obtained from the USDA websoilsurvey database to describe the soils of all sampling sites, type of vegetation was documented, and soil pH was determined in our laboratory.

MATERIALS AND METHODS

DESCRIPTION OF THE SAMPLING SITES The Western Mojave Desert where California City is located is characterized by high temperatures, reduced precipitation and daily southwesterly winds, particularly in the afternoons. A random sampling plan was developed to collect soil samples near California City (Kern County) based on soil physical and chemical parameters with information obtained from the U.S. Department of Agriculture websoilsurvey database. A state prison is located next to Mule Team Pkway, east of California City. In May of 2014, twenty soil samples were collected (5-7 cm depth) along transects from sites south and west of the Correctional Facility, along Proctor Boulevard and Mule Team Parkway (Figure 6 and 7, Table 1). These sampling sites covered several different soil types as indicated in the U.S. Department of Agriculture websoilsurvey database. In the spring of 2015, additional twenty-three soil samples were collected along transects on Mule Team Pkway and Sequoia Boulevard, but not again along Proctor Boulevard (Figure 8 and 9, Table 2 and 3). The soils at all sampling sites can be described as mostly undisturbed with sparsely distributed or no vegetation and with rodent burrows near some sites. The Sequoia Boulevard transect was 13 impacted by sheep grazing (plants damaged and organic matter input in form of sheep pellets). Soils appeared loamy, dry, with exposed coarse soil material on the surface (quartz pebbles) indicating partial erosion.

Proctor Blvd.

Sequoia Blvd.

Figure 6. Map of California City (eastern part) with purple arrows indicating transects where soil samples were collected during May 2014 and spring of 2015. Mule Team Pkway and Proctor Blvd. (n=20) for 2014, and Mule Team Pkway (n=11) and Sequoia Blvd. (n=12) for 2015. The numbers and the orange lines on the map represent areas with different soil types (USDA websoilsurvey database) (Photo: USDA websoilsurvey data base). Exact coordinates for all sampling sites are listed in tables below for all transects. Orange lines show borders of different soil types; soil map unit numbers are indicated as well.

Figure 7. Figures 7A and 7B show the map of California City (eastern part), the approximate location of the sampling sites nearby and along Proctor Blvd and Mule Team Pkway from May 2014. (photo: USDA websoilsurvey database). Exact coordinates are listed in Table 1. Orange lines show borders of different soil types; soil map unit numbers are indicated as well. Numbered blue dots indicate the individual sampling spots.

Table 1: Coordinates and elevation of locations Proctor Blvd and Mule Team Parkway, California City collected in May 2014 (USDA websoilsurvey database). Sample Coordinates Soil Soil Type General Site Appearance (5-7cm ID Map depth) Unit 1-1 CC N35.11497 254 Zerker loam, 2 to 5 percent Loamy and coarse soil, dense, moist W117.93250 slopes 1-2 CC N35.1150504 171 Rosamond clay loam Coarse soil, hard to dig, less moisture W117.931 than previous soil, no vegetation, loamy soil 1-3 CC N35.11510 171 Rosamond clay loam Loose soil, not very hard, dense, less W117.93180 moisture, dry 2-1 CC N35.12642 154 Neuralia sandy loam, 2 to 5 Near salt bush, soft, dry soil W117.91887 percent slopes 2-2 CC N35.12660 154 Neuralia sandy loam, 2 to 5 Near old Creosote bush, sandy, very loose W117.91878 percent slopes soil 2-3 CC N35.12675 154 Neuralia sandy loam, 2 to 5 sandy, very loose soil, near rodent W117.91861 percent slopes burrow, no bushes near sampling spot 3-1 CC N35.12549 100 Alko-Neuralia sandy loams, 0 Very lose, sandy, dry soil, near Creosote W117.92919 to 9 percent slopes bush 3-2 CC N35.12524 100 Alko-Neuralia sandy loams, 0 Loose, dry soil, near salt bush W117.9292 to 9 percent slopes 3-3 CC N35.12498 100 Alko-Neuralia sandy loams, 0 Near rodent burrow, dry, hard soil, W117.92905 to 9 percent slopes loamy, reddish color 4-1 CC N35.14441 154 Neuralia sandy loam, 2 to 5 Loose, sandy soil, quartz pebbles visible W117.9184 percent slopes on surface, near bush 4-2 CC N35.11412 154 Neuralia sandy loam, 2 to 5 Very loose, dry, loamy soil but sandier W117.91182 percent slopes than previous samples 4-3 CC N35.14340 154 Neuralia sandy loam, 2 to 5 Sandy, dry, powdery soil, low moisture W117.91184 percent slopes content 5-1 CC N35.15927 100 Alko-Neuralia sandy loams, 0 Extremely loose, dry soil, connected to W117.87272 to 9 percent slopes rodent burrow, quartz pebbles present near surface 5-2 CC N35.15983 100 Alko-Neuralia sandy loams, 0 Soil condensed not very loose, dry, a lot W117.87259 to 9 percent slopes of wild flowers nearby 5-3 CC N35.15999 100 Alko-Neuralia sandy loams, 0 Near a rodent burrow, some wild flowers W117.87259 to 9 percent slopes nearby, loose, dry, gravely soil 5-4 CC N35.15 battery 100 Alko-Neuralia sandy loams, 0 Loamy and sandy soil died to 9 percent slopes 6-1 CC N35.16397 114 Cajon loamy sand, 0 to 5 Loose, gravely, sandy, very dry soil W117.85561 percent slopes 6-2 CC N35.16367 114 Cajon loamy sand, 0 to 5 Extremely loose, dry, reddish tint, more W117.85574 percent slopes red on the surface 6-3 CC N35.16509 154 Neuralia sandy loam, 2 to 5 Loose and dry soil, near a bush, not much W117.85661 percent slopes different from previous site 6-4 CC N35.16453 114 Cajon loamy sand, 0 to 5 very loamy soil, near water tower W117.85593 percent slopes

Figure 8. Map of California City (eastern part) showing the approximate location of all the sampling sites along Mule Team Pkway from spring 2015. (photo: USDA websoilsurvey database). Exact coordinates are listed in Table 2. Orange lines show borders of different soil types; soil map unit numbers are indicated as well. Numbered blue dots indicated the individual sampling sites.

Table 2: Coordinates and elevation of locations along Mule Team Pkwy Road, California City, collected for the second sampling set in spring of 2015 Sample Coordinates Map Soil Type Site Description (5-7cm) ID Unit MT-1 N35.14023 154 Neuralia sandy loam, 2 to 5 loose soil, not red, creosote bush area, W117.92329 percent slopes small grasses, pink invasive annuals MT-2 N35.14108 154 Neuralia sandy loam, 2 to 5 small salt bush, gold fields, pink invasive W117.92097 percent slopes annuals MT-3 N35.14255 154 Neuralia sandy loam, 2 to 5 loose and dusty soil, creosote bush area, W117.91745 percent slopes white flowers, goldfields, small salt bushes MT-4 N35.14333 154 Neuralia sandy loam, 2 to 5 yellow flowers, purple flowers, small salt W117.91540 percent slopes bushes, lupine, owl-clover MT-5 N35.14302 154 Neuralia sandy loam, 2 to 5 goldfields, dry creosote bush area, loose W117.91520 percent slopes soil MT-6 N35.14504 154 Neuralia sandy loam, 2 to 5 very loose soil, new purple flower, dead W117.90984 percent slopes salt bushes, few creosote bushes MT-7 N35.14619 154 Neuralia sandy loam, 2 to 5 very loose and sandy soil, lots of yellow W117.90681 percent slopes annual flowers MT-8 N35.14631 154 Neuralia sandy loam, 2 to 5 W117.90674 percent slopes sandy soil, near salt bush MT-9 N35.15189 151 Muroc-Randsburg sandy W117.89068 loams, 5 to 9 percent slopes loose soil, not sandy 17

MT-10 N35.15284 151 Muroc-Randsburg sandy across the street from MT-9, very loose W117.89033 loams, 5 to 9 percent slopes soil, moist, water standing previously, plenty of salt bushes, other wildflowers MT-11 N35.16465 114 Cajon loamy sand, 0 to 5 very hard soil, ants, goldfields, near water W117.85629 percent slopes tower MT-12 N35.16465 114 Cajon loamy sand, 0 to 5 very hard soil, open, dried puddle, no W117.85629 percent slopes plants, close to MT-11 (about 10 m west)

Table 3: Coordinates and elevation of location Sequoia Boulevard, California City, collected for the second sampling set on 3/27/2015. Sample Coordinates Map Soil Type Site Description (5-7cm) ID Unit SQ-1 N35.09734 154 Neuralia sandy loam, 2 to 5 Hard soil, reddish color, quartz gravel on top, W117.91700 percent slopes tidy tips, small salt bushes, close to California City Ave. SQ-2 N35.09747 154 Neuralia sandy loam, 2 to 5 loose soil, large bush, rodent hole, small W117.91492 percent slopes grasses, sheep pellets SQ-3 N35.09758 154 Neuralia sandy loam, 2 to 5 creosote bush, loose soil, gravelly W117.91257 percent slopes SQ-4 N35.09735 154 Neuralia sandy loam, 2 to 5 small salt bush, ants nearby, also rodent holes, W117.91040 percent slopes loose soil SQ-5 N35.09700 154 Neuralia sandy loam, 2 to 5 very sandy soil, thistles (blue/purple), small W117.90668 percent slopes salt bushes, wildflowers, among them lots of Amsinckia plants SQ-6 N35.09686 114 Cajon loamy sand, 0 to 5 more compacted soil, small salt bushes, W117.90334 percent slopes Amsinckia sp., no thistles, many caterpillars SQ-7 N35.09675 114 Cajon loamy sand, 0 to 5 loose soil, goldfields, Amsinckia sp., thistles, W117.90154 percent slopes rodent holes, ants SQ-8 N35.09728 114 Cajon loamy sand, 0 to 5 hard on surface, more loose soil in deeper W117.89748 percent slopes layer, wildflowers, no plants SQ-9 N35.09701 114 Cajon loamy sand, 0 to 5 loose and sandy soil, green bush, creosote W117.89518 percent slopes bushes present, Amsinckia sp. SQ-10 N35.09731 114 Cajon loamy sand, 0 to 5 compacted soil, sparse vegetation, pink W117.89090 percent slopes invasive annual, creosote bush area SQ-11 N35.09724 114 Cajon loamy sand, 0 to 5 hard soil, rocky, top of hill, creosote bushes, W117.88669 percent slopes small salt bushes, sandy soil SQ-12 Battery Died 114 Cajon loamy sand, 0 to 5 percent slopes Top of hill, sandy soil

Figure 9. Map of California City (eastern part) showing the approximate location of sampling sites along Sequoia Boulevard from spring of 2015. (photo: USDA websoilsurvey database). Exact coordinates are listed in Table 3. Orange lines show borders of different soil types; soil map unit numbers are indicated as well. Numbered blue dots indicated the individual sampling sites.

COLLECTION OF SOIL SAMPLES A sterile spade (rinsed with 10% bleach before taking each sample) was used to collect soil in sterile 50 ml (VWR International, LLC Radnor, PA, USA) collection tubes with a screw top lid. Approximately 50 g of soil were collected from each individual sampling spot. Soil samples were placed on ice immediately after collection to prevent any changes in microbial diversity. In the laboratory, all the samples were stored at -20°C prior to DNA extraction. A total of forty-three samples (n=43) were collected altogether.

SOIL PH AND OTHER DESCRIPTIVE ENVIRONMENTAL PARAMETERS

Soil samples were analyzed for pH using an Oakton pH meter along with an Oakton Epoxy body pH Electrode sensor (Pasco Scientific, Roseville, CA, USA). In order to obtain the pH of the soil samples, soil slurries were prepared using 50% deionized water and 50% soil; and from that slurry, the pH was determined. To further characterize the soils, averaged soil parameter information including various soil physical and chemical parameters (e.g. pH, organic matter, water content, grain size percentages) for all sampling sites were obtained from the USDA websoilsurvey database. The pH of all soil samples determined in our laboratory was compared with the pH obtained from USDA websoilsurvey database.

IDENTIFICATION OF VEGETATION 19

Along with the investigation of important soil properties, such as soil pH and soil texture, the type of vegetation present at the sampling sites was investigated. For example, vegetation changes along transects and with elevation were documented and all dominant plants at the sampling site were identified. In order to identify the plants and to determine the plant associations present at the sampling sites, information about native plants of California was obtained from the California native plant society database: A manual of California vegetation, available at http://www.cnps.org/cnps/vegetation/manual.php.

FUGITIVE DUST EMMISION (PM10) DATA, ELEVATION, AND PRECIPITATION DATA Data for fugitive dust emission (PM10) was obtained from the California Air Resource Board website available at https://www.arb.ca.gov/adam for the city of Lancaster, CA and the Mojave Desert Air Basin which comprises northern Los Angeles County, Eastern Kern County and San Bernardino County (https://www.arb.ca.gov/desig/adm/basincnty.htm). We retrieved data for the years 2000 to 2015, and were able to compare the fugitive dust emission (PM10) for the entire Mojave Air basin combined from various weather stations in the basin with the data collected in the city of Lancaster only. The data was obtained for the State 24-Hour Average. The data for elevation was obtained using MapTools (https://www.freemaptools.com/elevation- finder.htm ) for all 4 transects (Figure 27). Data for precipitation (inches) per year from 2005 to 2015 was obtained from the Weather Underground website available at https://www.wunderground.com for the Mojave Desert Air Basin.

DNA EXTRACTION DNA from vegetative cells and from microbial conidia and spores in the soil samples was extracted using the MoBio PowerSoil® DNA Isolation Kit (MoBio Laboratories, Inc., Solana Beach, California). DNA was extracted from each soil sample by placing 0.25g of soil into a PowerBead tube followed by an incubation step at 70C for 30 min in order to weaken the protein coats of the spores. Then, 10 L of Proteinase K (Thermo Scientific) were added and the tubes were incubated at 56C for 30 min to increase DNA extraction efficiency from dormant microorganisms. The manufacturer’s protocol provided with the DNA Isolation Kit was followed for thorough homogenization of soil samples and DNA extraction. Extracted DNA was eluted in 20

100 L of EDTA buffer provided by the kit and stored at -20°C until processed. DNA extraction was performed with 2 replicates from each individual sampling spot. The successful DNA extraction was confirmed by loading 10 μL of each DNA extract onto a 2% (w/v) agarose gel in 1X TBE (20 mL Tris, 10mL acetate, 5mL ethylenediaminotetraacetic acid [EDTA], pH 8) buffer. The electrophoresis conditions were 180 V for 20 min. All gels were stained with ethidium bromide (10mg/L) for 20 min and were then visualized under UV light and photographed with the BioRad Gel Documentation Quantity One program (BioRad, Hercules, CA USA). The presence of distinct bands of good quality (non- sheared) confirmed the successful extraction of DNA.

DNA QUANTIFICATION

The amount of DNA present in the soil samples was measured using the Qubit 3.0 quantification system (Invitrogen Life Technologies-Thermo Fisher, MA, USA). The manufacturer’s protocol provided with the Qubit 3.0 quantitation system was followed for calibration of the Qubit 3.0. The calibration solution for the Qubit 3.0 flurometer was prepared using the dsDNA BR Assay Kit (Thermo Fisher Scientific). Five micro liter of the DNA were measured from each soil sample and was mixed with the appropriate solutions (Qubit dsDNA BR buffer) following the Qubit dsDNA BR Assay Kit protocol. The DNA amount was measured in ng/l and the data was recorded.

POLYMERASE CHAIN REACTIONS (PCRs) OF FUNGAL rDNA

The nested PCR used in this study is a three step PCR that is generally recommended to increase the probability of the diagnostic PCR to be able to amplify DNA of a single species and to reduce the numbers of false positive PCR products. The first PCR with a primer pair that targets all fungi and the second primer pair that targets predominantly Ascomycetes excludes DNA from all bacteria and other species in the amplification steps. This increases the probability that the final diagnostic PCR step leads to a positive result. DNA isolated from all soil samples was used in a nested PCR to amplify the inter-transcribe spacer region [ITS 1 and 2] (part of the ribosomal gene) of the ribosomal gene in fungi. In the first step of the nested PCR the forward primer NSA3 (5’- AAACTCTGTCGTGCTGGGGATA-3’) and the reverse primer NLC2 (5’- 21

GAGCTGCATTCCCAAACAACTC-3’) amplified a fragment of ~1,000bp, which is specific for all fungi present in the soil. A second amplification followed with the forward primer NSI1

(5’GATTGAATGGCTTAGTGAGG-3’) and the reverse primer NLB4 (5’-GGATTCTCACCCTCTATGAC-3’) amplified a fragment of ~900bp specific for Ascomycetes and some Basidiomycetes (Baptista-Rosas et al. 2012) which was diluted 1:50 in preparation of one of the final diagnostic PCR described below. For the diagnostic PCR, two different diagnostic primer pairs were used i) the primer pair ITSC1A/ITSC2 (Greene et al. 2000) was used instead of the primer pair originally developed by Binnicker et al. (2007) which resulted in high numbers of false positives in previous research (unpublished), and ii) the recently developed primer pair ITS1CF/ITS1CR (Vargas-Gastélum et al. 2015). Aliquots of all PCR reactions were loaded onto 2% agarose gels together with a DNA marker (Axygen Biosciences, Union City, CA) to verify the correct size of PCR fragments.

Each PCR reaction (25 μL final volume) was prepared using 2μL of DNA, 1.5 μL (10 picomol/ml) of each primer, 12.5 μL GoTaq Green Master Mix (Promega, WI, USA), and 7.5 of sterile DI H2O. GoTaq Green Master Mix (2X) is a premixed ready‐to‐use solution containing bacterially derived Taq DNA polymerase, 400μM dNTPs, 3mM MgCl2, and reaction buffers at optimal concentrations, which makes the DNA amplification more efficient. GoTaq Green Master mix(2X) also contains blue and yellow dyes that monitor the movement of DNA during electrophoresis. For all PCR amplifications, an initial denaturation and enzyme activation step of 2 min at 95°C was followed by 35 cycles that comprised a denaturing step for 30-45 secs at 95°C in which the hydrogen bonds holding the complementary strands of DNA together are broken. Then, for the first and second nested PCR reactions respectively, an annealing temperature of 55˚C for 40 secs was used for primer pair NSA3/NLC2 and 60°C for primer pair NSI1/NLB4, followed by an extension step for 60 secs at 72°C in which the Taq polymerase binds to each PCR primer and begins adding nucleotides, and a final 10 min extension at 72°C (Baptista-Rosas et al. 2012, Table 4 and 5). For the third step of the nested PCR, which is the diagnostic PCR step, the protocol suggested by Greene et al. (2000) was followed using primer pair ITSC1A/ITSC2. The diluted PCR product of the NSI1/NLB4 reaction was used in a PCR reaction with the diagnostic primer pair ITSC1A/ITSC2 to identify the presence of the pathogen. The expected length of the amplicon 22 was ~220bp for this primer pair. The products were amplified using the following conditions: 94°C for 2 min; 45x (94°C, 15 secs; 47°C, 30 secs); 68°C, 120 secs; 4°C, forever (see also Tables 4 and 5). An additional nested PCR method was included in order to determine and confirm the presence of the pathogen, based on Vargas-Gastélum et al. (2015). Two micro‐liters (undiluted) of the PCR product were taken from the second PCR reaction (NSI1/NLB4) and a nested PCR method using primer pair ITS1CF/ITS1CR (Vargas-Gastélum et al. 2015) was used to identify the samples positive for C. immitis. The expected length of the amplicon was ~135bp for this primer pair. For this PCR step, the products were amplified using the following conditions: 94°C for 2 min; 45x (95°C,30 secs; 70°C, 40 secs; 72°C, 60 secs); a final elongation step at 72°, 5 min, and 4°C, forever (see also Tables 4 and 5). Amplification of the obtained PCR fragments was verified via agarose gel electrophoresis as described above. The expected fragment size (sizes in table 4) for each PCR were compared to the position of bands in a PCR ladder (Axygen Biosciences, Union City, CA). In order to purify the PCR products obtained with the first (ITSC1A/ITSC2) and second (ITS1CF/ITS1CR) diagnostic PCR prior to sequencing, 5 μL of the PCR products of positive amplicons were mixed well with 2 μL EXO sap-it solution (Affymetrix USB Products, Santa Clara, CA) and incubated using the following conditions: 37°C for 30 min, 80°C for 15 min, prior to sequencing. Purified products were sequenced at the Sequencing Facility of the University of Florida using the forward primers (http://dnalims.dnatools.com). The sequences were identified using the BLAST (http://blast.ncbi.nlm.nih.gov/Blast.cgi) sequence analysis online database for nucleotides.

NS1/GcFung PCR

A second single PCR using primer pair NS1/GCFung was performed in order to obtain PCR products for DGGE. This primer pair provides high PCR amplification efficiency and reproducibility for DGGE applications (Hoshino and Morimoto 2008, Table 4). Two micro-liters of the extracted DNA were used to prepare the PCR reactions. Each PCR reaction (25 μL final volume) was prepared using 2 μL DNA, 1.5 μL (10 picomol/ml) of each primer (NS1/GCfung),

12.5 μL GoTaq Green Master Mix (Promega, WI, USA), and 7.5 of sterile DI H2O. Products were amplified using the following conditions: 94C for 2 min; 45x (94C, 30 secs; 50C, 30 secs; 72C, 23

30 secs) with a final extension of 10 min at 72C (Hoshino and Morimoto 2008). Four micro-liters of a 100bp PCR DNA ladder exACTGene (Axygen Biosciences, Union City, CA, USA) were used to confirm the length of amplicons separated on a 2% agarose gel. In the electrophoresis gel chamber, 4 μL of the amplified PCR product were investigated on 2% (w/v) agarose gels in 1X TBE buffer at 180 V (33mAmp) for 20 min. Gels were stained in an ethidium bromide (10 mg/L) solution for 20 min. PCR products obtained with primer pair NS1/GcFung were analyzed using a Molecular Imager Gel Doc System (BioRad, Hercules, CA USA) and the Quantity One Software. The expected product size was ~350bp.

Figure 10. Position of primers and annealing sites used for the nested PCR approach (locations for primer pairs NSA3/NLC2, NSI1/NLB4, ITSC1A/ITSC2, and ITS1CF/ITS1CR). The PCR with primer pair NS1/GCFung was performed in order to obtain PCR products for DGGE analysis. Information about the primers used in this project with sequence references are given in Table 4 (see also Hoegger and Kues 2007).

Table 4. Description of PCR primers for the amplification of fungal rDNA. Primer Pair Product Target Sequence (5’-3’) Reference size (bp) region NSA3/NLC2 ~1,000 Nuclear NSA3: 5’-AAACTCTGTCGTGCTGGGGATA-3’ Baptista-Rosas rRNA NLC2: 5’-GAGCTGCATTCCCAAACAACTC-3’ et al. 2012 NSI1/NLB4 ~900 Nuclear NSI1: 5′-GATTGAATGGCTTAGTGAGG-3’ Baptista-Rosas rRNA et al. 2012 NLB4: 5′-GGATTCTCACCCTCTATGAC-3’ ITSC1A/ITSC2 ~220 ITS2+5.8s ITS1: 5′-TCCGTAGGTGAACCTTGCGG-3’ Greene 2000 region ′ rDNA ITS2: 5 -GCTGCGTTCTTCATCGATGC-3’ ITS1CF/ITS1CR ~135 ITS1+ 5.8s ITS1F: 5’-GTGGCGTCCGGCTGCGCACCTCCCCCGCGG-3’ Vargas- region ITS1CR: 5′-GCGCAAGGCGGGCGATCCCCGGCAGCC- 3’ Gastélum et al. rDNA 2015 PCR for DGGE 350 Nuclear 18S NS1: 5′-GTAGTCATATGCTTGTCTC-3’ White et al. GCFung/NS1 rRNA Gene ′ 1990 GCFung: 5 -CGCCCGCCGCGCCCCGCGCCC May et al. 2001 GGCCCGCCGCCCCCGCCCCCATTCCCC GTTACCCGTTG-3’ *In a nested PCR NSA3/NLC2 served as outer primers and NSI1/NLB4 as inner primers followed by a diagnostic amplification step (Fig. 10). 24

Table 5. Experiment regimes for Polymerase Chain Reaction (PCR) and Denaturing Gradient Gel electrophoresis (DGGE) for fungal rDNA. PCR Experimental Conditions Main Cycling conditions Primer Set Expected Initial No. denaturing Annealing Extension Reference Product denaturing Cycles. temp (oC), temp (oC), temp temp (oC), time (s) time (s) (oC), time time (min) (s) NSA3/NLC2 ~1000 95, 2 35 95, 45 55, 40 72, 60 Baptista-Rosas et al. 2012 NSI1/NLB4 ~900 95, 2 35 95, 30 60, 40 72, 60 Baptista-Rosas et al. 2012 ITSC1A/ITSC2 ~220 94, 2 30 94, 15 47, 30 68, 120 Greene et al. 2000 ITS1CF/ITS1CR ~135 95, 2 30 95, 30 70, 40 72, 60 Vargas- Gastélum et al. 2015 GCFung/NS1 ~350 94, 2 45 94, 30 50, 30 72, 30 Hoshino and Morimoto 2008* DGGE Experimental Conditions Concentration of gel Denaturing Temp. Voltage (V) Time (h) Reference acrylamide/bisacrylamide (%) gradient range (oC) of gel (%) 7.5% 20-45 60 80 18 Hoshino and Morimoto 2008 *Hoshino and Morimoto (2008) used a different DGGE gradient, but the electrophoresis conditions were the same.

DENATURING GRADIENT GEL ELECTROPHORESIS (DGGE)

A fingerprinting method, known as DGGE, is a molecular approach for determining species richness and diversity of microbial populations (Muyzer et al. 1993). DGGE was performed with PCR amplicons obtained with primer pair NS1/GCfung to compare differences in the diversity of fungal communities present in all soil samples, comparing C. immitis positive and C. immitis negative samples by using the method published by Hoshino and Morimoto (2008) (see also Table 4 and 5). This method separates PCR amplicons by base composition and can indicate differences in microbial communities present in a sample by showing distinguishable bands in the separation patterns. Each DGGE band represents a single species, also referred to as operational taxonomic units (OTUs). To obtain a fingerprint of the fungal diversity in the soil samples, 20 µl of PCR product (NSI1/GCfung) that showed strong bands when analyzed on 2% agarose gels (the exact amount of amplified DNA was not determined) were loaded into each DGGE well. DGGE gels (7.5% polyacrylamide gels) were prepared using 18.75 ml of 40% acrylamide/bisacrylamide;

2 ml of 50X TAE buffer; 33 ml formamide (deionized), 33.6 g urea; and 100 ml deionized H2O) 25 and were run in 1X TAE buffer (242g Tris, 100 ml 0.5 M EDTA (pH 8) 57.1 ml glacial acetic acid). Along with the PCR products, an amplicon from C. immitis was also loaded on each DGGE gel as a positive control. DNA of C. immitis was obtained from sterilized cell material obtained from the Public Health Laboratory at Kern Medical Center, Bakersfield, CA. The gradient chosen for optimal separation of DGGE bands was 20-45 % urea and formamide. The detailed DGGE experimental conditions are listed in Table 5. Gels were stained with ethidium bromide solution (10 mg/L) for 20 minutes and then de-stained with distilled water for additional 20 minutes. Gels were photographed, and analyzed using a molecular imager system, the same system used to document agarose gels (Bio-Rad, Hercules, CA). The number of bands in each DGGE-lane were counted to assess fungal species richness, and banding patterns for samples from each transect were analyzed using the Quantity One software (version 5; Bio-Rad Hercules, CA USA).

BAND EXCISION, RE-AMPLIFICATION AND SEQUENCE ANALYSIS

Prominent DGGE bands were visualized on a Blue Reader (Dark Reader transilluminator, Clare Chemical Research; www.clarechemical.com) to avoid damaging the DNA and excised using a sterile scalpel, and then placed in 50 µl of sterile water in 0.6 ml Eppendorf tubes (Thermo Fisher Scientific). In order to avoid contamination from nearby bands, only the innermost portions of the bands were excised. The tubes were stored at 8C for 24-48 hours, so that DNA from the acrylamide pieces eluated into the water. Prior to re-amplification, the test tubes were vortexed for 5 seconds and a PCR reaction with the NSI1/GCFung primer pair was prepared using 2 µl of eluted DNA. Re-amplified DNA was again loaded and analyzed on a 2% (w/v) agarose gel in 1X TBE buffer at 180 V for 20 min. Gels were stained and analyzed as described earlier. In order to ensure that there was no contamination and only one band was present, re-amplified products were investigated again using DGGE. The bands without any contamination (only one band was present on the DGGE gel when re-analyzed) were purified using EXOsap-it solution (Affymetrix, Santa Clara, CA) and were sent out for sequencing to determine the closest match in the GenBank nucleotide database, as described above.

PHYLOGENETIC ANALYSIS

DGGE can display the fungal diversity of quantitatively numerous species present in the soil samples based on the separation of PCR amplicons obtained with primers specific for fungi. DGGE is semi-quantitative, which shows the dominant members of the fungal community that could be retrieved. The DGGE method allows a comparison of fungal diversity present in soil samples making it possible to conduct a comparable analysis of dominant fungal species in the samples by excision, re-amplification and sequencing of the amplicons obtained. The software Geneious version 6.1.6 (http://www.geneious.com, Kearse et al. 2012) was used to align 18S rDNA nucleotide sequences retrieved from DGGE bands. Occasionally, sequence mistakes in obviously conservative regions of the 18S rDNA fragment were corrected by hand after evaluating the sequence chromatograms. The software MEGA, version 5.22 was used for constructing a phylogenetic tree based on the neighbor joining algorithm for all sequenced amplicons. The corresponding sequence of the Basidiomycete Hannaella luteola (Accession# FJ527155), which was obtained from the GenBank nucleotide database was used as an out-group.

STATISTICAL ANALYSIS All data for pH, fungal diversity, DNA extraction, and fugitive dust emission (PM10) were pooled for exploratory data analysis. Appropriate statistical tests were performed to explore the relationship between the positive samples for C. immitis and amount of DNA extracted, USDA websoilsurvey data base parameters (pH only), and the fungal diversity or species richness present in the DGGE fingerprints using Microsoft Excel 2015 (version 15.13). Vegetation present on the sampling sites was identified visually; no statistics were used to determine difference in plant coverage or diversity. ANOVA was performed using Excel 2015 to explore the relationship between the pH, the amount of DNA (ng/l), and the samples positive and negative for C. immitis. The Pearson correlation was performed using Minitab 17 (2016) to show the correlation between the amount of DNA (ng/l) in the soil samples and the fungal diversity present in the soil samples. The Quantity one software (BioRad version 4.6.5, Hercules, CA) was used to mark and match the bands to perform Unweighted Pair Group Matching Analyses (UPGMAs) for a comparable analysis of DGGE fingerprints.

DATA ANALYSIS AND RESULTS 27

PHYSICAL AND CHEMICAL SOIL PARAMETERS

The soil types that prevailed at each sampling site along a transect were identified using the U.S. Department of Agriculture soil maps (Table 6, 7, 8 and 9). In total, six different soil types that varied in soil parent material and in regard to soil chemical and physical parameters, as indicated by different USDA soil map units, were characteristic for the sampling area as a whole. Variation in soil types and soil physical and chemical parameters were observed for all sites. The transect along Proctor Blvd (2014) comprised four different soil types (Soil Map Units 254, 171, 154, 100), whereas soils along Mule Team Pkway (2014) were composed of three different soil types (Soil Map Units 154, 100, 114). Mule Team Pkway was sampled in 2014 and 2015, but the individual sampling spots differed for both years. Samples collected along Sequoia Blvd and along Mule Team Pkway (2015) were comprised of three different soil types only (Soil Map Units 114, 154 and 151), respectively. The transects sampled in 2015 (Soil Map Units 114, 151, 154) were less diverse in regard to soil types compared to transects sampled in 2014 (Soil Map Units 254, 171, 154, 100, 114), soil types Zerker loam, Rosamond clay loam, and Alko-Neuralia sandy loam were not included in the 2015 sampling event. The Mule Team Pkway transect (2015) included a soil type that was not present in any of the other transects and was indicated as Muroc Randsburg sandy soil in the USDA websoilsurvey database (Soil Map Unit #151). The samples that were positive for C. immitis were found in all six different soil types. However, two soil types were most common in our sampling area and contained the highest number of C. immitis positive samples. These soils were characterized as Neuralia sandy loam (Soil Map Unit #154) and Cajon loamy sand (Soil Map Unit #114). Twenty-five soil samples that were positive for C. immitis positive samples were detected in Neuralia sandy loam (43% of the positive samples) and Cajon loamy sand (37% of the positive samples) from samples collected in both years, indicating these soil types as a preferable habitat for C. immitis. The chemical parameters for Neuralia sandy loam and Cajon loamy sand, such as available water capacity, pH, water content, and percent clay showed differences when compared to each other and other soil types (Table 6, 7, 8 and 9). Results showed that pH values when measured in the laboratory did not show a significant difference between samples that were positive or negative for the pathogen. Even though the USDA websoilsurvey data is averaged for the pH of soil samples, the pH values in the database and the ones determined in the laboratory did not differ significantly. The averaged pH determined in our 28 laboratory for samples that showed a very strong PCR amplicon (MT-4 and S-1) in the diagnostic PCRs was 7.61 and 7.64. Although the pH of C. immitis positive samples was slightly higher than the pH of C. immitis negative soil samples, a statistical analysis using ANOVA showed no significant difference (F1,41=0.041, p=0.840) between the pH of soils that supported or not supported the growth of C. immitis.

7.65

7.60

7.55

7.50

7.45

Measured Measured pH 7.40

7.35

7.30

7.25 Positive Samples Negative Samples

Figure 11. The mean number of the pH of samples positive (n=30) and negative (n=13) for C. immitis collected near California City, California. Error bars are 95% confidence intervals. (F1,41=0.041, p=0.840).

Table 6. Sampling sites indicating soil type and soil map unit symbols for all sampling sites. Averaged data for soil chemical and physical parameters for all soil samples collected at Proctor Blvd in 2014, as obtained from the USDA websoilsurvey database. PCR results for all primer combination are indicated with indication of results for replicate analyses. Samples highlighted in yellow were positive for C. immitis (see Table 7 for the remaining samples). 5/1/14 1-1CC 1-2CC 1-3CC 2-1CC 2-2CC 2-3CC 3-1CC 3-2CC 3-3CC 4-1CC Soil Map Unit 254 171 171 154 154 154 100 100 100 154 Soil Map Unit Name Zerker loam, 2 to Rosamond Rosamond Neuralia Neuralia Neuralia Alko-Neuralia Alko-Neuralia Alko-Neuralia Neuralia 5 percent slopes clay loam clay loam sandy loam, sandy loam, sandy loam, sandy loams, sandy loams, sandy loams, sandy loam, 2 2 to 5 2 to 5 2 to 5 0 to 9 percent 0 to 9 percent 0 to 9 percent to 5 percent Coordinates percent percent percent slopes slopes slopes slopes N35.11497 N35.115050 N35.11510 N35.12642 N35.12660 N35.12675 N35.12549 N35.12524 N35.12498 N35.14441 W117.93250 4 W117.931 W117.93180 W117.91887 W117.91878 W117.91861 W117.92919 W117.9292 W117.92905 W117.9184 Chemical parameters CaCO3 (%) 3.00 3.00 3.00 5.00 5.00 5.00 0.00 0.00 0.00 5.00 Cation Exchg Cap. (mEq/100g) 12.50 13.50 13.50 7.40 7.40 7.40 7.00 7.00 7.00 7.40 Electrical Conductivity (dS/m) 0.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 pH (USDA average) 7.90 8.20 8.20 7.50 7.50 7.50 8.20 8.20 8.20 7.50 pH (measured in the lab) 7.69 7.58 8.16 8.28 7.55 7.85 7.69 7.83 8.03 7.41 pH (measured in the lab replicate) 7.78 7.54 7.22 7.65 7.42 7.40 7.08 7.84 7.54 7.37 Physical parameters Available Water Capacity (cm/cm) 0.15 0.16 0.16 0.11 0.11 0.11 0.10 0.10 0.10 0.11 Available water storage (cm/cm) 3.00 3.20 3.20 2.14 2.14 2.14 2.05 2.05 2.05 2.14 Avail. Water supply 0-25cm (cm/cm) 3.75 4.05 4.05 2.99 2.99 2.99 2.68 2.68 2.68 2.99 Bulk Density, one-third bar (g/cm 2 ) 1.50 1.53 1.53 1.55 1.55 1.55 1.58 1.58 1.58 1.55 Linear extensibility (%) 4.50 3.80 3.80 1.80 1.80 1.80 1.50 1.50 1.50 1.80 Liquid Limit (%) 35.00 28.80 28.80 21.00 21.00 21.00 25.00 25.00 25.00 21.00 Organic Matter (%) 0.75 0.75 0.75 0.25 0.25 0.25 0.25 0.25 0.25 0.25 Percent clay 24.00 23.60 23.60 13.10 13.10 13.10 16.00 16.00 16.00 13.10 Percent Sand 58.20 42.90 42.90 64.60 64.60 64.60 65.10 65.10 65.10 64.60 Percent silt 17.80 33.50 33.50 22.30 22.30 22.30 18.90 18.90 18.90 22.30 Plasticity index (%) 12.50 11.90 11.90 5.80 5.80 5.80 2.50 2.50 2.50 5.80 Saturated Hydraulic Conductivity (um/sec) 9.00 9.03 9.03 25.47 25.47 25.47 28.00 28.00 28.00 25.47 Surface texture Sandy Clay Loam Sandy loam Sandy loam Sandy loam Sandy loam Sandy loam Sandy loam Sandy loam Sandy loam Sandy loam Water content, 15 bar (%) 14.70 15.20 15.20 8.40 8.40 8.40 10.10 10.10 10.10 8.40 Water content, one-third bar (%) 22.10 27.00 27.00 17.50 17.50 17.50 18.90 18.90 18.90 17.50 Wind erodibility group 5.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 PCR Results NSA3/NLC2 (1st) pos pos pos pos pos pos pos pos pos pos NSA3/NLC2 (2nd) pos pos pos pos pos pos pos pos pos pos NSI1/NLB4 (1st) pos pos pos pos pos pos pos pos pos pos NSI1/NLB4 (2nd) pos pos pos pos pos pos pos pos pos pos ITSC1A/ITSC2 (1st) pos neg pos pos neg neg neg neg Suspected pos* pos ITSC1A/ITSC2 (2nd) neg neg pos neg pos pos pos neg neg pos ITS1CF/ITS1CR (1st run) neg neg neg neg neg neg neg neg neg neg ITS1CF/ITS1CR (2nd run) neg neg neg neg neg neg neg neg neg neg NS1/GCfung (1st) pos pos pos pos pos pos pos pos pos pos NS1/GCfung (2nd pos pos pos pos pos pos pos pos pos pos *suspected positive diagnostic PCRs resulted in a noisy sequence, resulting from the contribution of more than one species to the sequence. 30

Table 7. Sampling sites indicating soil type and soil map unit symbols for all sampling sites. Averaged data for soil chemical and physical parameters for all soil samples collected at Mule Team Pkway in 2014 (continued), as obtained from the USDA websoilsurvey database. PCR results for all primer combination are indicated with indication of results for replicate analyses. Samples highlighted in yellow were positive for C. immitis. 5/1/14 4-2CC 4-3CC 5-1CC 5-2CC 5-3CC 5-4CC 6-1CC 6-2CC 6-3CC 6-4CC Soil Map Unit 154 154 100 100 100 100 114 114 154 114 Soil Map Unit Name Neuralia sandy Neuralia sandy Alko-Neuralia Alko-Neuralia Alko-Neuralia Alko-Neuralia Cajon loamy Cajon loamy Neuralia sandy Cajon loamy sand, loam, 2 to 5 loam, 2 to 5 sandy loams, 0 sandy loams, 0 sandy loams, 0 to sandy loams, 0 to sand, 0 to 5 sand, 0 to 5 loam, 2 to 5 0 to 5 percent percent slopes percent slopes to 9 percent to 9 percent 9 percent slopes 9 percent slopes percent slopes percent slopes percent slopes slopes Coordinates N35.11412 N35.14340 N 35.15927 N 35.15983 N 35.15999 N35.15 battery N 35.16397 N 35.16367 W N 35.16509 N35.16453 W117.91182 W117.91184 W117.87272 W117.87259 W117.87259 died W117.85561 117.85574 W117.85661 W117.85593 Chemical parameters CaCO3 (%) 5.00 5.00 0.00 0.00 0.00 0.00 0.00 0.00 5.00 0.00 Cation Exchg Cap. (mEq/100g) 7.40 7.40 7.00 7.00 7.00 7.00 3.90 3.90 7.40 3.90 Electrical Conductivity (dS/m) 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 pH (USDA average) 7.50 7.50 8.20 8.20 8.20 8.20 7.90 7.90 7.50 7.90 pH (measured in the lab) 7.81 7.59 8.82 7.13 8.01 7.78 7.46 7.35 7.33 7.46 pH (measured in the lab replicate) 7.73 7.01 7.29 7.13 7.14 7.80 6.98 6.84 7.15 7.40 Physical parameters Available Water Capacity (cm/cm) 0.11 0.11 0.10 0.10 0.10 0.10 0.08 0.08 0.11 0.08 Available water storage (cm/cm) 2.14 2.14 2.05 2.05 2.05 2.05 1.60 1.60 2.14 1.60 Avail. Water supply 0-25cm (cm) 2.99 2.99 2.68 2.68 2.68 2.68 2.00 2.00 2.99 2.00 2 Bulk Density, one-third bar (g/cm ) 1.55 1.55 1.58 1.58 1.58 1.58 1.65 1.65 1.55 1.65 Linear extensibility (%) 1.80 1.80 1.50 1.50 1.50 1.50 1.50 1.50 1.80 1.50 Liquid Limit (%) 21.00 21.00 25.00 25.00 25.00 25.00 0.00 0.00 21.00 0.00 Organic Matter (%) 0.25 0.25 0.25 0.25 0.25 0.25 0.75 0.75 0.25 0.75 Percent clay 13.10 13.10 16.00 16.00 16.00 16.00 4.00 4.00 13.10 4.00 Percent Sand 64.60 64.60 65.10 65.10 65.10 65.10 79.20 79.20 64.60 79.20 Percent silt 22.30 22.30 18.90 18.90 18.90 18.90 16.80 16.80 22.30 16.80 Plasticity index (%) 5.80 5.80 2.50 2.50 2.50 2.50 2.50 2.50 5.80 2.50 Saturated Hydraulic Conductivity (um/sec) 25.47 25.47 28.00 28.00 28.00 28.00 92.00 92.00 25.47 92.00 Surface texture Sandy loam Sandy loam Sandy loam Sandy loam Sandy loam Sandy loam Loamy sand Loamy sand Sandy loam Loamy sand Water content, 15 bar (%) 8.40 8.40 10.10 10.10 10.10 10.10 3.80 3.80 8.40 3.80 Water content, one-third bar (%) 17.50 17.50 18.90 18.90 18.90 18.90 12.10 12.10 17.50 12.10 Wind erodibility group 3.00 3.00 3.00 3.00 3.00 3.00 2.00 2.00 3.00 2.00 PCR Results NSA3/NLC2 (1st) pos pos pos pos pos pos pos pos pos pos NSA3/NLC2 (2nd) pos pos pos pos pos neg pos pos pos pos NSI1/NLB4 (1st) pos pos pos pos pos pos pos pos pos pos NSI1/NLB4 (2nd) pos pos pos pos pos neg pos pos pos pos ITSC1A/ITSC2 (1st) pos neg neg pos neg pos neg pos Suspected Pos* neg ITSC1A/ITSC2 (2nd) pos neg neg neg neg neg neg neg neg pos ITS1CF/ITS1CR (1st run) neg neg neg neg neg neg neg neg neg neg ITS1CF/ITS1CR (2nd run) neg neg neg neg neg neg neg neg neg neg NS1/GCfung (1st) pos pos pos pos pos pos pos pos pos pos NS1/GCfung (2nd pos pos pos pos pos pos pos pos pos pos *suspected positive diagnostic PCRs resulted in a noisy sequence, resulting from the contribution of more than one species to the sequence 31

Table 8. Sampling sites indicating soil type and soil map unit symbols for all sampling sites. Averaged data for soil chemical and physical parameters for all soil samples collected at Mule Team Pkway in 2015, as obtained from the USDA websoilsurvey database. PCR results for all primer combination are indicated with indication of results for replicate analyses. Samples highlighted in yellow were positive for C. immitis. 5/1/15 MT-1 MT-2 MT-4 MT-5 MT-6 MT-7 MT-8 MT-9 MT-10 MT-11 MT-12 Soil Map Unit 154 154 154 154 154 154 154 151 151 114 114 Soil Map Unit Name Neuralia Neuralia Neuralia Neuralia sandy Neuralia sandy Neuralia sandy Neuralia sandy Muroc- Muroc- Cajon loamy Cajon loamy sandy loam, 2 sandy loam, 2 sandy loam, 2 loam, 2 to 5 loam, 2 to 5 loam, 2 to 5 loam, 2 to 5 Randsburg Randsburg sand, 0 to 5 sand, 0 to 5 to 5 percent to 5 percent to 5 percent percent slopes percent slopes percent slopes percent slopes sandy loams, 5 sandy loams, 5 percent slopes percent slopes slopes slopes slopes to 9 percent to 9 percent Coordinates slopes slopes N35.14023 N35.14108 N35.14333 N35.14302 N35.14504 N35.14619 N35.14631 N35.15189 N35.15284 N35.16465 N35.16465 W117.92329 W117.92097 W117.91540 W117.91520 W117.90984 W117.90681 W117.90674 W117.89068 W117.89033 W117.85629 W117.85629 Chemical parameters CaCO3 (%) 5.00 5.00 5.00 5.00 5.00 5.00 5.00 3.00 3.00 0.00 0.00 Cation Exchg Cap. (mEq/100g) 7.40 7.40 7.40 7.40 7.40 7.40 7.40 7.50 7.50 3.90 3.90 Electrical Conductivity (dS/m) 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 pH (USDA average) 7.50 7.50 7.50 7.50 7.50 7.50 7.50 8.20 8.20 7.90 7.90 pH (measured in the lab) 7.77 7.39 7.85 7.31 7.62 7.81 7.02 7.83 7.9 7.8 7.90 pH (measured in the lab replicate) 7.25 7.44 7.42 7.07 7.27 6.72 6.81 7.38 7.68 7.72 7.84 Physical parameters Available Water Capacity (cm/cm) 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.10 0.10 0.08 0.08 Available water storage (cm/cm) 2.14 2.14 2.14 2.14 2.14 2.14 2.14 1.91 1.91 1.60 1.60 Avail. Water supply 0-25cm (cm) 2.99 2.99 2.99 2.99 2.99 2.99 2.99 2.39 2.39 2.00 2.00 2 Bulk Density, one-third bar (g/cm ) 1.55 1.55 1.55 1.55 1.55 1.55 1.55 1.55 1.55 1.65 1.65 Linear extensibility (%) 1.80 1.80 1.80 1.80 1.80 1.80 1.80 1.50 1.50 1.50 1.50 Liquid Limit (%) 21.00 21.00 21.00 21.00 21.00 21.00 21.00 20.00 20.00 0.00 0.00 Organic Matter (%) 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.75 0.75 Percent clay 13.10 13.10 13.10 13.10 13.10 13.10 13.10 13.00 13.00 4.00 4.00 Percent Sand 64.60 64.60 64.60 64.60 64.60 64.60 64.60 67.40 67.40 79.20 79.20 Percent silt 22.30 22.30 22.30 22.30 22.30 22.30 22.30 19.60 19.60 16.80 16.80 Plasticity index (%) 5.80 5.80 5.80 5.80 5.80 5.80 5.80 2.50 2.50 2.50 2.50 Saturated Hydraulic Conductivity (um/sec) 25.47 25.47 25.47 25.47 25.47 25.47 25.47 28.00 28.00 92.00 92.00 Surface texture Sandy loam Sandy loam Sandy loam Sandy loam Sandy loam Sandy loam Sandy loam Sandy loam Sandy loam Loamy sand Loamy sand Water content, 15 bar (%) 8.40 8.40 8.40 8.40 8.40 8.40 8.40 8.10 8.10 3.80 3.80 Water content, one-third bar (%) 17.50 17.50 17.50 17.50 17.50 17.50 17.50 17.10 17.10 12.10 12.10 Wind erodibility group 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 2.00 2.00 PCR Results NSA3/NLC2 (1st run) pos pos pos pos pos pos pos pos pos pos pos NSA3/NLC2 (2nd run) pos pos pos pos pos pos pos pos pos pos pos NSI1/NLB4 (1st run) pos pos pos neg neg pos pos pos pos pos pos NSI1/NLB4 (2nd run) pos neg pos pos neg pos pos pos pos pos pos ITSC1A/ITSC2 (1st run) neg neg pos neg neg neg neg Suspected pos* pos neg neg ITSC1A/ITSC2 (2nd run) neg neg neg neg neg neg neg neg neg neg neg ITS1CF/ITS1CR (1st run) N/A N/A N/A N/A N/A N/A N/A neg neg N/A N/A ITS1CF/ITS1CR (2nd run) neg neg pos neg neg pos pos pos pos pos pos NS1/GCfung (1st run) pos pos pos pos pos pos pos pos pos pos pos NS1/GCfung (2nd run) pos pos pos pos pos pos pos pos pos pos pos *suspected positive diagnostic PCRs resulted in a noisy sequence, resulting from the contribution of more than one species to the sequence. 32

Table 9. Sampling sites indicating soil type and soil map unit symbols for all sampling sites. Averaged data for soil chemical and physical parameters for all soil samples collected at Sequoia Blvd in 2015, as obtained from the USDA websoilsurvey database. PCR results for all primer combination are indicated with indication of results for replicate analyses. Samples highlighted in yellow were positive for C. immitis. 5/1/15 SQ-1 SQ-2 SQ-3 SQ-4 SQ-5 SQ-6 SQ-7 SQ-8 SQ-9 SQ-10 SQ-11 SQ-12 Soil Map Unit 154 154 154 154 154 114 114 114 114 114 114 114 Soil Map Unit Name Neuralia sandy Neuralia sandy Neuralia sandy Neuralia sandy Neuralia sandy Cajon loamy Cajon loamy sand, Cajon loamy Cajon loamy Cajon loamy Cajon loamy Cajon loamy loam, 2 to 5 loam, 2 to 5 loam, 2 to 5 loam, 2 to 5 loam, 2 to 5 sand, 0 to 5 0 to 5 percent sand, 0 to 5 sand, 0 to 5 sand, 0 to 5 sand, 0 to 5 sand, 0 to 5 percent slopes percent slopes percent slopes percent slopes percent slopes percent slopes slopes percent slopes percent slopes percent slopes percent slopes percent slopes

Coordinates N35.09734 N35.09747 N35.09758 N35.09735 N35.09700 N35.09686 N35.09675 N35.09728 N35.09701 N35.09731 N35.09724 W117.91700 W117.91492 W117.91257 W117.91040 W117.90668 W117.90334 W117.90154 W117.89748 W117.89518 W117.89090 W117.88669 battery died Chemical parameters CaCO3 (%) 5.00 5.00 5.00 5.00 5.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Cation Exchg Cap. (mEq/100g) 7.40 7.40 7.40 7.40 7.40 3.90 3.90 3.90 3.90 3.90 3.90 3.90 Electrical Conductivity (dS/m) 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 pH (USDA average) 7.50 7.50 7.50 7.50 7.50 7.90 7.90 7.90 7.90 7.90 7.90 7.90 pH (measured in the lab) 7.80 8.33 8.03 7.63 7.37 7.65 7.01 7.23 7.01 8.02 7.23 8.00 pH (measured in the lab replicate) 7.42 7.02 7.37 7.51 7.44 7.40 6.81 7.16 6.88 7.06 7.70 6.96 Physical parameters Available Water Capacity (cm/cm) 0.11 0.11 0.11 0.11 0.11 0.08 0.08 0.08 0.08 0.08 0.08 0.08 Available water storage (cm/cm) 2.14 2.14 2.14 2.14 2.14 1.60 1.60 1.60 1.60 1.60 1.60 1.60 Avail. Water supply 0-25cm (cm) 2.99 2.99 2.99 2.99 2.99 2.00 2.00 2.00 2.00 2.00 2.00 2.00 Bulk Density, one-third bar (g/cm 2 ) 1.55 1.55 1.55 1.55 1.55 1.65 1.65 1.65 1.65 1.65 1.65 1.65 Linear extensibility (%) 1.80 1.80 1.80 1.80 1.80 1.50 1.50 1.50 1.50 1.50 1.50 1.50 Liquid Limit (%) 21.00 21.00 21.00 21.00 21.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Organic Matter (%) 0.25 0.25 0.25 0.25 0.25 0.75 0.75 0.75 0.75 0.75 0.75 0.75 Percent clay 13.10 13.10 13.10 13.10 13.10 4.00 4.00 4.00 4.00 4.00 4.00 4.00 Percent Sand 64.60 64.60 64.60 64.60 64.60 79.20 79.20 79.20 79.20 79.20 79.20 79.20 Percent silt 22.30 22.30 22.30 22.30 22.30 16.80 16.80 16.80 16.80 16.80 16.80 16.80 Plasticity index (%) 5.80 5.80 5.80 5.80 5.80 2.50 2.50 2.50 2.50 2.50 2.50 2.50 Saturated Hydraulic Conductivity (um/sec) 25.47 25.47 25.47 25.47 25.47 92.00 92.00 92.00 92.00 92.00 92.00 92.00 Surface texture Sandy loam Sandy loam Sandy loam Sandy loam Sandy loam Loamy sand Loamy sand Loamy sand Loamy sand Loamy sand Loamy sand Loamy sand Water content, 15 bar (%) 8.40 8.40 8.40 8.40 8.40 3.80 3.80 3.80 3.80 3.80 3.80 3.80 Water content, one-third bar (%) 17.50 17.50 17.50 17.50 17.50 12.10 12.10 12.10 12.10 12.10 12.10 12.10 Wind erodibility group 3.00 3.00 3.00 3.00 3.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 PCR Results NSA3/NLC2 (1st run) pos pos pos pos pos pos pos pos pos pos pos pos NSA3/NLC2 (2nd run) pos pos pos pos pos pos pos pos pos pos pos pos NSI1/NLB4 (1st run) pos pos pos pos pos pos pos pos pos pos pos pos NSI1/NLB4 (2nd run) pos pos pos pos pos pos pos pos pos pos pos pos ITSC1A/ITSC2 (1st run) pos neg neg neg neg neg neg Suspected pos* neg neg neg neg ITSC1A/ITSC2 (2nd run) neg neg neg neg neg neg neg neg neg neg neg neg ITS1CF/ITS1CR (1st run) neg N/A neg N/A N/A N/A N/A neg N/A N/A N/A N/A ITS1CF/ITS1CR (2nd run) pos pos pos pos pos pos pos pos pos pos pos pos NS1/GCfung (1st run) pos pos pos pos pos pos pos pos pos pos pos pos NS1/GCfung (2nd run) pos pos pos pos pos pos pos pos pos pos pos pos *suspected positive diagnostic PCRs resulted in a noisy sequence, resulting from the contribution of more than one species to the sequence. 33

VEGETATION The plant associations of Larrea tridentata (Creosote bush) with Ambrosia dumosa (burro- weed or white bur-sage) and Atriplex polycarpa (cattle spinach or alkali salt bush) were dominant along all transects sampled in both years. A. polycarpa is also known to be associated with sparse playa (Sawyer et al. 2009) and was detected on dry playa sites from Proctor Blvd (2014). The Proctor Blvd and Mule Team Pkway (2014) transects exhibited predominantly vegetation of four plant species (L. tridentata, Ambrosia sp., A. polycarpa and the annual herb Phacelia fremontii). These sites where also showing spots with dry playas absent of vegetation. The Mule Team Pkway transect (2015) was dominated by numerous plant species which included Shismus arabica (grass), Erodium cicutarium (filaree), A. polycarpa, and L. tridentata (see Table 10 for details) with dry playa soils at two sampling spots that were positive for C. immits. The Sequoia Blvd (2015) transect exhibited more plant diversity than all other transects. The vegetation cover of Sequoia Blvd was dominated by Coreopsis sp., Malacothrix glabrata (Desert Dandelion), Ambrosia salsola (Cheesebush), Salvia sp. (thistle sage), Acamptopappus sphaerocephalus (Goldenheads), Coreopsis sp. (Bigelow's coreopsis), and Adenophyllum cooperi (Goldfields), and there were no dry playas present near any of the sampling spots (see Table 10 for details). There were no salt bushes established along Sequoia Blvd, but L. tridentata was present near some sampling spots. Some of the plants present at the sites positive for C. immitis were also present at sites that were negative for C. immitis, for example A. polycarpa was present at site MT-2. Two strong positive sampling sites, MT-4 and S-1, were rich in plant diversity (Figure 12A and B). All plant species were identified visually; no statistics were used to determine difference in plant coverage or diversity. Differences in vegetation were also documented between years for the same sampling site. Overall, the Mule Team Pkway and Sequoia Blvd transects (2015) exhibited higher diversity of plants compared to the transects sampled in 2014. In 2014, the sites positive for C. immitis were characterized by plants such as A. polycarpa and L. tridentata (Table 10), but some sites that were positive for C. immitis showed no plant species but dry playas, as mentioned before. In both years, none of the plant species stood out for the samples negative for C. immits; Phacelia fremontii was present on some of the negative sites (Table 10). In both years, the sites positive for C. immitis were characterized by several different plant species (Table 10) including 34

A. polycarpa and L. tridentata which were identified as the dominant plant species at sites that tested positive for C. immitis indicating a habitat suitable for C. immitis.

A B

Figure 12A and B. Figure 12A shows sampling site MT-4 and 12B shows the sampling site S-1, in California City (eastern part), from Spring 2015.

Table 10. The dominant plant species present at each site with indication of soil map units for each individual sampling spot. Sample IDs in red color with asterisks represent soil samples in which C. immitis was detected. Sample Soil Map Type of vegetation present on the sampling sites ID Unit Eastern border of California City (Proctor Blvd and Mule Team Pkway, 2014) 1-1* 254 Scattered vegetation of Phacelia fremontii 1-2 171 Dry Playa 1-3* 171 Dry Playa 2-1* 154 Atriplex polycarpa 2-2* 154 Larrea tridentata 2-3 154 scattered vegetation of Phacelia fremontii 3-1* 100 Larrea tridentata 3-2 100 Ambrosia sp. 3-3 100 Dry Playa 4-1* 154 Larrea tridentata 4-2* 154 Dry land 4-3 154 Dry land 5-1 100 Larrea tridentata 5-2* 100 Atriplex polycarpa 5-3 100 Phacelia fremontii 5-4 100 Phacelia fremontii 6-1 114 Dry land 6-2* 114 Larrea tridentata 6-3* 154 Larrea tridentata 6-4* 114 Dry Playa Mule Team Pkway, Western border of California City (2015) MT-1 154 Malacothrix glabrata and Shismus arabica MT-2 154 Atriplex polycarpa, Artemisia sp. MT-4* 154 Malacothrix glabrata and Phacelia fremontii, Malacothrix sp., and Erodium cicutarium 35

MT-5 154 N/A MT-6 154 Phacelia sp. and Larrea tridentata in the background MT-7* 154 Adenophyllum cooperi and Chaenactis sp. Fremontii pincushion MT-8* 154 Atriplex polycarpa MT-9* 151 Larrea tridentata MT-10* 151 Dry playa 114 Lasthenia sp., Erodium cicutarium, Dichelostemma capitatum, MT-11* Sisymbrium sp. (mustard) MT-12* 114 Dry Playa, Larrea tridentata Sequoia Blvd., Eastern border of California City (2015) SQ-1* 154 Coreopsis sp. (Bigelow's coreopsis), Leptosyne sp. or Leptosyne bigelovii, Larrea tridentat SQ-2* 154 N/A SQ-3* 154 Larrea tridentata SQ-4* 154 Krasheninnikovia sp. SQ-5* 154 Salvia sp. SQ-6* 114 Ambrosia salsola SQ-7* 114 N/A SQ-8* 114 Malacothrix glabrata SQ-9* 114 Acamptopappus sphaerocephalus SQ-10* 114 Dry playa SQ-11* 114 Adenophyllum cooperi, Lasthenia sp. SQ-12* 114 Malacothrix glabrata

DNA ISOLATION DNA was extracted successfully for all forty-three soil samples including the replicates. The quality of the extracted DNA was confirmed with agarose gel electrophoresis by loading an aliquot of DNA (8 µl combined with 2 µl of loading dye in each well (Figure 13). 36

Figure 13. Agarose gel electrophoresis (2%) showing successful DNA extraction results (DNA fragments appear larger than 3000bp). Note that the visible bands differ in intensity indicating that the amount of DNA extracted differed between individual sampling sites. The basepairs (bp) of some individual fragments in the PCR marker are indicated.

THE STATISTICAL ANALYSIS OF EXTRACTED DNA AMOUNTS Quantification of DNA was successful for all of the samples including the replicates except for sample MT-6. The DNA amount present in sample MT-6 could not be determined, indicating that it likely had a very low DNA amount that was under the detection limit of the Fluorimeter (Figure 13 indicates a very faint band of extracted DNA for sample MT-6 compared to other samples). An analysis of variance (ANOVA) showed no significant difference (F1,41=0.109, p=0.742) between the amount of DNA extracted and the samples positive and negative for C. immitis (Table 11; Figure 14).

Table 11. DNA extraction (ng/l) from soil samples collected from California City, California, with indication of sample IDs. The locations where C. immitis was detected and confirmed by sequencing are indicated by asterisks. Sample ID DNA extraction DNA Sample ID DNA DNA extraction (ng/l) extraction extraction (ng/l) of (ng/l) of (ng/l) Replicate Replicate 1-1* 2.57 1.52 MT-1 1.84 2.77 1-2 3.49 3.34 MT-2 0.808 3.65 1-3* 0.736 2.48 MT-4* 2.4 4.4 2-1* 7.24 4.92 MT-5 4.76 16.4 2-2* 4.2 4.52 MT-6 0.542 Under detection limit 2-3 1.16 2.36 MT-7* 1.21 1.34 3-1* 2.48 6.04 MT-8* 0.46 2.17 3-2 7.16 5.32 MT-9* 4.44 6.96 3-3 1.42 2.8 MT-10* 1.43 2.55 4-1* 3.16 1.72 MT-11* 5.32 3.19 4-2* 3.47 2.55 MT-12* 5.48 2.35 4-3 0.94 2.05 S-1* 3.5 0.46 5-1 3.68 0.472 S-2* 4.88 1.14 5-2* 1.88 2.08 S-3* 3.64 1.76 5-3 1.82 3.23 S-4* 8.48 8.88 5-4 1.04 5.04 S-5* 0.64 0.836 6-1 0.804 0.562 S-6* 3.94 1.08 6-2* 0.816 3.94 S-7* 1.26 2.83 6-3* 5.44 1.92 S-8* 1.02 1.45 6-4* 5.28 4.6 S-9* 1.03 3.36 S-10* 6.16 1.84 S-11* 2.15 10.4 SQ-12* 1.15 1.58

5.00

4.50

4.00

3.50

3.00

2.50

2.00

1.50

1.00 Measured Measured DNAamount (ng/µl) 0.50

0.00 Positive Samples Negative Samples

Figure 14. The mean amount of determined DNA (ng/l) of C. immitis positive samples (n=30, average =3.17) and negative samples (n=13, average=2.93) collected near California City, California. Error bars are 95% confidence intervals (F1,41=0.109, p=0.742). 38

NESTED PCR OF FUNGAL rDNA A nested PCR method using primers (NSA3/NLC2; NSI1/NLB4) was successful for all the samples and produced strong bands that were detectable with the Molecular Image Gel DOC System (BioRad version 4.6.5, Hercules, CA, USA) indicating that Ascomycetes and/or Basidiomycetes were present in all soil samples except MT-6 (Figure 15, Table 6, 7, 8 and 9).

A B

Figure 15. (A) Agarose gel electrophoresis (2%) showing successful PCR amplification results (~1,000bp) obtained with primer pair (NSA3/NLC2) using DNA extracts from twenty-three soil samples collected from California City. A positive DNA extract from C. immitis served as a control. The basepairs (bp) of individual fragments in the PCR marker are indicated (~1,000bp, POS= positive control, NEG=negative control). (B)Agarose gel electrophoresis (2%) showing successful PCR amplification results (~900bp) obtained with primer pair (NSI1/NLB4) using DNA extracts from twenty-three soil samples collected from California City. A positive DNA extract from C. immitis served as a control. The basepairs (bp) of individual fragments in the PCR marker are indicated (~900bp, POS= positive control, NEG=negative control).

Diagnostic PCR with primers ITSCIA/ITSC2 The diagnostic PCR using primer pair (ITSC1A/ITSC2) produced ~220bp bands indicating the presence of C. immitis for a total of thirteen samples out of all forty-three samples (Figure 16; Table 6, 7, 8 and 9). The nested PCR method produced consistent results for all the samples when repeated. 39

Figure 16. Agarose gel electrophoresis (2%) showing successful PCR amplification results (~220bp) obtained with primer pair (ITSC1A/ITSC2) specific to C. immitis using DNA extracts from twenty-three soil samples collected from California City. Primers ITSC1A/ITSC2 for the nested PCR allow the amplification of the ITS 2 region and part of the 5.8s region of fungal ribosomal DNA from the soil samples. A positive DNA extract from C. immitis served as a control. Arrows point to the general area showing bands (~220bp) of correct size that were confirmed by sequencing (POS= positive control, NEG=negative control). The basepairs (bp) of some individual fragments in the PCR marker are indicated.

Diagnostic PCR with primers ITS1CF/ITS1CR The diagnostic PCR using primer pair (ITS1CF/ITS1CR) produced ~135bp bands indicating the presence of C. immitis for a total of nineteen samples out of all forty-three samples Results using primers ITS1CF/ITS1CR were positive for all samples from the Sequoia Blvd transect and for 7 samples for the Mule Team Pkway transect (Figure 17). Results for the Proctor Blvd and Mule Team Pkway (2014) using primers ITS1CF/ITS1CR were negative for all samples. Agarose gel electrophoresis of PCR products indicated the amplification of ~135bp PCR products for samples from Sequoia Blvd and Mule Team Pkway that were confirmed positive by sequencing with the ITS1CF primer (Figure 17, Table 6, 7, 8, and 9). The product for sample S-1 from primers ITS1CF/ITS1CR was sequenced but came out noisy, indicating that more than one species contributed to the PCR amplicon (Figure 17). 40

Figure 17. Agarose gel electrophoresis (2%) showing successful PCR results (~135bp) obtained with primer pair (ITS1CF/ITS1CR) specific to Coccidioides spp. for the majority of samples collected along Mule Team Pkway and Sequoia Blvd. The arrow points toward a strong positive amplicon obtained for site S1. The basepairs (bp) of individual fragments in the PCR marker are indicated.

COMPARISON OF NESTED PCR RESULTS The nested PCR method using diagnostic primer pair ITSC1A/ITSC2 (Greene et al. 2000) produced a ~220bp amplicons for 13 soil samples (11 samples from 2014; 2 samples from 2015). Furthermore, there was a faint PCR signal produced for one of the Sequoia Blvd samples (S-3), but the sequence was not pure and the presence of the pathogen could not be confirmed. The second nested PCR method using primer pair ITS1CF/ITS1CR (Vargas-Gastélum et al. 2015) produced faint amplicons (~135bp) for all samples from 2015 and a strong amplicon for samples MT-4 and S-1 (Sequoia Blvd and Mule Team Pkway) which were all confirmed positive for C. immitis by sequencing. The first diagnostic PCR with primer pair (ITSC1A/ITSC2) produced a positive result for 30% of the samples whereas the second diagnostic PCR with primer pair ITS1CF/ITS1CR produced a positive result for 44% of the samples, showing that primer pair (ITS1CF/ITS1CR) is more sensitive for the detection of the C. immitis compared to the primer pair ITSC1A/ITSC2. The replicates were not identical for reactions with both of the primer pairs. Both of the diagnostic 41 primers pairs agreed (0.06%) for only two of the samples (S-1 and MT-4) that were strong positive with primer pair ITSC1A/ITSC2. Both of the samples belonged to Neuralia sandy loam (Soil Map Unit#154). Overall, the pathogen was detected in 70% of the samples when results from both diagnostic PCRs were combined. Of the overall forty-three samples investigated in this study, thirty samples (~70%) indicated the presence of C. immitis (Table 6, 7, 8 and 9) and thirteen samples were negative for C. immitis.

DENATURING GRADIENT GEL ELECTROPHORESIS (DGGE)

PCRs using primers NS1/GCFung were consistently resulting in ~350bp products on agarose gels for all samples (Figure 18; Table 6, 7, 8 and 9).

Figure 18. Agarose gel electrophoresis (2%) showing successful PCR results (~350bp) obtained with primer pair (NS1/GCFung) for samples collected along the Proctor Blvd. DNA ladder Promega g3161 (Fisher BioReagents, Madison, WI USA) was used. The basepairs (bp) of individual fragments in the PCR marker are indicated.

ANALYSIS OF DGGE BANDS AND BANDING PATTERNS PCR products obtained with primer pair NS1/GCfung resulted in clear band separation on the DGGE gels for all samples, except for sample MT-6. However, repeated trials with the same samples did not always produce identical DGGE profiles and showed different numbers of bands for some samples. Occasional frowning effects (distortion of the gel) seemed to be the only issue making it difficult to analyze positions of the bands. Bands that were successfully excised without 42 any contamination were re-amplified and sequenced for proper identification of the fungal diversity (Figure 19, Table 12). In most cases, DGGE was repeated several times for accuracy and clarity. For some samples the shortness of the amplicons only allowed an identification to genera or family level (see discussion).

Figure 19. DGGE (on the left) showing the fungal diversity for 10 different samples (Mule Team Pkway 2015). Numbers indicate which bands were excised and re-amplified. DGGE gel (on the right) showing the diversity of fungi from re-amplified and sequenced DGGE bands. Sample M6 did not produce any bands potentially due to low amounts of extracted DNA and/or potential inhibition of PCR. Samples shown in red with an asterisk indicate sites where the pathogen was detected by nested PCR.

FUNGAL DIVERSITY ALONG DIFFERENT TRANSECTS We compared the fingerprints of fungal communities as indicated by DGGE fingerprints from all individual soil samples collected along the various transects. This method can also be used to determine if C. immitis is a dominant member of the fungal community in the samples by running a positive control side by side to environmental samples. The overall number of dominant fungal species in all samples was low, as indicated by only between 1 and 5 strong bands, but numerous faint bands could be detected in many samples. In order to determine the similarity of these fungal community fingerprints among soil samples collected along a transect, the sequenced DGGE bands were marked and matched using the Quantity one software (BioRad version 4.6.5, Hercules, CA) in preparation for Unweighted Pair Group Matching Analyses (UPGMAs). An attempt to compare fingerprints of the fungal communities from sites positive for C. immitis with 43 those negative for the pathogen for samples analyzed within each transects, revealed no apparent “difference” using UPGMAs. Therefore, based on the genetic distance (UPGMAs), there was no correlation found in the fungal diversity or species richness present in different soil types and the samples positive and negative for C. immits (see Appendix for UPGMA results). It is an assumption that bands that are present in the same melting area of the DGGE gel very likely originate from the same species, especially when the overall diversity is low and the separation of bands is good. DGGE bands that were not sequenced but were present in the same melting area in the gels were therefore indicated as being the same fungal species in this study which was confirmed for several, but not all, bands analyzed from the same melting area. The highest number of faint DGGE bands was seen in samples from the Sequoia Blvd transect compared to all other transects. Also, the Sequoia Blvd transect contained the highest number of strong bands (up to 5 bands per sample) compared to all other transects, which only contained few (between 1-2 bands) strong bands per sample. C. immitis could not be confirmed in any of the DGGE gels, but a DGGE gel with samples from a Sequoia Blvd transect had many faint bands present in the lower area of the gel in the same melting area as the positive control C. immitis (see Figure 22 for details). However, these bands could not be excised and re-amplified. These faint bands could possibly represent C. immitis, since all the Sequoia Blvd samples came out positive with one of the nested PCR methods (see Figure 22 for details). The following pictures show the results of DGGE gels for all sampling sites (4 transects for 2 years), showing the fungal diversity present in each sample; each lane representing a single sampling spot. Closest matches of sequenced DGGE bands were obtained from the National Center for Biotechnology Information (NCBI) GenBank nucleotide database and is given in the legend next to the gels with accession number and percent similarity of the closest match.

Proctor Blvd and Mule Team Pkway (2014) The number of bands detected in the soil samples from Proctor Blvd and Mule Team Pkway transect (2014) indicated a total of 14 different fungal species (some were only identified to the genus level) from two different phyla, Ascomycota and Basidiomycota. Members of the following Ascomycete orders were found in soil samples from 2014 (Proctor Blvd and Mule Team Pkway): Pezizales, Diaporthales, Pleosporales, Eurotiales, and Helotiales (Figure 20 and 21; Table 12). Basidiomycete species that were detected in soils from 2014 included also members of the 44

Microstromatales and Cantharellales (Figure 20 and 21; Table 12). The members of Pleosporales sp. were dominant in the samples from 2014 and were present in all the samples that were positive for the pathogen.

1-1 to 4-3: Accession# % Band Closest match from Similarity # NCBI Genbank database 1 Ascobolus crenulatus AY544721.1 99% 2 Pleosporales sp. KF650048.1 97% 3 Sirococcus sp. KJ817840.1 98% Sympodiomycopsis DQ832239.1 99% 4 paphiopedili 5 Curvularia lunata KJ909964.1 99% 6 Agaricomycotina KX232675.1 99% 7 Unidentified

Figure 20. DGGE showing the fungal diversity for 12 different samples collected from Proctor Blvd (2014). Numbers indicate bands that were excised, re-amplified and sequenced (legend above). Closest matches to the sequences obtained from the GenBank nucleotide database are indicated with accession number and percent similarities. Samples 1-1,1-3, 2-1, 2-2, 3-1, 4-1, and 4-2, shown in red with an asterisk indicate sites where the pathogen was detected by nested PCR.

5-1 to 6-4: Closest Accession# % match from NCBI Similarity Band # Genbank database 1 Curvularia papendorfii KJ909964.1 99% 2 Curvularia lunata KJ909964.1 99% U53380.1 98% 3 Iodophanus carneus Anguillospora DQ310783.1 93% 4 longissima 5 Rhizoctonia sp. KU310598.1 96% Sympodiomycopsis DQ832239.1 99% 6 paphiopedili KR063174.1 96% 7 Aspergillus ustus 8 Pleosporales sp. DQ310782.1 98% 9 Ascobolus crenulatus AY544721.1 99% 10 Ascobolus viridis AF121077.1 99%

Figure 21. DGGE showing the fungal diversity for 8 different samples collected from Mule Team Pkway (closer to the road near Mule Team Pkway 2014). Numbers indicate bands that were excised, re-amplified and sequenced (legend above). Closest matches to the sequences obtained from the GenBank nucleotide database are indicated with accession number and percent similarities. Samples 5-2, 6-2, 6-3, and 6-4, shown in red with an asterisk indicate sites where the pathogen was detected by nested PCR.

Sequoia Blvd and Mule Team Pkway (2015) The fungal diversity was higher in samples collected along Sequoia Blvd (1-10 dark bands in the samples) compared to samples collected along Mule Team Pkway in 2015 (1-3 dominant DGGE bands in all the samples). A total of 7 samples were positive for the pathogen from the Mule Team Pkway transect, and all of the samples from Sequoia Blvd were at least weakly positive for C. immitis. DNA sequencing results identified Myrothecium sp., Iodophanus sp., Porostereum sp., and Leucoagaricus sp. and members of the order Pleosporales in soil samples from Mule Team Pkway and Sequoia Blvd (Figure 22, 23, 24, and 25; Table 12). A total of 5 different fungal species (some were only identified to the genus level) from two different phyla (Ascomycota and Basidiomycetes) and 5 different orders (Hypocreales, Pezizales, Pleosporales, Agaricales, and Polyporales) were present in the soil samples from Sequoia Blvd (Figure 22; Table 12). Members of the following Ascomycete orders were found in soil samples from Sequoia Blvd: Hypocreales, Pezizales, and Pleosporales (Figure 22; Table 12). 46

Basidiomycete species that were detected in soils from Sequoia Blvd included members of the Agaricales and Polyporales (Figure 22; Table 12).

S1 to S12: Closest Accession# % Band match from NCBI Similarity # Genbank database 1 Iodophanus sp. FJ393434.1 95% 2 Alternaria KF650048.1 95% Myrothecium KR063177.1 99% 3 verrucaria Porostereum sp. AB809163.1 99% 4 (Basidiomycota) Leucoagaricus sp. JN940440.1 99% 5 (Basidiomycota) 6 Unidentified sp.

Figure 22. DGGE showing fungal diversity for 12 different samples collected along Sequoia Blvd (2015). Numbers indicate bands that were excised, re-amplified and sequenced (legend above). Closest matches to sequences obtained from the GenBank nucleotide database are indicated with accession number and percent similarities. Samples, S-1 to S-12, shown in red with asterisks indicate sites where the pathogen was detected by nested PCR.

The Mule Team Pkway transect contained the lowest fungal diversity with only 4 different species from 2 different orders (Hypocreales and Pleosporales) within the Ascomycota (Figure 23, 24, 25; Table 12). One sample from the Mule Team Pkway transect (MT-6) had a low amount of DNA present when quantified and did not give a band when investigated by PCR/DGGE, but a faint band was produced for the DNA extraction, which could present mostly non-fungal DNA (Figure 15). When repeated, sample MT-6 showed two bands on the DGGE gel that were excised, re-amplified and re-investigated on the DGGE gel for purity (Figure 25). The DGGE gel showed two amplicons for sample MT-6 that were not pure and were therefore not sequenced.

M1 to M12: Closest match Accession# % Band from NCBI Gen bank Similarity # database 1 Myrothecium verrucaria KR063177.1 97% 2 Alternaria KF650048.1 97%

Figure 23. DGGE showing fungal diversity for 10 different samples collected from Mule Team Pkway (2015). Numbers indicate bands that were excised, re-amplified and sequenced (legend above). Closest matches to the sequences obtained from the GenBank nucleotide database are indicated with accession number and percent similarities. Samples MT-4, MT-7, MT-8, MT-9, MT-10, MT-11, and MT-12, shown in red with asterisks indicate sites where the pathogen was detected by nested PCR.

Closest match from Accession # % NCBI Genbank Similarity Band # database 1 Myrothecium sp. KU310601.1 99% Myrothecium KR063177.1 99% 2 verrucaria 3 Pleosporales sp. KJ917106.1 97% 4 Rhizoctonia KU310598.1 96% 5 Unidentified

Figure 24. DGGE replicate comparing fungal diversity for 8 different samples collected from Mule Team Pkway and Sequoia Blvd (2015). Numbers indicate bands that were excised, re- amplified and sequenced (legend above). Closest matches to the sequences obtained from the GenBank nucleotide database are indicated with accession number and percent similarities. Samples MT-12, S-3, S-7, S-8, S-9, and S-10, shown in red with asterisks indicate sites where the pathogen was detected by nested PCR.

Band Closest match from NCBI Accession # % Similarity # Genbank database 1 Myrothecium sp. KU310601.1 97% 2 Myrothecium verrucaria KR063177.1 99% 3 Peziza phyllogena AY789327.1 99%

Figure 25. DGGE replicate showing fungal diversity for 9 different samples collected from Mule Team Pkway (2015). Numbers indicate bands that were excised, re-amplified and sequenced (legend above). Closest matches to the sequences obtained from the GenBank nucleotide database are indicated with accession number and percent similarities. Samples MT-12, MT-10, MT-8, MT- 7 and MT-4, shown in red with asterisks indicate sites where the pathogen was detected by nested PCR. 50

Table 12. Overview of closest matches obtained from the GenBank nucleotide database for sequences derived from DGGE bands. Red color and asterisks represent C. immitis positive samples. NSI/GC- Taxonomy Closest GenBank database entry Accession # % Similarity FUNG Ascomycetes Sample ID order family Genus Species 1-1* N/A N/A N/A N/A N/A N/A 1-2 Pezizales Ascobolaceae Ascobolus Ascobolus crenulatus AY544721.1 99% 1-3* Diaporthales Sordariomycatidae Sirococcus Sirococcus sp. KJ817840.1 98% 2-1* Pleosporales Pleosporaceae Pleosporales Pleosporales sp. KF650048.1 97% 2-2* Pezizales Ascobolaceae Ascobolus Ascobolus crenulatus AY544721.1 99% 2-3 N/A N/A N/A N/A N/A N/A 3-1* Pleosporales Pleosporaceae Pleosporales Pleosporales sp. KF650048.1 97% 3-2 Pleosporales Pleosporaceae Pleosporales Pleosporales sp. KF650048.1 97% 3-3 Pleosporales Pleosporaceae Pleosporales Pleosporales sp. KF650048.1 97% 4-1* Pleosporales Pleosporaceae Cochliobolus Curvularia lunata KJ909964.1 99% 4-2* Pleosporales Pleosporaceae Cochliobolus Curvularia lunata KJ909964.1 99% 4-3 N/A N/A N/A N/A N/A N/A 5-1 Pleosporales Pleosporaceae Cochliobolus Curvularia lunata KJ909964.1 99% 5-2* Pezizales Pezizaceae Iodophanus Iodophanus carneus U53380.1 98% Pleosporales Pleosporaceae Pleosporales Pleosporales sp. DQ310782.1 98% 5-3 Pleosporales Pleosporaceae Cochliobolus Curvularia lunata KJ909964.1 99% Pleosporales N/A Anguillospora Anguillospora longissimi DQ310783.1 93% Pezizales Ascobolaceae Ascobolus Ascobolus crenulatus AY544721.1 99% 5-4 Pezizales Ascobolaceae Ascobolus Ascobolus crenulatus AY544721.1 99% 6-1 Pleosporales Pleosporaceae Curvularia Curvularia papendorfii KJ909964.1 99% 6-2* Pleosporales Pleosporaceae Curvularia Curvularia papendorfii KJ909964.1 99% Eurotiales Trichocomaceae Aspergillus Aspergillus ustus KR063174.1 96% Helotiales Sclerotiniaceae Sclerotinia Sclerotinia 6-3* Pleosporales Pleosporaceae Curvularia Curvularia papendorfii KJ909964.1 99% Pleosporaceae Pleosporlaes Pleosporales sp. DQ310782.1 98% 6-4* Pleosporales Pleosporaceae Curvularia Curvularia papendorfii KJ909964.1 99% MT-1 Hypocreales Incertae sedis Myrothecium Myrothecium verrucaria KR063177.1 97% Pleosporales Pleosporaceae Alternaria Alternaria KF650048.1 97% MT-2 Hypocreales Incertae sedis Myrothecium Myrothecium verrucaria KR063177.1 97% Pleosporales Pleosporaceae Alternaria Alternaria KF650048.1 97% MT-4* Hypocreales Incertae sedis Myrothecium Myrothecium verrucaria KR063177.1 97% Pleosporales Pleosporaceae Alternaria Alternaria KF650048.1 97% MT-5 Pleosporales Pleosporaceae Alternaria Alternaria KF650048.1 97% 51

MT-6 N/A N/A N/A N/A N/A N/A MT-7* Hypocreales Incertae sedis Myrothecium Myrothecium verrucaria KR063177.1 97% MT-8* Hypocreales Incertae sedis Myrothecium Myrothecium verrucaria KR063177.1 97% Pleosporales Pleosporaceae Alternaria Alternaria KF650048.1 97% MT-9* Hypocreales Incertae sedis Myrothecium Myrothecium verrucaria KR063177.1 97% Pleosporales Pleosporaceae Alternaria Alternaria KF650048.1 97% MT-10* Hypocreales Incertae sedis Myrothecium Myrothecium verrucaria KR063177.1 97% MT-11* Hypocreales Incertae sedis Myrothecium Myrothecium verrucaria KR063177.1 97% Pleosporales Pleosporaceae Alternaria Alternaria KF650048.1 97% MT-12* Pleosporales Pleosporaceae Alternaria Alternaria KF650048.1 97% SQ-1* Hypocreales Incertae sedis Myrothecium Myrothecium verrucaria KR063177.1 99% SQ-2* Hypocreales Incertae sedis Myrothecium Myrothecium verrucaria KR063177.1 99% Pezizales Pezizaceae Iodophanus Iodophanus sp. FJ393434.1 95% SQ-3* Hypocreales Incertae sedis Myrothecium Myrothecium verrucaria KR063177.1 99% Pezizales Pezizaceae Iodophanus Iodophanus sp. FJ393434.1 95% SQ-4* Hypocreales Incertae sedis Myrothecium Myrothecium verrucaria KR063177.1 99% SQ-5* Hypocreales Incertae sedis Myrothecium Myrothecium verrucaria KR063177.1 99% SQ-6* Pleosporales Pleosporaceae Alternaria Alternaria KF650048.1 95% SQ-7* Hypocreales Incertae sedis Myrothecium Myrothecium verrucaria KR063177.1 99% Alternaria KF650048.1 99% SQ-8* Hypocreales Incertae sedis Myrothecium Myrothecium verrucaria KR063177.1 99% SQ-9* Hypocreales Incertae sedis Myrothecium Myrothecium verrucaria KR063177.1 99% SQ-10* Hypocreales Incertae sedis Myrothecium Myrothecium verrucaria KR063177.1 99% SQ-11* Hypocreales Incertae sedis Myrothecium Myrothecium verrucaria KR063177.1 99% SQ-12* Hypocreales Incertae sedis Myrothecium Myrothecium verrucaria KR063177.1 99% Pleosporales Pleosporaceae Alternaria Alternaria KF650048.1 99% Basidiomycetes 2-3 Microstromatales Microstromataceae Sympodiomycopsis Sympodiomycopsis paphiopedili DQ832239.1 99% 4-3 Agaricales Bartheletiaceae Agaricomycotina Agaricomycotina sp. KX232675.1 99% 5-4 Microstromatales Microstromataceae Sympodiomycopsis Sympodiomycopsis paphiopedili DQ832239.1 99% 6-4* Cantharellales Ceratobasidiaceae Rhizoctonia Rhizoctonia KU310598.1 96% SQ-7* Agaricales Agaricaceae Leucoagaricus Leucoagaricus sp JN940440.1 99% SQ-10* Polyporales Phanerochaetaceae Porostereum Porostereum sp. AB809163.1 99% Agaricales Agaricaceae Leucoagaricus Leucoagaricus sp. JN940440.1 99% SQ-11* Agaricales Agaricaceae Leucoagaricus Leucoagaricus sp. JN940440.1 99% 52

PHYLOGENETIC ANALYSIS OF DGGE BANDS

A neighbor joining analysis was conducted with sequenced DGGE bands to investigate how closely related the fungal species were to each other (Figure 26). Most of the positive samples contained members of the Ascomycota. The dominant species present in soils from Mule Team Pkway and Sequoia Blvd were identified as Myrothecium sp. being present in all of the samples from these two transects (Figure 26). The bootstrap values for members of the Pleosporales sp. were higher compared to those for Myrothecium sp., showing the node of Pleosporales sp. is well supported (Figure 26). About ~55% of the samples from Proctor Blvd were positive for C. immitis and none of these samples contained Myrothecium spp. Members of the Pleosporales were mostly found in soils collected along Proctor Blvd and Mule Team Pkway from 2014, that were positive for C. immitis (Figure 26).

Pleosporlaes

Diaporthales Ascomycota

Hypocreales

Helotiales Pezizales Onygenales

Basidiomycota

Figure 26. Diversity of fungi from re-amplified and sequenced DGGE bands using a neighbor- joining tree. The samples positive for C. immitis are highlighted in red. Bootstrap values are shown on each node (100 replicates). The tree is rooted by Basidiomycete species, the Hannaella luteola (out-group). Only one type of species were found in order Helotiales (Scierotina sp.), and order Diaporthales (Sirococus sp.). 54

CORRELATION OF FUNGAL DIVERISTY AND AMOUNT OF EXTRACTED DNA The overall number of dominant fungal species in all samples was low, as indicated by between 1 and 5 strong DGGE bands only for most samples, but numerous faint bands could be detected in many samples. To investigate the fungal species richness that contributed to DNA extracts obtained from soil samples of the 4 different transects, the number of strong and distinct DGGE bands on all gels were counted using the Quantity One program (BioRad version 4.6.5, Hercules, CA). The Pearson correlation showed no significant (p=0.776) correlation between the amount of DNA (ng/l) in the soil samples and the fungal diversity present in the soil samples (Figure 27).

18 16 14 12 10 8 6

DNAextracted (ng/µl) 4 2 0 0 2 4 6 8 10 12 14 16 Number of bands (Low Sensitivity)

Figure 27. Amount of DNA (ng/l) extracted from all samples and fungal diversity (as numbers of strong DGGE bands) present in the samples as determined by DGGE analysis (p=0.776). The number of bands were counted using low sensitivity level (~1 to 2) on the Quantity one program (BioRad version 4.6.5, Hercules, CA) to detect only dominant bands.

FUGITIVE DUST EMISSION (PM10) Fugitive dust emission (PM10) in the Mojave Desert Air Basin was higher compared to values documented for the City of Lancaster, CA, using the State 24-Hour Average data between 2000 and 2015 (Figure 28). In 2010, an increase in PM10 for both Lancaster and the Mojave Air Basin in general was documented which was likely due to increases in soil disturbance during that time and the effects of the ongoing drought. The ongoing drought was interrupted by precipitation 55 events, which contributed to increased PM10 in 2010 (Figure 29), likely due to a storm prior to the rain in 2010.

800 700 600 500 400 300 200

ParticulateMatter µm) (10 100 0 2000 2002 2004 2006 2008 2010 2012 2014 Year

State Lancaster State Mojave

Figure 28. Particulate matter (10 micrometer/mm3) detected in the City of Lancaster and the Mojave Air Basin between 2000 – 2015 (State 24-Hour Average). The data for each year was calculated from January to December of the year. 30

10 Precipitation (Inchs) 5

0 2005 2007 2009 2011 2013 2015 Year

Figure 29. Precipitation (inches) in the Mojave Desert per year from 2005 to 2015 (https://www.wunderground.com). The precipitation (inches) values from 2005 to 2009 were below 1. The data for each year was calculated from May to May of next year.

ELEVATION There was no pattern or correlation found between the elevation and the samples positive and negative for C. immitis. The samples positive for C. immitis were found at both lower and 56 higher elevations. Four samples were positive at the Mule Team Pkway (2014) transect, which had the highest elevation compared to the other three transects (within 725.50-783.71 meters above sea level). The sample 6-4 positive for C. immitis, from the Mule Team Pkway transect, was collected at the highest elevation (783.37 meters above sea level). Six samples were positive for C. immitis from the Proctor Blvd (2014) transect, that ranged in elevation between 717.34 and 719.37 meters above sea level. The Sequoia Blvd transect (2015), for which all samples were at least weakly positive for C. immitis, ranged in elevation between 718.12 and 739.38 meters above sea level. Seven samples were positive for C. immitis from the Mule Team Pkway transect (2015) ranging between 723.85 and 783.29 meters above sea level showing no correlation or pattern between the samples positive for C. immitis and elevation. The samples that were positive for C. immitis were present at various spots at different elevations (Figure 30).

790

780

770

760

750

740

730

720 Elevation Elevation (Meters above sea level) 710

700 1 2 3 4 5 6 7 8 9 10 11 12 Sample

Proctor Blvd (2014) Mule Team Pkway (2014) Sequoia Blvd (2015) Mule Team Pkway (2015)

Figure 30. Overview of the elevation (meters above sea level) of individual transects from 2014 and 2015. The x-axis shows the samples collected (1 represents the first sample collected on the transect and 12 represents the last sample collected on the transect). There were only eight samples collected from Mule Team Pkway in 2014 (1 being the 1st sample and 8 being the last sample on the transect). Six samples from Proctor Blvd and five samples from Mule Team Pkway (2014) were positive for the pathogen. Seven samples from Mule Team Pkway (2015) and all the samples from Sequoia Blvd were positive for the pathogen. 57

DISCUSSION

The purpose of our study was to identify how widespread C. immitis is distributed in soils east of California City where an increase in incidence of coccidioidomycosis has been reported and where prisoners of the California City Correctional Facility have raised concerns about being exposed to the pathogen. In our study, we investigated if the presence or absence of C. immitis in soils is linked to certain environmental parameters. In this context, we also studied the type of vegetation present in the sampling area and determined the fungal diversity in all soil samples that were collected along several transects in Spring 2014 and 2015. We used a culture independent nested PCR approach with diagnostic primer pairs to detect the pathogen and applied traditional PCR with primers that target all fungi followed by DGGE to reveal fingerprints of the dominant fungal species present in the samples. We were able to verify the presence of the pathogen in about 70% of the soil samples, which means that the pathogen is widely distributed in this area. Mostly weak signals were retrieved from PCR suggesting that C. immitis was not a dominant member in any of the soil samples investigated. An understanding of the ecology of C. immitis is required in order to interpret and predict disease incidence and the spread of the pathogen due to climate change.

INCIDENCE OF COCCIDIOIDOMYCOSIS IN CALIFORNIA PRISONS The San Joaquin Valley of California is known as a highly endemic zone of valley fever. Cases of valley fever have been reported from most counties in California, but over 75% of cases were diagnosed in people who live in the San Joaquin (Central) Valley (California Department of Public Health 2016). Of the reported cases in California over 75% occurred in people who live in the San Joaquin (Central) Valley (CDPH 2016). This puts the residents of the Central Valley at a much higher risk for developing valley fever compared to residents in other parts of California. Also, the annual incidence rate of valley fever was calculated as 6 to 32% among those performing military maneuvers within endemic zones of the Central Valley (Crum et al. 2004). In the Mojave Desert, extreme heat and wind helps the spread of C. immitis arthroconidia, but no details are known about the northwestern part of the Mojave Desert where California City is located. However, it has been noted that prisoners incarcerated in the California City correctional 58 facility have contracted coccidioidomycosis quiet frequently while serving time in the San Joaquin Valley prisons and jails (detailed numbers not published). Coccidioidomycosis has been recognized in inmates of California state prisons since 1919 and two prisons that are located in highly endemic areas of C. immitis are the state prisons in Pleasant Valley (Fresno County) and Avenal (Kings County) (Pappagianis 2007). From 2008 to 2012, about two-thirds of valley fever cases identified among California’s state prisoners occurred in Pleasant Valley and Avenal state prisons (SLO Public Health Dept. 2014). Most of the inmates in California that were diagnosed with valley fever served sentences in prisons located in the San Joaquin Valley. A lawsuit was filed on behalf of current and former inmates who contracted valley fever while at Avenal or Pleasant Valley State Prison (Cook 2013). One of the lawsuits involved a $425,000 settlement from the federal government for a former inmate who developed valley fever while incarcerated at Taft Correctional Institution in Kern County (Adlin 2012). Another lawsuit nicknamed “petri dish for valley fever” has been filed by a prisoner, who contracted valley fever while serving a 14-year sentence in the Taft Correctional Institution (see http://Voiceofco.org 2012 for details). Inmates, workers and staff in correctional facilities located in endemic areas of the pathogen face an ongoing challenge of maintaining or restoring vegetation and grass on the grounds because of the ongoing drought and water restrictions, which puts them at an increased risk of exposure to airborne dust that might contain C. immitis arthroconidia (Perio et al. 2015). With prisons and jails housing generally a high number of immunocompromised inmates, valley fever is much more prevalent and dangerous within correctional facilities in endemic areas of the pathogen compared to the general public. Immunocompromised humans are at highest risk of contracting valley fever. Inmates with depressed immune systems such as those with HIV are at high risk of disseminated infections in general (SLO Public Health Dept. 2014). Prisoners also have a rate of HIV that is about five times that of the general public in the United States (Weinstein 2010). In closed environments such as prison systems, diseases that affect the immune system, such as HIV, predispose many inmates to develop valley fever during the time of their sentence. Currently, the California Department of Corrections and Rehabilitation (CDCR) has a policy that prohibits housing prisoners with depressed immune system or with certain medical conditions in nine CDCR prison which includes Pleasant Valley and Avenal State prisons (Valley Fever and CDCR Housing 2015). Diagnostic practices for detecting coccidioidomycosis vary in different institutions. The CDCR policy offers a voluntary cocci skin test (Spherusol, Nielsen Biosciences) 59 to inmates to determine if they had been already exposed to the pathogen and have formed antibodies against the fungus. In 2015, the state spent $5.4 million to provide skin tests for about 90,000 inmates, however nearly 60,000 inmates refused to be tested (Thompson 2015). In this case, training and education needs to be provided to educate inmates about the risk of valley fever and benefit of the skin test. Results of our study can be used to educate the inmates and staff and to increase awareness of coccidioidomycosis, and thus, to reduce the incidence of the disease. We therefore recommend that inmates from areas where there is no valley fever should not be sent to prisons in C. immitis endemic areas since they might have a low immunity to the pathogen. Inmates from non-endemic areas generally have a low tolerance to valley fever and even a mild infection with Coccidioides spp. can put their lives at risk. Only people who spent their lives in endemic areas of the pathogen, such as Southern and Central California, and therefore have likely built up immunity against this pathogen, should be sent to prisons in these areas. Prisoners should also be tested for any other immunocompromised disease that puts them at risk to succumb to coccidioidomycosis.

COMPARISON OF NESTED PCR RESULTS We were able to detect the pathogen in soil samples using the ITSC1A/ITSC2 (Greene et al. 2000) and the ITS1CF/ITS1CR (Vargas-Gastélum et al. 2015) primer pair in a nested PCR approach, which target two different ITS regions in the ribosomal gene of fungi. The primer pair ITS1CF/ITS1CR was more sensitive compared to the primer pair ITSC1A/ITSC2 because it produced a higher number of C. immitis positive samples, although most of the signals were weak. Since the ITS1CF/ITS1CR primer pair produced mostly faint amplicons for samples from Sequoia Blvd and Mule Team PkWay (2015), in contrast to the ITSC1A/ITSC2 primer pair which did not produce any amplicons for the same samples, it is possible that C. immitis was not dominant in these samples. The results for both of the diagnostic PCRs only agreed for two out of all 43 samples. Furthermore, the ITS1CF/ITS1CR primer pair did not produce any positive results for samples collected in 2014, but primer pair ITSC1A/ITSC2 produced positive results for eleven of the samples from 2014. A possible explanation for this result could be that the DNA from 2014 samples was degraded overtime due to repeated defrosting. However, when loaded onto agarose gels, the DNA appeared non-sheared. Another explanation could be that slightly different climate patterns or other unknown factors might have caused a shift in the fungal diversity in the soil which 60 we observed by detecting differences in the DGGE fingerprints of the fungal communities that led to this result. This change in fungal diversity might have led to the suppression of the growth of the pathogen in some soils.

DGGE AND FUNGAL DIVERSITY This study identified mostly Ascomycetes and a few Basidiomycetes in soils collected near California City, CA. The re-amplified and sequenced DGGE-bands resulted in short fragments of about 350bp covering only 2 hypervariable areas of the 18S rRNA gene (V1–V2 regions). Therefore, identifications to the species level were impossible and often several entries in the GenBank nucleotide database were suggested as closest matches to the retrieved sequence (Table 10); however, these closest matches fell into the same order and were members of the same family. Repeated DGGE analyses did not always yield the same amount of operational taxonomic units (UTUs) which could be due to differences in DNA extract replicates from incompletely homogenized soil particles or, if fewer bands were detected in the DGGE profile, potential degradation of some of the DNA over time due to repeated defrosting as mentioned earlier. Some sites showed plenty of faint bands, which could indicate minority species present in the soil sample. Overall, we were able to re-amplify and sequence most of the excised DGGE bands, but not all of these PCR products resulted in pure sequences that could be identified. The number of the bands (OTUs) per soil sample in the DGGE profiles ranged from one to ten. Some of the faint bands in the lower melting area were not excised and re-amplified, so the presence of C. immitis using DGGE method could not be confirmed. DGGE is a reliable tool for indicating differences in fungal communities; it only represents dominant fungi that contribute to about 10% of the fungal diversity in a sample (Muyzer et al. 1993; Gelsomino et al. 1999). Our DGGE results of environmental samples did not indicate a band in the melting area of C. immitis; therefore, this result may indicate that C. immitis was most likely not a dominant member of the fungal community in any of our soil samples, which was also suggested by many weakly positive diagnostic PCR results. DGGE results showed that there was a lower fungal diversity present at sampling sites from 2015 compared to sampling sites from 2014, which could be due to the amount of precipitation received per year. The rainfall amount was higher in 2013 compared to 2014, likely contributing to increased fungal diversity in 2014. Disturbance of soil surfaces through rainfall, leads to runoff of the organic material from higher 61 elevations to lower elevation, therefore organisms tend to colonize preferentially in the lower elevation areas, because more nutrients accumulate there after a wash. The Proctor Blvd (2014) transect had the highest fungal diversity, but had the lowest elevation compared to all other transects. Furthermore, soil samples collected in 2015 resulted in a higher number of C. immitis positive samples compared to 2014. For example, sample S-1, which was strong positive for C. immitis with the nested PCR method, indicated a lower fungal diversity compared to other sampling sites on Sequoia Blvd when investigated by DGGE, suggesting that C. immitis might benefit from reduced competition. A higher number of samples were positive for C. immitis, when fungal diversity appeared low which indicates a potential correlation between lower fungal diversity and presence C. immitis. Indeed, the Pearson’s correlation indicated a weak correlation between fungal diversity and the the amount of DNA (ng/l) in the soil samples; however, an increased sampling size would be necessary in order to confirm this correlation. Out of thirteen soil samples total, three of the C. immitis negative soil samples collected along Proctor Blvd (1-2) and Mule Team Pkway (5-3, 5-4) revealed the presence of Ascobolus sp. None of the positive samples from both years contained this fungal species suggesting that it may serve as an antagonist to the pathogen, which can be a focus of a future study. Since, only three negative samples contained Ascobolus sp., a higher sampling size is needed to investigate if Ascobolus sp. can serve as an antagonist to the pathogen. Antagonists to Coccidioides spp. can also be found among other microorganisms (bacteria and archaea), which were not investigated in this study. Members of the fungal order Pleosporales were detected in most of the soil samples from both years, and we can therefore say that these fungal species might be ubiquists in the region. In another research study, Pleosporales spp. were also ubiquitous in the samples that were collected around Bakersfield from three different depths indicating them as being representative fungal species for an arid or semi-arid desert environment (English 2010). In the samples from 2015, mostly Pleosporales spp. and Myrothecium sp. (Hypocreales) were present in soils along the transects. Members of the genus Myrothecium were not dominant in Bakersfield soils, which instead indicated the presence of Onygenales and Sordariales sp. (English 2010) manifesting that the fungal communities in the California City area are different to some degree from the ones that can be detected in soils around the Bakersfield area. Soil types such as Neuralia sandy loam (Soil Map Unit 154) are not dominant in Southern San Joaquin Valley, which often show a higher clay content (e.g. Garces loam) (Lauer et al. 2012). In the samples from 2014, along with Pleosporales 62 spp. other fungal species such as Ascobolus sp. were detected along the transect, which were not found in samples collected in 2015. The fungal diversity discovered in the samples from both years indicated that members of the Pleosporales, Pezizales, Hypocreales and Eurotiales are typical for a desert environment and species that belong in these orders were also detected in other arid environments, such as soils of the Sonoaran Desert (Ranzoni 1968; Knudsen and Kocourkova 2010).

CHARACTERIZATION OF SOIL SAMPLES (SOIL TYPE; PHYSICAL AND CHEMICAL PARAMETERS OF THE SOILS) Variation in soil type and characteristics for all sampling sites was indicated by information obtained from the USDA websoilsurvey database for our study area. It should be noted that the USDA soil parameter data is averaged; therefore, one has to be careful in performing statistical analyses to determine potential correlations between environmental parameters such as pH and positive sites for the pathogen because averaged data might not represent the real data situation in a microhabitat. Individual sampling spots where the pathogen was detected likely represent suitable microhabitats of C. immitis that might differ from soils only a few cm away indicating a spotty distribution of many members for the soil microbial community including C. immitis. Since the USDA websoilsurvey data is averaged for physical and chemical parameters, it is impossible to determine differences and similarities between sampling sites that are only a few meters apart by using information from this database; however, this information is useful when developing a sampling plan. Furthermore, soil samples in this study were taken near the surface which is more exposed to influences such as weather, erosion, plant growth and pollution, compared to deeper layers of the soil which can result in quite different microhabitats characterized by different soil parameters and microbial populations. Our results showed that positive and negative sites for C. immitis were not distinguished by soil pH (Table 1). The pH (measured in the laboratory) ranged between 6.72-8.33 for all soil samples, and no significant difference was detected in soils that contained the pathogen compared to soils that did not, showing that C. immitis can likely survive in soils with a somewhat wide variety of pH. A study by Rousk et al. (2009) showed that fungi generally are able to cope with a wide pH ranges for optimal growth if nutrients are abundant and competition is low, supporting the results of our research. In vitro experiments have shown that C. immitis is able to grow at a 63 range of pH from 3.5 – 9.0 (Kolivras et al. 2001; Cordeiro et al. 2006). In our research, most of the C. immitis samples were characterized by neutral pH (~7 pH).

VEGETATION The Sequoia Blvd and Mule Team Pkway transect from 2015 exhibited a higher plant diversity compared to the Proctor Blvd and Mule Team Pkway transects from 2014, which could be due to differences in elevation. The vegetation covers present along the transects from both years were dominated by drought tolerant perennial such as Larrea tridentata and Atriplex polycarpa. L. tridentata is a prominent species of the Mojave, Sonoran, and Chihuahuan Deserts of western North America, including portions of California, Arizona, Nevada, Utah, New Mexico and western Texas in the United States (California Native Plant Society; Sawyer et al. 2009). L. tridentata was found at lower elevations of the Mule Team Pkway transect from 2015 (MT-6), but also at higher elevation (MT-9), suggesting a spatial variability. L. tridentata is extraordinarily tolerant of drought, saline or alkaline soils and adapted to desert conditions (California Native Plant Society; http://www.cnps.org). A. polycarpa, was present at both transects in 2014 and is native to the southwestern United States and northern Mexico. A. polycarpa was present at the Mule Team Pkway transect in 2015, but was not found at any of the sampling sites at Sequoia Blvd (2015). In terms of elevation, A. polycarpa was found at lower elevations of the Mule Team Pkway transect (MT-2), and also at higher elevation (MT-8) from 2015. L. tridentata and A. polycarpa are perennial plants generally found in the Mojave Desert ecosystem. These plants contribute to soil fertilization by litter fall adding organic matter to the environment and might indicate a suitable habitat for C. immitis on the larger scale, which is known to tolerate salty soils (Titus 2002; Elconin et al. 1963). The Sequoia Blvd (2015) transect exhibited the highest diversity of plant species and also comprised the highest number of C. immitis positive samples, suggesting a correlation between the nutrient availability and the presence of C. immitis. For future studies, investigating the type of organic matter and presence/absence of the pathogen might be an area of interest.

OTHER ENVIRONMENTAL PARAMETERS Environmental parameters, such as fugitive dust emission (PM10) pose a risk of increasing valley fever incidence. Over the last few years, the drought in California has resulted in a 64 significant reduction in farming activities in the Antelope Valley south of California City, which has resulted in large areas of abandoned fields that slowly erode. At the same time, a renewable energy boom had resulted in increased soil disturbance and fugitive dust emissions in the Western Mojave Desert. A study by Lauer et al. (2016) in the West Lancaster area using the same nested PCR approach found 40% of soil samples positive for C. immitis due to renewable energy construction, whereas in this study we found 70% of the samples positive for C. immitis in the California City prison area indicating that the northwestern Mojave Desert might even be a better habitat for C. immitis. In the West Lancaster area, 5.5% of the samples positive for C. immitis were discovered in Cajon loamy sands (Lauer et al. 2016), whereas in this study 37% of the samples positive for C. immitis samples were found in soil of this type, but most positive soil samples were associated with Neuralia sandy loam. Both studies indicate that Cajon loamy sand can support the growth of the pathogen and therefore might indicate a preferable habitat for C. immitis. The higher fugitive dust emission in California City, compared to the West Lancaster area (https://www.arb.ca.gov/desig/adm/basincnty.htm) indicates a higher risk of contracting coccidioidomycosis in the northwestern Mojave. California City is surrounded with more natural or semi-natural, undisturbed soils that can support the growth of the pathogen with few agricultural fields and other human influenced environments which poses the population living near California City at a higher risk of contracting coccidioidomycosis compared to inhabitants of the Antelope Valley (Cities of Lancaster and Palmdale) where more soil disturbance for various construction projects occurred in recent years. The results of this study showed that California City appears more endemic for C. immitis compared to the Antelope Valley and one can therefore say that the general public and inmates that reside in the northwestern Mojave Desert area are at a greater risk of contracting coccidioidomycosis compared to residents further south.

CONCLUSION Based on the high numbers of soils in which we detected the fungal pathogen C. immitis, we can conclude that our sampling area in general can be characterized as a suitable habitat for C. immitis. Even though soil types were indicated to be different by the USDA websoilsurvey database, the surface soils from which soil samples were collected might have been more similar and can be investigated in future studies for the presence of certain minerals and type of organic matter. Not all microhabitats supported the growth of the same fungal species as it was indicated 65 by different fingerprints of the fungal communities which indicates difference in soil physical and chemical parameters and might also explain why the presence of the pathogen in soils that were sampled over a 2-year period varied during both years. Our results confirmed that C. immitis is able to thrive in diverse environments that differ in pH, but this observation could also indicate that multiple strains within C. immitis exist that are adapted to survive in slightly different environmental conditions, or that the pathogen is present but dormant in some less supportive soils. The entire area has been identified as a hotspot for C. immitis in this study, and prisoners and general public alike are at a high risk of contracting valley fever when exposed to fugitive dust from these soils. This threat is increased by the ongoing drought, which may accelerate the problem along with urbanization, and construction that generates increased PM10 in the air. In this study, C. immitis appeared to be spottily distributed along Proctor Boulevard and Mule Team Pkway, but one particular area (Sequoia Boulevard, California City) stood out with the highest numbers of C. immitis positive soil samples, even though mostly weak PCR products were obtained. There is currently no vaccine for valley fever; the only prevention is to avoid inhaling dust contaminated with arthroconidia of the pathogen. Disease mitigation efforts such as wetting soil before disturbing it as a short-term dust mitigation effort, planting and maintaining vegetation cover as a long-term mitigation effort, closing the prison yards at times when PM10 emissions are high, advising inmates and employees to stay indoors, in addition to improved education, may lower the risk for airborne dispersion of Coccidioides arthroconidia and also reduce disease incidence among prisoners, staff and the general public, and thus, will reduce human suffering and health care costs substantially. 66

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Appendix

A B

Figure 1. DGGE (A) showing the fungal diversity for 12 different samples collected from Proctor Blvd (2014). Numbers indicate bands that were excised. Green color indicates bands that were marked and matched. Samples 1-1,1-3, 2-1, 2-2, 3-1, 4-1, and 4-2, are the sites where the pathogen was detected by nested PCR. Figure 1B, shows the analysis of matched DGGE bands using a UPGMA tree. Genetic distance percentage is shown on each node and numbers on the nodes represent the lane number from DGGE gel. A B

Figure 2. DGGE (A) showing the fungal diversity for 8 different samples collected from Mule Team Pkway (2014). Numbers indicate bands that were excised. Green color indicates bands that were marked and matched. Samples 5-2, 6-2, 6-3, and 6-4, are the sites where the pathogen was detected by nested PCR. Figure 2B, shows the analysis of matched DGGE bands using a UPGMA tree. Genetic distance percentage is shown on each node, numbers on the nodes represent the lane number from DGGE gel. 75

A B

Figure 3. DGGE (A) showing the fungal diversity for 12 different samples collected from Sequoia Blvd (2015). Numbers indicate bands that were excised. Green color indicates bands that were marked and matched. Samples, S-1 to S-12, are the sites where the pathogen was detected by nested PCR. Figure 3B, shows the analysis of matched DGGE bands using a UPGMA tree. Genetic distance legend is shown above the tree, numbers on the nodes represent the lane number from DGGE gel.

A B

Figure 4. DGGE (A) showing fungal diversity for 10 different samples collected from Mule Team Pkway (2015). Numbers indicate bands that were excised. Green color indicates bands that were marked and matched. Samples MT-4, MT-7, MT-8, MT-9, MT-10, MT-11, and MT-12 are the sites where the pathogen was detected by nested PCR. Figure 4B, shows the analysis of matched DGGE bands using a UPGMA tree. Genetic distance legend is shown above the tree, numbers on the nodes represent the lane number from DGGE gel.

A B

Figure 5. DGGE (A) showing fungal diversity for 9 different samples collected from Mule Team Pkway (2015). Numbers indicate bands that were excised. Green color indicates bands that were marked and matched. Samples MT-4, MT-7, MT-8, MT-10, and MT-12 are the sites where the pathogen was detected by nested PCR. Figure 5B, shows the analysis of matched DGGE bands using a UPGMA tree. Genetic distance legend is shown above the tree, numbers on the nodes represent the lane number from DGGE gel. A B

Figure 6. DGGE (A) showing fungal diversity for 9 different samples collected from Mule Team Pkway and Sequoia Blvd (2015). Numbers indicate bands that were excised. Green color indicates bands that were marked and matched. Samples MT-12, S-3, S-7, S-8, S-9, and S-10 are the sites where the pathogen was detected by nested PCR. Figure 6B, shows the analysis of matched DGGE bands using a UPGMA tree. Genetic distance legend is shown above the tree, numbers on the nodes represent the lane number from DGGE gel. Sampling Sites Proctor Boulevard, 2014 (California City, CA) 78

1‐1 1‐2

1‐3 2‐1

2‐2 2‐3

3‐1 3‐2

3‐3 4‐1

Sampling Sites Mule Team Parkway, 2014 (California City, CA)

4‐2 4‐3

5‐1 5‐2

5‐3

5‐4

6‐1 6‐2

6‐3 6‐4

Sampling Sites Mule Team Parkway, 2015 (California City, CA)

MT‐1 MT‐2

MT‐4 MT‐6

MT‐7 MT‐8

MT‐9 MT‐10

MT‐11 MT‐12

Sampling Sites Sequoia Boulevard, 2015 (California City, CA)

S‐1 S‐3

S‐4 S‐5

S‐6 S‐8

S‐9 S‐10

S‐11 S‐12