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We collected data from all US states where school vaccine exemption information was freely available from the Department of Health website in any format. We were able to locate that data in 24 states (see Table 1 for a list of states included). Within these states, the number of years available varied relatively widely, between 19 years in California and a single year in 6 states. The most represented year in our dataset was 2017 (corresponding to school year 2017–2018). Because the dataset was compiled in June-July 2019, we note that it is likely that additional data for more recent years may be available, or that data may have become available in additional states not included in our dataset.
Table 1 Exemption data reporting varies widely across states.
Full size table

The data format varied widely between states, and exemptions were reported either as a number of exemptions or as a percentage of the enrolled students. We have elected to use number of students rather than percentages, and have transformed data as needed. For most states included in our dataset, the data are provided at the county level. In several states (Arizona, Colorado, Illinois, Maine, Michigan, South Dakota, Tennessee, Vermont, Oregon, and Washington), the data was provided at the school level, which we aggregated to the county.
Additional data processing was necessary in some cases. In Virginia, data was provided by school name, but county or city information was not included. We used a list of public and private schools to match school names with their respective county using fuzzy matching (with the ‘fuzzywuzzy’ Python package) with an 80% matching requirement. Our algorithm was unable to find a suitable match for between 3.8% and 6.8% of schools (depending on year), and these schools were not included in the final counts at the county level. Similarly, in Idaho, data at the school level included city information but county was not provided. We first matched city and county names, before aggregating the exemption data at the county level. Finally in New York state, exemptions were provided as percentages at the school level but enrollment information was not included. We obtained enrollment for public and private schools separately from the New York State Education Department, and used the school unique code to calculate exemption number from enrollment and exemption percentages. We then aggregated these numbers at the county level.
States reported data for exemptions based on varying definitions, so we selected data records based on data availability to make the data comparable across states. We aimed to achieve parsimonious definitions of total medical exemptions (Fig. 1a), total non-medical exemptions (Fig. 1b), and total exemptions (Fig. 1c), which includes both types of exemptions. We define medical exemptions as reported total medical exemptions. In Florida, permanent medical exemptions were reported separately from temporary medical exemptions, so permanent medical exemptions was chosen to represent total medical exemptions. To define total non-medical exemptions, we considered the state law regarding non-medical exemptions and the data availability. If the state reported total aggregated non-medical exemptions, that was selected as total non-medical exemptions. If the state reported only religious exemptions and only allows religious exemptions, that was selected as total non-medical exemptions. If the state reported only religious exemptions, but also allows philosophical exemptions, that was considered missing data. If the state allows philosophical exemptions and only reports philosophical exemptions, that was selected as total non-medical exemptions, as the state may not differentiate religious from philosophical. If the state allows philosophical exemptions and reports both religious and philosophical exemptions separately, these values were summed for total non-medical exemptions. To define total exemptions, if the state reported a total exemptions value, this value was used. If the state did not report a total exemptions value, but reported values for total medical exemptions and total non-medical exemptions, as defined above, these were summed for total exemptions. If the state was missing either medical or non-medical exemptions, but reported the total number of students with completed vaccinations, the total exemptions was the difference between the number of students enrolled and the number of students completed. This classification process is visualized in Fig. 1.
Fig. 1

Exemptions were classified by type to standardize reporting. Exemptions were classified as medical exemptions (a), non-medical exemptions (b), and total exemptions (c) to standardize reporting across states with different values reported.

Full size image

We also considered disease-specific exemptions reports. If a state reported the number of exemptions for a vaccine specific to a given infection, that value was used. If the state did not report exemptions, but did provide the total number complete for that disease, the difference between the enrolled students and the completed students was used. For pertussis-specific vaccination, we used DTaP exemptions where available, and TDaP exemptions where DTaP was not available. For measles-specific vaccination, if separate reports were available for measles, mumps, and rubella, the value for measles was used. If measles was not available, then the mumps or rubella exemptions were used, if available.
The data in the figures is only data reported for kindergartens in states where kindergarten-specific data was available, or K-12 data in states where kindergarten-specific data was not reported. States reported age groups heterogeneously, and data by other age groups is available in the data file. We note that Oregon reports kindergarten-specific data in 2014–2015, then K-12 data in 2016–2018. More

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Greenhouse experiments and sampling
Wheat (Triticum turgidum cv. Negev) was cultivated in sandy loam soil (19% clay, 6% silt, 75% sand) classified as Calcic Haploxerept. The soil was obtained from intensive agriculture field located in Eshkol region, Israel (31.248,949, 34.379,872). Potatoes, wheat and peanuts were previously grown in this field. Initial soil parameters were: pH 8.78 ± 0.04, electrical conductivity 99 ± 1 (µS/m), NO3-N 0.22 ± 0.02 (mg/kg), NH4 0.30 ± 0.01 (mg/kg), P-PO4 0.09 ± 0.01(mg/kg), total soluble organic carbon 4.0 ± 0.04 (mg/kg) and total soluble nitrogen 0.70 ± 0.02 (mg/kg).
The plants were grown for 6 weeks (from December 2016 to February 2017) as described previously [25]. Briefly, 750 g of soil was distributed in a 700-mL plastic pot, with four seeds per pot. Those pots were able to sustain up to four wheat plants for six weeks under the experimental conditions. The wheat was grown in a greenhouse with two closed-system chambers at day/night temperatures of 25 °C/18 °C ± 1 °C, and with an automatically adjusted CO2-supply system (Emproco Ltd., Ashkelon, Israel). The photoperiod was 9 h and the daily light integral was 12.5 MJ/day. Wheat plants were grown in a sequence of three independent experimental cycles of 6 weeks each (five pots per treatment per cycle), with a 1-week shift between cycles. Plants were grown under either ambient (400 ppm) or elevated (850 ppm) atmospheric CO2 levels. Nutrient solution was prepared with 90% nitrogen supplied as nitrate and 10% supplied as ammonium using KNO3 and NH4NO3 to provide final concentrations of 30, 70 and 100 ppm nitrate [26]. Other macronutrients were supplied in each treatment at the following rate: P-15 ppm, K-150 ppm, Mg-24 ppm, Ca-120 ppm and S-40 ppm provide by NH4NO3, KNO3, CaCl2, KCl, MgCl2 and KH2PO4 salts. 40 ppm S and Ca were present in the tap water. Micronutrients were supplied at a rate of 1.3 ppm Fe, 0.7 ppm Mn, 0.3 ppm Zn, 0.05 ppm Cu, and 0.0375 ppm Mo using Korotin (Haifa Chemicals, Israel), a commercial micronutrient mix. Each pot was irrigated with 50 mL of the nutrient solution four times a week. The total amount of nitrogen in the 30 ppm nitrate treatment was 36 mg/pot (equivalent of ca. 73 kg N/ha), 70 ppm nitrate treatment was 84 mg/pot (equivalent of ca. 170 kg N/ha) and in the 100 ppm treatment, 120 mg/pot (equivalent of ca. 250 kg N/ha).
Soil and plant analyses
At the end of the 6th week of growth, 15 pots (5 pots per cycle) from each treatment were sampled for soil, shoots and roots, and the following parameters were measured: soil nitrate and ammonia content, soil EC and soil pH, shoot and root dry biomass, nitrogen concentration and content in shoot and roots. Soil properties and relevant methods were as described previously [25]. Briefly, soil EC and pH were determined in a solution of 1:5 air dry sieved soil:distilled water (w/v). Nitrate and ammonium concentrations were determined using an autoanalyzer (Lachat Instruments, Milwaukee, WI or Gallery Plus, Thermo Fisher Scientific, Waltham, MA, USA). Sampled shoots and roots were dried at 60 °C for 48 h, ground and weighed to obtain dry biomass. Total nitrogen concentration was determined using an autoanalyzer (Lachat Instruments or Gallery Plus) following digestion with sulfuric acid and peroxide [27].
Root DNA extraction for sequencing and qPCR
At the end of the 6th week of wheat growth, pots were randomly selected for DNA extraction. To obtain the root-surface-associated microbiome, wheat roots were collected in triplicate from each of the three cycles and were vortexed three time with 85% saline solution, until no visible soil particles were attached to the roots. Total DNA was extracted from 0.4 g of complete root system, using the Exgene Soil DNA mini isolation kit (GeneAll, Seoul, Korea) according to the manufacturer’s instructions.
Generation of qPCR plasmid standards
Plasmids containing the 16S rRNA gene were generated as described previously [28, 29]. Each PCR amplification product was ligated into pGEM-T Easy Vector (Promega, Madison, WI, USA) and plasmids were transformed into BioSuper Escherichia coli DH5α competent cells (Bio-Lab, Jerusalem, Israel). Circular plasmid DNAs were used as the standards to create calibration curves at 10-fold dilutions for gene quantification by real-time qPCR.
Assessment of gene copy numbers by qPCR
Copy numbers of the total bacterial community (16S rRNA gene) and translation elongation factor 1 (TEF, a plant housekeeping gene) were assessed using selected primers (Table S1) in roots of 6-week-old wheat plants with the StepOnePlus Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). Triplicates from whole genomic DNA were diluted to 6 ng/µL and 1 µL was used in a 20-µL final reaction volume together with 50 µM forward and reverse primers and 10 µL 1X FAST MasterMix (Thermo Fisher Scientific). Three biological and three technical replicates were analyzed for each root DNA sample. Reaction efficiency was monitored in each run by means of an internal standard curve (constructed plasmids) using duplicates of 10-fold dilutions of standards ranging from 108–102 copies per reaction. Efficiency was 89–98% for all target genes and runs, and R2 values were greater than 0.99. Copy numbers of the target genes were calculated based on the relative calibration curve of the plasmid copy numbers. All data analyses were conducted using StepOne software v2.3 (Applied Biosystems).
Shotgun sequencing
Root DNA was extracted from each of the biological triplicates, in each of the three cycles. For sequencing, the DNA of the triplicates was combined, resulting in three biological replicates per treatment (one from each batch) and 18 samples altogether. Shotgun metagenome libraries were prepared using the Celero DNA-Seq library preparation kit (NuGen, Takara Bio, USA) with enzymatic shearing, according to the manufacturer’s instructions. All libraries were then pooled in equal volumes and size selection (350–400 bp fragments) was performed using a Blue PippinPrep instrument (Sage Scientific). The libraries were then sequenced using an Illumina MiniSeq instrument employing a mid-output kit. Based on the number of reads per sample, the samples were repooled with varying volumes, and size selection was performed again using the same size range. The final size-selected pool was sequenced on an Illumina NovaSeq instrument with an S4 flow cell, employing 2 × 150 base reads. Library preparation and pooling were performed at the University of Illinois at Chicago Sequencing Core (UICSQC), and sequencing was performed by Novogene Corporation (Chula Vista, CA, USA).
In total, we obtained 310 Gb of information, with 30–44 million sequences per root sample. These sequence data were submitted to the Sequence Read Archive (SRA) of the NCBI databases under accession numbers SUB6631533 and SUB8385777, BioProject: PRJNA592741.
All reads were subjected to quality control using FastQC v0.11.3 [30] and barcode trimming using Trimmomatics v0.32 [31]. Reads were mapped to the whole wheat metagenome using Bowtie2 v2.3.5.1 [32], and mapped reads were filtered out from each sample. Then, short Illumina reads from triplicates of each nitrate treatment (30, 70 and 100 ppm) were assembled using SPADES v3.13.0 [33] into longer contigs, to create three wheat root microbiome catalogs for each treatment separately. The 30 ppm nitrate catalog had 677,271 contigs with N50 of 964 bp, 70 ppm nitrate catalog had 644,394 contigs with N50 of 971 bp, and the 100 ppm catalog had 677,271 contigs with N50 of 964 bp. Those three catalogs were combined and Prodigal v2.6.2 [34] was used for protein-coding gene prediction. To create a non-redundant set of genes, we used CD-HIT-EST software v4.8.1 [35] with a similarity threshold of 95%. Those genes were used as the root gene catalog, which included 35 million partial genes. This gene catalog was searched against the non-redundant NCBI protein database using DIAMOND sensitive algorithm v0.9.24.125 [36] to assign taxonomic and functional annotations. Results were then uploaded to MEGAN Ultimate edition software v6.15.2 [37]. The LCA (lowest common ancestor) algorithm was applied (parameters used with minimum bit-score of 70, minimum support of 5% and 30% top threshold) to compute the assignment of genes to specific taxa. For functional annotation, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database [38] was used. Following annotation, to generate taxonomic and functional count tables, each library was mapped to the gene catalog with Trinity mapping software v2.8.4 [39], with Bowtie2-modified parameters (–no-unal –gbar 99999999 -k 250 –dpad 0 –mp 1,1 –np 1 –score-min L,0,−0.9 -L 20 -i S,1,0.50).
Data analyses
Significance of interactions between CO2 and nitrate levels on soil and plant parameters was calculated using two-way ANOVA the least-squares method, in JMP 14 Pro software (SAS Institute Inc., Cary, NC, USA). Differences between soil and plant parameters as influenced by interactions between CO2 and nitrate levels was calculated using Student’s t test in JMP 14 Pro software and statistical significance was set at P  More