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An aldo-keto reductase is responsible for Fusarium toxin-degrading activity in a soil Sphingomonas strain

Degradation of toxins by microorganisms is a promising approach for detoxification of agricultural products. Here, a bacterial strain, Sphingomonas S3-4, that has the ability to degrade the mycotoxin deoxynivalenol (DON) was isolated from wheat fields. Incubation of Fusarium-infected wheat grains with S3-4 completely eliminated DON. In S3-4 DON is catabolized into compounds with no detectable phytotoxicity, 3-oxo-DON and 3-epi-DON, via two sequential reactions. Comparative analysis of genome sequences from two DON-degrading strains, S3-4 and Devosia D17, and one non-DON-degrading strain, Sphingobium S26, combined with functional screening of a S3-4 genomic BAC library led to the discovery that a novel aldo/keto reductase superfamily member, AKR18A1, is responsible for oxidation of DON into 3-oxo-DON. DON-degrading activity is completely abolished in a mutant S3-4 strain where the AKR18A1 gene is disrupted. Recombinant AKR18A1 protein expressed in Escherichia coli catalyzed the reversible oxidation/reduction of DON at a wide range of pH values (7.5 to 11) and temperatures (10 to 50 °C). The S3-4 strain and recombinant AKR18A1 also catabolized zearalenone and the aldehydes glyoxal and methyglyoxal. The S3-4 strain and the AKR18A1 gene are promising agents for the control of Fusarium pathogens and detoxification of mycotoxins in plants and in food/feed products.

major determinant of toxicity 10,11 . Based on this discovery a new detoxification pathway was proposed where DON is catabolized into 3-oxo-DON (3-keto-DON) and 3-epi-DON 5 (Fig. 1), both of which exhibit little to no toxicity 12,13 . Production of 3-oxo-DON occurs via oxidation of the DON C3 hydroxyl group, and a mixed culture, D107, isolated from soil could convert DON to 3-oxo-DON 14 . The soil bacteria Nocardioides sp. WSN05-2 was found to catabolize DON to 3-epi-DON via DON C3 epimerization 15 . Recently, a set of 13 bacteria isolated from a variety of environmental samples, such as soils and wheat leaves, were also found to efficiently transform DON into 3-epi-DON 10 . These studies have led to the speculation that DON is oxidized to 3-oxo-DON 5 , possibly by a 3-acetyl deoxynivalenol oxidase 16 , and then, 3-oxo-DON is epimerized into 3-epi-DON. However, to date no genes responsible for de-epoxidation, oxidation or epimerization of DON have been identified, and, thus, the components of these detoxification pathways remain unclear.
Sphingomonas is an unusual genus of the Gram-negative alphaproteobacteria known for their extraordinary ability to degrade recalcitrant environmental pollutants. Members of this genus are widely distributed and have been isolated from a variety of terrestrial and aquatic environments 17 . Sphingomonas can degrade a wide range of xenobiotic compounds such as herbicides 18 and pesticides 19 , and thus is considered a good choice for bioremediation 20 . Recently, a Sphingomonas strain, KSM1, isolated from lake water was found to catabolize DON into 16-hydroxy-DON (16-HDON), which is 10-fold less phytotoxic to wheat than DON 21 . The conversion of DON to 16-HDON in this strain is mediated by a cytochrome P450 system.
Aldo-keto reductases (AKRs) have also been implicated in detoxification and are widely distributed across all organisms 22 . The AKR superfamily comprises 17 families, where members in different families share less than 40% amino acid identity. AKRs are mostly NAD(P)(H)-dependent oxidoreductases and have wide substrate spectrum. These enzymes typically catalyze the reversible reduction of a number of ketone-and/or aldehyde-containing compounds to the corresponding alcohols. Plant AKRs are involved in diverse metabolic reactions including reactive aldehyde detoxification, biosynthesis of osmolytes, secondary metabolism and membrane transport and have been exploited to generate stress tolerant transgenic plants. AKRs have also frequently been implicated in the metabolism of endogenous and exogenous toxicants 22 . AKR7A-related members from rat and human may protect against aflatoxin B1 (AFB1)-induced hepatotoxicity 23 . However, AKR superfamily members capable of detoxifying Fusarium toxins have not been investigated.
Here, a novel member of the AKR superfamily responsible for detoxifying Fusarium toxin DON was cloned from a soil Sphingomonas strain through comparative genome sequence analysis of bacteria with and without DON-degrading activity combined with functional screening of a Sphingomonas BAC library. The ability of the recombinant AKR protein to detoxify DON and other toxins was also evaluated.

Results
Isolation of a DON-degrading bacterial strain. An enrichment procedure was used to identify wheat field soil bacterial communities that were consistently able to degrade DON. Three antibiotics were then added to cultures with DON-degrading activity to reduce the diversity of the bacterial population. One antibiotic, streptomycin, substantially reduced population diversity without affecting the ability to degrade DON. Therefore, serial dilution was done in the presence of streptomycin to isolate single colonies with DON-degradation activity. These single colonies were then cultured in mineral salts medium (MM) containing DON, and DON levels were monitored by HPLC. One pure bacterial strain, designated S3-4, displayed stable DON-degradation activity and was selected for subsequent study.
The identity of the Gram-negative S3-4 strain was determined by sequencing its 16S rDNA gene (GenBank ID in NCBI: KY575150) and performing BLAST searches against 16S rDNA sequences retrieved from NCBI. The S3-4 rDNA sequence had the highest identity (97%) to the rDNA of Sphingomonas leidyi DSM 4733 ( Supplementary Fig. S1). Thus, we conclude that strain S3-4 is a member of the genus Sphingomonas.
To investigate DON degradation activity of S3-4 in the presence of DON versus time, S3-4 was grown in MM containing DON (100 μg/mL), and changes in DON concentration were monitored every 12 hours by HPLC. Levels of DON were significantly reduced at 12 hours after inoculation (hai), were sharply decreased at 36 hai and completely absent at 72 hai ( Fig. 2A). We investigated the degradation of 3-acetyl deoxynivalenol (3A-DON), which is the predominant DON-acetylated form produced by FHB pathogens in China 2 and other countries 24 . Similar to DON, levels of 3A-DON were reduced in S3-4, but not to the same extent as DON. The number of These results indicate that S3-4 cells grew slowly and retained high DON degradation activity under oligotrophic culture conditions.
To evaluate the ability of S3-4 to degrade DON in agricultural products, wheat grains infected with F. graminearum and contaminated with DON were ground, incubated with S3-4 and monitored by HPLC for DON degradation. Wheat samples contained 112 μg of DON before treatment, and after incubation with S3-4 for 72 hours, no DON was detectable (Fig. 2B). In contrast, levels of DON in control samples that were incubated in MM without S3-4, were not significantly reduced after 72 hours. These results demonstrate that strain S3-4 can completely eliminate DON from wheat.
To detect the products of DON degradation, metabolites from S3-4 cultures (100 μg/mL DON) at 0 hai and 120 hai were extracted and analyzed by HPLC. At 120 hai, there were two new compounds with retention times of 7.65 min (compound A) and 3.29 min (compound B), respectively (Fig. 3A). These compounds were produced at different times after inoculation with DON; significant levels of compound A accumulated at 12 hai, then levels sharply increased at 60 hai, and remained high at 120 hai. In contrast, compound B was not detectable until 72 hai and then levels slightly increased (Fig. 3B). In addition, based on the peak areas from the HPLC profiles, levels of compound A were 80-fold (72 hai) and 8-fold higher (120 hai), respectively, than compound B. These results indicate that strain S3-4 sequentially catabolizes DON into two compounds, with compound A being the first and predominant product.
Identification of DON catabolites by gas-chromatograph/mass spectroscopy and nuclear magnetic resonance. Compounds A and B were purified by semi-preparative HPLC and along with DON were subjected to analysis by gas-chromatograph/mass spectroscopy (GC/MS) to determine their identities. Derivatized DON and its two catabolites ( Supplementary Fig. S2) each had a distinct retention time (Fig. 4), confirming that they are structurally distinct. Molecular mass for each derivatized compound was determined from MS: DON is 512.2 Da (the molecular mass of DON is 296 Da, and the molecular mass of the three trimethylsilyl groups used for derivatization is 216 Da), compound A is 438.2 Da (the difference from derivatized DON was 74, and the molecular mass of the trimethysilyl group is 72, leaving a 2 dalton difference between DON and compound A), and compound B is 512.2 Da (the same as DON).
These compounds were further characterized by 1 H nuclear magnetic resonance (NMR) spectroscopy ( Supplementary Fig. S3). All NMR assignments for compounds A and B were compared with previously published NMR data for 3-oxo-DON and 3-epi-DON 11,15 . The NMR experimental parameters, including 1 H chemical shifts, multiplicity and J-coupling constants, of compounds A and B closely match those of 3-oxo-DON and 3-epi-DON, respectively (Supplementary Table S1). GC/MS (Fig. 4) and NMR results strongly indicate that compounds A and B are 3-oxo-DON and 3-epi-DON, respectively.

Reduced impact of 3-oxo-DON and 3-epi-DON on wheat seedlings.
To determine whether the two DON-degradation compounds have reduced phytotoxicity, wheat coleoptiles were inoculated with 100 μg/mL of DON, 3-oxo-DON and 3-epi-DON, respectively, and the lengths of wheat leaves and coleoptiles were measured 24 hai. As shown in Fig. 5A, there were no significant differences in coleoptile length between the four treatments. However, DON inoculation significantly reduced wheat leaf length (56%) compared with the water control, whereas 3-oxo-DON or 3-epi-DON inoculation did not significantly affect leaf length. Thus, in wheat, both 3-oxo-DON and 3-epi-DON have no or undetectable phytotoxicity.

3-oxo-DON and 3-epi-DON have reduced impact on the expression of DON-responsive genes
in wheat seedlings. We next evaluated the effect of DON, 3-oxo-DON and 3-epi-DON on DON-responsive gene expression in wheat seedlings at 24 hai. Five wheat marker genes known to be induced by DON were assayed by qRT-PCR. Transcript levels for all five genes were significantly lower in 3-oxo-DON or 3-epi-DON-treated seedlings than in seedlings treated with DON, with reduction in transcript levels ranging from 2.0-to 6.6-fold (Fig. 5B). These results indicate the impact of 3-oxo-DON and 3-epi-DON on gene expression is significantly reduced compared with DON.

Identification of oxidoreductase genes by comparative genomic sequence analysis. To perform
comparative genome analysis with other bacteria, the S3-4 genome was sequenced. The S3-4 genome comprises three scaffolds (3,153,523 bp, 1,058,422 bp and 397,147 bp in length, respectively) and two plasmids (155,520 bp and 12,966 bp in length, respectively), with a total genome size of 4.7 Mb. There are 4,593 predicted genes, including two 5 S rRNA, two 23 S rRNA, two 16 S rRNA, and 50 tRNA genes.
The annotated protein sequence of S3-4 was compared with the genome sequences of Devosia sp. 17-2-E-8 (D17), which is able to degrade DON 25 . Comparative analysis revealed that 1,859 protein-coding genes are conserved between D17 and S3-4 ( Fig. 6A). A second BLASTp search was conducted to compare these 1,859 proteins against the genome of Sphingobium japonicum UT26 (S26), which is a close relative of S3-4 but does not have DON-degrading activity 21 . This comparison revealed that 188 of the 1,895 protein-coding genes conserved between D17 and S3-4 are not present in S26 (Fig. 6A). These 188 genes unique to the DON-degrading strains were subsequently used for identification of candidate genes responsible for DON-degradation in the S3-4 strain.
Functional annotation was done using the COG (Clusters of Orthologous Groups) database. Of the 188 unique genes, 163 could be classified into 19 functional categories (Fig. 6B). Eleven genes in the class of energy production and conversion were selected for further analysis. This class was chosen because genes responsible for energy production and conversion likely play an essential role in allowing S3-4 to grow in MM media containing DON as the only carbon source by metabolizing DON to generate energy for bacterial growth. Because the transformation of DON into 3-oxo-DON is actually the oxidation of alcohol, which may be catalyzed by a dehydrogenase, 5 of the 11 genes that encode predicted oxidoreductases (related to aryl-alcohol dehydrogenases) were considered potential DON degradation candidate genes.  Identification of an oxidoreductase gene by functionally screening a BAC library. To identify the gene responsible for DON-degradation, a S3-4 BAC library was screened for enzymatic activity. This library consists of 2,304 clones that were arrayed in six 384-well plates. Analysis of 30 random BAC clones showed that the library had an average insert size of 120 kb with a size range from 20 to 227.5 kb and an empty-vector rate of lower than 5% (Supplementary Fig. S4). The genome coverage of this library is estimated to be more than 50-fold (based on a genome size of 4.7 Mb). When this BAC library was screened for DON-degrading activity, one positive clone was identified. The insert of this clone was 148 kb in length and contained 152 genes. These genes can be classified into 20 categories based on COG database annotation, with 6 genes belonging to the energy production and conversion category (Fig. 7). One of these genes, which was also identified as a gene unique to S3-4 and D17, encodes a predicted oxidoreductase, designated A1 (GenBank accession number: MF314460). This oxidoreductase gene is one of the five predicted oxidoreductases that were identified by comparative genomic sequence analysis (Fig. 6B) and was then further characterized.

A new aldo/keto reductase family member AKR18 A1 is responsible for DON-degradation.
Alignment of the amino acid sequences of the S3-4 A1 protein and aldo/keto reductases retrieved from BLAST searches showed that the A1 protein contains typical AKR signature motifs, such as an (α/β) 8 -barrel motif and catalytic tetrad (Asp-57, Tyr-62, Lys-90, and His-131), which are conserved among aldo-keto reductases ( Supplementary Fig. S5). The AKR superfamily comprises 17 families, with more than 190 members (as of July 2015, http://www.med.upenn.edu/akr/). No AKR protein has more than 40% amino acid sequence identity to the A1 protein. A phylogenetic tree was generated based on amino acid sequence alignments of all 24 annotated AKRs from bacteria (http://www.med.upenn.edu/akr/) and the A1 protein. Based on this tree, A1 is most closely related to AKR12, but only has 37%, 36% and 35% identity to AKR12A1, AKR12B1 and AKR12C1, respectively ( Supplementary Fig. S6). The nomenclature criterion for the AKR family is >40% identity 26 ; therefore, these results led us to propose a new AKR family, family 18, and A1, designated AKR18A1, as the first member of this family.
Enzymatic properties of recombinant AKR18A1 expressed from E. coli. AKR18A1 was expressed in E. coli BL21, purified by affinity-chromatography and assayed for its DON-degrading activity and enzymatic properties. This recombinant protein had the expected size based on SDS-polyacrylamide gel electrophoresis (Fig. 8A) and was used in all enzymatic activity assays. The recombinant AKR18A1 protein oxidized DON to form 3-oxo-DON in the presence of cofactor NADP + (Fig. 8B), whereas no activity was seen in the presence of NAD + (data not shown), indicating that NADP + is essential for DON-degradation activity. The AKR18A1 protein catalyzed a reverse reaction of 3-oxo-DON into DON in the presence of the cofactor NADH ( Supplementary  Fig. S7), whereas no activity was seen in the presence of NADPH (data not shown).
Recombinant AKR18A1 had the highest activity at pH 10.6 and 45 °C, and activity was maintained over a wide range of pH values (from 7 to 11) and temperatures (from 10 to 50 °C), implying high stability and activity under different conditions (Fig. 8C). Km and Vmax values for AKR18A1 were 1214.4 ± 73.3 μM and 25.7 ± 0.8 nmo-l·min −1 ·mg −1 protein, respectively (Fig. 8D), and for the cofactor NADP + in the oxidation reaction, the Km and Vmax values were 480 ± 70 μM and 53.7 ± 2.4 nmol·min −1 ·mg −1 protein, respectively. Regarding reversible reduction reaction from 3-oxo-DON into DON, the Km and Vmax values for AKR18A1 were 547.1 ± 121.4 μM and 176.1 ± 19.5 nmol·min −1 ·mg −1 protein, respectively, while for the cofactor NADH the Km and Vmax were 78 ± 28 mM and 147.5 ± 5.2 nmol·min −1 ·mg −1 protein, respectively. Disruption of AKR18A1 in Sphingomonas sp. strain S3-4. To further verify that AKR18A1 is responsible for DON-oxidation in strain S3-4, its coding sequence was disrupted by gene replacement with a disruption plasmid pK18mobsacB, generating an isogenic Sphingomonas strain, Δakr18a1, that differs from the strain S3-4 only in a single gene, AKR18A1. The specific disruption of AKR18A1 was confirmed by PCR ( Supplementary  Fig. S8). The mutant Δakr18a1 and wild-type (WT) S3-4 strains had similar growth patterns in nutrient broth (NB) medium. When WT S3-4 was cultured in MM supplemented with 100 μg/mL DON, DON was substantially degraded at 36 hai and completely degraded at 72 hai. However, when the mutant strain Δakr18a1 was cultured under the same conditions, DON was not degraded at all during the entire culture period while the number of cells remained constant (Supplementary Fig. S9). The abolishment of DON-degrading activity in Δakr18a1 demonstrates that AKR18A1 is the only gene responsible for oxidation of DON in S3-4.

Degradation of aldehydes by recombinant AKR18A1 in vitro and in E. coli.
To determine if AKR18A1 protein degrades aldehydes, an E. coli strain BL21 expressing the AKR18A1 gene was grown in the presence of glyoxal (GO) and methyglyoxal (MG). This strain was highly tolerant to GO and MG, whereas a BL21 strain containing a control plasmid pET-22b was sensitive to GO and MG ( Supplementary Fig. S11A and B). In  Fig. S11C and D). These results demonstrate that AKR18A1 can catabolize aldehyde compounds.

Discussion
In this study function-based screening and comparative analysis were used to isolate a DON-detoxifying bacterial strain, S3-4, and the gene responsible for catabolic activity, AKR18A1. Biotransformation of DON into two different compounds, 3-oxo-DON and 3-epi-DON, in the same strain provides direct evidence that DON degradation occurs via sequential reactions. To date, only two bacterial strains, one Devosia 10, 11 and one mixed culture 14 , are known to oxidize DON into 3-oxo-DON, whereas several Devosia and Nocardioides strains have been shown to epimerize DON into 3-epi-DON 10,15,27 . However, there is no a single strain that can catabolize DON into both compounds. It has been proposed that DON is first oxidized to 3-oxo-DON and then converted to 3-epi-DON 5 .
In the present study we demonstrate that these reactions are sequential (Fig. 9). First, AKR18A1 catalyzes the reversible oxidation/reduction of DON to 3-oxo-DON and second, 3-oxo-DON is converted to 3-epi-DON by an unknown enzyme. In strain S3-4 there is much higher oxidation activity than epimerization activity, but the mechanistic basis for this differential activity remains unknown.
The S3-4 AKR18A1 protein contains conserved domains typical of AKR family members but is less than 40% identical to other AKR proteins. Therefore AKR18A1 represents a new member of the AKR superfamily. The AKR18A1 protein carries conserved sequences that have been identified in sequence alignments and structural analysis of AKRs. For instance, the residues of the cofactor-binding pocket across all the AKRs are strictly conserved (Asp, Asn, Gln, and Ser) 26 , and these residues are all found in the AKR18A1 sequence (Asp-50, Asn-162, Gln-187, Ser-268) (Supplementary Fig. S5). The substrate-binding pocket for the AKRs is formed by the carboxyl-terminal regions of the central β-strands where there is a conserved catalytic tetrad of Tyr, Lys, His, Asp 26 . This catalytic tetrad is also present in AKR18A1 (Supplementary Fig. S5).
The AKR18A1 protein appears to have favored catalytic direction towards the reverse reduction reaction of 3-oxo-DON into DON in vitro assays, whereas in the S3-4 strain, DON is completely converted into 3-oxo-DON and 3-epi-DON (Fig. 2B). This discrepancy between recombinant the AKR18A1 protein and the S3-4 strain may relate to the presence of other proteins in the S3-4 strain that may catalyze the 3-oxo-DON to 3-epi-DON; it has been speculated that during microbial conversion from DON into 3-oxo-DON and 3-epi-DON the coupling of the transformation to another pathway may drives it away from equilibrium 26 .
In addition to degrading DON, AKR18A1 catalyzes the reduction of ZEN into α-ZOL and β-ZOL ( Supplementary Fig. S10), as well as the reduction of the aldehydes, GO and MG ( Supplementary Fig. S11). Thus, AKR18A1 can catalyze the reduction of a wide range of ketones and/or aldehyde-containing compounds and the oxidation of alcohols. It is likely that AKR18A1 could reduce aflatoxin dialdehyde to mono and bis-alcohols, as has been demonstrated for the AKR superfamily members AKR7A2 and 7A3. This reaction can prevent aldehydes from forming Schiff 's bases with lysine residues and thus reduce the hepatotoxicity of aflatoxins 22 . Therefore, the AKR18A1 protein could be used for degrading different toxins.
The lower degrading activity for 3A-DON than for DON (Fig. 2) suggests that an additional reaction to remove acetyl group at the C3 position by unknown enzymes is required before oxidation of DON into 3-oxo-DON. Supporting this view, recombinant AKR18A1 enzyme did not oxidize 3A-DON into 3-oxo-DON (data not shown). Indeed, when strain S3-4 was grown in the presence of 3A-DON, HPLC assays revealed that DON appeared first, followed by 3-oxo-DON (data not shown). The acetyl group at the C3 position may thus interfere with oxidation activity. In addition, neither the AKR18A1 enzyme or strain S3-4 degraded nivalenol (NIV) (data not shown), another mycotoxin produced by some species of F. graminearum. NIV and DON are structurally similar sesquiterpenes bearing C3 hydroxyl groups. The only difference between them is a C4 hydroxyl group in NIV and a C4 hydrogen group in DON; the NIV C4 hydroxyl group may reduce the accessibility of the C3 hydroxyl group to AKR18A1. This finding is consistent with a previous report that UDP-glucosyltransferase can act on DON but not NIV 28 . Thus, although AKRs are promiscuous enzymes, individual AKRs have unique substrate specificities. The strain S3-4 was isolated from a screen where DON was the sole carbon source, and it is conceivable that strains capable of detoxifying NIV may also be isolated if NIV is used as the carbon source for screening.
The toxicity of 3-oxo-DON is much lower than DON, and in mouse has only one tenth the immunosuppressive toxicity of DON 11 . Recent studies have reported that 3-epi-DON has almost no toxicity 13 . In the present study both 3-oxo-DON and 3-epi-DON were not toxic to wheat seedlings (Fig. 5). DON in wheat grains at levels 112-fold higher than the mycotoxin limit for consumption (1 μg/g) was completely eliminated by strain  (Fig. 2), indicating the great practical potential of S3-4 in decontaminating food/feed stuff. A large-scale culture of this strain via fermentation and its application for detoxification of DON-contaminated food/feed products are in progress. The recombinant AKR18A1 protein can also be used to detoxify DON. Moreover, because DON is a virulence factor that is required for the spread of plant FHB pathogens 1 , the AKR18A1 gene can potentially be used to control plant diseases caused by DON-producing pathogens. The improvement of plant resistance against Fusarium pathogens and mycotoxins is a major challenge because most cultivars currently grown are susceptible to this disease and no highly resistant germplasm is available. Thus the identification of new genes to control Fusarium mycotoxins, such as AKR18A1, is particularly important.

Conclusion
In the present study a bacterial strain, S3-4, capable of detoxifying Fusarium toxins was isolated and the gene responsible for detoxification, AKR18A1, was cloned. Both the S3-4 strain and the recombinant AKR18A1 protein could be used as detoxifying agents to control FHB pathogens and to reduce mycotoxin levels in food and feed products. These results serve as the basis for future isolation of novel genes that detoxify mycotoxins and dissection of the complete pathway for degradation of Fusarium toxins via oxidation and epimerization.

Materials and Methods
Soil, chemicals, media and plants. DON-biotransforming microorganisms were isolated from soil samples collected from a wheat field at Huazhong Agricultural University in Wuhan, China, where there are frequent FHB epidemics. DON was purified from F. graminearum 52701 2 as previously described 9 . Standard chemicals such as DON, 3A-DON, NIV, ZEN, α-ZOL, β-ZOL, GO and MG were purchased from SIGMA (St Louis, MO, USA). MM supplemented with DON as a carbon source was used to screen microbial cultures for DON biotransformation capability 15 . Nutrient agar (NA) and 10-fold-diluted NA plates were used to isolate single colonies. Wheat (Triticum aestivum) cv. Sumai 3 was used for seedling inoculation.

Enrichment of bacterial cultures with DON degradation activity. Soil samples (5 g) were suspended
in sterile water (50 mL), and after the soil settled, 250 μL of the resulting supernatants were incubated in 2.5 mL of MM supplemented with 50 μg DON per mL as previously described 9 . The cultures were incubated at 28 °C for 7 days with shaking at 220 rpm. 100 μL of each culture was then transferred to fresh MM medium containing DON, followed by 7 days of incubation under the same conditions. This procedure was repeated seven times. Levels of DON in the cultures were monitored by HPLC as described below. Cultures with decreased DON concentrations were selected.
Isolation of a DON-degrading strain from a bacterial community. The cultures obtained from enrichment were treated with antibiotics and serially diluted in water to reduce the diversity of the microbial population without affecting the ability of the community to degrade DON. The antibiotics used were: ampicillin (100 μg/mL), rifampicin (100 μg/mL) and streptomycin (100 μg/mL). Aliquots (0.1 mL) of cultures were spread onto plates containing 10-fold-diluted NA and incubated at 28 °C for 5 days. Randomly selected bacterial colonies were tested for DON-degradation activity, which was monitored by HPLC as described below. Strains with DON-degrading activity were stored in 25% glycerol at −80 °C until use.
Taxonomic characterization of the isolated bacterial strain. The strain S3-4 was subjected to Gram staining, morphological observation, and 16 S rDNA gene analysis. The partial 16 S rDNA gene was amplified by PCR using the primers shown in Table S2 and sequenced to obtain 1,445 bp of 16 S rDNA sequence. Sequences similar to the S3-4 16 S rDNA were identified using BLAST (National Center for Biotechnology Information, http://www.ncbi.nlm.nih.gov). Maximum likelihood (ML) phylogenetic trees of the 16 S rDNA sequences from 26 bacterial species were constructed using MEGA 6 software. All sequences were retrieved from NCBI (http:// www.ncbi.nlm.nih.gov).

Extraction and detection of trichothecenes toxins and metabolites. DON and its metabolized
products were extracted and measured as previously described 15 using HPLC 9 . DON-biotransformed products were purified by semi-preparative HPLC. The purified compounds A and B were analyzed by GC/MS and NMR.

GC/MS and NMR analysis. GC/MS analysis of the purified compounds A and B was performed using an
Agilent 7890 A gas chromatograph equipped with an Agilent 5975 C mass spectroscopy detector as previously described 9 . MS data were collected in full scan mode at mass range m/z 100 to 600. 1 H NMR spectra of both compounds were acquired at 298 K on a Bruker Avance III 600 MHz spectrometer equipped with a Bruker inverse cryogenic probe (Bruker Biospin, Germany). A standard one-dimensional NMR spectrum was recorded for each sample dissolved in deuterated chloroform by employing the first increment of NOESY pulse sequence (NOESYPR1D) and 90° pulse set to approximately 10 μs. A total of 64 scans were collected with 32 K data points for each spectrum with a spectral width of 20 ppm and recycle delay of 2 s. All free induction decays were multiplied by an exponential function with a 1 Hz line broadening factor prior to Fourier transformation. 1 H NMR chemical shifts were determined with respect to the residual chloroform and referred to sodium 3-trimethylsilyl [2,2,3,3-d 4 ] propionate (TSP-d 4 ). 1 H NMR resonances of the metabolites were assigned to specific protons based on previous publications 11, 15 . Assay of S3-4 DON-degrading activity in wheat grains contaminated with DON. Wheat grains from spikes that were infected with FHB pathogens in the field at Huazhong Agricultural University, Wuhan, China were ground, passed through a 40-mesh screen and then autoclaved at 121 °C for 18 min. Five grams of autoclaved wheat sample was mixed with 20 mL MM in a 50-mL flask. The mixture was inoculated with 5 mL SCIeNtIFIC RepoRTs | 7: 9549 | DOI:10.1038/s41598-017-08799-w bacterial suspension of Sphingomonas sp. S3-4 (1 × 10 10 CFU mL −1 ) or MM and incubated at 28 °C for 72 h in an aerobic chamber. The samples were freeze-dried. DON was extracted and assayed by HPLC as described above.

Inoculation of wheat seedlings with DON and quantitative real-time PCR.
Wheat seedlings were inoculated with DON and its metabolites as described 29 and seedling lengths were scored 24 h after inoculation. Total RNA from wheat seedlings that were harvested 24 hai were used for reverse transcription and quantitative real-time PCR as previously described 29 . Five genes, P450 (cytochrome P450), 221 (Efflux family protein), C12 (Glutathione S-transferase), C7 (methionyl-tRNA synthetase) and C14 (Putative kinase), which are induced in wheat in response to DON 30 , were quantitatively assayed. Primers are listed in Table S2. BAC library construction. A BAC vector was prepared with Hind III (Fermentas, MA, USA) from the high-copy composite vector pHZAUBAC1 and was used for construction of a BAC library with Hind III digested genomic DNA isolated from strain S3-4 as previously described 31 . One mL overnight culture for each BAC clone was suspended in 1 mL MM containing DON (20 μg/mL). After incubation for 5 days at 37 °C, metabolites were extracted and analyzed by HPLC. The positive clone was sequenced at both ends by BGI (Shenzhen, China) with primers listed in Table S2.
Sequencing, assembly, annotation, and genome comparisons. Bacterial DNA was isolated using a DNA extraction kit (Axygen, Hangzhou, China). The genome of strain S3-4 was sequenced by Personalbio (Shanghai, China) using an Illumina Miseq system. A combination of three libraries containing 450 bp (paired-end), 3 kb and 8 kb (mate-paired) inserts was sequenced. The generated sequences were assembled using Newbler de novo (version 2.6) 32 , and the pre-assembled contigs were scaffolded using the SSPACE program 33 . Gaps between contigs were closed using GapCloser software (version 1.12; http://soap.genomics.org.cn) and PCR amplification. Prediction of open reading frames (ORFs) was accomplished using Glimmer 3.0 (http://www.cbcb. umd.edu/software/glimmer/), whereas RNAmmer 1.2 34 and tRNAscan-SE (Version 1.3.1) 35 were used for the identification of rRNA and tRNA. Functional annotation of genes was done using BLAST2GO software 36 and the refseq-protein database (https://www.ncbi.nlm.nih.gov/refseq/). A putative function was assigned to each gene using a cutoff E-value of ≤1 E −06 . Predicted protein sequences of strains with and without DON-degradation activity, Devosia sp. 17-2-E-8 25 and Sphingobium japonicum UT26 21 , respectively, were compared with strain S3-4 using bidirectional BLASTp comparisons with an E value cutoff of 10 −5 as previously described 37 . Cloning, expression and purification of recombinant protein in E. coli. The AKR18A1 gene was amplified by PCR from genomic DNA of strain S3-4 using KOD-plus-DNA polymerase (Toyobo, Shanghai, China) with the primers listed in Table S2. The PCR products were cloned into a pET-22b vector (Novagen, CA, USA) and transformed into E. coli BL21 for expression as a His 6 -fusion protein. Recombinant AKR18A1 protein was purified by affinity-chromatography and analyzed by SDS-PAGE as previously described 38 . Enzyme assays. Purified AKR18A1 protein (6 μg) was assayed for DON-degrading activity in a 50 μL mixture containing DON (100 μM) and 2 mM NADP + at different temperatures. The reaction was terminated by adding an equal volume of methanol. The initial rate of substrate decrease was quantified by HPLC. Activity at different pH values was assayed using 50 mM solutions of the following buffers: sodium phosphate (pH 6.0-7.0), Tris-HCl (pH 7.0-9.0), and glycine NaOH (pH 9.0-11.0). Optimum temperatures were determined by assaying activity at temperatures ranging from 10 °C to 60 °C in 50 mM glycine NaOH buffer (pH 10.6).
Enzymatic kinetics of the AKR18A1 with each substrate and cofactor were measured with 6 μg of recombinant AKR18A1 enzyme in a final volume of 50 μL. For the oxidation reaction, the kinetics were determined in the glycine-NaOH buffer (pH 10.6). The DON concentrations in the assay ranged from 10 to 1500 µM, using an NADP + concentration of 2 mM. For the reduction reaction, the kinetics were measured in sodium phosphate buffer (pH 7.0). The 3-oxo-DON concentration ranged from 50 to 1000 µM were used for the assays, with an NADH concentration of 0.2 mM. The reaction was incubated for 30 min at 45 °C. Reactions were stopped and the decrease in substrate quantity was quantified by HPLC analysis of the resultant mixture. Michaelis-Menten constants (Km and Vmax) were calculated with Origin 8 (Origin Software, CA).
Disruption of an AKR18A1 gene in S3-4. The ~850 bp upstream and downstream regions flanking the AKR18A1 gene were PCR-amplified using primers listed in Table S2 and joined by PCR overlap extension. The resulting sequence was cloned into a suicide plasmid pK18mobsacB 39 . This plasmid was introduced into S3-4 via conjugal transfer, and the mutant strain was identified as described previously 40 with the primers listed in Table S2.

ZEN catabolism by strain S3-4 and recombinant AKR18A1 protein. Strain S3-4 was assayed for
ZEN, α-ZOL and β-ZOL catabolic activity in 1 mL of MM containing 100 μM of each substrate. Substrate concentration was determined by HPLC at 72 hai. For the recombinant AKR18A1 protein, assays of ZEN, α-ZOL and β-ZOL catabolism were performed in a 50 μL mixture containing 100 μM each substrate, 0.2 mM NADH or 2 mM NADP + and 6 μg of purified protein. The reaction was terminated by adding an equal volume of methanol to the mixture, and substrate concentration was determined by HPLC.
Glyoxal and methylglyoxal catabolism by recombinant AKR18A1 in an E. coli strain. The tolerance of the recombinant E. coli strain to GO and MG was determined as previously described 41 . E. coli BL21-(DE3) cells harbouring the pET22b::AKR18A1 expression construct or control pET22b vector were grown at 37 °C in LB liquid medium to early exponential phase (OD 600 = 0.2), and the expression of His-AKR18A1 fusion SCIeNtIFIC RepoRTs | 7: 9549 | DOI:10.1038/s41598-017-08799-w protein was induced by the addition of 0.2 mM IPTG. After 60 min 10 mL aliquots were exposed to a 2 mM concentration of GO or MG, and OD 600 was determined over the course of 4 h.

GO and MG catabolism by recombinant AKR18A1 protein.
The GO and MG catabolic activity of AKR18A1 was measured in a 50 μL mixture containing 100 μM each substrate, 0.2 mM NADPH and 6 μg purified protein. The reaction was terminated as described above, and substrate concentration was determined by HPLC as previously described 42 . Statistical analysis. All assays were performed in triplicate. The results were analyzed using ANOVA for multiple comparisons followed by the Duncan test using SAS software v.8.1 (SAS institute, Cary, NC, USA), with significance levels of 0.01.


Gestational diabetes mellitus: Case definition & guidelines for data collection, analysis, and presentation of immunization safety data

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Preamble
1.1. Need for developing case definitions and guidelines for data collection, analysis, and presentation for gestational diabetes mellitus as an adverse event following immunization Gestational diabetes mellitus (GDM) is a common condition in pregnancy that can result in significant morbidity and mortality to both mother and fetus. According to the International Diabetes Federation (IDF), about 16.8% of live-births are born to women with hyperglycemia in pregnancy [1]. Approximately 16% of these women will have pre-existing diabetes mellitus, diagnosed prior to pregnancy or during the first trimester of pregnancy. The remainder will have GDM. The incidence of GDM follows the incidence of insulin-resistance and type 2 diabetes mellitus (T2DM) in a given country's population [2]. The prevalence of GDM can range anywhere from 1% to 15% depending on screening methods used, risk factors and ethnicity [3]. The Global Burden of Disease Project and IDF estimate that the rates of T2DM, including those of reproductive-age women, will continue to rise annually especially in low-and middle-income countries (LMICs) due to increasing risk factors such as obesity and sedentary lifestyle [4].
The pathophysiology for GDM centers around the inability of a pregnant woman to develop an adequate insulin response to a glucose load to maintain her blood sugar in a normal range. This is due to decreasing insulin sensitivity as the pregnancy progresses. Risk factors for GDM include family history of diabetes, GDM in prior pregnancy, ethnicity and obesity. However it has been found that screening based on these factors will miss approximately 50% of women with GDM [5]. GDM places mothers at increased risk for gestational hypertension, pre-eclampsia and cesarean section during pregnancy [6]. In addition, women with a history of GDM are at higher risk for developing T2DM in the future [7]. Fetal complications of pregnancies with GDM include increased risk of macrosomia, operative delivery, shoulder dystocia, birth trauma and neonatal hypoglycemia and hyperbilirubinemia. The Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study demonstrated a continuous association between maternal glucose levels and increased birth weight, cesarean section deliveries and neonatal hyperinsulinemia [8]. In addition, in utero exposure to maternal hyperglycemia may predispose to obesity and insulin resistance later in life [9,10]. Given the risk for significant maternal and fetal morbidity and mortality in pregnancies complicated by GDM, strict glycemic control during pregnancy is recommended [11,12] it is also important to be cognizant of medications that may cause transient hyperglycemia or that exacerbate hyperglycemia in mothers with GDM, such as beta-adrenergic agents and corticosteroids that are often administered to women with threatened preterm labor [13].
The association between maternal immunization and GDM, whether the development or exacerbation of the disease, or even the mitigation of disease, has not been well studied and is unknown. Multiple large prospective and retrospective vaccination studies have included GDM as a potential adverse outcome, often relying on ICD-9 or ICD-10 codes for diagnosis. While these studies did not find an increased incidence of GDM after maternal immunization, they had multiple confounders [14][15][16][17][18][19][20][21][22]. One of these confounders is the specific timing of vaccinations in pregnancy in relation to the diagnosis of GDM. Vaccinations are predominantly administered in the first and third trimesters, and GDM testing and diagnosis occurs in the second and third trimesters. This inevitably leads to the diagnosis of GDM after vaccine administration has occurred or concurrently. This timing makes determining the actual effect of vaccines on glucose tolerance difficult. Other medications, such as corticosteroids and beta-mimetics, are known to alter glucose tolerance, although only transiently [13]. Ideally, studies that examine the effect of vaccines on glucose tolerance would include a time period close to the vaccination administration, perhaps 0-14 days. Since this definition is not currently in use the studies included in this review have not limited the time between vaccination and diagnosis of GDM. Another confounder to the prior studies is that the true incidence of GDM is not known and ranges widely based on the population examined and the diagnostic criteria used [23]. If the baseline incidence in the study population is not known then determining the change in incidence after vaccination is not feasible. For this review, we have focused on the development of GDM as a possible adverse event following vaccination. We have excluded treatment of GDM, as well as maternal, fetal and neonatal complications attributed to GDM as these poor outcomes were likely due to GDM and other co-morbidities, not directly to the vaccine.
There is wide variation for the diagnostic criteria for GDM globally depending on country, consensus statements and resources available. Recommendations for GDM screening in pregnancy, usually between 24 and 28 weeks gestational age, are increasingly becoming universal, however, are often based on risk factors in resource-limited settings. While the gold standard for diagnosis of GDM is an oral glucose tolerance test, the blood glucose cut-offs often vary between and within countries, and sampling methodology can range from laboratory results based on venous serum samples to plasma samples using calibrated handheld glucometers [24]. In resource-limited settings, alternative methods of diagnosis have been proposed including fasting glucose levels, glucosuria or diagnosis based on other risk factors.
There is hence no uniformly accepted definition of Gestational Diabetes Mellitus. This is a missed opportunity, as data comparability across trials or surveillance systems would facilitate data interpretation and promote the scientific understanding of GDM in general, and for our purposes, any possible relationship of maternal vaccination with the development of GDM.
1.2. Methods for the development of the case definition and guidelines for data collection, analysis, and presentation for gestational diabetes mellitus as an adverse events following immunization Following the process described on the Brighton Collaboration Website http://www.brightoncollaboration.org/internet/en/index/ process.html, the Brighton Collaboration Gestational Diabetes Working Group was formed in 2016 and included members from clinical, academic, public health, and industry backgrounds. The composition of the working and reference group as well as results of the web-based survey completed by the reference group with subsequent discussions in the working group can be viewed at: http://www.brightoncollaboration.org/internet/en/index/working_ groups.html.
To guide the decision-making for the case definition and guidelines, a literature search was performed using MEDLINE and Embase databases. Due to the extensive and diverse topic of gestational diabetes mellitus, the search was limited to systematic reviews conducted in the prior five years. The search term for Pubmed is shown below and was modified for Embase search terminology: ((''pregnancy induced diabetes" AND diagnosis) Filters: published in the last 5 years; Humans; English) OR (gestational diabetes/diagnosis OR (Diabetes mellitus AND diagnosis AND pregnancy) OR ''diabetes in pregnancy" OR Glucose intolerance of pregnancy OR hyperglycemia in pregnancy OR hyperglucosuria) Filters: Systematic Reviews; published in the last 5 years; Humans; English) A separate search was done to identify any studies or reports associating gestational diabetes mellitus with immunizations and vaccinations, using MEDLINE, Embase, the Cochrane Database of Systematic Reviews, Clinical Key medical reference books, and the Centers for Disease Control and Prevention (CDC) and National Institutes for Health (NIH) websites. The following search string was used: ((maternal NEXT/1 (vaccin ⁄ OR immuniz ⁄ OR immunis ⁄ )) OR ((('vaccine'/exp/mj OR 'immunization'/exp/mj OR vaccin ⁄ :ti OR immuniz ⁄ :ti OR immunis ⁄ :ti OR revaccin ⁄ :ti OR postvaccin ⁄ :ti OR reimmuni ⁄ :ti OR postimmuni ⁄ ) AND ('pregnancy'/exp/mj OR 'child bearing':ti OR 'childbearing':ti OR 'gestation':ti OR 'gravidity':ti OR ((labor OR labor) NEXT/ 1 presentation):ti ORpregnan ⁄ :ti OR 'pregnant woman'/exp/ mj OR 'expectant mother'/exp/mj OR (expectant NEXT/ 1 mother ⁄ ) AND ('pregnancy diabetes mellitus'/exp AND diabetes NEAR/2 (gestational OR pregnancy)) Two committee members reviewed the literature search results for duplications and appropriateness to the topic, retaining 111 of 209 included documents. Of the search for gestational diabetes related to vaccinations, 11 of 14 were retained after review. Differences in literature review were adjudicated by a third committee member.
In addition, we identified latest edition Obstetrics and Gynecology text books in common usage in North America, Europe and Africa and reviewed four of these for definitions of GDM. Fifteen national endocrine and obstetrics and gynecology guidelines were reviewed. A flow diagram of identified sources is shown in Fig. 1.
Each member of the Gestational Diabetes work group was assigned approximately eight of the 141 articles to review for identification of a working definition of gestational diabetes and the preferred method of diagnosing it or for describing any association of gestational diabetes as a complication of vaccination.
Findings of the literature search included varying definitions of gestational diabetes mellitus in the literature as well as different diagnostic criteria described in the systematic reviews and research studies. In the majority of cases, oral glucose tolerance tests were used to diagnose gestational diabetes with venous blood draws. The specific glucose tolerance test as well as the glucose level cut-offs vary between reviews and consensus guidelines. An inventory spreadsheet of definitions and diagnostic criteria of the 141 pieces of literature as well as a summary page comparing the most common guidelines for the definition of gestational diabetes was made available to working group members. The full ref-erence list of documents, consensus guidelines and textbooks are available upon request. Please contact the corresponding author for further information. Alternate terminology for GDM includes ''pregnancy-induced hyperglycemia." Diabetes in pregnancy is frequently used to describe pregestational diabetes or is used as an umbrella term for pregestational diabetes AND GDM.

Related term(s) of gestational diabetes mellitus
Pregestational diabetes mellitus (DM): Pregestational DM is the diagnosis of diabetes mellitus prior to pregnancy. Two subtypes are frequently described: -Type 1 DM: Type 1 DM typically has an onset early in life usually due to an autoimmune process. It necessitates insulin therapy. It is commonly considered to result from insufficient production of insulin in the pancreas. -Type 2 DM: Type 2 DM is the more common form of pregestational DM. It is characterized by insulin resistance or relative insulin deficiency and can be treated by dietary and lifestyle modifications, oral agents and/or insulin therapy.

Formulating a case definition that reflects diagnostic certainty: weighing specificity versus sensitivity
It needs to be emphasized that the grading of definition levels is entirely about diagnostic certainty, not clinical severity of an event. Thus, a clinically very severe event may appropriately be classified as Level Two or Three rather than Level One if it could reasonably be of non-GDM etiology. Detailed information about the severity of the event should additionally always be recorded, as specified by the data collection guidelines.
The number of symptoms and/or signs that will be documented for each case may vary considerably. The case definition has been formulated such that the Level 1 definition is highly specific for the condition. As maximum specificity normally implies a loss of sensitivity, two additional diagnostic levels have been included in the definition, offering a stepwise increase of sensitivity from Level One down to Level Three, while retaining an acceptable level of specificity at all levels. In this way it is hoped that all possible cases of GDM can be captured.

Timing post immunization
Timing criteria for considering GDM as a possible adverse event from vaccination are important. We considered only vaccinations given during pregnancy, not prior to pregnancy. GDM is usually diagnosed in the second or third trimester of pregnancy, related to increasing pregnancy-related insulin resistance. Some medications used in pregnancy, such as corticosteroids and beta-mimetics, which can result in transient hyperglycemia which can last from hours to several days. Given the paucity of identified information about GDM associated with vaccination, we are unable to provide an evidence-based estimate of time interval for the possible development of GDM following maternal immunization. Future studies on time intervals between maternal immunizations and GDM are needed (please see Section 3.2).
Timed criteria should be used, since development of hyperglycemia in pregnancy can occur at any time in the second and third trimesters of pregnancy and vaccinations are usually administered in the 1st and 3rd trimesters of pregnancy.
We postulate that a definition designed to be a suitable tool for testing causal relationships requires ascertainment of the outcome (e.g. GDM) independent from the exposure (e.g. immunizations). Therefore, to avoid selection bias, a restrictive time interval from immunization to onset of GDM should not be an integral part of such a definition. Instead, where feasible, details of this interval should be assessed and reported as described in the data collection guidelines.
Further, GDM usually occurs outside the controlled setting of a clinical trial or hospital. In some settings it may be impossible to obtain a clear timeline of the event, particularly in less developed or rural settings. In order to avoid selecting against such cases, the Brighton Collaboration case definition avoids setting arbitrary time frames.  -Elements to differentiate pregestational DM from GDM include timing and trimester of diagnosis and severity of hyperglycemia as well as postnatal testing results. Further details are available in Section 2. Please see below.

Guidelines for data collection, analysis and presentation
As mentioned in the overview paper, the case definition is accompanied by guidelines that are structured according to the steps of conducting a clinical trial, i.e. data collection, analysis and presentation. Neither case definition nor guidelines are intended to guide or establish criteria for management of ill infants, children, or adults. Both were developed to improve data comparability.

Periodic review
Similar to all Brighton Collaboration case definitions and guidelines, review of the definition with its guidelines is planned on a regular basis (i.e. every three to five years) or more often if needed.

Gestational diabetes mellitus (GDM) is a clinical syndrome characterized by
The absence of pre-gestational diabetes diagnosis defined by Previous diagnosis of diabetes while not pregnant OR First trimester hemoglobin A1c level of P 6.5% (47.5 mmol/mol) OR First trimester fasting blood glucose 126 mg/dL/P7 mmol/L AND Identification of sustained hyperglycemia during pregnancy not due to other known causes (i.e. corticosteroids, beta-mimetics, etc.)

Level 1 of diagnostic certainty
Absence of pregestational diabetes mellitus diagnosis in the first trimester as defined above with level 1-2 certainty for gestational age using GAIA definition for gestational age (please see Appendix A) AND Diagnosis of gestational diabetes based on a positive internationally recognized oral glucose tolerance test (see below ''major criteria") using venous blood sample/samples.

Level 2 of diagnostic certainty
Absence of pregestational diabetes mellitus diagnosis in the first trimester as defined above with level 1-2 certainty for gestational age using GAIA definition for gestational age (please see Appendix A) AND Diagnosis of gestational diabetes based on positive internationally recognized oral glucose tolerance test (see below ''major criteria") using capillary blood sample/samples.

Level 3 of diagnostic certainty
Absence of pregestational diabetes mellitus diagnosis in the first trimester as defined above with at least level 3 certainty for gestational age using GAIA definition for gestational age (please see Appendix A) AND Diagnosis of gestational diabetes based on positive internationally recognized oral glucose tolerance test (see below ''major criteria") using venous blood or capillary blood sample/samples OR Diagnosis of gestational diabetes based on fasting plasma glucose of 5.1-6.9 mmol/l (92-125 mg/dL) using venous or capillary blood samples.
Glucometers should be calibrated according to local standards/ research protocols.
All participants in maternal immunization trials should have at minimum a fasting venous blood or capillary glucose sample prior to vaccination.  Table 1). Ideally, a postpartum or interpregnancy glucose tolerance test would be performed to confirm that the diagnosis of diabetes mellitus is confined to pregnancy and to exclude diabetes mellitus outside of pregnancy. Postpartum or interpregnancy GTTs, however, are infrequently performed and therefore the absence of this test would not be exclusionary.

Guidelines for data collection, analysis and presentation of gestational diabetes mellitus
It was the consensus of the Brighton Collaboration Gestational Diabetes Mellitus Working Group to recommend the following guidelines to enable meaningful and standardized collection, analysis, and presentation of information about gestational diabetes. However, implementation of all guidelines might not be possible in all settings. The availability of information may vary depending upon resources, geographical region, and whether the source of information is a prospective clinical trial, a post-marketing surveillance or epidemiological study, or an individual report of gestational diabetes. Also, as explained in more detail in the overview paper in this volume, these guidelines have been developed by this working group for guidance only, and are not to be considered a mandatory requirement for data collection, analysis, or presentation.

Data collection
These guidelines represent a desirable standard for the collection of data on availability following immunization to allow for comparability of data, and are recommended as an addition to data collected for the specific study question and setting. The guidelines are not intended to guide the primary reporting of GDM to a surveillance system or study monitor. Investigators developing a data collection tool based on these data collection guidelines also need to refer to the criteria in the case definition, which are not repeated in these guidelines.
Guidelines 1-44 below have been developed to address data elements for the collection of adverse event information as specified in general drug safety guidelines by the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use [28], and the form for reporting of drug adverse events by the Council for International Organizations of Medical Sciences [29]. These data elements include an identifiable reporter and patient, one or more prior immunizations, and a detailed description of the adverse event, in this case, of GDM following immunization. The additional guidelines have been developed as guidance for the collection of additional information to allow for a more comprehensive understanding of GDM following immunization.

Source of information/reporter
For all cases and/or all study participants, as appropriate, the following information should be recorded: (1) Date of report.
(2) Name and contact information of person reporting (Footnote 2) and/or diagnosing the GDM as specified by country-specific data protection law. (5) Case/study participant identifiers (e.g. first name initial followed by last name initial with medical record number/booking number/subject number) or alpha-numeric code (or in accordance with country-specific data protection laws). (6) Date of birth, age, and sex. (7) For infants: Gestational age and birth weight and length, and whether multiple gestation. Infant's name and identifier (medical record number/booking number/subject number or alpha-numeric code) should also be recorded.

Clinical and immunization history.
For all cases and/or all study participants, as appropriate, the following information should be recorded: (8) For the purposes of this definition, any hyperglycemia or diabetes diagnosis prior to current pregnancy or between pregnancies. (9) Past medical history, including hospitalizations, underlying diseases/disorders, pre-immunization signs and symptoms including identification of indicators for, or the absence of, a history of allergy to vaccines, vaccine components or medications; food allergy; allergic rhinitis; eczema; asthma. Risk factors for GDM including family history of diabetes, GDM in prior pregnancies, eating habits and physical activity, weight or body mass index (BMI) at first prenatal visit. (10) Any medication history (other than treatment for the event described) prior to, during, and after immunization including prescription and non-prescription medication as well as medication or treatment with long half-life or long term effect. (e.g. immunoglobulins, blood transfusion and immunosuppressants). (11) Immunization history (i.e. previous immunizations and any adverse event following immunization (AEFI)), in particular occurrence of GDM after a previous immunization.

Details of the immunization
For all cases and/or all study participants, as appropriate, the following information should be recorded: If this is not feasible due to the study design, the study periods during which safety data are being collected should be clearly defined.

Data analysis
The following guidelines represent a desirable standard for analysis of data on GDM to allow for comparability of data, and are recommended as an addition to data analyzed for the specific study question and setting.
(33) Reported events should be classified in one of the following five categories including the three levels of diagnostic certainty. Events that meet the case definition should be classified according to the levels of diagnostic certainty as specified in the case definition. Events that do not meet the case definition should be classified in the additional categories for analysis.

Event meets case definition
(1) Level 1: Criteria as specified in the GDM case definition (2) Level 2: Criteria as specified in the GDM case definition (3) Level 3: Criteria as specified in the GDM case definition GDM is first detect via diagnostic criteria described above. GDM is usually screen detected and therefore asymptomatic. This may only be possible to determine in retrospect. 5 The date and/or time of first observation of the first sign or symptom indicative for GDM can be used if date/time of onset is not known. 6 The date of diagnosis of an episode is the day post immunization when the event met the case definition at any level. 7 The end of an episode is defined as the time the event no longer meets the case definition at the lowest level of the definition. 8 E.g. recovery to pre-immunization health status, spontaneous resolution, therapeutic intervention, persistence of the event, sequelae, death. 9 An AEFI is defined as serious by international standards if it meets one or more of the following criteria: (1) it results in death, (2) is life-threatening, (3) it requires inpatient hospitalization or results in prolongation of existing hospitalization, (4) results in persistent or significant disability/incapacity, (5) is a congenital anomaly/ birth defect, (6) is a medically important event or reaction. 10 To determine the appropriate category, the user should first establish, whether a reported event meets the criteria for the lowest applicable level of diagnostic certainty, e.g. Level three. If the lowest applicable level of diagnostic certainty of the definition is met, and there is evidence that the criteria of the next higher level of diagnostic certainty are met, the event should be classified in the next category. This approach should be continued until the highest level of diagnostic certainty for a given event could be determined. Major criteria can be used to satisfy the requirement of minor criteria. If the lowest level of the case definition is not met, it should be ruled out that any of the higher levels of diagnostic certainty are met and the event should be classified in additional categories four or five.

Event does not meet case definition
Additional categories for analysis (4) Reported GDM with insufficient evidence to meet the case definition 11 (5) Not a case of GDM 12 (34) The interval between immunization and reported GDM could be defined as the date/time of immunization to the date/time of onset (Footnote 3) of the first abnormal glucose measurement. If few cases are reported, the concrete time course could be analyzed for each; for a large number of cases, data can be analyzed in the following increments: Subjects with gestational diabetes mellitus by interval to presentation (35) The duration of a possible GDM could be analyzed as the interval between the date/time of onset (Footnote 2) of the first symptoms and/or signs consistent with the definition and the end of episode (Footnote 6) and/or final outcome (Footnote 7). Whatever start and ending are used, they should be used consistently within and across study groups. (36) If more than one measurement of a particular criterion is taken and recorded, the value corresponding to the greatest magnitude of the adverse experience could be used as the basis for analysis. Analysis may also include other characteristics like qualitative patterns of criteria defining the event. (37) The distribution of data (as numerator and denominator data) could be analyzed in predefined increments (e.g. measured values, times), where applicable. Increments specified above should be used. When only a small number of cases is presented, the respective values or time course can be presented individually. (38) Data on GDM obtained from subjects receiving a vaccine should be compared with those obtained from an appropriately selected and documented control group(s) to assess background rates of hypersensitivity in non-exposed populations, and should be analyzed by study arm and dose where possible, e.g. in prospective clinical trials.
Ultimately, careful analysis of data is necessary given overlap between usual timing of vaccination in pregnancy and routine GDM screening between 24 and 28 weeks gestational age in order to avoid misleading conclusions regarding GDM and associations with vaccinations purely based on timing of vaccine administration and GDM diagnosis.

Data presentation
These guidelines represent a desirable standard for the presentation and publication of data on GDM following immunization to allow for comparability of data, and are recommended as an addition to data presented for the specific study question and setting. Additionally, it is recommended to refer to existing general guidelines for the presentation and publication of randomized controlled trials, systematic reviews, and meta-analyses of observational studies in epidemiology (e.g. statements of Consolidated Standards of Reporting Trials (CONSORT), of Improving the quality of reports of meta-analyses of randomized controlled trials (QUORUM), and of meta-analysis Of Observational Studies in Epidemiology (MOOSE), respectively) [30][31][32].
(39) All reported events of GDM should be presented according to the categories listed in guideline 32. (40) Data on possible GDM events should be presented in accordance with data collection guidelines 1-25 and data analysis guidelines 32-37. (41) Terms to describe GDM such as ''well-controlled", ''poorly controlled", ''low-grade", ''mild", ''moderate", ''high", ''severe" or ''significant" are highly subjective, prone to wide interpretation, and should be avoided, unless clearly defined. (42) Data should be presented with numerator and denominator (n/N) (and not only in percentages), if available.
Although immunization safety surveillance systems denominator data are usually not readily available, attempts should be made to identify approximate denominators. The source of the denominator data should be reported and calculations of estimates be described (e.g. manufacturer data like total doses distributed, reporting through Ministry of Health, coverage/population based data, etc.).
(43) The incidence of cases in the study population should be presented and clearly identified as such in the text. (44) If the distribution of data is skewed, median and range are usually the more appropriate statistical descriptors than a mean. However, the mean and standard deviation should also be provided. (45) Any publication of data on GDM should include a detailed description of the methods used for data collection and analysis as possible. It is essential to specify: The study design. The method, frequency and duration of monitoring for GDM. The trial profile, indicating participant flow during a study including drop-outs and withdrawals to indicate the size and nature of the respective groups under investigation.
The type of surveillance (e.g. passive or active surveillance). The characteristics of the surveillance system (e.g. population served, mode of report solicitation). The search strategy in surveillance databases. Comparison group(s), if used for analysis. The instrument of data collection (e.g. standardized questionnaire, diary card, report form). Whether the day of immunization was considered ''day one" or ''day zero" in the analysis. Whether the date of onset (Footnote 3) and/or the date of first observation (Footnote 4) and/or the date of diagnosis (Footnote 5) was used for analysis.
Interval ⁄ Number <7 days after immunization 7 -<14 days after immunization 14 -<28 days after immunization 28 -<42 days (6 weeks) after immunization Week increments thereafter Total 11 If the evidence available for an event is insufficient because information is missing, such an event should be categorized as ''Reported GDM with insufficient evidence to meet the case definition". 12 An event does not meet the case definition if investigation reveals a negative finding of a necessary criterion (necessary condition) for diagnosis. Such an event should be rejected and classified as ''Not a case of GDM".
Use of this case definition for GDM, in the abstract or methods section of a publication. 13

Disclaimer
The findings, opinions and assertions contained in this consensus document are those of the individual scientific professional members of the working group. They do not necessarily represent the official positions of each participant's organization (e.g., government, university, or corporation). Specifically, the findings and conclusions in this paper are those of the authors and do not necessarily represent the views of their respective institutions.