Desulfobacterium N47 and the rod-shaped Spirochete Rectinema cohabitans HM. Genome sequencing and proteome analysis suggested that the Spirochete is an obligate fermenter that catabolizes proteins and carbohydrates, resulting in acetate, ethanol, and molecular hydrogen (H2) production. Physiological experiments inferred that hydrogen is an important link between the two bacteria in the enrichment culture, with H2 derived from fermentation by R. cohabitans used as reductant for sulfate reduction by Desulfobacterium N47. Differential proteomics and physiological experiments showed that R. cohabitans utilizes biomass (proteins and carbohydrates) released from dead cells of Desulfobacterium N47. Further comparative and community genome analyses indicated that other Rectinema phylotypes are widespread in contaminated environments and may perform a hydrogenogenic fermentative lifestyle similar to R. cohabitans. Together, these findings indicate that environmental Spirochaetes scavenge detrital biomass and in turn drive necromass recycling at anoxic hydrocarbon-contaminated sites and potentially other habitats.Spirochaetes are frequently detected in anoxic hydrocarbon- and organohalide-polluted groundwater, but their role in such ecosystems has remained unclear. To address this, we studied a sulfate-reducing, naphthalene-degrading enrichment culture, mainly comprising the sulfate reducer  The bacterial phylum Spirochaetes is well known for its commensal and pathogenic species, among them the causative agents of syphilis and Lyme disease . HoweverSphaerochaeta pleomorpha and Sphaerochaeta globosa from trichloroethene-dechlorinating enrichment cultures provided evidence that some environmental Spirochaetes are morphologically and metabolically distinct from host-associated representatives from the phylum [2–CO2 was not observed [The recent isolation of e phylum . Contrare phylum , 8. It we phylum , 12, butobserved . Neverth2 by the stable enrichment culture N47 derived from sediments of a contaminated aquifer [Desulfobacterium strain N47 that has been shown to be responsible for naphthalene degradation [Rectinema cohabitans strain HM, which is neither able to degrade naphthalene nor reduce sulfate or other sulfur compounds in pure culture [We have shown that, under sulfate-reducing conditions, naphthalene can be anaerobically mineralized to CO aquifer , 13. Basradation , 14. The culture . Other p culture  because  culture .Rectinema species, we also provide evidence that environmental Spirochaetes may generally contribute to necromass recycling in anoxic hydrocarbon- and organohalide-contaminated environments as well as other anoxic habitats.Here, we intended to elucidate the functional role of environmental Spirochaetes using the enrichment culture N47 as an example. By means of physiological, proteogenomic, and comparative genome analyses, we show that environmental Spirochaetes are involved in necromass recycling and provide hydrogen and nutrients to associated hydrocarbon degraders. Through reconstructing the genomes of two other putative Desulfobacterium strain N47, and a spirochete, Rectinema cohabitans [The naphthalene-degrading, sulfate-reducing enrichment culture N47 was cultivated in bicarbonate-buffered freshwater medium as described previously . After ahabitans .Desulfobacterium strain N47 in pure culture by serial dilution in agar roll tubes resulted in a highly enriched Desulfobacterium culture that lacked the Spirochete R. cohabitans and was 96% dominated by Desulfobacterium strain N47 . The supernatant, together with the heptamethylnonane phase containing the naphthalene, was discarded. The cell pellets were suspended in 2.5 mL anoxic fresh medium (without substrate or yeast extract), and autoclaved three times in a closed 100 mL serum bottle at 121 °C for 20 min. Thereafter 0.5 mL dead biomass was transferred to serum bottles filled with 30 mL fresh medium and the bottles were inoculated with 3 mL cultures of R. cohabitans. Due to the carryover from the previous cultures, glucose, yeast extract, and fermentation products could have been transferred with the inoculum. Thus, fresh media with only starter cultures of R. cohabitans or only dead biomass were incubated as control experiments. To investigate whether R. cohabitans can utilize putative carbon sources from the supernatant of the highly enriched Desulfobacterium culture, 30 mL centrifuged supernatant were filtered through 0.22 μm pore-size membranes , transferred to fresh serum bottles, and inoculated with 3 mL starter cultures of R. cohabitans. Non-inoculated bottles served as controls.To test whether R. cohabitans has the capability to ferment necromass, different substrates relevant to subsurface habitats were added to 50 mL bicarbonate-buffered freshwater medium and inoculated with 5 mL starter cultures of R. cohabitans. These substrates included 5 mM sucrose, 5 mM valine, 5 mM pimelic acid, or 0.1% tryptone. Furthermore, living cells of the highly enriched Desulfobacterium culture grown in 75 mL cultures were harvested by centrifugation and added as substrate as described above. Fresh media with only starter cultures of R. cohabitans were incubated as carryover controls.To further test whether 2 gases (Messer Schweiz) . For the latter, the GC was equipped with a Molsieve 5A pre-column and Porabond column . The carrier gases were N2 for the H2 channel and He for the rest of the gases . The concentrations (>100 ppmv) were determined using a thermal conductivity detector.Hydrogen was measured with a Shimadzu GC-8A Gas Chromatograph  or a ComR. cohabitans, uncultured Spirochete bacterium bdmA 4, and uncultured Spirochete bacterium SA-8. The latter two genomes contained 16S rRNA gene sequences with 95% identity to R. cohabitans. The draft genome of the uncultured Spirochete bacterium SA-8 was derived from the metagenome of the terephthalate-degrading community TA, which is available on IMG/M  as taxon object ID 3300001095 [R. cohabitans and the metagenome of enrichment culture Sob are described in the Supporting Information. The draft genomes of R. cohabitans, uncultured Spirochete bacterium bdmA 4, and uncultured Spirochete bacterium SA-8 were loaded into the MicroScope genome annotation platform and automatically annotated [−10 and cover fraction >0.4) [−10) [For comparative genomic analyses, three high-quality draft genomes were obtained, namely those of 00001095  for the nnotated . The annnnotated , and metnnotated , 23. Theon >0.4)  and MERO4) [−10) , respectR. cohabitans and Desulfobacterium N47 by screening translated sequences through the web database HydDB [Potential genes encoding hydrogenase catalytic subunits were identified in the genomes of se HydDB . Sequencse HydDB  and branse HydDB  and visuse HydDB  construcse HydDB  and bootse HydDB  and ironse HydDB .The phylogeny of the Spirochaetes was inferred using 16S rRNA gene sequences retrieved from the NCBI  Genome Database. Sequences were aligned using the MUSCLE algorithm included in MEGA7 and trimmed to uniform length . A maximDesulfobacterium culture (without R. cohabitans) were grown with naphthalene . In addition, four cultures of R. cohabitans were grown with 10 mM glucose and 0.1% yeast extract. The cultures were harvested after 6 weeks for the enrichment culture or 10 days for R. cohabitans and then immediately frozen at −20 °C. The frozen cell pellets were molten at room temperature and taken up in 1 mL phosphate-buffered saline . The samples were lysed by ultrasonification in a Bioruptor® (Diagenode) for 5 min to break the cells, release the proteome, and shear the genomic DNA ; sonication cycle, 30 sec ON, 30 sec OFF; total sonication time, 5 min). The turbid solution was cleared by centrifugation  and the supernatant was transferred to a fresh 1.5 mL Eppendorf tube. The protein concentration was determined by using a modified Bradford assay  following the manufacturer’s instructions. A detailed protocol for the subsequent sample preparation for Liquid chromatography tandem-mass spectrometry, as well as the settings during data acquisition and data analysis, can be found in the Supporting Information. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [https://www.ebi.ac.uk/pride/archive/) with the dataset identifier PXD005624.For proteomics, the enrichment culture N47 and the highly enriched he PRIDE  partner R. cohabitans HM  to develop hypotheses about its ecophysiological role in the enrichment culture N47. Analysis of the metabolic capacity from the genome suggests that R. cohabitans performs a fermentative lifestyle. It is predicted to be capable of degrading proteins and carbohydrates, resulting in the production of the endproducts ethanol, acetate, and H2  as an additional electron donor for the reduction of sulfate (20 mM). Consistent with genome-based predictions of hydrogenases for Desulfobacterium N47, we observed a gradual decrease in H2 concentration , a potent inhibitor of sulfate reduction [Desulfobacterium N47 directly couples H2 oxidation to sulfate reduction through an anaerobic respiratory chain.The highly enriched ion Fig. . Consumpeduction , was addeduction , Desulfo2 concentrations in the enrichment culture N47 (R. cohabitans + Desulfobacterium N47) with naphthalene and sulfate. The concentration of H2 remained constant (~2 ppmv) for 1 month  used for H2 production by R. cohabitans. Considering that R. cohabitans cannot utilize naphthalene and no other exogenous organic sources were added, the potential carbon sources for R. cohabitans include metabolites formed during naphthalene degradation and/or dead biomass from the deltaproteobacterium.While the above results demonstrate that HDesulfobacterium culture  . For somproducts , 39, 40.products , 41. Refproducts . Our stuDesulfobacterium strain N47 degrades naphthalene as energy and carbon source with sulfate as electron acceptor, leading to primary biomass production in the absence of sunlight. Under nutrient-rich conditions, if there is sufficient sulfate and naphthalene in the aqueous system  [R. cohabitans to supply additional electron donors. In contrast, R. cohabitans relies on organic carbon sources derived from detrital biomass of Desulfobacterium N47 to sustain its growth. In turn, necromass degradation via hydrolysis and fermentation results in production of hydrogen and potentially short-chain fatty acids and alcohols. These products are then utilized by Desulfobacterium N47, thereby resulting in the recycling of necromass.In the enrichment culture N47, the chemo-organo-heterotrophic >~50 μM) , the sulR. cohabitans is only one example out of many fermentative microorganisms that might mediate equivalent functions in necromass recycling. Even sulfate reducers themselves are quite versatile microorganisms and some perform hydrogenogenic fermentation when lacking electron acceptors such as sulfate or nitrate [R. cohabitans have genes encoding several extracellular peptidases  and carbohydrate-converting enzymes including hydrolases  and DNA-SIP experiments [In anoxic hydrocarbon- and organohalide-contaminated habitats, the biomass turnover rates are potentially high  because eriments , 46. Fureriments , 47, 48.2 and other substrates such as acetate and ethanol has frequently been reported to stimulate microbial blooms  [Given the potential high biomass turnover, recycling of necromass is therefore important for ecosystem function and prevents loss of nutrients in contaminated ecosystems Fig. . Most noriming”)  or serveriming”) . Secondlriming”) , 52.R. cohabitans constituted ~7% of the microbial population. Typical growth yields for anaerobic microorganisms are around 5–10% of the consumed substrates, as exemplified by the terephthalate-degrading culture mentioned above [R. cohabitans, at least a tenfold number of Desulfobacterium cells  must have died to form necromass and serve as substrate for R. cohabitans to produce the observed 7% of the total cell numbers. Considering the actual proportion of 93% Desulfobacterium cells, this means that 163% of the total Desulfobacterium cell numbers (93% living cells plus 70% necromass) must have grown. Thus, approximately 43% of all Desulfobacterium cells that have grown in the culture died and were recycled by R. cohabitans. The necessary difference in growth rate to account for the necromass recycling is quite small here. For example, if the culture grows exponentially from a 10% parent inoculum to full density  in 6 weeks, the doubling time is approximately 13 days. Taken the described necromass recycling calculated above into consideration, it only shortens to ~11 days for the Desulfobacterium cells . However, if such values of cell death and necromass recycling are transferable to subsurface habitats, and assuming that cell death is much higher under non-optimal conditions in the environment, we may speculate that fermentative organisms like R. cohabitans might constitute a subsurface microbial loop [At this point, we can only speculate on the importance of necromass recycling in the subsurface because quantitative data are not available. In our enrichment culture N47, ed above . If necrial loop , 54 thatSupporting InformationSupplementary Table S1Supplementary Table S2Supplementary Table S3Supplementary Table S4Supplementary Table S5Supplementary Table S6Supplementary Database 1Supplementary Database 2Supplementary Database 3Supplementary Database 4


Glycine max (L.) Merr.] often involves completely resequencing a line at 5X or greater coverage. Currently, hundreds of soybean lines have been resequenced at high depth levels with their data deposited in the NCBI Short Read Archive. This publicly available dataset may be leveraged as an imputation reference panel in combination with skim (low coverage) sequencing of new soybean genotypes to economically obtain high-density SNP information. Ninety-nine soybean lines resequenced at an average of 17.1X were used to generate a reference panel, with over 10 million SNPs called using GATK’s Haplotype Caller tool. Whole genome resequencing at approximately 1X depth was performed on 114 previously ungenotyped experimental soybean lines. Coverages down to 0.1X were analyzed by randomly subsetting raw reads from the original 1X sequence data. SNPs discovered in the reference panel were genotyped in the experimental lines after aligning to the soybean reference genome, and missing markers imputed using Beagle 4.1. Sequencing depth of the experimental lines could be reduced to 0.3X while still retaining an accuracy of 97.8%. Accuracy was inversely related to minor allele frequency, and highly correlated with marker linkage disequilibrium. The high accuracy of skim sequencing combined with imputation provides a low cost method for obtaining dense genotypic information that can be used for various genomics applications in soybean.Obtaining genome-wide genotype information for millions of SNPs in soybean [ Genomics research has yielded a variety of tools which allow for more efficient and precise translation of genetic variation into crop improvements. Panels of single nucleotide polymorphisms (SNPs) obtained through SNP arrays or genotyping-by-sequencing (GBS) are the most common tool used to explore and make associations between genetic and phenotypic variation. Genomics-assisted crop breeding continues to demand increasing densities of genotype information to successfully dissect and predict genetically complex traits . The raw reads were filtered for adapter sequence contamination, base quality, and truncated reads using Trimmomatic  were sequenced to a depth of 1X or greater on an Illumina NextSeq 500  using the manufacturer’s protocol and 150 base pair paired end reads. DNA was isolated from lyophilized leaf tissue collected from twenty plants per genotype using a CTAB based extraction method  scaled dGlycine max Wm82.a2.v1 reference genome with Bowtie2 using the “very sensitive” option. Mapped reads below a quality score of 20 were filtered. The genotypes at all 10,803,148 SNP positions in the reference panel were called in the low coverage imputation panel using GATK3.7 Haplotype Caller. Genotyping SNPs from a single read has been found accurate in rice whole genome sequencing and maize GBS applications  score above 0.9, pairwise rFor the sake of computational efficiency, imputation was performed on a per chromosome basis using Beagle 4.1  with the2 statistics were calculated for all pairwise reference panel SNPs using Plink1.9 (2 and error.Error in relationship to linkage disequilibrium (LD) was examined as a potential metric of extensibility to other soybean population and crop species. D' and rClose relatedness between the sample and reference genotypes has been previously reported to increase imputation precision. Relatedness matrices were generated based on five different coefficients and averaged the top five scores from each sample genotype as a metric for gauging degree of relatedness to the reference panel. These measures were plotted against concordance scores from the imputed data filtered for GP scores above 0.9 and averaged across all depth levels. A simple linear regression model was fit to assess potential correlation. Relatedness matrices were calculated using the R package “synbreed”, using options corresponding to measures described by vanRaden, Astle and Balding, Reif, Hayes and Goddard, and Euclidean distances  of interest. To assess how well the panel represented variation across the genome, the distribution of LD in the imputed experimental dataset was compared to the SoySNP50k Array positions extracted from the imputed experimental dataset . The retained cost per sample and average raw concordance were plotted as depth decreased.https://doi.org/10.25387/g3.7975751.Raw sequencing data directly generated by this project for use in creating the study panel has been deposited in the NCBI Short Read Archive under accession number PRJNA512147. The reference panel used for genotype imputation was generated using previously publicly available sequence data deposited in the NCBI Short Read Archive from study number SRP062245 .2 and D' LD measures for the Soy50KSNP Array and imputed dataset demonstrated that whole genome sequencing with imputation had a greater concentration of values toward higher linkage values. Generally, a D' or r2 of over 0.8 between is considered “strong linkage”. The imputed dataset provided 1,716,234 SNPs after common quality control filters, with 36.00% and 85.66% of r2 and D' values above 0.8, respectively. This is in comparison to the 42,133 SNPs in the fixed array, where 24.20% and 80.00% of r2 and D' values are above 0.8  to improve dataset quality was successful. When imputed positions with a GP score of less than 0.45 were discarded, accuracy improved by an average of 2.50% across sequencing depths. A more stringent filter that only kept positions with a GP score over 0.9 resulted in a 4.26% increase in accuracy (Supplementary Table 2). This practice did reintroduce some missing data, which varied across depth and filtering level. Data loss as a result of post imputation filtering was below 5% for all depths at a filtering level of GP > 0.45, but quickly inflates when filtering for imputation quality of GP > 0.9 to a missing data rate of 20.82% at 0.1X . While filtering on Beagle’s posterior genotype probability may reduce falsely imputed genotypes, it must be balance with the reintroduction of missing data it causes.The error rate at individual marker loci may not be well captured by the overall concordance across all SNPs. Examining concordance in the context of minor allele frequency (MAF) reveals as MAF values approach a maximum of 0.5, concordance decreases. Application of post imputation filters of GP values increases overall accuracy through improved concordance at these increased MAFs . This tr2 approach 1, where neighboring SNPs are in perfect linkage with each other, error rates are at their lowest. Both relationships demonstrate a very strong correlation with R2 values of 0.98 and 0.89 for r2 and D' respectively (Error rates in imputation may be influenced by characteristics specific to the population and crop species to which it is applied. The correlation between variants is a cornerstone to the success of imputation. If the correlation between alleles is high then imputation accuracy should also be high and as the correlation between alleles decrease then the accuracy of imputation should also decrease. This correlation between alleles can be measured with LD. Soybean is a historically inbred crop with long ranging LD (ectively , indicat2 of 0.26. Examination of the standard error, shows that the study population varies narrowly in terms of relatedness to the reference panel. Additionally, assessing the raw values suggests that the study population is weakly related to reference genotypes. This is best illustrated with the vanRaden and Astle & Balding measurements, where a “strong” relationship is usually indicated by values approximately >= 0.4. In both these cases, the largest measure does not exceed 0.18 and 0.16 (Relationship of the study genotypes to the reference panel genotypes has been suggested as a strong influencer of imputation accuracy. Plotting calculated values for five unique kinship metrics against concordance for each genotype did not demonstrate any strong linear relationships. The maximum correlation for any of the measures was for Reif’s method, at an Rand 0.16 . The comUnderstanding the effect error rate has on genomic studies is important when selecting an appropriate genotyping technique. To determine the effect of the error rate of skim sequencing and imputation has on GWAS we performed power simulations of detecting a moderate effect QTL in a panel of 300 individuals. This power study showed significantly decreased power to detect QTL with increasing errors at MAF from 0.1-0.3. This was most pronounced when the minor allele was incorrectly imputed as the major allele. Above 0.3 MAF, power for QTL detection was minimally affected by error . StudyinThis study illustrates the potential of low coverage sequencing with imputation as an economical approach to obtaining high density SNP genotype information in soybean. Accelerating improvement of complex phenotypes through genomics necessitates high quality, high resolution marker data. However, studies are often limited by the cost required to obtain this information through high coverage sequencing. The combination of low coverage sequencing with imputation presents an option that drastically cuts costs while retaining a high level of accuracy. Implementing a similar method in rice allowed researchers to generate a high quality, dense SNP dataset using 1X depth whole genome sequence (While SNP arrays and GBS are popular options for obtaining genotype information, high precision genomics demands markers to be in close linkage to the contributing genes. Regions of the genome with sparsely correlated markers may therefore contain overlooked causal variation . Skim seThe accuracy and extensibility of this approach in other soybean populations, as well as other crops is based on several factors. To explore potential limitations in this method, population LD and the relatedness of reference panel to study lines were examined. Both of these factors have been implicated as strong influencers of imputation accuracy due to the innate reliance of the technique on the presence of sample haplotypes within the reference panel, as well as the extent of correlation between observed markers (The power to detect a QTL is partially dependent on the allele frequency at that loci (The cost savings associated with this method can be used to include more sample genotypes, not only recovering power losses at low minor allele frequencies, but potentially increasing total power. Similar results in humans have indicated sampling more genotypes with small error is more beneficial over fewer genotypes with perfect accuracy (Here it is demonstrated that low coverage sequencing accompanied with imputation from a reference panel can be extended below 1X depth in soybean to capture high density, reasonably accurate SNP genotype information economically. The tremendous drop in per sample sequencing cost over high depth methods may allow researchers to expand the number of study genotypes in their investigations, while representing a larger portion of the genome than fixed SNP arrays and GBS data. The potential for success of this genotyping method within and outside of soybean is highly reliant on population LD. Furthermore, researchers should examine accuracy and power within the context of minor allele frequency to make informed decisions about sequencing depth tolerances. As genomics demands increasing SNP panel densities across a wide range of genotypes, skim sequencing with imputation constitutes a financially feasible and highly accurate way to meet these requirements.


Oyster is rich in glycogen and free amino acids and is called “the milk of sea”. To understand the main genetic effects of these traits and the genetic networks underlying their correlation, we have conducted the whole genome resequencing with 427 oysters collected from the world-wide scale.After association analysis, 168 clustered significant single nucleotide polymorphism (SNP) loci were identified for glycogen content and 17 SNPs were verified with 288 oyster individuals in another wide populations. These were the most important candidate loci for oyster breeding. Among 24 genes in the 100-kb regions of the leading SNP loci, cytochrome P450 17A1 (CYP17A1) contained a non-synonymous SNP and displayed higher expressions in high glycogen content individuals. This might enhance the gluconeogenesis process by the transcriptionally regulating the expression of phosphoenolpyruvate carboxykinase (PEPCK) and glucose 6-phosphatase (G6Pase). Also, for amino acids content, 417 clustered significant SNPs were identified. After genetic network analysis, three node SNP regions were identified to be associated with glycogen, protein, and Asp content, which might explain their significant correlation.Overall, this study provides insights into the genetic correlation among complex traits, which will facilitate future oyster functional studies and breeding through molecular design.The online version of this article (10.1186/s12864-019-5971-z) contains supplementary material, which is available to authorized users. Crassostrea gigas is an important marine fishery resource cultivated globally. Its annual global production continues to increase at a rate of 4 million tons per year [The Pacific oyster per year . Howeverper year . These tper year , 4. Howeper year . Recentlper year , 7. TherC. gigas, which is significantly related to the glycogen content [Studies on the genetic basis of glycogen, amino acid, and protein content have been extensively conducted in various domesticated animals, as well as in crops –10, catt content . HoweverRecently, more and more evidences revealed that genome-wide association (GWA) studies was a powerful method to identify the nucleotide polymorphisms associated with agronomics traits in plants and animals , 22. In Several traits exhibit heritable covariation, adding to the complexity of breeding. However, information on specific trait is insufficient for molecular breeding. In plants, the correlation analysis of complex traits has been conducted by many studies , 31. In In the present study, we aimed to understand the main genetic effects of nutrient traits and genetic networks underlying phenotypic correlation in oysters. Four hundred and twenty-seven individuals were collected to conduct genome resequencing, which were used to perform GWAS to identified new SNP loci for these characteristics. The candidate SNP loci were further verified with the 190 K SNP array using other populations and the candidate genes were used for the functional analysis. Further, genetic networks between different traits were also analyzed. The results will facilitate future breeding of oysters to improve these traits through genomic selection.In the present study, we collected 427 individuals from different geographical populations Fig. a, coveriC. gigas in North China can be classified into four main clades, based on their geographical distribution [C. angulata was used as the outgroup. The PCA analysis further supported this difference , which decayed to 0.25–0.33 beyond 100 bp in different populations . The 30 individuals were collected in each family and were used for phenotype detection , which represented 427 female parents’ phenotypic values. The F1 breeding method and phenotypic values have been presented in Additional file r > 0.8, P < 0.01) with each other, as they are physiologically correlated. However, the content of Met exhibited negative correlation with several amino acids, but it positively correlated with the content of Cys, Arg, His, and Try.The 427 female individuals collected from different locations were usd for breeding and produce 427 half-sib families. The FP < 1 × 10 − 6 in a mixed model; false discovery rate (FDR) < 0.05). For glycogen analysis, 168 of 175 SNPs were clustered into a peak on chromosomes 6, 8, and 9   based on 4.2 million high quality SNP markers  genotyped from 427 individuals using the mixed linear model   associated with glycogen content . For the high LD values (r2>0.4) of SNPs on scaffold1243, 1597, 340 and 426   to T  at 425 amino acid position in the protein sequence  resulted in a change from F (Phe) to L (Leu), accounting for 6.2% of the phenotypic variation  were identified to be associated with these traits  at 548-bp position of an unknown gene (Crassostrea_gigas_GLEAN_10005147) induced amino acid transformation from T to R. The other was a non-synonymous SNP mutation from A to G  at 3097-bp position of collagen α-1(XXIV) chain gene (Crassostrea_gigas_GLEAN_10013041) induced amino acid transformation from M to V , which was further used to analyze  positive relationship between protein and Asp content, as well as the negative relationship between glycogen and protein or Asp content.Especially, in oysters, the content of glycogen, protein, and Asp has significant positive or negative correlation with each other Fig. b. We havWe have performed an integrated GWAS analysis with expression profiling data and GBA, which we used to rapidly identify candidate genes associated with glycogen and amino acids contents in oysters. We have mapped 175, 32 and 787 GWAS signals significantly associated with glycogen, protein and types amino acids contents. A total of 17 GWAS signals in four genomics regions  were further validated to be with glycogen contents in another wild population cultured in Qingdao . For the high LD of SNPs in these four regions, we think these were most important candidate regions for glycogen content. Further, gene function analysis found that cytochrome P450 17A1 (CYP17A1) contained a non-synonymous SNP between high and low glycogen contents individuals and displayed the higher expressions in high glycogen content individuals. This might enhance the gluconeogenesis metabolism process by the transcriptionally regulating the expressions of phosphoenolpyruvate carboxykinase (PEPCK) and glucose 6-phosphatase (G6Pase). Finally, using genetic network analysis, we found several SAL that control both glycogen and several amino acids contents, which will provide important basis for their molecular breeding.1 individuals in different families were cultured in the same marine environments and have the same male parent, their differences will reflect the genetic differentiation of their female parents. So, we have collected 30 individuals from each family, and their average phenotypic values were used to represent their female parents’ values. The same phenotypic detection method has been widely used in many other species [For GWAS analysis, the traits were usually controlled by many genes with small effects. Therefore, increasing the sample size will improve the power to recover meaningful associations . Given t species –49, whic species . However species .Apart from genetic heterogeneity, another major challenge for GWAS analysis with our samples is population structure. It is well known that population structure generates associations between phenotypes and there need to do a statistical analysis of accounting for structure in GWAS. In our previous study, it was revealed that oyster could be divided into four populations based on their resequencing results . In our C. gigas is rich in glycogen content, which is higher than that in other mollusks [CYP17A1 contained a non-synonymous SNP located in the enzyme active center and exhibited high expressions in individuals with higher glycogen content. CYP17A1 is a single gene-encoded protein that mediates 17α-hydroxylase and promotes gluconeogenesis by activating the transcriptional activity of the gluconeogenesis metabolism process in mammals [Glycogen is the major source of glucose reserve stored in specific vesicular cells and is known to play a central role in providing energy for the maintenance and gametogenic development of bivalves . The Pacmollusks . In prevmollusks  and PPP1mollusks . Especia mammals . In the  mammals . In the The aim of breeding is to aggregate many ideal traits into one variety, which required to understand the positive or negative correlation of these candidate traits . Using GIn summary, the present study provides a large dataset of loci and genes responsible for important traits in oyster, such as glycogen, protein, and amino acids, which will facilitate future functional studies and breeding development. Furthermore, we have constructed association networks among different traits and identified some SALs that function as key nodes connecting different traits, but most SALs specifically regulated individual traits. This information will guide breeders attempting to establish a clear strategy for genetic improvement. However, the genes associated with nutrient traits were not involved in their traditional key metabolism pathways, indicating the existence of a new regulation mechanism of glycogen, protein, and amino acid metabolism processes in oysters. Further work will be necessary to verified the functional mechanism of these specific genes underlying these breeding target traits.C. gigas) from 26 different environmental conditions were collected during the summer of 2013. After culturing for approximately 1 month in farms, the gill of 427 females were collected and used for resequencing. At the same time, these females were mated with one male cultured in Qingdao, generating 427 half-sub families. These 427 families were cultured in the same environment for approximately 1 year, and then 30 F1 individuals from each family line were collected and used for phenotype measurement [1 individuals have the same male parent and the same culturing environments, their phenotypic differentiation may be induced by the female parent’s differentiation. The same methods have been widely used in many other species [For the GWAS, oysters  was fragmented using the Covaris system  to obtain 200–800 bp fragments. The DNA fragments were then treated according to the Illumina DNA sample preparation protocol. The fragments were end-repaired, A-tailed, ligated to paired-end adaptors, and PCR amplified with 500-bp inserts for library construction. Sequencing was performed to generate 100-bp paired-end reads on the HiSeq 2000 platform (Illumina) according to the manufacture’s standard protocol.The SNP calling and population structure analysis were conducted as reported previously . The Burhttp://treesoft.sourceforge.net/treebest.shtml) using the SNPs at a population scale. Furthermore, the Discriminant Analysis of Principal Components (DAPC) analysis was performed using the R package.The phylogenetic tree was constructed with TreeBeST . Finally, we conducted gene-based analysis (GBA) with PCR-based sequencing to confirm the associated non-synonymous SNPs. We also conducted the association analysis with SHEsis Plus Online Version-Beta  and classified the samples into distinct haplotypes.We used the following strategy to narrow down candidate genes. First, according to the GWAS associated loci, we estimated the candidate region by pairwise LD correlation. Second, based on our assembled genome sequence for r2) between all SNPs from SAL1 region to all SNPs from SAL2 region; PmaxLD (SAL1) or PmaxLD (SAL2) are the largest possible LD value within the SAL1 or SAL2 region, respectively, obtained by calculating the mean r2 of each SNP to all the SNPs from the SAL1 or SAL2 region and choosing the maximum mean LD to represent the region’s PmaxLD; Pairwise r2 values represent all significant SNPs. Only the average r2 ≥ 0.1 were selected to draw the networks.The software Cytoscape was used to construct association networks . The traAmong the 288 individuals, the top 15 oysters with extreme high and low glycogen or amino acid content were selected for the real-time PCR. Forty oysters were collected for RNA extraction. The total RNA was isolated using the Trizol reagent (Invitrogen). The RNA yield and purity were determined spectrophotometrically  at 260 and 280 nm. The RNA integrity was assessed by 1.2% agarose gel electrophoresis.EF) gene was chosen as the internal standard. The qRT-PCR was carried out in triplicate with a reaction mixture of total volume 20 μL containing 10 μL of SYBR Green 2X Supermix (Takara), 1 μL of 1:100 diluted cDNA, 0.4 μL each of the forward and reverse primers, 0.4 μL of ROX Dye II, and 7.8 μL of DEPC H2O. The PCR involved two steps: 95 °C for 30 s, followed by 40 cycles at 95 °C for 3 s and 60 °C for 30 s. The analysis was based on the Ct values of the PCR products. Melting curve analysis of the products was performed at the end of each PCR amplification. The Ct values for the comparison between the amplified genes and EF gene (ΔCt) were calculated. The blank group was used as the reference sample . The ΔCt of each sample was then subtracted from the ΔCt of the calibrator, and this difference is called the ΔΔCt value. The expression level of the target genes was then calculated as 2-ΔΔCt.For the quantitative real time PCR (q-PCR), the RNA sample was reverse transcribed using a cDNA synthesis kit  and qRT-PCR was performed using the ABI7500 fast Real-Time Detection System . The elongation factor Additional file 2: Table S2 Summary of the results of whole-genome resequencing. (XLSX 11 kb)Additional file 3: Table S3 Four hundred and twenty-seven oysters collected from the world-wide scale and their phenotyping values, including the content of glycogen, protein, and 17 different amino acids. (XLSX 66 kb)Additional file 4: Table S4 Fold changes of different nutrient quality traits in 426 individuals. (XLSX 12 kb)Additional file 5: Table S5 Correlation analysis among different traits. Upper matrix indicates correlation value and lower matrix indicates significance . (XLSX 14 kb)Additional file 6: Table S6 Genome-wide significant association signals of quality traits in C. gigas using the compressed MLM model. (XLSX 65 kb)Additional file 7: Table S7 The significant SNP loci of glycogen contents with the genome-wide analysis for 288 individuals using the compressed MLM model. (XLSX 243 kb)Additional file 8: Table S8 The significant SNP loci of protein contents for the genome-wide analysis with 288 individuals using the compressed MLM model. (XLSX 250 kb)Additional file 9: Table S9 Link loci across different traits. (XLSX 33 kb)Additional file 10: Figure S1 Phylogenetic tree analysis of Pacific oysters using the whole-genome SNPs. C. angulata was used as the outgroup. (DOCX 141 kb)Additional file 11: Figure S2 F1 breeding methods of 427 individuals collected from the world-wide scale. The females collected were mated with the males cultured in Qingdao and generated 427 family lines. They were cultured in the same environment for approximately one year, and then 30 individuals in each family line were collected and used for phenotype measurement. (DOCX 124 kb)Additional file 12: Figure S3 Histogram of glycogen and amino acids content in oysters. All quality traits presented normal distribution, except for Met and Cys. X axis represents the value of different traits, whereas, the y axis represents number of individuals. (DOCX 235 kb)Additional file 13: Figure S4 Amino acid composition in all 427 individuals. (A) Among all detected amino acids. Glu and Tau accounted for 14 and 11% of TAA content, respectively, and they represent the amino acids with the highest content in C. gigas. (B) Among all detected amino acids, six DAA accounted for 41% of TAA content. (DOCX 692 kb)Additional file 14: Figure S5 Genome-wide analysis of glycogen, protein, and amino acids components. The left panel shows the Manhattan plots of the MLM model. The X axis shows the genomic position in 10 chromosomes and the Y axis shows the significance expressed as -log10-transformed P-value. The right panel shows the Quantile-quantile plot of the MLM model. (DOCX 2787 kb)Additional file 15: Figure S6 Pairwise LD analysis of the 100 kb-region on both sides of the leading SNP of glycogen and protein. The upper panel shows the GWAS results of the 100 kb-region on both sides of the leading SNP of the trait, whereas, the panel below shows the pairwise LD analysis of the SNPs (P < 2 × 10− 4) included in the region. (DOCX 4156 kb)Additional file 16: Figure S7 Pairwise LD analysis of the 100 kb-region on both sides of the leading SNP of amino acids. The upper panel shows the GWAS results of the 100 kb-region on both sides of the leading SNP of the trait, whereas, the panel below shows the pairwise LD analysis of the SNPs (P < 2 × 10− 4) included in the region. (DOCX 2658 kb)Additional file 17: Figure S8 Indidate gene identification. First part shows the Manhattan plots of traits. Red horizontal dashed line indicates the genome-wide significance threshold. Second part presents 0.1-Mb region on each side of the peak SNP, the position of which is indicated by a vertical red line with red triangle. Bottom of each panel shows the annotated genes of the 200-kb region. Gene is indicated by black boxes. If the significant SNP was located in the gene, the gene name is highlighted in red bold font, and the coding region of the gene is indicated by black vertical lines. (DOCX 220 kb)Additional file 18: Figure S9 Predicted 3D structure of CYP17A1 proteins of different genotypes with SWISS-MODEL Workspace (https://swissmodel.expasy.org/). (DOCX 1099 kb)Additional file 19: Figure S10 Genome-wide association study of Asp (A), His (B), Leu (C), Cys (D), and Met (E) content. The first section of each panel shows the Manhattan plot of traits. The negative log10-transformed P-values from the genome-wide scan are plotted against position on each of the 10 chromosomes. Red horizontal dashed line indicates the genome-wide significance threshold. Second part exhibits the 0.1-Mb region on each side of the peak SNP and its position is indicated by a vertical red line with red triangle. The bottom of each panel shows the annotated genes of the 200-kb region. Significant SNP that surpassed the threshold is indicated by red plots and peak SNP is indicated by purple plots. The dotted lines with arrows represent the traits related to the peak SNP. Gene is indicated by black boxes. If the significant SNP was located in the gene, the gene name is highlighted in red bold font and the coding region of the gene was indicated by black vertical lines. (DOCX 768 kb)


Ostrea edulis) is a highly appreciated mollusk with an important aquaculture production throughout the 20th century, in addition to playing an important role on coastal ecosystems. Overexploitation of natural beds, habitat degradation, introduction of non‐native species, and epidemic outbreaks have severely affected this important resource, particularly, the protozoan parasite Bonamia ostreae, which is the main concern affecting its production and conservation. In order to identify genomic regions and markers potentially associated with bonamiosis resistance, six oyster beds distributed throughout the European Atlantic coast were sampled. Three of them have been exposed to this parasite since the early 1980s and showed some degree of innate resistance , while the other three were free of B. ostreae at least until sampling date . A total of 14,065 SNPs were analyzed, including 37 markers from candidate genes and 14,028 from a medium‐density SNP array. Gene diversity was similar between LTA and NV groups suggesting no genetic erosion due to long‐term exposure to the parasite, and three population clusters were detected using the whole dataset. Tests for divergent selection between NV and LTA groups detected the presence of a very consistent set of 22 markers, located within a putative single genomic region, which suggests the presence of a major quantitative trait locus associated with B. ostreae resistance. Moreover, 324 outlier loci associated with factors other than bonamiosis were identified allowing fully discrimination of all the oyster beds. A practical tool which included the 84 highest discriminative markers for tracing O. edulis populations was developed and tested with empirical data. Results reported herein could assist the production of stocks with improved resistance to bonamiosis and facilitate the management of oyster beds for recovery production and ecosystem services provided by this species.The European flat oyster ( Additionally, oyster reefs play an important ecosystem role providing multiple benefits such as water filtration, food and habitat for other animals, shoreline stabilization, carbon burial, nutrient regeneration, and coastal fisheries .The flat oyster Ostracoblabe implexa . Bonamiosis, endemic in many areas of flat oyster distribution, is currently the main concern for flat oyster production and the main contributory factor to its decline  and in only one or two families.Overexploitation of natural beds, introduction of non‐native oysters, adverse effects of climate change and specific epidemic outbreaks, such as the “shell disease” caused by the fungus B. ostreae , and this low but significant differentiation has been suggested to be driven by oceanic fronts present in the region , according to periodical surveys since the 1980s , following the manufacturer recommendations. Two different strategies were followed for single nucleotide polymorphisms (SNPs) selection and genotyping to identify genetic markers associated with resistance to bonamiosis. The first one was focused on the search for SNPs associated with candidate (denoted “CAND” markers) differentially expressed genes (DEGs) between LTA vs. NV oysters  and O. edulis  , observed (Ho), and expected (He) heterozygosity, departure from Hardy–Weinberg equilibrium (HWE) and the estimators of Wright  F‐statisFST values between oyster beds were calculated with ARLEQUIN v3.5 using 10,000 permutations to test for significance. Genetic subdivision was explored using an approach that does not require defining populations a priori. In particular, a model‐based clustering approach, implemented in STRUCTURE v2.3.4  from a collection of individual multilocus genotypes and estimates for each individual the fraction of its genome that belongs to each cluster, was applied. A burn‐in period of 10,000 steps and 100,000 Monte Carlo replicates for different values of K ranging from 1 to 7 was used. For each K tested, five replicates were run to assess their reliability. After checking that K = 1 was not the most probable scenario (see Results), the STRUCTURE results were reanalyzed with the program STRUCTURE HARVESTER v0.3 . Data were transformed using PCA , and a number of principal components (PC) and discriminant functions (DF) were retained to explain >90% of the variance.Pairwise d Goudet . The sofd Goudet  was usedd Goudet  for the 2.4ST values under an inland model  BYPOOL, where oyster beds were pooled according to their bonamiosis status (LTA vs. NV). The other approach, ARLEQUIN v3.5, identifies outliers using the FDIST FST method using a maximum likelihood approach  ARLPOOL, but additionally including a third one, ARLHIER, where a hierarchical island model was applied grouping oyster beds by their resistance to bonamiosis. All ARLEQUIN runs were performed with 25,000 simulations, 10 groups, and 100 demes per group. Strategies to identify outliers under selection can produce type I  or type II  errors. BAYESCAN follows a more conservative approach than ARLEQUIN  <0.05 with BAYESCAN were considered consistent outliers. ARLEQUIN has the tendency to produce a higher proportion of false positives, and therefore, loci at p < 0.01 were considered consistent outliers, while those at p < 0.05 suggestive outliers. Markers shared (or excluded) by the different methods and scenarios were assumed to be the most reliable ones (see Results).Different statistical strategies were used to identify outlier loci subjected to selection. BAYESCAN v2.01, a method based on a Bayesian approach to identify outliers using FO. edulis database  to lower (less discriminant) values, and then, subsets of markers were set up by progressively adding one by one SNPs following the FST rank. The USEPOPINFO option of the program STRUCTURE 2.3.4 was used to define the six reference populations studied. Then, for each SNP subset and individual tested, the admixture coefficient q was obtained for each of the six reference populations. With this information, we estimated the proportion of individuals assigned to their population according to the highest q  and the highest one was considered the correct allocation. Using this information, we also estimated the average q value for each population and subset of SNPs tested.Outlier SNPs putatively related to other factors than bonamiosis resistance were identified by discarding, from the total outliers detected with all strategies, those detected in the LTA vs. NV comparisons, putatively associated with bonamiosis since this was the factor used for grouping the set of populations analyzed covering the whole Atlantic area. This subset of outlier SNPs was used to estimate their capability for discriminating populations and for allocating individuals to their populations. These outliers were ordered according to global Finally, an assignment test using the Bayesian method of Rannala and Mountain  implemen33.1Among the 837 DEGs identified by Ronza et al. , 411 conp > 0.175 for all Mann–Whitney U tests performed for each estimator), suggesting no loss of genetic diversity in the whole genome due to the selection pressure caused by the parasite. FIS values were not significant in all cases excluding LIM (p < 0.05) which showed a slight heterozygote excess  was lower than 5% in all locations, excluding LIM .A total of 971 monomorphic loci were detected among the 14,065 analyzed; some of them might be monomorphic due to the limited sampling in our study, but also they might be false positive SNPs from the SNP discovery pipeline based on RAD Sequencing of pooled DNA samples  and grouping scenarios . CAND outliers were exclusively detected with ARLEQUIN , and most of them were suggestive for balancing selection, excluding Oed_rep_c4836_2869e and Oed_rep_c2446_3082e, which fitted to a divergent selection pattern Table . The for3.3IND vs. ARLIND) and pooled (ARLPOOL/HIER and BYPOOL) scenarios, suggesting a certain proportion of false positives with ARLEQUIN , was in accordance with previous reports suggesting a notable proportion of false positives with this program when checking for balancing selection  and from 345 for ARPOOL to 1,226 for ARLIND with ARLEQUIN (p < 0.01). On the other hand, a higher number of outliers were detected with ARLHIER than with ARLPOOL, when using pooling strategies, although these figures were very similar for divergent selection (p < 0.01:355 vs. 345).The number of outliers detected with BAYESCAN was much lower than those detected with ARLEQUIN in both the individual (BYIN Table . Accordip < 0.01) strategies outlined previously is shown in the Venn diagram shown in Figure POOL were also detected with BYIND (87.5%). With ARLEQUIN, 273 out of 396 outliers detected with pooling strategies (ARLPOOL ARLHIER) were also detected with ARLIND (68.9%). This is an expected result since, hypothetically, the individual scenario should detect outliers for most if not all selective factors. A total of 21 markers were consistently detected as divergent outliers in the five scenarios considered, including pooling strategies, and thus, we considered this marker set along with the CAND Oed_rep_c2446_3082e as the 22 most consistent outlier loci putatively associated with bonamiosis resistance in our study (strict (S) criterion, denoted outlierPOOL‐S). Additionally, we considered other less strict, although consistent panel, including the CAND Oed_rep_c2446_3082e, all loci detected with BYPOOL (24 loci) and the intersection of ARLEQUIN pooling strategies at p < 0.01 , which rendered a total of 87 outliers (less strict (L) criterion, denoted outlierPOOL‐L).The correspondence between the BAYESCAN and ARLEQUIN  in our study, the first strategy should show higher resolution. Additionally, we performed the same analysis with 50 randomly selected loci from the whole 14,065 loci to be taken as an unlinked dataset reference. Using the 22 outlierPOOL‐S, a total of 206 significant LD tests were detected out of 231 performed , 173 after Bonferroni correction , which strongly supports linkage among several of these loci (Table p < 0.05 out of 903 performed (6.2%) and 3 after Bonferroni correction (0.3%). A LD pairwise matrix was constructed for integrating data, and strong linkage signals were observed among 19 loci , putatively constituting a single QTL related to flat oyster resistance to bonamiosis   and 633 after Bonferroni correction (16.9%) (Table POOL‐S SNP panel (Table POOL‐S SNPs) showed strong signals of LD. However, when this information was integrated with the most consistent 19 outlierPOOL‐S loci outlined before, they showed strong linkage signals  showed a strong effect on bonamiosis resistance, determining a significant selective sweeping around the favorable mutations. Thus, we checked for linkage disequilibrium (LD) among the 22 outlierci Table . When us%) Table . We also%) Table . The mosel Table . A set ols Table , thus suO. edulis and Crassostrea spp transcriptomic and genomic databases for their annotation and mining. There were only four markers for which significant alignment with the flanking sequence was identified ‐containing protein 5‐like gene. WDRs have been related to many biological functions like signal transduction, transcription regulation, and apoptosis  = −2419.74; p = 0.000) as the most consistent hypothesis, which agrees with the existence of two genomic clusters relating to bonamiosis resistance; one of the clusters was predominant in the LTA group, while the other was in the NV one  suggested by STRUCTURE following the Evanno's approximation was K = 3 (Ln P (K = 1) = −1082309.4; p = 0.000) and the DAPC showed these three groups spatially segregated . The number of population units (K) suggested by STRUCTURE in this case was K = 5, but each of the six populations showed a unique composition , excluding LIM, which reached nearly the maximum q‐value with only five SNPs.The sampling design in our study was thought to identify SNP outliers related to bonamiosis resistance following a population genomics approach. Therefore, despite signals for selection related to other unknown environmental factors were detected, the correlation between genetic and environmental data  was out of the scope of our work. Furthermore, one of the populations evaluated (ROS) has been subjected to artificial selection for several generations, and thus, it would not be suitable for this analysis. However, the capability of outlierse Figure a and thed Figure b. As sho4B. ostreae in the European Atlantic area, and despite the connectivity among oyster populations mainly through larvae advection  was similar to that previously documented using 16 microsatellite loci on a broader sample collection in the same sampling area  , although not with BAYESCAN. In the study by Ronza et al.  related to divergent selection between LTA and NV groups taking as reference the neutral background for Atlantic oyster outlined before. Remarkably, most of these markers showed strong LD among them, apparently targeting to a specific genomic region in the flat oyster genome, suggestive of a large QTL as the primary determinant of resistance to bonamiosis. When a less strict, although consistent 87 SNP set was used by adding another 65 SNPs, another cluster of nine loci demonstrated to be linked to that outlined before, providing additional support for the existence of a single major QTL. We looked for annotation of the outlier ARRAY loci using the flat oyster database and the IND loci putatively not related to bonamiosis resistance but to abiotic environmental variables. This SNP panel enabled the full differentiation of the six populations studied in the Atlantic area and the correct allocation of all individuals to their populations. Even the very close geographic samples in the British Isles were allocated with 100% accuracy to their original populations. The use of outlier loci for distinguishing populations and successful allocation of individuals to populations has been applied in other studies with aquatic organisms showing low population structuring , a fish species whose dispersal mechanism is mainly related to larval advection (Prado, Vera, Hermida, Bouza, et al., Finally, although not the main goal of the study, we could identify a broad set of 324 ourliersIn this study, we used a population genomics approach to identify putative selective sweeps harnessing QTLs related to bonamiosis resistance in flat oyster, the main threat for its production and viability. The combination of candidate gene and genomic screening strategies enabled preliminary ascertainment of the genomic architecture of resistance to bonamiosis, where a putative major QTL appears to explain most of the divergence between LTA and NV populations. Other minor QTLs scattered across the genome, including two candidate genes associated with enriched pathways and functions activated against the parasite, would also explain the divergence between both population sets. The lack of a flat oyster reference genome precluded a high‐resolution analysis genes and genetic variants associated with resistance, which remains an urgent task for the near future. In any case, the information obtained provides a set of markers to be evaluated as tools to enhance selective breeding programs and for appropriate management and recovery of flat oyster beds.None declared. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Carya illinoinensis seedlings inoculated with the Chinese black truffle (Tuber indicum). The growth of T. indicum-mycorrhized seedlings and their mycorrhizal colonization rate were investigated. Additionally, the denitrifying bacterial community harboring NO reductase (norB) genes and the fungal community in the rhizosphere of the host were analyzed by high-throughput sequencing. The results showed that the colonization rate of T. indicum was significantly influenced by SNP treatments and P stress, with the highest level being obtained when the SNP was 100 μmol/L under low P stress (5 μmol/L). Treatment with 100 μmol/L SNP alone also increased the colonization rate of T. indicum and had positive effects on the plant height, stem circumference, biomass, root-shoot ratio and root POD activity of the seedlings at different times after inoculation. Under low P stress, the 100 μmol/L SNP increased the richness of the norB-type denitrifying bacterial community. Interestingly, the diversity and richness of norB-type denitrifying bacteria were significantly positively correlated with the colonization rate of T. indicum. SNP treatments under low P stress altered the abundance of some dominant taxa such as Alphaproteobacteria, Gammaproteobacteria, Pseudomonas, Ensifer, and Sulfitobacter. Evaluation of the fungal community in the rhizosphere revealed that 100 μmol/L SNP treatment alone had no noticeable effect on their richness and diversity, but it did shape the abundance of some fungi. Buellia, Podospora, Phaeoisaria, Ascotaiwania, and Lophiostoma were more abundant following exogenous NO application, while the abundance of Acremonium, Monographella, and Penicillium were decreased. Network analysis indicated that T. indicum was positively and negatively correlated with some fungal genera when treated with 100 μmol/L SNP. Overall, these results revealed how exogenous NO and P stress influence the symbiosis of truffles and host plants, and indicate that application of SNP treatments has the potential for ectomycorrhizal synthesis and truffle cultivation.In the artificial cultivation of truffles, ectomycorrhizal colonization level, host plant quality, and the associated microbes in the rhizosphere soil are vitally important. To explore the effects of nitric oxide (NO) and phosphorus (P) stress on the early symbiosis of truffles and host plants, different concentrations of exogenous NO donor sodium nitroprusside (SNP) and P were applied to  Tuber spp., commonly known as truffles, are ascomycete fungi that form ectomycorrhizae in a symbiotic relationship with plant roots and are prized for their hypogeous, edible fruiting bodies which adds a unique flavor to dishes  genes can perform this process  (f 1:1:1) . After 3f 1:1:1) , 2018. TTuber indicum was from Yanbian County, China. The truffle inoculum was prepared as previously described  was applied to the inoculated C. illinoinensis seedlings every 15 days, while uninoculated seedlings were treated with 0 μmol/L SNP. Samples were treated with 100 mL SNP per pot at each treatment time, half of which was applied to the cultivation substrate, while the remainder was sprayed on the leaf surface. The inoculated seedlings treated with 0, 10, 100, and 1000 μmol/L SNP were denoted S0, S1, S2, and S3, respectively.After 2PO4 and the final concentration of each element in the nutrient solution except P is shown in C. illinoinensis seedlings that were irrigated with 0, 5, and 2000 μmol/L P nutrient solution were denoted as the no P treatment (P0), low P treatment (P5), and high P treatment group (P2000), respectively. Uninoculated seedlings treated with 0 μmol/L SNP were only irrigated with 0 μmol/L P nutrient solution and were assigned to CK group. Overall, there are 12 treatments (excluding CK): P0S0, P0S1, P0S2, P0S3, P5S0, P5S1, P5S2, P5S3, P2000S0, P2000S1, P2000S2, and P2000S3. Each treatment contained at least three C. illinoinensis seedlings, all of which were timely irrigated with corresponding modified Hoagland nutrient solution, while sterile water was periodically applied to keep the cultivation substrate moist.When the seedlings were treated with different concentrations of SNP, they were also treated with different levels of P. Briefly, modified Hoagland nutrient solution containing three different concentrations of P was prepared . The P originated from KHC. illinoinensis seedlings with T. indicum colonization in the early symbiotic stage, the appropriate concentration (100 μmol/L) of SNP was applied alone as described above. Overall, half of the inoculated seedlings cultivated in Substrate II were treated with 100 mL of 100 μmol/L SNP every 15 days after inoculation of T. indicum until day 90 (SNP treatment), while the remaining inoculated seedlings were treated with an equal amount of water at the same time (Control-M treatment). The seedlings in these two treatments were irrigated with water every 2–3 days to keep the cultivation substrate moist.Exogenous treatment of only NO was applied to seedlings that were cultivated in Substrate II . To furtC. illinoinensis seedlings colonized by T. indicum were successfully detected by morphological analysis using a microscope. In each treatment, seedlings and their root system were harvested. Moreover, the rhizospheres soil of seedlings in CK and low P treatments  were also collected aseptically. The mycorrhizal colonization rate was determined by counting the number of root segments colonized by T. indicum under a stereomicroscope based on the mycorrhizal fungal structures, with 30 root segments randomly selected in total for each seedling, which was finally expressed as:  × 100%  × 100% . The plaT. indicum was calculated at month 6.For the seedlings of two treatments cultivated in Substrate II, their root systems and rhizosphere soil were collected every month after inoculation and used to determine the plant morphology and physiology, as well as for high-throughput sequencing of the fungal communities. Samples harvested at 0, 1, 2, 3, 4, 5, and 6 months after inoculation were denoted M0, M1, M2, M3, M4, M5, and M6, respectively. The ectomycorrhizae in samples from each month were detected and the colonization rate of The whole experimental design and sampling strategy in this study can be seen in The measured plant morphological and physiological indices included the plant height, stem circumference, root-shoot ratio, biomass, root activity, and superoxide dismutase (SOD) activity in roots and peroxidase (POD) activity in roots.C. illinoinensis seedlings were measured using a ruler and vernier caliper. Next, the seedlings were put in 100°C water for 20 min to halt respiration, then they were oven-dried at 75°C until constant weight for determination of the dry-weight, which was taken as the biomass. Next, the seedlings were divided into their underground and aboveground parts and the dry weights of the two parts were determined. The root-shoot ratio was expressed as the ratio of the dry weight of underground to aboveground parts of the seedlings  according to the manufacturer’s instructions. The extracted DNA was detected by 0.8% agarose gel electrophoresis and quantified by ultraviolet spectrophotometry.Total genomic DNA of the rhizosphere soil samples was extracted using a Power Soil5S0, P5S1, P5S2, P5S3, and CK treatments (each treatment was performed in triplicate), the norB gene was amplified with the universal primers cnorB2F (5′-GACAAGNNNTACTGGTGGT-3′) and cnorB6R (5′-GAANCCCCANACNCCN GC-3′). The PCR reaction mix was 25 μL, which included the DNA template (2 μL), reaction buffer (5 μL), GC buffer (5 μL), 2 μL dNTPs (2.5 mmol L–1), 1 μL forward primer (10 μmol L–1), 1 μL reverse primer (10 μmol L–1), 0.25 μL Q5 DNA polymerase, and 8.75 μL ddH2O. The cycling conditions were as follows: initial denaturation at 98°C for 2 min, followed by denaturation at 98°C for 15 s, and annealing at 55°C for 30 s, extension at 72°C for 30 s, after which samples were subjected to final extension at 72°C for 5 min. For the DNA extracted from the soil samples in Substrate II, the ITS1 region was amplified using primers ITS1 (5′-GGAAGTAAAAGTCGTA ACAAGG-3′) and ITS2 (5′-GCTGCGTTCTTCATCGATGC-3′). The PCR products were checked by 2% agarose gel electrophoresis and the target fragments were recovered using an Axygen Axy Prep DNA Gel Extraction kit . The recovered PCR products obtained from three technical replicates were combined in equidense ratios for each sample and purified with a Qiagen Gel Extraction Kit . The PCR products were then quantified using a Quant-iT PicoGreen dsDNA Assay Kit . An Illumina TruSeq Nano DNA LT Sample Prep Kit  was used to generate PCR amplicon libraries, after which the library quality was assessed with Agilent High Sensitivity DNA Kit .For the DNA extracted from the soil samples of the PnorB-denitrifying bacteria was obtained using Ribosomal Database Project (RDP) (Release 11.11) and the fungal sequences were taxonomically classed using UNITE database (Release 5.02) . The alpha diversity of the species complexity of each sample was determined using the Chao1, ACE, Shannon, and Simpson indices. The beta diversity was determined by non-metric multidimensional scaling (NMDS) using R software, which reflects the differences in microbial communities among groups. Permutational multivariate analysis of variance (PERMANOVA) was performed by QIIME accompanied with NMDS. Linear discriminant analysis effect size analysis was used to respectively reveal the bacterial and fungal taxa at all taxonomic levels with significantly differential abundance between groups, which was carried out by Galaxy online analysis platform3. Network analysis for investigation of the interactions between the dominant genera was also performed using Mothur software  on an Illumina MiSeq sequencing platform. The overlapping paired-end reads were assembled using PEAR software and poor-quality sequences were removed using QIIME (v1.8.0) and USEARCH (v5.2.236) . High-quse 5.02) . The alpsoftware .PRJNA544895/SRP199549.All of the raw sequencing data used in this study were submitted to the NCBI Sequence Read Archive (SRA) database with the accession number t-tests, and the results reported were the means ± standard deviation (SD) of three biological replicates for each treatment. The least significant difference (LSD) test was performed using P < 0.05 as the threshold. Spearman’s correlation coefficient (rho) was calculated using SPSS 22.0.Statistical analyses were performed using SPSS v22.0 . The data were analyzed by one-way analyses of variance (ANOVAs) and independent C. illinoinensis seedlings, while other uninoculated seedlings (CK) had not been colonized by truffles based on morphological evidence   . The rooT. indicum and the physiological indices of C. illinoinensis seedlings were significantly affected by different concentrations of exogenous NO and P (T. indicum on C. illinoinensis seedlings significantly increased as the SNP concentration increased from 0 to 100 μmol/L (P < 0.05), but when the SNP concentration increased to 1000 μmol/L, there were no ectomycorrhizae successfully detected. Under the same concentration of SNP (except 1000 μmol/L), the colonization rate was significantly higher when the P concentration was 5 μmol/L (P < 0.05) and reached a maximum of 81 ± 3% in P5S2 treatment.The colonization rate of NO and P . Under t0 and P5 treatments, no differences in these four indices under different SNP concentrations were observed, while in the P2000 treatments, the stem circumference and biomass were significantly higher when the SNP concentration was 10 μmol/L compared to when SNP was 0 and 100 μmol/L (P < 0.05).The different concentrations of P had no noticeable effect on the plant height, stem circumference, root-shoot ratio or biomass of seedlings . In the 0S2 treatment. Specifically, no significant differences were observed among the three different P levels when the SNP concentration was 0 or 10 μmol/L, but at 100 μmol/L, the root activity significantly decreased as the P concentration increased (P < 0.05). In the P0 and P5 treatments, root activity was significant higher when SNP was 100 μmol/L (P < 0.05), and there were no significant differences in P2000 treatments. The SOD activity of roots in the P0 treatments were significantly lower than in the P5 and P2000 treatments (P < 0.05). There was no noticeable effect of different SNP concentrations on root SOD activity. The maximum POD activity was observed in the P0S0 treatment, followed by the P5S2 treatment. In the P5 treatments, POD activity was significantly higher when the SNP was 100 μmol/L (P < 0.05) and there were no significant differences between P2000 treatments. Overall, the colonization rate and the physiological indices of the root system were higher when the SNP concentration was 100 μmol/L and the P content was low.The highest root activity was observed in the PT. indicum colonization were more noticeable in the SNP treatment. On the sixth month after inoculation, the colonization rate of T. indicum reached 88 ± 2% on the seedlings to which 100 μmol/L SNP were applied, which was significantly higher than that of the seedlings in the Control-M treatment (62 ± 3%) (P < 0.05).The morphology of the inoculated seedlings and their root systems are shown in P < 0.05), and the stem circumference became significantly thicker in the SNP treatment from the third to the sixth month (P < 0.05) (P < 0.05). SNP treatment also significantly increased the root-shoot ratio on the third, fifth and sixth month (P < 0.05). The POD activity in roots differed significantly between the two treatments on the first and second month, and was also higher in response to SNP treatment (P < 0.05). However, SNP treatment had no noticeable effect on the SOD activity in roots because there were no significant differences between the two treatments after inoculation. Root activity was significantly lower in response to SNP treatment during the fourth and fifth month (P < 0.05).Sodium nitroprusside treatment significantly increased the plant height from the second month to the sixth month compared with the Control-M ( < 0.05) . The bio5S2 being highest, followed by that in the P5S3 treatment. The unique number of OTUs in P5S0 and P5S1 was even lower than that in CK.Sequencing of the rhizosphere soils of the CK treatment and those treated with different concentrations of SNP under low P stress were yielded 947,615 high-quality sequences from all 15 samples after quality control, which were clustered into 4,387 OTUs . The VennorB-type denitrifying bacteria were not significant. The estimated richness indices (Chao1 and ACE) revealed that the norB-type denitrifying bacterial community richness was highest in the P5S2 treatment, and was significantly higher than that in the P5S0 and CK treatments (P < 0.05).The two diversity indices (Shannon and Simpson) showed no significant differences among the five treatments , indicatnorB-type denitrifying bacterial communities were detected. At the phylum level, Proteobacteria was the most abundant phylum, accounting for 98.33% (Actinobacteria (1.07%) and Acidobacteria (0.42%). The relative abundance of these three phyla showed no significant differences among the five treatments.In the 15 samples from the five different treatments, a total of 10 phyla, 16 classes, 35 orders, 56 families, and 95 genera of r 98.33% , followeAlphaproteobacteria (75.70%), Gammaproteobacteria (20.07%), and Betaproteobacteria (2.50%) were the dominant taxa. Under low P stress, the abundance of Alphaproteobacteria gradually increased as the SNP concentrations increased from 0 to 100 μmol/L and then decreased when the SNP concentration was 1000 μmol/L. Alphaproteobacteria was significantly more abundant in the CK and P5S2 groups than in the P5S0 and P5S3 groups (P < 0.05). The abundance of Gammaproteobacteria was lowest in the CK and P5S2 groups, while the P5S3 treatment contained significantly more Gammaproteobacteria than the CK (P < 0.05). Betaproteobacteria was more abundant in the P5S1 group.At the class level , AlphaprPseudomonas (19.97%), Sinorhizobium (16.27%), Rhizobium (13.20%), Ensifer (12.80%), Rhodobacteraceae_unidentified (8.70%), Polymorphum (5.96%), Bradyrhizobium (3.52%), and Sulfitobacter (2.88%). Under low P stress, Pseudomonas abundance gradually decreased as the SNP concentrations increased from 0 to 100 μmol/L, then increased to the maximum when the SNP concentration was 1000 μmol/L. The abundance of Pseudomonas in the P5S3 group was significantly higher than in the CK (P < 0.05) (Sinorhizobium and Rhizobium showed no significant differences among the five treatments. Ensifer was significantly more abundant in the CK (P < 0.05) , while Sulfitobacter was significantly more abundant in the P5S1 group (P < 0.05) than in the other groups  . Sinorhi < 0.05) , and ther groups .norB-type denitrifying bacterial community structure among the five treatments were visualized by NMDS analysis   . The nornorB-type denitrifying bacterial communities, 36 showed correlations with others   . Based oOverall, 1,824,064 high-quality sequences were obtained from the 42 samples collected during different months after quality control procedures. These sequences were clustered into 1,452 OTUs in all, and the rarefaction curves of the fungal OTUs in different samples are shown in P < 0.05). In general, the SNP treatment did not have any noticeable effect on the diversity and richness of fungal communities in rhizosphere soil at different growth times.Based on the Chao1 and ACE indices, fungal community richness of the rhizosphere soil did not differ significantly between the SNP treatments and Control-M treatments during each month . AdditioAscomycota (80.26%) was the dominant fungal phylum, followed by Basidiomycota (10.41%) and Zygomycota (4.08%)  . The relPezizomycetes (41.06%), Sordariomycetes (18.32%), Agaricomycetes (10.53%), and Saccharomycetes (8.58%) were the dominant taxa .At the class level, ant taxa . The relTuber (32.68%), Rhizoctonia (5.64%), Phaeoacremonium (4.93%), Pichia (3.81%), Basidiobolus (2.53%), Fusarium (2.27%), Acremonium (1.71%), Buellia (1.44%), Bullera (1.30%), and Monographella (0.98%) . The abundance of Fusarium was also significantly lower in the SNP treatment on the first and second month (P < 0.05). In the SNP groups, Tuber showed significantly greater abundance from the second to the sixth month compared with month 0 and 1 (P < 0.05). However, in Control-M treatment, Tuber abundance increased from the fourth month, and was significantly more abundant compared with months 0, 1, 2, and 3 (P < 0.05).At the genus level, the top 10 of the most abundant genera were  (0.98%) . Tuber aP < 0.05)  analysis was used to reveal the fungal taxa that showed significantly different abundance between the SNP and Control-M treatments ( < 0.05) . At the Tuber was negatively correlated with Archaeorhizomyces, Podospora and Penicillium, but positively correlated with Tricholoma.Among the top 50 genera, 39 showed correlations with others . Tuber wP = 0.008)  . In SNP C. illinoinensis seedlings. The shifts in the colonization levels of T. indicum, in the growth of host plants, and in the associated microbes of rhizosphere soil were then investigated during the early symbiotic stage.As ectomycorrhizal fungi, the successful and efficient synthesis of ectomycorrhizae is the basis for the artificial cultivation of truffles . The abiT. indicum with C. illinoinensis seedlings. The colonization rate reached a high level (81 ± 3%) when SNP was 100 μmol/L under low P stress (5 μmol/L). Previous research showed that plants could be less dependent on ectomycorrhizae for P absorption when more soil P is available, and that ectomycorrhizal colonization may be greater under P-limited conditions  did not contribute to colonization of T. indicum. The trend in colonization levels was P5 > P0 > P2000. These findings indicated that the plants could adjust their root architecture in response to low P conditions . In previous studies, the colonization rate of truffles was between approximately 40 and 60%, depending on the truffle species species and host plant  on soil rhizosphere fungi associated with truffles were investigated. Not surprisingly, ing soil , 2018. Ning soil . In addiulations . During T. indicum and the growth of host seedlings, with the shift of colonization rate, plant physiology, and some microbial communities in the rhizosphere, which could have potential application in the artificial cultivation of truffles in the future. Also, the mechanism of how exogenous NO and P stress affect the symbionts of truffles and the host also needs to be further explored.Both exogenous NO and P stress affected the ectomycorrhizal synthesis of PRJNA544895/SRP199549.The datasets generated for this study can be found in the NCBI Sequence Read Archive (SRA) database with the accession number XL, XZ (first author), and XZ (last author) conceived and designed the experiments. CW, ZK, and LY performed the experiments. XZ (first author) and XL wrote and revised the manuscript. All of the authors approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.