Paeonia ostii, a member of tree peony, is an emerging oil crop with important medical and oil uses and widely cultivated in China. Dissolving the genetic diversity and domestication history of this species is important for further genetic improvements and deployments. We firstly selected 29 simple sequence repeats (SSRs) via transcriptome mining, segregation analyses and polymorphism characterizations; then, 901 individuals from the range-wide samples were genotyped using well-characterized SSR markers. We observed moderate genetic diversity among individuals, and Shaanxi Province was identified as the center of genetic diversity for our cultivated plants. Five well-separated gene pools were detected by STRUCTURE analyses, and the results suggested that multiple independent domestication origins occurred in Shaanxi Province and Tongling City (Anhui Province). Taken together, the genetic evidence and the historical records suggest multiple long-distance introductions after the plant was domesticated in Shandong, Henan and Hunan provinces. The present study provides the first genetic evaluation of the domestication history of P. ostii, and our results provide an important reference for further genetic improvements and deployments of this important crop. With the increasing demand for fats and oils, researchers have targeted plant sources to explore their uses and functional properties3 with the goal of developing improved varieties of edible oils that can withstand future challenges, such as climate change, sustainability and food security4. Paeonia ostii is a traditional medicinal plant that represents a newly discovered and underutilized woody oilseed plant with a considerable agronomic potential for edible oil production. Originally, this species was grown for the root bark, which is used as an antispasmodic throughout Asia5. Recent studies have indicated that this species presents a competitive agronomic performance as compared to established oil crops for its high seed yield and unsaturated fatty acids content especially high α-linolenic acid content in the seed oil7. Grain yields of over 13.34 kg/ha have been achieved with common cultivars of P. ostii, and a considerable variation in oil content in seed has been recorded in this species, with values ranging from 20–35%9. α-Linolenic acid, which is a necessary element that cannot be produced within the human body and must be acquired through diet, is commonly found at unusually high levels (above 40%)12 in the seed oil of P. ostii, which indicates that this plant is a valuable crop for linolenic acid production13. Moreover, seed oil of P. ostii is a versatile oil as its use is not limited to food, but it is also widely used in non-food applications, such as in oleochemicals, cosmetics, and pharmaceuticals15. Therefore, P. ostii is an important economic plant with simultaneous medicinal and oil applications. The rediscovery of the value of this oil crop and its rapid deployment in recent years represent unprecedented opportunities for farmers and have attracted considerable interest among plant breeders17.Edible oils represent an important food source for the rapidly growing global populationP. ostii belongs to the section Moutan of the genus Paeonia and the family Paeoniaceae18, and this diploid tree peony species, with 2n = 2x = 10 chromosomes, is indigenous to China19. This plant is cross-pollinated by insects and propagated by seeds20. Compared with other tree peonies in this section, P. ostii is clearly characterized with white or pale rose petals without the base blotch, and the leaves contain no more than fifteen leaflets and present red or purple filaments, a disc and a stigma21. Literature on its cultivation has been recorded since the Qin to Han dynasties , and its root bark was originally used as a medicine. However, the plant was not used for gardening22, although it may represent one of the parental species for breeding the popular ornamental tree peony24. Currently, P. ostii is being cultivated at high concentrations in Bozhou and Tongling city in Anhui Province and the southwest region of Hunan Province, and these areas supply thousands of kilograms of root bark materials for the medical industry annually. In addition, this species is sparsely cultivated across southern and northern China, mainly in Shanxi, Shandong and Shaanxi provinces. Shaanxi Province is often reported as the region with the largest species diversity of Paeonia25, and in this region, the largest number of wild populations of different woody and herb peonies have been reported26. In addition, historical literature records Shaanxi as the original cultivation location of P. ostii28. However, the cultivation history of P. ostii remains largely unclear. Studies have indicated that P. ostii cultivation originated in the Fenghuang Mountain area of Tongling city in Anhui Province30. However, multiple independent cultivations may have also occurred in Henan Province, which has represented a famous location for the cultivation and breeding of the ornamental peony for more than 1,000 years31. P. ostii is an important rootstock resource for grafting the ornamental tree peony and one of the parents for the ornamental tree peony30. The current specimens cited in the original taxonomic description were derived from plants collected in three Chinese provinces: Henan, Hunan and Shaanxi28. Recent searches for the species as a wild plant have been largely unsuccessful33. The plant may have been common in the wild at one time but has suffered from over-collection of its roots for use as a Chinese medicine32. When phenotypic variation was qualified across the popular cultivars34, little information was obtained on the divergence and relationship among cultivars. However, genetic evaluations may help to clarify the genetic diversity and relationship among the populations and provide insights on the domestication history and future breeding.P. ostii has been cultivated as a new crop for edible oil production, which represents an unprecedented opportunity for the peony oil industry; however, the genetic background of this species is poorly understood. Thus, proper initiation of breeding populations by the selection of optimal genotypes from the available germplasm is lacking and a proper resource management strategy across the entire country has not been developed. These questions present the main challenges for the deployment of P. ostii. Hence, comprehensive information on the genetic diversity and population structure of P. ostii is urgently required.In recent years, 36. To date, more than 500 SSR markers have been developed for the Moutan section46. Segregation analyses that incorporate genetic mapping may provide a good strategy for selecting a set of optimal SSR markers. These markers can then be used to determine the allele inheritance and independent assortment of P. ostii47 and for conducting a population genetic study.Population genetics analyses rely primarily on microsatellite markers, which are also powerful tools for marker-assisted selection (MAS) in plant breeding. Co-dominant inheritance, a high level of transferability, close associations with genes of known function and a low cost of development are some of the advantages that led to an increased focus on simple sequence repeat (SSR) markersP. ostii are still unknown, a complex pattern of population divergence is expected because of the long domestication history and outcrossing property of these plants. P. ostii presents an opportunity to investigate the genetic effects of migration, hybridization, adaptive divergence and anthropogenic influence on the genetic diversification of outcrossing woody plants. Clarifying the genetic variation could provide valuable information for the proper management of P. ostii genetic resources as well as for the deployment of these resources and directive breeding. We first screened a set of SSR markers that are suitable for population genetic evaluations based on segregation analyses, amplifications and polymorphic properties. Then, we performed a population genetic evaluation of a large collection of samples collected over a wide range in China. The goal of our research was to understand the genetic diversity among and within the population and the genetic structure among populations, infer the domestication history, and provide information for genetic assessments, regional genetic improvements and management of this emerging oil crop.Although the population structure and genetic diversity of Paeonia suffruticosa ‘LuoYang Hong’)48, which is a related species related to P. ostii, was mined for SSR markers. In total, 2,989 SSR loci were detected, and 788 SSR loci were selected and tested. Of these loci, 373 yielded fragments of expected sizes43. In addition, we developed a F1 mapping population for segregation analyses and genetic map construction. This mapping population included 195 progenies derived from a cross between P. ostii ‘FenDanBai’  and P. × suffruticosa ‘HongQiao’ . We then tested the 373 validated SSR loci in our mapping population and determined that 74 SSRs were polymorphic loci (Table 2) segregation test was performed and the genetic map was constructed using Joinmap 4.149. In total, 68 SSR loci were sorted into 5 segregation types  and successfully mapped to 5 linkage groups. Among these 68 SSR loci, 48 present Mendelian inheritance  of the 40 SSR loci. Furthermore, Ewen-Watterson tests performed using Arlequin v3.5.2.251 targeted 4 loci  that deviated significantly from a neutral equilibrium model. Additionally, an analysis using Genecap v1.452 determined that a pair of samples had an identical genotype; therefore, one member of this pair was removed. Finally, 29 markers (19 in Mendelian inheritance) considered unlinked and neutral with all 901 samples were remained for further analyses.Of the 68 mapped SSR loci, 40 showed polymorphisms among 902 sampled individuals of generally cultivated populations of tii Fig. . Using MN) ranged from 2 to 8, with an average of 4.897 alleles per locus. The mean polymorphism level of the loci (PIC) was 0.318, with a range from 0.108 (at the locus PS183) to 0.613 (PS112) (Table HO) ranged from 0.080 (at the locus PS074) to 0.801 (PS101), with an average of 0.343, and the expected heterozygosity (HE) ranged from 0.081 (at the locus PS183) to 0.625 (PS112), with an average of 0.321 (Table PA) were found at 20 loci distributed in the 14 populations (Table H) and allelic richness (AR) were 0.330 and 2.194, with H ranging from 0.286 for SXSLA to 0.415 for SXFX (two populations from Shaanxi Province) and AR ranging from 1.610 for XXFH (an isolated population in the south) to 2.510 for SXSL (a population from Shaanxi Province). We observed that the genetic diversity and allelic richness were higher in populations from Shaanxi Province (Table FIS) at the locus and population levels were −0.053 and −0.045 (Table F) values indicated an excess of heterozygotes in P. ostii, which is consistent with the self-incompatibility system of this species.A total of 142 alleles were found, and the number of alleles per locus (2) Table . The obs21 Table . At the ns Table . The ave45 Table . The negFST estimated for the 29 loci was 0.106 and ranged from 0.022 at locus PS260 to 0.529 at locus PS187. The pairwise multi-locus FST estimated for these loci ranged from 0.001 to 0.208, which indicated moderate population differentiation among the populations  reached a clear mode at K = 8 before decreasing, and the highest delta K was detected when K = 8 with the second highest values were detected at K = 2, K = 4 and K = 7 . Four major well-separated genetic clusters  were identified at K = 4–8, and one minor well-separated genetic cluster  was observed at K = 6–8. Models assuming different K values helped to detect admixed groups . Group (AHTL) of populations from Tongling in Anhui Province almost entirely made up cluster I; similarly, populations  from Shandong Province were always assigned to cluster II; three populations  from Hunan, Shaanxi and Henan Province were assigned to cluster III; three populations  from Shaanxi and Shandong Province were assigned to cluster IV. These four major and one minor well-separated genetic clusters revealed the five gene pools in the domestication of the species, indicating probably five times of independent domestication events. Two populations  from Bozhou City in Anhui Province showed a closer affiliation with cluster I at any K value, which may be attributed to recent migration from Tongling to Bozhou City in Anhui Province. When K = 4, the group (AHBZ) of populations from Bozhou City in Anhui Province, Linfen City in Shanxi Province  and Liaocheng City in Shandong Province  appeared to be admixed groups of clusters I and III, highlighting the important role of clusters I and III in recent breeding activities through hybridization. At K = 8, populations from Shaanxi Province were assigned to four different ancestral gene pools and populations from Shandong Province were assigned to two ancestral gene pools.STRUCTURE analyses indicated that the ln A principal coordinate analysis (PCA) partitioned 67.40% and 1.80% of the variance in the experimental data to the first two axes Fig. . CollectHNSY, HNLY and SXSN populations shared the same proportion of genetic ancestry in the STRUCTURE analyses; therefore, these samples were pooled into a single group (HN). Similarly, samples from the SDHZB, SDHZC, SDHZD and SXMXA populations were pooled together to form the SD2 group, samples from the SXTG and SXGQ populations were pooled together to form the SHX1 group, and samples from the SXSLA, SXSLB and SDHZA populations were pooled together to form the SHX3 group. Samples collected from Tongling and Bozhou City in Anhui Province were pooled into the AHTL and AHBZ groups, respectively. Similarly, samples from Shandong and Shaanxi Provinces were pooled into the SD1 and SHX2 groups, respectively. Because a significant structure was not observed for certain samples, samples from SXLFA, SXLFB, XXFH, HBBK and BJJF were pooled into a single group (MIX). In total, nine groups were analyzed: AHTL, AHBZ, SD1, SD2, SHX1, SHX2, MIX, HN and SHX3  showed the highest degree of genetic differentiation from the other groups (average FST = 0.130). Groups SHX1 and HN  showed moderate genetic differentiation compared with the other groups (average FST = 0.109–0.105). Group AHTL  and SD2 showed low genetic differentiation compared with group AHBZ, SD1, SHX2 and MIX, and this finding was consistent with the STRUCTURE analyses. The AMOVA analyses of the nine cluster groups revealed that 5.83% of the genetic variation occurred among regions, whereas 2.48% of the genetic variation occurred among populations within regions , clusters were grouped in a pattern similar to that of the STRUCTURE analyses and pairwise FST analyses. Four admixed groups  that grouped close together displayed a high degree of gene flow, and two genetically pure groups (AHTL and SD2) exhibited a relatively high gene flow with these four admixed groups. Three genetically distinct groups  appeared relatively isolated from the other groups.The directional relative migration networks as estimated by divMigrateups Fig. . For allP. ostii, and we selected unlinked and neutral markers and performed range-wide genotyping and genetic evaluation for this commercially important woody plant with a long history of cultivation.To establish breeding and management strategies for emerging crops, a thorough understanding of the population genetics of the species of interest has become essential for producing long-term sustainable goals. This work represents the first systematic population genetic study of P. ostii presents a wide distribution range, reproduces via insect pollination and outcrossing and has a self-incompatible mating system, and these characteristics allow this species to develop a higher genetic diversity. Consistent with other cross-pollinating plants55, most of the total genetic variation (>90%) in P. ostii occurs among individuals within a population, whereas a small proportion of the genetic variation occurs between populations. Compared with previous studies of wild peony species in the Moutan section, the genetic diversity indices obtained in the present study are lower than those in Paeonia jishanensis56  and Paeonia rockii57 . This could be attributed to long-term domestication of P. ostii involving selecting and modifying its wild progenitor to meet human needs58. The elite P. ostii cultivars that growers use today are the result of intensive selection applied to the founding stock and its descendants, which is presumed to have led to additional losses in genetic diversity. The reduction in genetic diversity does not bode well for future genetic gains in productivity and could result in broad susceptibility to newly emerging diseases or insect pests, thereby threatening long-term food and feed security60. Future germplasm collections for the creation of new cultivars should account for the vast reservoir of genetic diversity to maintain the genetic base of P. ostii.P. ostii samples. Interestingly, we found that samples in Shaanxi Province were assigned to four ancestral gene pools. In addition, the genetic diversity analyses demonstrated that both the diversity and allelic richness are generally higher in populations from Shaanxi Province. Based on these results, we proposed that Shaanxi Province is the main genetic diversity center of the cultivated materials of the species. The other important group of populations from Tongling (AHTL) in Anhui Province contained a well-separated gene pool and displayed extensive genetic exchanges with admixed groups as revealed by the divMigrate analyses. Therefore, the resources from these two provinces could represent priorities for future germplasm conservation and exploration.STRUCTURE analyses identified five main ancestral gene pools across the wide range of P. ostii for medicinal use originally occurred in Shaanxi, Henan, Shandong and Anhui (Tongling) provinces61. The hypothesis that the domestication origin of P. ostii occurred in Shaanxi Province has generally been accepted, although other hypotheses of its domestic origin are still popular61. However, the domestic origin hypotheses have not been tested using genetic data. The present study presented for the first time a genetic test for P. ostii based on an analysis of multi-locus data. Five genetically well-separated gene pools were identified in geographically isolated regions of Shaanxi Province, Tongling City in Anhui Province, and in Shandong, Henan and Hunan provinces. The allopatric distribution of distinct gene pools of cultivated P. ostii provided support for the hypothesis of independent domestication origins of this species. Consistent with the cultivated ornamental tree peony62, our study provided another case of multiple independent domestication for the cultivated tree peonies of the Moutan section.Cultivation of P. ostii. Interestingly, four distinct gene pools were found in Shaanxi Province, with two of these pools also found in Shandong. The richest diversity of both the wild populations and species of tree peony was observed in Shaanxi26, and this province also presents the longest recorded history of cultivation of tree peonies65. Reports have suggested that the tree peony was introduced from Shaanxi to Shandong Province several different times from ancient to modern times65. Given these facts, it is easy to predict that at least four times domestication origins of cultivated P. ostii ever happened in Shaanxi Province. The reconstruction of four well-separated gene pools identified by the STRUCTURE analyses clearly suggested that protection and utilization should be prioritized for the cultivars of different domestication origins that are isolated from each other in areas such as SXSN , SXMXA , SXSLA , SXSLB , SXGQ , and SXTG . Because most of these areas are located in the vicinity of the Qinling mountains, which presents the largest specific diversity of the Moutan section, we suspect that the Qinling Mountains in Shaanxi represents the likely origin of domesticated P. ostii. As the wild plants of P. ostii have been completely eradicated33 and all populations sampled from Shaanxi were cultivated, further analysis of P. ostii, including models of the domestication history and phylogenetic reconstructions based on the low-copy nuclear genes relative to that of the chloroplast and rDNA sequences, will be needed to validate this hypothesis.In the present study, the STRUCTURE results of five well-separated gene pools indicated that five independent domestication origins may have occurred over the long-term cultivation of P. ostii resources that are extensively used as rootstock for ornamental cultivated tree peony grafting. Our present study suggested that at least two rounds of independent long-distance P. ostii introductions likely occurred from Shaanxi to Shandong. We observed that one well-separated gene pool was identified in three distinct populations of SXSN , HNLY  and HNSY , reflected probably unique domestication in Shaanxi Province and following long-distance introduction to Henan and Hunan provinces. It is not difficult to see the importance of Shaanxi Province in domestication and deployment of P. ostii.As the world’s largest producer of ornamental tree peony, Heze City in Shandong Province cultivates P. ostii is Tongling City in Anhui Province, where the long-term cultivation of this species has been recorded27. We found a group (AHTL) of populations from Tongling that almost solely consisted of a unique well-separated gene pool (cluster I). This finding indicates at least one independent domestication origin in Tongling City, which is distinct from the origins in Shaanxi Province. It is interesting that all cultivated samples from Tongling City were assigned to one unique gene pool without introduction were found from Shaanxi Province.Another important region in the domestication of P. ostii. Bozhou City, which is located close to Tongling City, has earned a reputation as the “Capital of Traditional Herbs” and has become the central region for the cultivation of P. ostii66. The production of P. ostii in Bozhou accounts for more than 70% of the annual total production66. In our present study, both the STRUCTURE and divMigrate analyses revealed that cultivars in Bozhou City and admixed cultivars popular in other regions are similar in genetic makeup and present the closest relationships with cultivars from Tongling City in Anhui Province, Shaanxi and Shandong provinces. This finding demonstrates the important role of domestication in Tongling City in Anhui Province.Our study suggested that Tongling City has likely played an important role in the domestication and breeding of modern cultivars of Alongside with those from well-separated gene pools, admixed individuals were identified extensively across most of the sampled regions . We hypothesized that these individuals were likely derived from recent hybridization breeding processes, but we failed to find records that corroborated this hypothesis. Genomic data may help elucidate the origin of the admixed populations.P. ostii; (2) Shaanxi Province has the largest genetic diversity; (3) multiple independent domestication origins occurred in Shaanxi Province and Tongling City in Anhui Province, and this species was introduced to Shandong, Henan and Hunan provinces. Our results provide comprehensive information that can be used in genetic assessments, regional genetic improvements and management of P. ostii and insights for the development of breeding strategies for this emerging oil crop.This study was the first comprehensive work to select well-characterized SSR markers for population genetics studies on peony species. Our study settled long-running debates by confirming that (1) moderate genetic diversity and differentiation in 67, and maintained at room temperature for DNA extraction.A total of 902 individuals from 36 populations that cover the vast majority of the distribution area in China were collected Fig. . The geoP. suffruticosa ‘Luo Yang Hong’48, and 4,373 potential SSRs were identified from 3,787 unigene sequences. In total, 2,989 SSR-containing sequences were appropriate for primer design, and 788 primer pairs were further selected for the primer synthesis. Of these, 373 primer pairs generated PCR amplification products of the expected sizes43. Those 373 SSR loci were then screened for polymorphism in a genetic mapping population. The mapping population was composed of 197 individuals, including the two parents and 195 F1 progeny. The parents were P. ostii ‘FenDanBai’  and P. × suffruticosa ‘HongQiao’ . The F1 mapping population plants were grown in the Beijing Guose Peony Garden, Beijing, China. The validity and polymorphism of 373 SSR loci were analyzed by using 6 progeny individuals randomly selected from F1 progeny, and 74 SSR loci were found to be polymorphic. We kept these 74 SSR loci for genotyping of 195 mapping individuals. All SSR genotypes we generated were combined with SNP genotypes68 from the same mapping population, and then used for chisquare test (χ2) of segregation and genetic map construction with Joinmap 4.1. Fifty-three SSR loci showed no deviation from Mendelian inheritance ratios (P < 0.05). Then, the optimal arrangement order was determined via regression analyses with the single-linkage clustering algorithm at logarithm of odds (LOD) ≥ 4.0 and recombination rates (r) ≤ 0.3. Based on the above results, the LOD threshold was relaxed to 3.0 and markers that showed segregation distortion with higher LOD values in the linkage analyses were also mapped to the linkage group without changing the order of marker linearity. The genetic distance between adjacent markers was then estimated. The graph of the genetic map with 4 SNP markers  and 68 SSR markers was visualized using MapChart v2.369.A total of 59,275 unigenes were obtained from the transcriptome assembly of  P. ostii individuals, 68 SSR loci on the genetic linkage map  were selected.To further examine the genetic diversity and structural analyses of the 902map Fig.  were use2O, 5 μL 1 × Taq PCR Master Mix , and 0.5 μL (10 pmol) each reverse and forward primer with fluorescent labeled 6-FAM, HEX, TAMRA or ROX . PCR amplification was performed with the following amplification protocol: pre-denaturation at 95 °C for 4 min, followed by 35 cycles of denaturation at 94 °C for 35 s, annealing at 47–60 °C for 35 s . The purity and concentration of the extracted genomic DNA was measured using a NanoDrop 2000 spectrophotometer at 260 nm  and adjusted to 50 ng/μL. PCR amplification was performed in a total volume of 10 μL containing 1 μL (50 ng) genomic DNA, 3 μL ddHWe further checked the suitability of each marker for the population genetic analyses. Micro-Checker was used to identify possible genotyping errors, including stuttering, large allele drop-outs and null alleles. We determined whether selected polymorphic microsatellite markers deviated significantly from a neutral equilibrium model using Ewen-Watterson tests in Arlequin.N), number of different alleles (Na), number of effective alleles (Ne), Shannon’s information index (I), observed heterozygosity (HO), expected heterozygosity (HE), percentage of polymorphic loci (PPL), and F-statistics  were calculated using GeneAlEx v6.50171. The polymorphism information content (PIC) was calculated with PowerMarker v3.2572. Hardy–Weinberg equilibrium (HWE) was calculated with the assistance of Genepop (http://genepop.curtin.edu.au/). Samples with identical genotypes were detected by GeneCap.The number of alleles per locus , ten independent runs were performed with a burn-in period of 200,000 followed by 200,000 Markov Chain Monte Carlo (MCMC) replications. The most likely K value was determined using Structure Harvester74 based on both the log likelihood and the maximum ΔK. Because clustering algorithms may incorporate stochastic simulations as part of the inference, independent analyses of the same data may result in several distinct outcomes; therefore, we used the computer program CLUMPP v1.1.275 to analyze the results from replicate analyses for optimal alignments of replicate clusters. The output from CLUMPP was graphically displayed by the cluster visualization program DISTRUCT76.The model-based (Bayesian) cluster software STRUCTURE v2.3.4FST distance matrix was performed using the “adegenet” package77 in R78.A PCA based on the pairwise P. ostii samples, sampled populations were pooled according to their geographic origin and genetic composition, and the global multi-locus FST for the 29 loci was calculated with GenAlEx. The pairwise multi-locus FST and significance of differences between FST values was assessed in exact tests conducted with Arlequin, and the gene flow (Nm) based on the FST values was calculated as Nm = 0.25 (1 − FST)/FST. A genetic distance matrix of pairwise FST values was also used for AMOVAs79 in Arlequin. Significance levels were determined using 1000 permutations.To identify the genetic differences in the 901 Supplement Table


Disorders of sex development (DSD) are congenital conditions in which chromosomal, gonadal, or phenotypic sex is atypical. Clinical management of DSD is often difficult and currently only 13% of patients receive an accurate clinical genetic diagnosis. To address this we have developed a massively parallel sequencing targeted DSD gene panel which allows us to sequence all 64 known diagnostic DSD genes and candidate genes simultaneously.We analyzed DNA from the largest reported international cohort of patients with DSD . Our targeted gene panel compares favorably with other sequencing platforms. We found a total of 28 diagnostic genes that are implicated in DSD, highlighting the genetic spectrum of this disorder. Sequencing revealed 93 previously unreported DSD gene variants. Overall, we identified a likely genetic diagnosis in 43% of patients with 46,XY DSD. In patients with 46,XY disorders of androgen synthesis and action the genetic diagnosis rate reached 60%. Surprisingly, little difference in diagnostic rate was observed between singletons and trios. In many cases our findings are informative as to the likely cause of the DSD, which will facilitate clinical management.Our massively parallel sequencing targeted DSD gene panel represents an economical means of improving the genetic diagnostic capability for patients affected by DSD. Implementation of this panel in a large cohort of patients has expanded our understanding of the underlying genetic etiology of DSD. The inclusion of research candidate genes also provides an invaluable resource for future identification of novel genes.The online version of this article (doi:10.1186/s13059-016-1105-y) contains supplementary material, which is available to authorized users. Disorders of sex development (DSD) are defined as congenital conditions in which the chromosomal, gonadal, or phenotypic sex is atypical . This grProviding a molecular diagnosis for patients with a DSD and families can serve multiple purposes: naming the underlying cause contributes to acceptance, reduces stigma or blame, and provides crucial clues and guidance for clinical management, including information on the malignancy risks associated with some types of DSD . A diagnMassively parallel sequencing (MPS) has been widely adopted for the diagnosis of genetic diseases, especially for monogenic congenital disorders, as it promises to improve diagnosis and alter patient management through rapid sequencing of many genes simultaneously at a lower cost compared with sequential testing of multiple genes. The process of deploying these genomic assays involves extensive evaluation of technology, bioinformatics, and clinical concerns to choose the right configuration for a given setting. As technology advances and whole genome sequencing (WGS) or whole exome sequencing (WES) becomes more accessible, the choice of platform must take both performance and cost into consideration. In some countries either government or private health insurance funding covers or contributes to the cost of WES to diagnose DSD patients, and this has been reported for a number of individuals affected by 46,XY DSD . In AustHere, we report the application of an MPS targeted gene panel to a cohort of patients affected by DSD . This panel contains genes of both clinical and research relevance that are associated with gonadal or genital development as well as steroidogenic pathways. It includes the majority of known diagnostic genes for DSD, allowing us to perform the same diagnostic test on all DSD patients and their participating family members irrespective of their DSD phenotype. Performance evaluation of our MPS targeted DSD gene panel in comparison to both WGS and well-characterized reference samples shows that it offers high sensitivity and specificity. The results from targeted genetic testing of 326 patients with DSD (and 129 of their family members) from a wide spectrum of clinical presentations (the largest known such cohort) are presented.We designed a targeted gene panel for DSD using HaloPlex (Agilent) technology. This system allowed us to simultaneously sequence 64 known diagnostic genes for DSD and an additional 967 candidate genes. HaloPlex technology uses custom molecular inversion probes  that are then used for selective circularization-based target enrichment. The known diagnostic genes have been compiled from current knowledge of DSD sourced from PubMed and clinical variant databases (such as HGMD and ClinVar) Table . The canTo provide a benchmark of assay quality, we created an evaluation data set that included 16 samples, three of which had been previously sequenced using WGS. These 16 samples were sequenced using our targeted gene panel on a single run using an Illumina MiSeq instrument, configured to produce 2 × 150-bp paired-end reads.This dataset was evaluated to ascertain performance of the panel with respect to several standard benchmarks for MPS assays, including coverage, targeting efficiency, and variant calling accuracy.A commonly accepted threshold for variant calling is approximately 30× in research settings, while higher thresholds are frequently sought for diagnostic use. In aggregate, the targeted gene sequencing of our evaluation data set yielded mean (median) coverage depths well in excess of these thresholds, varying between 135× 115×) and 190× (161×). However, the coverage depth was highly variable across different genomic regions. Approximately 10% of bases were covered at lower than 30× and the upper 10% of bases were covered at greater than 280× Fig. . WGS sho5× and 19Averaged across the evaluation samples, we observed that 92% of sequenced fragments overlapped the target region by at least 1 bp. This percentage compares favorably with commonly cited targeting accuracy for competing platforms such as Agilent SureSelect and Nimblegen . Howeverhttps://github.com/jstjohn/SeqPrep) and Agilent’s MPS ReadTrimmer (http://download.chem.agilent.com/software/ngs_readtrimmer). Thus, a custom trimming program was designed, resulting in nearly 100% of reads being correctly trimmed of adapter sequences  and under-trimming  was not achieved using a number of tools, including Trimmomatic , SeqPrepces see “”.Performance of our targeted gene panel at any given genomic locus is critically dependent on the performance of the handful of amplicons that span the locus. The 29,928 amplicons in our evaluation design showed highly variable performance, including a substantial number of amplicons (8% on average) to which no reads are mapped. Some of these “failures” occurred consistently between samples: 38% of amplicons that failed did so in all of our evaluation samples. However, we also observed that 13% of failures occurred sporadically, in only a single sample.http://www.illumina.com/platinumgenomes/). In comparison to the gold standard reference call set, we observed high sensitivity and specificity of our targeted gene panel. At a false positive rate of 2%, variant calls for NA12878 and NA12877 achieved an overall sensitivity of 97%  and 95% , respectively. Variant calls were compared using the RTG vcfeval  utility for single nucleotide changes and INDELs smaller than 10 bp  of samples from the 1000 Genomes CEPH pedigree. These samples have been intensively studied and sets of gold standard variant calls are available for comparison from the Illumina Platinum Genomes Project .We also have DNA samples from 48 patients with 46,XX DSD (including 12 with family members). This cohort includes 26 patients with a disorder of gonadal (ovarian) development, including seven with 46,XX OT DSD, 16 with testicular (T) DSD, and three with gonadal dysgenesis. Nine individuals with 46,XX Mayer-Rokitansky-Küster-Hauser syndrome (MRKH) and one with dysplastic ovaries were also included. In addition, we have DNA from ten patients with 46,XX virilization of unknown origin , New Zealand (7), Indonesia (97), the Netherlands (38), Pakistan (25), Vietnam (35), Cambodia (16), Austria (15), Belgium (6), Canada (2), India (1), and Italy (2).Prior to filtering, 1,097,162 variants were observed in the entire cohort of patient samples in diagnostic genes and research candidates. Of these variants, 48% were observed recurrently in the cohort, with the total set comprising only 57,320 unique variants; 12,257 variants were novel  and 23% of novel variants were observed recurrently in our samples and were interpreted as either sequencing artifacts or common population variants that are endemic to specific ethnicities in our cohort. These are largely removed by our variant filtering process see “”. The maSRY , AKR1C2, CDKN1C, CYP11B1, FGF8, LHX3, and CYP21A2  . All genes except six had at least 90% coverage at 20× or greater . In total we found 28,785 observations in diagnostic genes including recurrent variants; 2016 of these were protein changing and rare , meaning that, on average, each patient had around four diagnostic gene variants. These were further filtered for frequency in our database, inheritance, and quality/depth see “”. RemainSRY material, suggesting a Y-translocation had occurred, which explained the patient’s phenotype. One patient carried a VUS. Our screen provided little insight into the basis of the DSD in the 46,XX patients, who were confirmed SRY-negative; they were thus excluded from the rest of the analyses. All curated variants are presented for each patient in Additional file In the 46,XY DSD cohort (278 patients) we found a total of 159 individuals (57%) had a variant in a clinically relevant DSD gene Fig. , Table 2A large and diverse DSD cohort enabled us to determine the proportion of genetic diagnoses across the various subgroups of 46,XY DSD patients Table . Of the Patients in our cohort have been recruited from 12 countries. To investigate whether our panel is informative for different global regions, we grouped patients into Asia, Australia/NZ, or Europe. Each region showed a similar proportion of patients with a DSD gene variant; however, the diagnostic rate varied between regions from 33% (58 of 174 patients from Asia) to 45% for Australia/NZ (41 of 90 patients) . Reductions or expansions of these tracts have been suggested to contribute to numerous conditions, including hypospadias [Variants in the mon Fig.  with 26 ospadias –19 and uospadias . We ofteNR5A1 and SRD5A2 had the second and third highest number of variants called . Despite the preponderance of NR5A1 publications associated with DSD, the majority of the variants we found in NR5A1 had not been previously described (81%), including seven null and six missense variants  were previously reported and a large proportion of them occurred recurrently within our cohort  and MAP3K1 (six variants in 11 patients). Both of these genes have only been described in a limited number of DSD cases [ZFPM2 variants found in our study had been previously reported as pathogenic variants in congenital heart disease [MAP3K1, the majority of variants were unreported; however, three of these variants were observed in more than one patient with 46,XY DSD  and in the other three it was a variant in a testis development gene. Three individuals had a pathogenic variant in a testis development gene  in combination with a less damaging DSD gene variant  in combination with a VUS in an additional DSD gene, while one patient had two pathogenic variants, one in a DASA gene (HSD3B2) and the other in a congenital hypogonadotropic hypogonadism (CHH) gene (GNRHR). In most cases with oligogenic inheritance, at least two of the genes were predicted to be pathogenic and/or contribute to the phenotype.Of the five patients with hypospadias, three were found to have a likely pathogenic variant in a testis development gene  had a variant in a diagnostic DSD gene, and for trios 31 out of 63 (43%) had a DSD variant Fig. , b. HoweWDR11 VUS; patients 112 and 223  had a novel NR5A1 frameshift mutation; patients 33 and 34 were 46,XY DSD patients with a reported pathogenic variant in SRD5A2  and those with “isolated hypospadias”. The first group (133 patients) consisted of the following: limited clinical information, noted to have ambiguous genitalia, undervirilization phenotypes including hypospadias, bifid scrotum, micropenis, cryptorchidism, often with no further description of either internal structures or hormonal levels. When we reassessed this group by the type of DSD variant identified, a significant proportion had variants in genes known to cause disorders of androgen synthesis and action  or disorders of gonadal (testis) development  Fig. , highligOur cohort also included 46 patients with 46,XY DSD who were defined as having isolated hypospadias. Again, this group of individuals were often referred with limited clinical information. While ten of these patients (22%) did have a variant in a gene known to cause isolated hypospadias, six of the 46 patients (13%) had a variant in an androgen synthesis or action gene and seven (15%) had a variant in a gonadal (testis) development gene Fig. .CHD7, KAL1, WDR11, PROK2, PROKR2, FGF8, and FGFR1; Additional file One interesting observation limited to both 46,XY origin unknown and isolated hypospadias groups was that 9% of patients carried a variant in a known CHH/Kallmann syndrome gene  of the patients had undergone genetic pre-screening (such as single-gene Sanger sequencing or microarrays), which ultimately affects our overall diagnostic rate. This suggests that if applied as a first-tier diagnostic test, we could expect our panel to provide an even greater diagnostic outcome. Our results support previous conclusions reached by others , 8, 10 iAR and SRD5A2. The publically available AR database has a total of 546 unique entries (this includes recurrent variants associated with different phenotypes), with 339 of them associated with DSD [AR variants found in our 46,XY DSD cohort, only six were previously unreported (four null mutations and two missense), suggesting the vast majority of DSD-causing AR variants have been defined.Our highest diagnostic rate of 60%  is for patients who have disorders of androgen synthesis and action. A large proportion of these patients had variants previously described in DSD , primarily variants in with DSD . Of the SRY. We independently identified eight patients carrying SRY (indicative of translocation) from our 46,XX DSD cohort. Translocation of SRY accounts for approximately 80% of individuals with 46,XX testicular DSD [SOX9 [FGF9 [Large-scale MPS sequencing has not been previously reported for 46,XX DSD; therefore, we analyzed 48 patients with various forms of 46,XX DSD to determine how a targeted gene panel would perform. We found that gene panel testing is not informative for 46,XX DSD in its current format. The majority of 46,XX DSD patients included in our study were reported to have had a prior test to examine gain of ular DSD . The majSD [SOX9 –33, FGF9X9 [FGF9 , RSPO1 [X9 [FGF9 , 36, WNTX9 [FGF9 , 38; revX9 [FGF9 ). A receX9 [FGF9 , highligMAP3K1, a gene previously associated with 46,XY CGD [MAP3K1 representing six separate variants. Interestingly, a variant we detected in two patients with 46,XY CGD (p.L189R) had been previously reported in individuals with a similar phenotype [This study has allowed us to identify a total of 76 pathogenic, 42 likely pathogenic variants, and 41 VUS in known DSD genes, more than half of which were previously unreported. This substantially expands our current knowledge of diagnostic DSD variants. In a study on DSD patients using WES, Baxter et al.  identifi6,XY CGD . Similarhenotype .MAP3K1 variants (p.M312L and p.A1443V) that recurred in multiple patients who presented with a diverse range of phenotypes . This suggests variants in this gene may be associated with a greater phenotypic variability than had been previously thought, although population specific polymorphisms may be involved with the less severe phenotypes. While a high level of variability between the number of variants in each diagnostic gene was observed, MAP3K1 showed intolerance to protein changing variation compared to other genes, both in our data and also on ExAC (with a missense Z-score of 1.53 and a probability of LOF intolerance of 1). Given this, and previous reports using exome sequencing in a smaller cohort [MAP3K1 variants amongst 46,XY disorders of gonadal (testicular) development classification (5 of 52 patients); however, this could be up to 18% if the MAP3K1 phenotypic spectrum is expanded. Further functional analysis will be required to fully test these previously unreported variants.We also observed two r cohort , we can DHH variants in individuals presenting with a range of gonadal dysgenesis , with or without polyneuropathy [DHH missense variants (none were reported to have polyneuropathy). Homozygous or potentially compound heterozygous DHH variants were identified in four patients presenting with 46,XY DSD female phenotype, while the three individuals with heterozygous DHH variants had diverse phenotypes, including DASA, DSD origin unknown, and hypospadias. The clinical significance of heterozygous DHH variants is still unclear; however, variants in this gene can present as apparent DASA due to an impairment of Sertoli cell–Leydig cell interaction during gonad development [DHH can impact clinical management due to the increased risk of gonadal malignancy in such patients [A number of studies have identified uropathy –42. The uropathy . We idenelopment . Identifpatients , 40.ZFPM2 have commonly been shown to be associated with congenital heart disease [ZFPM2 missense and one frameshift mutation in six patients with 46,XY disorders of testicular development (52 patients), providing a genetic outcome for 12% of these patients.In humans, mutations in  disease  but only disease . We idenZFPM2 variants in three individuals with hypospadias and in some instances this was in conjunction with another DSD gene variant that had not previously been reported. In the case of MAP3K1, DHH, and ZFPM2 it is difficult to distinguish whether variants identified in patients categorized as isolated hypospadias expand the known mutation spectrum of these genes or whether these patients have underlying gonadal dysgenesis.We also observed NR5A1 mutations was attributed to oligogenic inheritance in other testis development genes such as MAP3K1 [MAP3K1 and ZFPM2) in combination with a VUS (often in a CHH gene). Another patient (251*), also with severe hypospadias, was found to have two pathogenic variants, one in HSD3B2  [GNRHR. Finally, in patients with 46,XY DSD of unknown origin we found five with an AR mutation in combination with an additional variant in either androgen action or gonadal development. This suggests that like NR5A1, AR may show oligogenic involvement in DSD.A recent report suggested that the expanded DSD phenotypic spectrum associated with s MAP3K1 . Similarspadias)  and the CHH leads to a reduction in gonadotrophin release from the pituitary and can present as an inability to enter puberty or even as mild undervirilization at birth in 46,XY males . This haWhere MPS is concerned, trios are often encouraged as the gold standard, to allow better variant filtering and curation. Although the total number of individuals sequenced in our study as singletons versus trios/duos was substantially different (215 versus 63), we found that the proportion of patients with a probable genetic diagnosis was similar between these groups. We observed a higher number of variants curated and deemed VUS in the singletons, variants which may not have stood up to scrutiny if the mode of inheritance was known . Screening patients with DSD as singletons provides a cost-effective clinical genetic diagnosis that is comparable to trio analyses, although trio analysis can reduce overall curation time. Nevertheless, in a gene discovery setting, trio analysis will still be highly valuable as it eliminates rare familial variants, confirms modes of inheritance, and detects de novo events.NR5A1 mutations presented in our 46,XY cohort as CGD (two patients), PGD (four patients), hypospadias (one patient) and DASA (one patient); additionally, it has also been shown to include spermatogenic failure [NR5A1 cannot be strictly classified as having a disorder of testis development. However, genetic etiology is crucial for informing clinical management and provides insights into the diverse heterogeneous nature of DSD.We found our panel to be highly informative for patients affected by DSD with an unknown biological basis. Given that this kind of sequencing is relatively inexpensive and quick and has a high genetic diagnostic rate, it has potential as a first-tier clinical test to help inform clinical management. A molecular diagnosis can provide clues as to the biological basis of DSD and may direct clinicians towards a specific clinical test. This could be particularly useful in situations or countries in which clinical tests such as histopathological examination, hormonal profiling, and advanced imaging are costly or not routinely performed. We have shown that our gene panel will assist with DSD classification in a situation where an in-depth clinical presentation is not available. The caveat to this is that the mutation spectrum of a number of genes encompasses multiple clinical presentations. For instance, the spectrum of In clinical genomics, systematic classification guidelines are constantly evolving as evidence-based tools, resources, and databases become available. We followed the same process employed by previous genomic studies of DSD patients , 10, basAlthough a success on many levels, our genetic panel did not provide answers for 39 patients with 46,XX DSD and 52 patients with 46,XY DSD where no diagnostic variant was detected. Like many sequencing technologies, there are regions in our panel that have low coverage. As we do not use alternative methods to fill these gaps, it is possible that we might miss diagnostic variants that fall within these regions. One limitation of targeted gene sequencing is that detection of CNVs is significantly more challenging than single nucleotide variants or INDELs. While a range of CNV detection methods have been developed to work with targeted sequencing data, specialized bioinformatic expertise is required to obtain accurate results. Furthermore, standard methods are generally not optimized well for use with the HaloPlex technology. CNVs are known to contribute to DSD, and our current inability to detect these in our targeted gene panel means that we may be missing diagnostic changes in DSD patients. We are currently working to create a bioinformatic pipeline designed to use these data to assay for CNVs, which will be a useful additional tool in the future.While this study has focused on diagnostic DSD genes, our targeted panel also includes 967 candidate genes identified from animal model studies, implicated genetic pathways, and gonad RNA-seq experiments. Currently, our research group is pursuing several novel candidate genes identified from these data, although these studies are ongoing and beyond the scope of this article. Further analysis of these genes (as well as WES or WGS sequencing and microarrays on select patients) promises to reveal novel candidate genes that may contribute to the development (and disease) of the reproductive system in humans. Future detailed analysis of these genes and their function will further improve genetic diagnosis and clinical management of DSD.Our targeted DSD gene panel is an effective means of providing a genetic diagnosis for patients with 46,XY DSD (43% of cases). Employing this in a large, diverse cohort of patients with DSD has provided us with a better understanding of the underlying genetic etiology of this condition. In particular, we have expanded the range of phenotypes associated with several DSD genes. Given the rapid turn-around time and reduced cost compared to WES or WGS, we believe that this targeted gene panel could be used as a first tier clinical diagnostic test for 46,XY DSD to assist in optimizing clinical management for these patients.This project  has been approved by the Royal Children’s Hospital  ethics committee. Patients and family members were enrolled after signing informed consent . For patients recruited in countries other than Australia, consent was also obtained using local ethics and consent and DNA transferred through a memorandum of understanding between the Murdoch Childrens Research Institute (MCRI) and the corresponding institute/hospital. This study was conducted in compliance with the Helsinki Declaration.SRY screening. Some had had single-gene sequencing . Only patients that were negative for these tests were included in the cohort discussed here. De-identified DNA from each patient was stored in a secure DNA storage facility.Clinical notes were collected for each patient during their standard clinical care by trained clinicians, and these data were transferred to us under the informed consent (HREC 22073). This often included a description of their external genitalia, internal reproductive organs, hormonal profile, and additional notes of interest . All of the patients had undergone karyotyping. Many patients had previous clinical microarrays and or Genomic DNA extraction from EDTA-blood samples was performed in an independent laboratory such as Victorian Clinical Genetics Service (VCGS), or at local hospitals. DNA quality was assessed using an Agilent gDNA ScreenTape run on 2200 TapeStation (Agilent Technologies Inc.) and concentration was measured in our laboratory on a Qubit 3.0 Flurometer using the broad range DNA quantification kit (ThermoFisher scientific).https://earray.chem.agilent.com/suredesign/). The gene panel currently includes a total of 1031 genes, microRNAs, and potential regulatory regions. These targeted regions comprise 64 known diagnostic genes for DSD  was digested with 16 different restriction enzymes at 37 °C for 30 min to create a library of gDNA restriction fragments. Both ends of the targeted fragments were then selectively hybridized to biotinylated probes from the HaloPlex DSD panel (Agilent Technologies Inc.), which resulted in direct fragment circularization. During the 16-h hybridization process, HaloPlex Illumina Barcodes were incorporated into the targeted fragments. Circularized target DNA–HaloPlex probe hybrids containing biotin were subsequently captured by HaloPlex magnetic beads on the Agencourt SPRIPlate Super magnet magnetic plate. DNA ligase was added to close the nicks in the hybrids and freshly prepared NaOH was used to elute the captured target libraries.The target libraries were then amplified and purified using AMPure XP beads. Amplicons ranging from 150 to 550 bp were finally quantified using an Agilent D1000 DNA ScreenTape on the 2200 TapeStation to validate the enrichment of the libraries.MPS was carried out according to the manufacturer’s instructions at the Translational Genomics Unit at the Murdoch Childrens Research Institute /VCGS, using either the Illumina MiSeq, NextSeq500, or HiSeq4000 or at the Centre for Translational Pathology, The University of Melbourne using an Illumina HiSeq2500. For the case of MiSeq samples, paired-end 2 × 150-bp reads were used, while the HiSeq 2500 produced 2 × 150-bp reads.https://github.com/ssadedin/halo_dsd and in Zenodo .The sequencing data were analyzed using Cpipe, an exome analysis pipeline designed at MCRI . Cpipe wSRD5A2 as the transcript was retired (transcript reference NM_000348.3).Four in silico models, SIFT , PolypheVariant files were filtered to include only rare , functional variants . As we did not run control samples from each ethnic subgroup, the allele frequency of population subgroups most reflecting the ethnic background were checked on ExAC and variants discounted if they were common (>0.01). In addition to public databases, the variant frequency within our cohort  was also tracked. This allows us to identify variants that may be the result of amplification or sequencing error (which are common in one sequencing run), or that may be common in a subpopulation but not well represented on publically available databases . Thus, we also discounted variants found in greater than 15 samples in the same run or in the total database. Following this, only variants in known diagnostic DSD genes (64) were considered.http://www.broadinstitute.org/igv/). In some cases of low coverage or depth, validation by Sanger sequencing using the standard protocol for BigDye® Terminator v3.1 Cycle Sequencing Kit  was carried out at the Centre for Translational Pathology, The University of Melbourne.Variants in known DSD genes were evaluated for coverage depth and read quality and also visually inspected using the Integrative Genomics Viewer . These were further separated into VUS-1 , VUS-2 (predicted deleterious (in at least three of four in silico predictors) yet not within the known spectrum of phenotypes), or VUS-3 .


Archaea, Bacteria and Fungi community structure, and their relationship with the operational conditions of the bioreactors by the means of quantitative PCR, massive parallel sequencing and multivariate redundancy analysis. The results showed dominance of Archaea and Bacteria members in the bioreactors. The activated sludge systems showed strong selection of Bacteria but not for Archaea and Fungi, as suggested by diversity analyses. Core OTUs in influent and bioreactors were classified as Methanobrevibacter, Methanosarcina, Terrestrial Group Thaumarchaeota and unclassified Euryarchaeota member for Archaea; Trichococcus, Leptotrichiaceae and Comamonadaceae family, and Methylorosula for Bacteria and Trichosporonaceae family for Fungi. All influents shared core OTUs in all domains, but in bioreactors this did not occur for Bacteria. Oligotype structure of core OTUs showed several ubiquitous Fungi oligotypes as dominant in sewage and bioreactors. Multivariate redundancy analyses showed that the majority of core OTUs were related to organic matter and nutrients removal. Also, there was evidence of competition among Archaea and Fungi core OTUs, while all Bacteria OTUs were positively correlated among them. The results obtained highlighted interesting features of extremely cold temperature bioreactors.Seven full-scale biological wastewater treatment systems located in the Polar Arctic Circle region in Finland were investigated to determine their  Despite this, in practice, bioprocesses for WWT have been designed from an engineering point of view, but the inherent microbiological aspects of these systems need to be also considered2. Prospects in WWT systems design and operation should consider their natural, intrinsic microbiological aspects3. Moreover, it has been defended that the understanding of the microbial communities and their activities are essential for the successful exploitation of biological WWT facilities5.Undoubtedly, microbial ecology of bioprocesses is a factor of major importance for the functioning of bioprocesses, especially wastewater treatment (WWT) systems7. The AS has been extensively studied, and established and recent results have shown that full-scale AS systems present a core microbiome which activity is responsible for decontamination of wastewater9. Nevertheless, the knowledge of microbiome in AS systems is not enough for systems with extreme operational conditions, such as Arctic temperatures10.Activated sludge (AS) systems have been widely used for urban WWT during the last century11. Cold temperature has been reported to affect microbial growth due to decrease in water availability, molecular motion and energetics, and increase in solute concentration due to decrease in water availability13. However, the adaptation of microbial communities to low temperature has been found to be problematic for WWT systems. Specifically, biological nitrogen removal processes have a strong negative correlation with temperature, with complete inhibition of mild temperature (around 20 °C) adapted AS when subjected to temperatures of 10 °C or lower14. Nevertheless, WWT systems in cold regions obtain high performances in terms of organic matter and nutrients removal. Thus, the microbial communities growing at extremely low temperature in AS systems have adapted to these conditions and an insight of these OTUs would be useful for the exploration of life thriving under cold temperatures and for the design, operation and control of WWT bioprocesses.It has been found that temperature is a parameter that greatly affects microbial community diversity and structure in WWT plantsArchaea, Fungi and Bacteria communities. Quantitative PCR and massive parallel sequencing (MPS) were used for the quantification of microbial OTUs and for the determination of their diversity and relative abundance. Core Archaea, Bacteria and Fungi OTUs were explored for the presence of an oligotype distribution by oligotyping analysis, using for the first time, in full-scale WWTPs studies. Core OTUs were also linked with the operational parameters of the bioreactors in order to unravel their influence over the performance of the system and their susceptibility to temperature. Also, the inter-domain interactions among core OTUs were observed.In this study, seven full-scale WWT systems, located within the Polar Arctic Circle have been sampled for a characterization of their The bioreactors analyzed in this study showed an operational temperature ranging from 3 to 7 °C Table . RemovalArchaea, 16S rRNA gene of Bacteria, and the 18S rRNA gene of Fungi is shown in Figure Archaea only the samples from KA and M had more copies in the influent than inside the bioreactor. This was true for Bacteria for samples KA, M and S. On the other hand, the majority of samples had more copies of Fungi 18S rRNA gene in the influent than in the bioreactor. Thus, it is possible that Fungi could not proliferate under the conditions given in the bioreactors or could not compete with Archaea and Bacteria OTUs within them.The number of copies of the 16S rRNA gene of Archaea, Bacteria and Fungi are shown in Table Archaea, Bacteria and Fungi were relatively low, with none higher than 6%. Therefore, the redundancy abundance-weighted coverage analysis showed that the MPS of Archaea, Bacteria and Fungi was successful.The results of the redundancy abundance-weighted coverage analysis for all MPS samples used for ecological study of 7. Thus, it is possible that cold climate conditions could favor the growth of different OTUs in the sewer system than inside the bioreactor.The α-diversity analysis showed that the three domains suffered a decrease of diversity when the influent wastewater entered the bioreactors sampled Table . These rArchaea, Bacteria and Fungi communities in the influent and mixed liquor of the bioreactors are shown in Figure Bacteria was the domain presenting the smoother differences within the samples collected. Interestingly, the influent samples showed significant differences among them and were grouped in different groups for Archaea, Bacteria and Fungi domain. In this sense, influent and bioreactor communities for WWTPs KE, M and R were similar for the three domains. This was different that other results found for Bacteria in influent wastewater in 10 full-scale WWT plants in The Netherlands and Spain, where influent samples clustered within the same group at the 60% similarity as measured by UniFrac and Bray-Curtis similarity distance, showing a similar Bacteria community composition7. Also, there was no differentiation in the technological configuration of bioreactors as the Mellanaapa biorotor (sample MB) with respect to the other activated sludge systems. This also contrasts with the results obtained for conventional and A-stage activated sludge, which were well differentiated when operating in warmer climates7. It is possible that low temperature or cold climate conditions in general greatly affects diversity in influent wastewater leading to very different compositions within the same geographical location, contrarily as has been reported before8.The clustering of Archaea, Bacteria and Fungi in the bioreactor and influent samples is given in Fig. The community structure of Archaea domain belonged to phylum Euryarchaeaota. Only two of these most important OTUs belonged to other phyla, namely Thaumarchaeota, which account for high relative abundance in samples KAI, MB, SB and YI . The dominant Archaea OTU was found at high relative abundance in all samples and was related to Methanobrevibacter genus (9.6–70.3%). This is the first time that a cold-adapted Methanobrevibacter OTU has been found as ecologically important in bioreactors. Following the criteria established by Gonzalez-Martinez et al.7 for the definition of core genera, OTUs A_Otu0002 and A_Otu0003 were influent core genera and were classified as Methanosarcina and Terrestrial Group Thaumarchaeota. Also, OTUs A_Otu0007 was classified as bioreactor core genera and belonged to Methanobacteriaceae family. Interestingly, OTU A_Otu0004 had a clear dominance of sample KAB (30.8%) and was identified as an unclassified Euryarchaeota.The dominant phylum among the most important OTUs for the Archaea community was defined by members of Archaea that have been widely reported in anaerobic digestion processed, such as Methanobrevibacter, Methanosarcina and Methanobacteriaceae family14. Besides, the presence of methanogenic Archaea in permafrost suggest that these microorganisms are adapted to cold environments15. Since none of the bioreactors have anaerobic digestion processes, the presence of methanogenic archaea could be possible due to generation of anaerobic zones within the bioreactors where these organisms can develop their metabolisms.The structure of the Thaumarchaeota member could possible drive ammonium oxidation, since the majority of ammonium oxidizing archaea found in soil develop this metabolism16.The terrestrial Bacteria domain were related to Trichococcus, Methylorosula, Polaromonas, Arcobacter, and members of the Leptotrichiaceae, Comamonadaceae, Alcaligenaceae and Holophagaceae families. Among these, OTUs B_Otu0001, B_Otu0003 and B_Otu0004 were considered as core OTUs in influent samples. These were affiliated with Trichococcus genus, Leptotrichiaceae and Comamonadaceae families. Trichococcus is a common pathogen in sewage17, and has been associated with bulking as well as with protein degradation in AS systems at low temperature19. With respect to Polar environments, facultatively anaerobic Trichococcus have been isolated from Arctic tundra soil20. The high metabolic versatility of Trichococcus may help this genus to adapt to severe temperature conditions such as Polar Arctic Circle WWTPs.The dominant OTUs of Methylorosula, which refers to a genus of psychrophilic, chemoorganoheterotrophic, aerobic bacteria21. Methylorosula was dominant in the bioreactor operating at lower minimum temperature (3 °C), while it was not present in any other influent or bioreactor at relative abundance >2%. This fact may indicate that, among other factors, this genus becomes of importance when no other competitors can adapt to temperatures lower than 4 °C. This is the first time that Methylorosula genus has been identified as dominant OTU in a wastewater treatment bioreactor, and thus it is possible that this occurs only at very low operational temperatures. Several uncultured Burkholderiales representatives were found as dominant OTUs for samples such as the putative GAO Comamonadaceae spb280 representative22.Interestingly, OTU B_Otu0002 had a clear dominance of sample KAB (75.2%) and was classified as 7. In this sense, the Bacteria community structure in Polar Arctic Circle full-scale WWTPs is not driven by technological configuration as in warmer climates. On the other hand, and as previously reported by Gonzalez-Martinez et al.7, there were core Bacteria OTUs in the influent wastewater. Nevertheless, core genera from different climate zones (The Netherlands and Polar Arctic Circle) were not shared, which indicates that sewage Bacteria community structure might be driven by environmental temperature, among other factors.No OTUs could be considered as core genera in bioreactors, which contrasts with previous results obtained in full-scale WWTPs in The Netherlands and Spain where core genera were found within bioreactors of the same technological configurationsFungi domain, the five most dominant OTUs were affiliated to the Trichosporonaceae family and could be classified as core genera in both influent and bioreactor samples according to Gonzalez-Martinez et al.7 criteria. Genera Trichosporon has been reported as arctic-adapted yeast23, and the results obtained suggested that Trichosporonaceae could play an important role in microbial metabolism in both influents and bioreactors in Polar Arctic Circle full-scale WWTPs, since it is known that Trichosporon can denitrify and help Bacteria OTUs in nitrogen removal24. Besides Trichosporonaceae family members, the other OTUs represented were related to Mortierella and Naganishia genera, which have been isolated from Arctic and Antarctic soil samples, respectively26.Among the Archaea and Bacteria only accounted for one OTU with a well-expressed oligotype structure (OTUs A_Otu0001 and B_Otu0002), while in contrast, all Fungi OTUs of interest had a well-expressed oligotype structure  with the lowest performance in solids removal   Table . The facArchaea OTUs that were more positively related to performance parameters in the bioreactors were A_Otu0004, A_Otu0003 and A_Otu0002, phylogenetically related to an unclassified Euryarchaeota member, a terrestrial group Thaumarchaeota OTU and Methanosarcina genus, respectively  and the FastPrep apparatus following the instructions given by the manufacturer. The five biomass samples from the same bioreactor or influent flow yielded five different DNA extracts which were finally merged into the same DNA pool, as has been done beforeBacteria, Archaea and Fungi OTUs. The primer pairs 28FF-519R (5′-GAGTTTGATCNTGGCTCAG-3′ and 5′-GTNTTACNGCGGCKGCTG-3′)7, 519F-1041R (5′-CAGCMGCCGCGGTAA-3′ and 5′-GGCCATGCACCWCCTCTC-3′)14 and ITS1F-ITS2 (5′-CTTGGTCATTTAGAGGAAGTAA-3′ and 5′-GCTGCGTTCTTCATCGATGC-3′)24 were chosen for the amplification of the hypervariable regions V1-V3 of 16S rRNA gene of Bacteria, the hypervariable regions V4-V6 of 16S rRNA gene of Archaea, and ITS region of Fungi, respectively.The DNA pools were subjected to MPS procedure using the Illumina MiSeq apparatus and the Illumina MiSeq Reagent v3. This protocol was done three times for each DNA pool to independently identify Bacteria, Archaea and Fungi using mothur41 and VSEARCH42 software. First, MiDAS S123 2.1.3 release43 was aligned against SiLVA NR v128 release using mothur, and the UNITEv6 database44 was aligned against itself using MUSCLE algorithm45. MiDAS database was used for the processing of Bacteria and Archaea sequences, while UNITE database was used for Fungi.The treatment of raw data from MPS was done for et al.46. The contigs were then screened to eliminate those with >0 ambiguous bases and >8 homopolymers.For the treatment or raw data, paired-end reads were merged into contigs avoiding the generation of ambiguous bases in the overlap region following Unno Archaea and Fungi and 97% similarity threshold for Bacteria, respectively, using distance-based greedy clustering method48 implemented in VSEARCH.The remaining contigs were then aligned against the database of choice using the Needleman criteria. Then, contigs that did not align at the position of the forward and reverse primers were considered as alignment failures and removed from the analysis. The remanent contigs were then subjected to chimera slaying process using VSEARCH. The remaining contigs were then taxonomically affiliated using the database of choice by the k-nearest-neighbor method and searching algorithm using k-mer size of 8. Those that failed to affiliate with their respective domain were removed. Then, all remaining sequences were clustered into OTUs in a 95% similarity threshold for Bacteria and Archaea 16S rRNA and Fungi 18S rRNA gene of each of the extracted DNA pools was measured by the means of quantitative real time PCR (qPCR). qPCR was performed using an Mx3000P QPCR system (Agilent Technologies) and the primers and annealing conditions described by Muyzer et al.49, Yu et al.50 and Nishizawa et al.51, for Bacteria, Archaea and Fungi, respectively. All quantitative amplifications were performed in duplicate. qPCR calibration curves were constructed with the aid of plasmid standards harboring inserts of the targeted genes. The calibration curves for the absolute quantification in the DNA samples were generated using serial tenfold dilutions (10−2–10−8) of linearized plasmid standards. The reaction mixture was made in a total volume of 25 μL contained 0.125 μL of SYBR Green PCR, 2.5 μL of buffer, 1.5 μL of MgCl2, 0.5 μL of dNTPs, 0.15 μL of each primer (10 µM), 0.125 μL of Taq Polymerase, 0.0625 μL of BSA, 17.88 of MilliQ water and 2 μL template DNA diluted 1:10 and 1:50. Melting curves were used at the end of each qPCR to check amplification specificity and purity of negative controls. Real-time PCR data were analyzed using a MxPro QPCR software version 3.0 .The number of copies of Archaea, Bacteria and Fungi were checked for diversity coverage using a redundancy abundance-weighted coverage method. This was done using NonPareil software53. The calculations were done using a query set size of 1000 sequences and a 95% similarity threshold in a minimum 50% sequences overlap.The MPS samples used for the determination of ecology of The OTU ecology of the MPS samples were used for the calculation of α-diversity indices Shannon-Wiener and Simpson, and a clustering of samples computed through Bray-Curtis distances. This was done using mothur software.Archaea, Bacteria and Fungi domains, based on their presence  or high dominance (>25% relative abundance in at least one sample). These OTUs were subjected to an oligotyping analysis following the procedure described by Meren et al.54. First, Shannon entropy was calculated for each of the OTUs. Based on these results, oligotypes were constructed for each OTU by repeated calculation until the purity score of the oligotypes with >100 reads was >0.9055. Removal of noise during the oligotyping process was set as: (i) each oligotype had to appear in at least one sample; (ii) each oligotype had to account for at least 1% relative abundance in at least one sample; (iii) each oligotype had a substantive abundance of 3056.Several OTUs were selected for the Archaea, Bacteria and Fungi members; (ii) the performance parameters of the bioreactors and their dominant Archaea, Bacteria and Fungi OTUs; (iii) the influent concentrations of organic matter, nutrients and solids and their dominant Archaea, Bacteria and Fungi OTUs; (iv) the temperature parameters of the bioreactors and the dominant archaea, Bacteria and Fungi OTUs; (v) the dominant OTUs across different domains . All multivariate redundancy analyses were developed using the CANOCO 4.5 for Windows and calculated by 499 unconstrained Monte-Carlo simulations in a full permutation model. Variable with units not consistent represented in the same multivariate redundancy analysis were normalized under a logarithmic model as in Gonzalez-Martinez et al.7.Several multivariate redundancy analyses were developed to link: (i) the operational conditions with the number of copies of Supplementary material


The temperature sensitivity of Rsoil was strongly influenced by labile C additions. Furthermore, estimation of the temperature response of the Michaelis-Menten parameters supports the use of substrate-based kinetics in modeling efforts. Results from both field and laboratory experiments demonstrated a general decrease in Rsoil with increasing soil available N that was variably dependent on carbon (C) availability. Both the field and the laboratory measurements demonstrated a consistent decrease in the Michaelis-Menten parameter kM with increasing soil available N, indicating an increase in the efficiency of soil C decomposition with increasing N. Furthermore, these results provide evidence of interactions between N deposition and temperature sensitivity, which could influence C storage under combined anthropogenic global change drivers.China experiences some of the highest rates of anthropogenic nitrogen deposition globally, with further increases projected. Understanding of soil feedbacks to the combined anthropogenic influences of climate change and nitrogen deposition in these systems is critical to improve predictive abilities for future climate scenarios. Here we used a Michaelis-Menten substrate-based kinetics framework to explore how soil CO In many regions soil C dynamics are influenced simultaneously by increased climate warming and nitrogen (N) deposition. With strong theoretical justification from thermodynamics and enzymatic processes4, warming should lead to increased rates of soil CO2 production , however, a broad range of responses have been observed5. The effects of N enrichment are even more complex with conflicting theoretical predictions and empirical evidence showing increases, decreases and no response of Rsoil to increased N availability10. The interactions between warming and nitrogen availability on soil C emissions are likely equally complex and may create a positive climate change feedback in a future warming climate. Resolving these uncertainties in Rsoil responses to both warming and N availability is important for defining soil functioning and the repercussions of multiple interacting global change drivers.Developing a mechanistic and predictive understanding of soil carbon (C) pool responses to interactive global change drivers remains an important research goal12. This approach is hierarchical in that the effects of warming and nitrogen availability are conceptualized as influencing parameters in a substrate dependent model. A commonly-used model of substrate-based kinetics is the Michaelis-Menten model, which describes a saturating function of substrate concentration, with parameters Vmax, the maximum reaction velocity, and kM, the half-saturation constant, which corresponds to the substrate concentration [S] when Vmax/2, as follows:soil temperature sensitivity, but there are competing hypotheses13. The inverse carbon-quality hypothesis predicts that low quality, recalcitrant substrates will require higher activation energy, and therefore be more temperature sensitive than high quality, labile substrates15. Alternatively, the availability of C may be a greater regulator of the temperature sensitivity of Rsoil via the temperature sensitivities of the Michaelis-Menten parameters, Vmax and kM. If both are influenced by temperature this can create a canceling effect at low substrate concentrations when the importance of the kM temperature sensitivity is greater16. Therefore, it is unclear whether carbon quality or availability is more important in regulation of the temperature sensitivity of Rsoil. Furthermore, few studies have empirically tested the temperature sensitivity of Vmax and kM in soil18.Substrate-based mechanisms for describing soil biogeochemical processes can be a valuable tool for modeling and predicting soil feedbacks to anthropogenic global change drivers2 emissions to N availability are also yet to be resolved19. Through an N limitation perspective, relief of N limitation should increase soil microbial activity and lead to a positive response of soil CO2 emission. However, this cannot explain the suppressive influence of N on soil CO2 emission seen in many studies21. Alternatively, the N mining hypothesis describes the process where microbes “mine” recalcitrant carbon for N22. Through this process, increasing N alleviates the need for mining of recalcitrant C, thereby decreasing Rsoil. Another promising hypothesis incorporates the divergent response of N addition on Rsoil through dynamic carbon-use efficiency (CUE) of soil microorganisms in order to adjust metabolic activity to better fit inconsistent resource availability25. This hypothesis predicts that the influence of N availability on Rsoil is dependent on the stoichiometry between C and N. When C is limiting compared to N, microbes will increase their CUE, thereby decreasing Rsoil. Alternatively, when C is abundant compared to N, decreased CUE results in increased Rsoil. When both resources are abundant, metabolic activity and growth are at a maximum. Initial laboratory tests have demonstrated Rsoil dynamics consistent with dynamic CUE in arid and semi-arid environments18, but not in a mesic system or from field experiments.How soil biogeochemical processes are influenced by N deposition, and the divergent responses of CO26. China experiences some of the highest rates of N deposition globally, with further increases projected27. The selected study sites along an urban to rural transect allow for the unique opportunity to study soil respiration under high rates of anthropogenic N deposition  How does soil N availability influence the response of Rsoil to labile C addition? (2) How does labile C addition affect the temperature sensitivity of soil respiration, and (3) Does soil N availability influence this temperature response? We hypothesized that N availability would influence Rsoil based on C:N stoichiometry, following the dynamic CUE hypothesis, and that temperature sensitivity would be mediated through the Michaelis-Menten parameters, Vmax and kM.In addressing this uncertainty, we examined how soils from a subtropical moist forest in Guangdong Province, China respond to increasing soil N availability. This region in the highly industrialized Pearl River Delta has experienced rapid land use change and the urbanized areas, including Guangzhou, have become a large source of N deposition to the outlying forest ecosystems4+ plus NO3−) from 14.7 to 71.4 mg kg−1 . To maintain consistency, the N addition plots have been relabeled to reflect differences in soil available N, similar to the urban to rural transect. The plots receiving 50 kg N ha−1 y−1 presented the highest soil total available N and will therefore be referred to as “High N”, the plots receiving 100 kg N ha−1 y−1 will be referred to as “Mid N”, and the control plots will be referred to as “Low N”.Soils from the urban to rural transect sites spanned a range of available N . However, CO2 production in the High N soils was not significantly different from the low N soils . In the N addition plots . In the urban to rural transect, the response of Rsoil to soil N was the same at both ambient soil C concentrations .For all measurements, soil COe N Fig.  showed dots Fig. , CO2 proons Fig.  and at ssoil with increasing soil N, evident at all sampling times , but no relationship between soil N and the Q10 of kM , and high N addition (100 kg N ha−1 y−1) for a total of nine plots. N was applied as ammonium nitrate in 10 L of water with a backpack sprayer once a month for two years prior to analysis40. Additional site characteristics are summarized in Table Additionally, soils were collected from N addition plots in a mixed legume and native forest at Heshan National Field Research Station of Forest Ecosystems in Heshan County, Guangdong Province. The plots consisted of three replicates each of a control (no additions), medium N addition  and four levels of glucose concentration: 5, 15, 30 and 90 g/L. Each treatment was replicated four times for a total of 20 samples per site. 200 mL of water or glucose solution were applied to each collar, and soil respiration was measured with a portable infrared gas analyzer  at 1, 8 and 24 hours after addition of water or glucose solution.Plots were established at the three sites along the urban to rural transect to compare 41. Water holding capacity (100%) was determined by the gravimetric water content of soil placed in a filter funnel and saturated with deionized water, then allowed to drain for two hours.Soils were collected from the three sites along the urban to rural transect as well as the nine N addition plots at Heshan Station to perform laboratory incubations to explore the response of soil respiration to glucose addition under controlled temperature and moisture conditions. Five field replicates were collected from each plot. Each field replicate was composed of approximately ten soil cores collected from 0–15 cm of the mineral soil layer. The soils were brought back to the lab and air dried for 3 to 5 days at room temperature. The soils were then sieved to 2 mm and homogenized. Before drying, an aliquot from each field replicate was placed in the freezer for nitrate and ammonium analysis. An additional aliquot from the dried and sieved samples was used for total organic C and total N analysis. Soil chemical analysis was performed by the Key Laboratory of Vegetation Restoration and Management for Degraded Ecosystems at the South China Botanical Gardens, Chinese Academy of Sciences using the copper cadmium reduction-diazotization coupling method for nitrate, potassium chloride leaching-indophenol blue method for ammonium, Kjeldahl method for total nitrogen and external heating using potassium dichromate oxidation method for total organic carbon2 flux measurements were then fit to the Michaelis-Menten model with nonlinear least squares regression to determine Vmax and kM using Matlab (2015b). Temperature sensitivity (Q10) was determined by the following equation:soil, Vmax or kM where applicable) measured at temperatures T1 and T2, respectively.The same five treatments were applied to the incubation experiments as were used in the field: control (water only), 5, 15, 30 and 90 g/L of glucose. In total, the three sites along the urban to rural transect included five treatments and five field replicates for a total of seventy-five samples. For the N addition plots at Heshan station, there were five glucose treatments, three N levels, and three replicates (one for each plot) for a total of forty-five samples. For each replicate, 50 g of soil was placed in a 200 ml Erlenmeyer flask with a vented rubber stopper. The glucose solutions (and water in the case of the control) were used to bring the soils to 40% WHC, and then they were incubated at 25 °C for 24 hours. After the incubation period, soil respiration was measured with an infrared gas analyzer (LI-6262 Licor Biosciences) at 13, 19, 25, and 31 °C. Fluxes were calculated from the linear portion of the curve generally between 30 and 90 seconds after the jar was sealed, and adjusted for chamber volume, soil weight and chamber temperature. The COStatistical significance for treatment effects for the laboratory incubations were determined by 2- and 3-way Analysis of Variance (ANOVA) using the (R version 3.2.5). Assumptions of normality and homoscedacity were tested with the Shapiro-Wilks and Bartlett’s tests, respectively. When these assumptions were not met, the Box-Cox family of transformations was used. Analysis of field measurements used linear mixed-effects model by maximum likelihood with time as a random factor followed by Tukey Contrasts with Bonferroni-Holm correction for multiple comparisons using the lme4 and multcomp packages in R version 3.3.3. Linear regressions for the relationship between kM and available soil nitrogen were performed in Matlab (2015b).The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.