The prevalence of obesity has been increasing in children over the last few decades, becoming a concern for health professionals and governments. Gut microbial community structure in obese people have been found to differ from that of lean subjects for some taxa which could result in different production of microbial metabolites. The aim of the present work was to study whether the gut microbiota from obese children extracts a different concentration of antioxidant capacity than the gut microbiota from lean children. For this purpose, different foods were in vitro digested and in vitro fermented using fecal material from obese and lean children. FRAP, DPPH and Folin-Ciocalteu methods were used to measure the antioxidant capacity released during digestion and fermentation. Overall, when using lean gut microbiota, antioxidant capacity released was higher when measured via DPPH and FRAP. Moreover, according to DPPH results, lean gut microbiota could potentially release more antioxidant power from vegetables than from animal products, while obese gut microbiota did the opposite. On the contrary, with the FRAP method obese gut microbiota released higher levels of antioxidant power from plant products than from animal products, but the final antioxidant capacity was still lower than that released by lean gut microbiota. Therefore, these results reflect that the total antioxidant capacity of foods is influenced by the gut microbiota, although whether that antioxidant capacity is released from plant or animal products can be slightly influenced by the method used for analysis. According to the World Health Organization, obesity is defined as a preventable risk factor that involves an abnormal or excessive accumulation of fat that can be harmful to health and is one of the most serious health problems of the 21st century. In particular, the global prevalence of childhood obesity has markedly increased in recent decades and is considered a global pandemic .Firmicutes and Bacteroides are the most dominant. Obese patients have been found to show some disturbances in the community such as lower alpha diversity [Firmicutes/Bacteroides ratio [Methanobacterial, Lactobacillus, Bifidobacteria, and Akkermansia [The gut microbiota is considered a metabolic organ consisting of a set of microorganisms and their genomes . It is civersity ,5. Addites ratio ,9,10,11.The gut microbiota plays an important role in human metabolism. In fact, it is involved in modulating host nutrition, resulting in vitamin production, fermentation of indigestible dietary components, production of short-chain fatty acids, vitamins and essential amino acids, enrichment of specific lipopolysaccharides, regulation of energy absorption, central appetite, fat storage, chronic inflammation and circadian rhythms ,13,14. SDifferent foods of animal origin  and most vegetables are consumed after being cooked under different conditions . Heat trThe aim of this work was to study the antioxidant capacity of Mediterranean diet foods after in vitro digestion and in vitro fermentation. In vitro fermentation was performed with gut microbiota obtained from fecal material from obese and lean children.Pancreatin from porcine pancreas was acquired from Alpha Aesar . Tryptone, cysteine, resazurin, sodium dihydrogen phosphate, sodium sulfide, porcine bile acids, pepsin and salivary alpha-amylase were purchased from Sigma-Aldrich .Trolox ((±)-6-Hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid), DPPH , sodium acetate, TPTZ -s-triazine), iron (III) chloride hexahydrate, gallic acid, Folin-Ciocalteu® reagent and hydrochloric acid. All the reagents for the analysis of antioxidant capacity were obtained from Sigma Aldrich .A total of 47 foods belonging to the following groups were studied: nuts (nut mix and peanuts), cereals , vegetables , legumes (kidney beans and lentils), fruits , tubers (potato and sweet potato), cocoa (dark chocolate and cocoa butter), dairy , meat  fish  and egg. Samples were analyzed either in their raw form or submitted to boiling in water for those foods that are usually cooked. Boiling was carried out in ultra-pure water for 20 min at a 5:1 ratio (water:food) on the following foods: pasta, whole grain pasta, rice, whole grain rice, kidney beans, lentils, potato, sweet potato, chicken, beef, lamb, pork, salmon, cod fish, egg, carrot, cauliflower, onion, pepper, spinach and zucchini. The food was acquired from 3 retail shops and stored refrigerated or in a cold room, according to the label or retailer instructions.Every single sample was in vitro digested and fermented following previous protocols ,23. For Fecal samples from 5 lean donors and 5 from obese were used for the in vitro fermentation. The common inclusion criteria for both groups of children were age between 8 and 10 years as well as not having taken antibiotics or probiotics 3 months before the start of the study. Common exclusion criteria were diagnosis of chronic gastrointestinal disorders or any other chronic disease or special diet. Only lean children with a BMI between the 5th and 85th percentile for sex, height and age were considered. For the obese group, BMI had to be greater than the 95th percentile for sex, weight and age. Recruitment of the study participants was done via the pediatric unit at the hospital in Athens (Greece). Parents were given an informed consent from as well as information and questionnaires for inclusion/exclusion criteria. The study was approved by ethics committee at the University General Hospital in Athens. v/v) was added to the fermentation tube at a volume of 7.5 mL. A fecal inoculum mas made from fecal material by mixing it with PBS at a concentration of 33%. Two milliliters of inoculum were added to the fermentation tube. Afterwards, nitrogen was bubbled into the tube until reaching anaerobic conditions (transparent solution as opposed to pink when oxygen is dissolved). After 20 h at 37 °C, microbial activity was halted by immersion in ice for 15 min and tubes were centrifuged to collect the supernatant (fraction available for absorption at the large intestine), which was stored at −80 °C until further analysis. Blanks carrying water instead of the sample were included in the in vitro digestion as well as in the in vitro fermentation. Fecal material was pooled by donor group (lean children and obese children) to account for inter-individual variability. In vitro fermentation was carried out at 37 °C for 20 h, in oscillation. For this purpose, 0.5 g of the pellet obtained after in vitro gastrointestinal digestion were used, as well as 10% of the supernatant. Fermentation medium composed of peptone  as well as in the fraction obtained after in vitro fermentation (absorbable in the large intestine). The sum of these two fractions would make for the total antioxidant capacity of the sample . Three dThe Folin–Ciocalteu (FC) assay was followed with a previous protocol . It was FRAP assay . The protocol of Benzie and Strain [TEACd Strain  was follDPPH assay . This method was carried out following the protocol of Rapisarda et al. [TEACa et al. . The prop value < 0.05. Statgraphics Plus software , version 5.1 was used to perform all the statistical analysis.Statistical differences were computed using unpaired Kruskal–Wallis test with a 95% confidence comparing the antioxidant capacity of each food group with the base mean antioxidant capacity . Therefore, we are showing whether one particular food group exhibits higher or lower antioxidant capacity than the average. A multivariate Principal Component Analysis (PCA) was carried out to explore differences between groups. Spearman parametric statistic was calculated to show the lineal relation between antioxidant capacity at a Antioxidant capacity was measured in the supernatant obtained after in vitro digestion as well as in the one obtained after in vitro fermentation. Both of them account for the total antioxidant capacity of a given food. It is important to bear in mind that antioxidant values obtained after in vitro digestion are the same for both type of subjects, lean and obese, since food was only digested once and then fermented with both lean fecal material and obese fecal material. Overall, in vitro fermentation accounted for most of the total antioxidant capacity of the samples, as will be laid out in the following sections. Three different methods  were used to measure the antioxidant capacity. All values obtained were corrected for the antioxidant capacity provided by chemicals, enzymes and fecal material.p < 0.05) correlations for the all the pairs: DPPH-FRAP (coefficient = 0.29), DPPH-FC (coefficient = 0.161) and FRAP-FC (coefficient = 0.632). We checked for linear correlations using Pearson method between the different antioxidant assays. Considering all the values (n = 1472), we obtained significant (p < 0.05) whereas dairy presented significantly lower values. The Folin–Ciocalteu assay (p < 0.05). Regarding TEACFRAP (p < 0.05).DPPH analysis  showed teu assay  showed tTEACFRAP , cocoa pAs summary, in vitro digestion presented cocoa products as the food group with highest antioxidant potential in all three methods whereas opposite results were found for other food groups. There are two situations catching the eye: high Folin–Ciocalteu values achieved by meats and fish and low ones obtained by vegetables and fruits in some assays. This will be discussed in p < 0.05), while meat, fruits and fish (this one not significantly) exhibited the lowest values. When we consider total antioxidant capacity . Tubers, on the other hand, showed significantly lower values (p < 0.05). Here, again, it is surprising to have eggs or meats at similar level than fruits or cocoa, whereas legumes are among the lowest in antioxidant capacity. If we account for the antioxidant capacity released during digestion , due to differences between different types of nuts. Vegetables showed, on the other hand, the lowest values. Similar results were obtained when considering total antioxidant capacity . On the other hand, no food group showed significantly lower values than the average though tubers and legumes were the ones with lowest antioxidant capacity. Total antioxidant capacity showed similar results . Cocoa products had a mean antioxidant capacity higher than meat products though probably due to differences between products, there was no significance. However, when considering total antioxidant capacity  was carried out to study the distribution of the samples. p < 0.05) antioxidant capacity from vegetables (DDPH), tubers (DPPH and FRAP), nuts (DPPH and FRAP), meat (FRAP), legumes (DPPH and FRAP), fruits (DPPH and FRAP), fish (FC and FRAP), dairy products (DPPH and FRAP), cocoa products (DPPH and FRAP) and cereals (DPPH and FRAP). On the other hand, fecal material from obese children only exhibited higher (p < 0.05) antioxidant capacity when fermenting vegetables and measured via Folin–Ciocalteu. Overall, when foods were fermented with fecal material from lean children, the antioxidant capacity released was higher, particularly in DPPH and FRAP assays . Folin–CWe also looked at how much each of the fractions (digestion and fermentation) contributed to total antioxidant capacity or, in other words, which process was able to extract the highest antioxidant power from the foods under study A,B. As wIn this work, a total of 48 different foods of plant and animal origin were submitted to in vitro digestion followed by in vitro fermentation using fecal material from different donors: healthy children and obese children. Antioxidant capacity  was measured in the potentially absorbable fraction obtained after in vitro digestion as well as after gut microbial fermentation. Regarding in vitro digestion results, cocoa showed the highest antioxidant capacity for all three methodologies. Cocoa is known to possess a high amount of phytochemicals, especially phenolic compounds from the flavan-3-ol family such as catechin or epicatechin . This cop < 0.05).However, meats and fish, followed by nuts, also exhibited significantly higher antioxidant capacity than the base-mean when antioxidant capacity was measured via FC. At the same time, vegetables, legumes and fruits showed lower values than the base-mean, only significant for vegetables and fruits. These results did not fall within expectations. The Folin–Ciocalteu assay is often used to estimate total polyphenol content . HoweverFRAP, on the other hand, did not show much variation between different food categories with the exception of cocoa and nuts, though the latter did not show significantly higher antioxidant values than the rest. These results would make sense regarding animal products which are not known for their antioxidant potential and would also agree with DPPH.Regarding in vitro fermentation with fecal material from lean children, cocoa was once again among the top three foods with the highest antioxidant capacity, though it was only significantly in the case of FRAP. Nuts were also among those top three when using DPPH and FRAP methods, though only significantly for DPPH. As commented above, cocoa  as well p < 0.05). Here, again, we found a statistically significant correlation between protein content and FC values . However, here is not clear whether high antioxidant values are due to interactions as described above or to actual antioxidant compounds. It has been proven before that several bacterial species such as different Bacteroides, Eubacterium hallii or Clostridium barlettii can metabolize aromatic amino acids into small phenolic compounds, same as the ones produced in plants [FC showed similar results to those obtained during in vitro digestion with high-protein foods showing some of the highest antioxidant values, though only fish was statistically significant  found to have a higher antioxidant power when measured via FRAP. The rest of the groups showed similar antioxidant capacity and only vegetables were significantly below the base mean. Although there are great differences within the group, some vegetables did show low antioxidant values whereas others displayed values comparable to other groups. Bacteroidetes and higher abundance of Firmicutes [Regarding in vitro fermentation with fecal material from obese children, cocoa was once more the most antioxidant group though only significantly in the case of DPPH. We can probably suggest now that, overall, cocoa was the most antioxidant food. DPPH showed that high protein foods were among the most antioxidant food groups, which was, in part, opposite to what we found using lean fecal material. Since the DPPH reagent has not been reported to react with proteins (as FC), we tried to find another explanation. This finding could be related to differences between gut microbiota from lean and obese. As depicted in rmicutes ,35. Thisrmicutes . Thereformicutes . Howeverrmicutes  carried rmicutes ,35. TherHowever, when antioxidant capacity was measured via FRAP and FC using obese gut microbiota, though meat showed high values via FC, animal foods presented in general lower values than plant-origin products. Nevertheless, FC values were similar among samples and only tubers and meats were significantly different to the base mean. When comparing FC values between lean and obese samples, they were very similar, only showing differences for egg and fish, which would require further and deeper research. FRAP, on the other hand, exhibited higher antioxidant values when fermenting with lean fecal material except for vegetables, though fruit values were also close to one another. Although this coincides with DPPH, the specific behavior of the different food groups was different than that found for DPPH. FRAP showed that lean gut microbiota produced similar antioxidant capacity from animal and plant origin products whereas obese gut microbiota produced higher values from plant products. Whether this behavior is gut microbe-related or due to the assays’ chemistry is hard to conclude and additional experiments with more subjects from each group would be needed. However, regarding the antioxidant assays chemistry, pH could be playing an important role here. Whereas DPPH is carried out at pH 7, FRAP is carried out at pH 3.6. Both assays are based on the same redox principle, transference of electrons from the antioxidant to the radical (DPPH) or to the metal (FRAP) . HoweverIn conclusion, we evaluated a number of foods grouped in several categories. Cocoa products (dark chocolate and cocoa butter) displayed, overall, the highest antioxidant capacity. Antioxidant capacity of foods is released in a two-step process: first, some of this antioxidant capacity is released during digestion, and, usually, more antioxidant power released from those foods more easily digested whereas others, such as vegetables, release lower concentrations. Regardless, most of the antioxidant capacity is released during gut microbial fermentation, which could be due to two main reasons: for plant-origin foods (hard to digest due to lack of enzymes) most phytochemicals could be released only after gut microbes have broken down vegetal cells. Secondly, animal products that lack phytochemicals  could exhibit their antioxidant power after amino acids are fermented into small phenolics but not before as larger peptides. Additionally, large differences were detected depending on whether foods were fermented with lean or obese fecal material. Overall, when using lean gut microbiota, antioxidant capacity released was higher, though this was only true for DPPH and FRAP methods. Moreover, according to DPPH results, lean gut microbiota could potentially release more antioxidant power from vegetables than from animal products, while obese gut microbiota did the opposite. FRAP, on the other hand, showed how it was obese gut microbiota the one that released higher levels of antioxidant power from plants products. Although this could be related to the chemistry of the antioxidant assay, it is not clear and further investigation would help elucidate it. Regardless, different antioxidant assays could show different antioxidant behaviors although FC has showed a potential interaction with proteins or amino acids which would limit its usefulness.


Nuclear receptor related 1 (Nurr1) is an orphan ligand-activated transcription factor and considered as neuroprotective transcriptional regulator with great potential as therapeutic target for neurodegenerative diseases. However, the collection of available Nurr1 modulators and mechanistic understanding of Nurr1 are limited. Here, we report the discovery of several structurally diverse non-steroidal anti-inflammatory drugs as inverse Nurr1 agonists demonstrating that Nurr1 activity can be regulated bidirectionally. As chemical tools, these ligands enable unraveling the co-regulatory network of Nurr1 and the mode of action distinguishing agonists from inverse agonists. In addition to its ability to dimerize, we observe an ability of Nurr1 to recruit several canonical nuclear receptor co-regulators in a ligand-dependent fashion. Distinct dimerization states and co-regulator interaction patterns arise as discriminating factors of Nurr1 agonists and inverse agonists. Our results contribute a valuable collection of Nurr1 modulators and relevant mechanistic insights for future Nurr1 target validation and drug discovery. Nurr1 is a promising therapeutic target for neurodegenerative diseases, but mechanistic understand of its modulation is limited and Nurr1 modulators as tools are lacking. Here, non-steroidal anti-inflammatory drugs are shown to act as inverse Nurr1 agonists, and their interactions with the NR4A family are characterised. Levels of Nurr1 are diminished in Parkinson’s disease (PD) patients and midbrain dopaminergic neuron development is Nurr1 dependent. Moreover, Nurr1 knock-out in mice in mature dopamine neurons resembled the progressive pathology seen in early stage of PD3 suggesting therefore Nurr1 as promising target in PD treatment.Nuclear receptor related 1 , a member of the nerve growth factor-induced β subfamily of orphan nuclear receptors1. However, recent reports indicate that Nurr1 activity can be modulated with small-molecule ligands. Despite the lack of a canonical binding site in the apo structure of the Nurr1 ligand-binding domain (LBD), dynamic NMR, hydrogen deuterium exchange, and mutagenesis studies have defined two potentially overlapping ligand-binding regions within the Nurr1 LBD for unsaturated fatty acids (UFAs) and amodiaquine type ligands6. These putative, highly solvent accessible binding sites5 are located on the LBD surface around helix 3, which is distant from helix 12 that often has a canonical activation function in other nuclear receptors. In addition, X-ray structures of the Nurr1 LBD in complexes with prostaglandin A1 , prostaglandin A2 , and 5,6-dihydroindole  have been reported recently, in which the ligands are covalently bound in an induced pocket between helices 5 and 12 that is not present in the apo structure.Nurr1 was originally considered as a ligand-independent nuclear receptor (NR) due to its closed ligand-free conformation and its high constitutive activity10. Neutral antagonistic effects of DHA and other UFA metabolites without intrinsic activity have been suggested10, however, their cellular effects on Nurr1 activity and potential biological relevance remain elusive. Conversely, the prostaglandins E1 and A1 have recently been reported as naturally occurring Nurr1 activators and found to exhibit Nurr1-dependent neuroprotective effects7. A series of isoxazolopyridinones has been described as synthetic Nurr1 activators, albeit with weak activation efficacies not exceeding 2-fold activation11. Recently, the antimalarials amodiaquine (AQ) and chloroquine (CQ) with chloroquinoline scaffold were also discovered as Nurr1 modulators with a slightly higher efficacy (~3-fold activation) but markedly lower potency6. While the putative neutral Nurr1 antagonism of UFAs requires further characterization, the prostaglandins E1 and A1 as well as all synthetic Nurr1 ligands activate the nuclear receptor and further promote its already high basal transcriptional inducer activity. The limited efficacies of these Nurr1 activators coupled with the lack of inverse Nurr1 agonists that suppress the receptor’s intrinsic activity prompt further efforts in the search for potent Nurr1 ligands that can be used as a tool for biological studies of the receptor’s roles in health and disease.Some recent studies have reported a number of Nurr1 ligands, demonstrating the potential of modulation of the receptor activity by small molecules. UFAs such as docosahexaenoic acid (DHA), arachidonic acid, linoleic acid, and oleic acid were identified as the first natural Nurr1 ligandsHere we report the discovery of several structurally diverse cyclooxygenase (COX) inhibitors as Nurr1 modulators with distinct activity profiles. The tricyclic compounds oxaprozin, valdecoxib, and parecoxib, as well as meloxicam markedly diminish constitutive Nurr1 activity and thereby act as inverse Nurr1 agonists. Meclofenamic acid (MFA) is characterized as selective Nurr1 modulator with agonist and inverse agonist properties. Together with the previously reported Nurr1 agonists AQ and CQ, these Nurr1 ligands serve as chemical tools to study the receptor’s mode of action. Both classes of binders with converse effects demonstrate that Nurr1 activity can be modulated by small-molecule ligands in a bidirectional fashion. The use of Nurr1 agonists and inverse agonists in cofactor recruitment assays reveals potential interactions between Nurr1 and several nuclear receptor co-regulators, including NCoR-1, NCoR-2, NRIP1, and NCoA6, in a ligand-concentration-dependent manner. Moreover, the Nurr1 modulators interestingly affect heterodimerization between Nurr1 and RXRα as well as Nurr1 homodimerization with distinctive profiles. Cross-titration additionally demonstrates that Nurr1 can be modulated simultaneously by the different types of binders suggesting distinct binding sites. These results contribute to our understanding of Nurr1 biology, and may open new avenues for pharmacological Nurr1 modulation.7) and the dopamine metabolite DHI (PDB-ID: 6DDA9) reveal a ligand-binding site differing from typical nuclear receptor LBDs , the binding of PGA1 and DHI requires an outward movement of helix 12 by ~10–21° from its closed position, essential for activation, to create the ligand-binding pocket. Such movement upon ligand binding affects and may weaken the salt bridge between Lys590 (H12) and Glu440 (H5) observed in the ligand-free state, suggesting that this region in proximity to helix 5 and 12 might provide a handle to modulate transcriptional inducer activity of Nurr1. In parallel, a different binding site has been postulated for the Nurr1 activator AQ, and is located at the Nurr1 LBD surface between helices 3 and 66. Mutagenesis experiments support this assumption indicating that Nurr1 has two, potentially independent, ligand-binding pockets that enable modulation by small-molecule ligands.The recently published co-crystal structures of the Nurr1 LBD bound to prostaglandin A1  and neuron-derived orphan receptor 1 , we included all three NR4A receptors in this primary screen. For this, we employed cellular hybrid reporter gene assays based on chimeric receptors composed of the human NR4A receptor LBD and the DNA binding domain of Gal4 from yeast. A Gal4-sensitive firefly luciferase reporter construct served as reporter gene and constitutively expressed renilla luciferase (SV40 promoter) was employed to normalize for transfection efficiency and to monitor test compound toxicity. In agreement with their natural behavior1, the chimeric Gal4-NR4A receptors revealed strong ligand-independent, intrinsic transcriptional inducer activity in this setting. AQ and CQ in conformity with literature6 activated Gal4-Nurr1 with EC50 values in an intermediate micromolar range and up to 3.6-fold activation efficacy. To exclude non-specific effects in this cellular test system, control experiments were performed for all compounds affecting NR4A receptor activity, in which the potent ligand-independent transcriptional inducer Gal4-VP1612 replaced Gal4-NR in the assay setup. Only compounds affecting NR4A-dependent but not VP16-dependent reporter activity were further considered as NR4A modulators.Inspired by the processed COX metabolite PGA1 as Nurr1 ligand50 value of 4.7 ± 0.1 µM and 3.52 ± 0.05-fold maximum activation on Gal4-Nurr1 , suggesting that the repressive effect at high concentration is likely due to inhibition of firefly luciferase. Nurr1 repression (<0.80-fold activation) was observed for oxaprozin, valdecoxib, and parecoxib at 10 or 30 µM. Control experiments on Gal4-VP16 indicated no non-specific reporter suppression  covering all chemotypes of drug-approved COX inhibitors were initially screened in the Gal4-NR4A reporter gene assays at concentrations of 10 and 30 µM for NR4A modulatory activity Fig. . Mecloferr1 Fig. , Table 1ion Fig.  and doseion Fig. . OxaprozNeither the Nurr1 modulators discovered in our screening nor the previously known ligands AQ and CQ were selective for Nurr1 over the related NR4A receptors. AQ (100 µM) revealed even stronger activation efficacy on Nur77 and NOR1 while CQ (100 µM) and MFA (10 µM) as well as inverse agonists parecoxib (30 µM) and oxaprozin (50 µM) caused similar modulation of all three NR4A receptors Fig.  without 13-activated nuclear receptors  which confirmed selectivity except weak RXR agonism of oxaprozin which has been described previously14.To profile also the selectivity of Nurr1 modulators MFA, parecoxib and oxaprozin Fig.  outside AQ, CQ, MFA, parecoxib, and oxaprozin displayed distinctive activity profiles of Nurr1 modulation ranging from agonism to inverse agonism, and therefore, emerged as a valuable set of tool compounds to assess Nurr1 modulation by chemical ligands.15. Understanding of Nurr1 modulation by ligands, thus, must also take ligand effects in more physiological settings into consideration where reporter activity is controlled by the native human full-length Nurr1 protein as monomer, homodimer, or RXR heterodimer. To study the effects of AQ, CQ, MFA, parecoxib, and oxaprozin on the activity of full-length human Nurr1 in cellular settings, we employed reporter constructs bearing a single repeat of the human DNA response elements for the Nurr1 monomer , the Nurr1 homodimer , or the Nurr1:RXR heterodimer  to control reporter gene expression. Nurr1  was overexpressed by co-transfection of a CMV-dependent expression plasmid. These cellular assay settings revealed further differences for the individual Nurr1 modulators  the latter of which has also been suggested for Nurr1ors Fig. , Table 1ors Fig. , but witThe observation of opposed effects of MFA on Nurr1 monomers and dimers suggested crucial involvement of Nurr1 dimerization in mediating responses to ligands. Therefore, we studied association of the LBDs of Nurr1 and RXR in time-resolved fluorescence resonance energy transfer (TR-FRET) based settings using GFP-labeled RXRα or Nurr1 LBDs and Tb-labeled Nurr1 LBD, from which the results demonstrated robust homodimeric Fig.  and heteThese observations further supported our hypothesis that the selective Nurr1 modulatory effects of MFA are mediated by changes in the dimerization state of the nuclear receptor. To observe this activity in another cellular setting, we studied how co-transfection of Gal4-RXRα affected MFA-mediated activation of Gal4-Nurr1 Fig. . In accoChanges in the dimerization state of Nurr1, thus, emerge as key mechanism of Nurr1 modulation by small-molecule ligands. Therein, agonists  promote dimerization while inverse agonists  diminish Nurr1 dimer formation.16, no strong direct association of the Nurr1 LBD with the canonical steroid receptor co-activators (SRC) was observed.In addition to dimerization, nuclear receptor activity depends on interactions with various co-regulators. To capture also the molecular mechanisms by which the Nurr1 co-regulator network responds to small-molecule ligands, we studied co-regulator binding to Nurr1 by TR-FRET in cell-free setting. We used a Tb-cryptate-labeled Nurr1 LBD and twenty nine Fluorescein-labeled co-regulator peptides Fig. . In abse17. Full dose–response characterization further confirmed distinctive ligand effects on co-regulator recruitment. MFA, characterized as Nurr1 modulator in the cellular settings, displaced NCoR-1, SMRT, PRIPRAP, and RIP140 from the Nurr1 LBD in a dose-dependent fashion with similar potencies (IC50 of 17–33 µM)  on the recruitment of all twenty nine co-regulators to the Nurr1 LBD. While no pronounced effects were detected for AQ and CQ in this primary screen, MFA, parecoxib, and oxaprozin markedly altered the Nurr1 recruitment ability for NCoR-1, SMRT, PRIPRAP, and RIP140. In addition, the peptide D22 was recruited by Nurr1 in a ligand-dependent fashion but not further considered due to its artificial originM) Table . The co-nds Fig. . Since lThus, in addition to differential control of the Nurr1 dimerization state, ligands modulate Nurr1 activity by regulating cofactor recruitment. Agonists  enhance recruitment of co-regulators such as NCoR-1, SMRT, PRIPRAP, and RIP140 while inverse agonists  promote displacement of these co-factors.7) together with the mutagenesis and NMR-based analysis6 of the putative binding site for AQ on Nurr1 suggest the existence of two independent ligand-binding pockets within the Nurr1 LBD  and then monitored the activation upon cross-titrating the other respective agonist into the assay vice versa  which comprise high intrinsic activity and possess agonistic and inverse agonistic ligands2. Moreover, the simultaneous Nurr1 modulation by NSAIDs  and AQ observed in cross-titration experiments suggests potentially the existence of two ligand-binding pockets in Nurr1, both of which can control the receptor activity.To assist validation of Nurr1 as future therapeutic target, there is a need for Nurr1 ligands as template for drug discovery and as chemical tools for biological studies to improve our knowledge on this orphan nuclear receptor. We have screened for alternative and additional Nurr1 modulators with higher potencies and distinct activity profiles, and employed them as in vitro tools for mode of action studies. Based on the recently published X-ray complex structure of the Nurr1 LBD bound to PGA126 recently demonstrated that Nurr1 monomers, Nurr1 homodimers, and Nurr1:RXR heterodimers address distinct response elements on DNA. To transfer our findings to endogenous conditions with different Nurr1 DNA response elements and dimerization states, we profiled the entire set of initial Nurr1 tool compounds  in reporter gene assays involving the human Nurr1 response elements NBRE, NurRE, and DR5 as well as the full-length human Nurr1 protein as monomer, homodimer, or heterodimer. Dose–response experiments on NBRE-, NurRE-, and DR5-dependent reporter expression confirmed agonism of AQ and CQ as well as inverse agonism of parecoxib and oxaprozin on Nurr1 monomers (NBRE), homodimers (NurRE), and heterodimers (DR5). MFA, however, exhibited a less consistent activity profile with agonism on Gal4-Nurr1, inverse agonism on Nurr1 dimers  and no activity on full-length Nurr1 monomers (NBRE). This observation suggests promising potential for (gene-)selective modulation of Nurr1 but also demonstrates limitations of the Gal4-hybrid assay setting to study Nurr1 modulation since it was not predictive of MFA’s mode of activity on full-length human Nurr1.The Gal4-hybrid reporter gene assay employed for our primary screening is a robust test system but does not fully capture the physiological behavior of nuclear receptors in terms of dimerization. Jiang et al.In order to observe the molecular modes by which agonists and inverse agonists differentially modulate Nurr1, we studied the dimerization behavior of the Nurr1 LBD and its interaction with co-regulators in presence and absence of ligands. We observed high affinity of Nurr1 to homodimerize and to form heterodimers with RXR. These interactions were markedly affected by Nurr1 modulators and enable distinction between agonism and inverse agonism with agonists promoting dimer formation and inverse agonists decreasing dimerization. MFA was most effective in countering Nurr1 dimer formation regarding both, homo- and heterodimer which suggests that MFA shifts the binding equilibrium of Nurr1 to a fully monomeric state. This is in line with different behavior of the compound in cellular settings involving monomers  or dimers  and provides preliminary explanation why the observed activities of MFA differed in different cellular settings. In addition, the ligand-induced Nurr1 dimerization state appears to affect Nurr1 activation efficacy since AQ which primarily promoted Nurr1 homodimerization in cell-free setting concomitantly exhibited the strongest activation efficacy on the human Nurr1 homodimer response element .We then assessed how ligands modulate the interaction between Nurr1 and co-regulators, and screened a library of twenty nine known nuclear receptor co-regulator fragments for their recruitment to Nurr1. We observed robust binding of NCoR-1, NCoR-2, NCoA6, and NRIP1 to the Nurr1 LBD and discovered that these interactions are responsive to Nurr1 ligands. The Nurr1 agonist CQ revealed a trend to enhanced recruitment of co-regulators and both AQ and CQ increased the affinity between the Nurr1 LBD and NCoR-1 while inverse agonists parecoxib and oxaprozin displaced co-regulator interaction motifs from the Nurr1 LBD. Different effects of Nurr1 modulators on co-regulator recruitment to the Nurr1 LBD therefore arise as further contributing factor to discriminate Nurr1 agonism and inverse agonism. In addition, different effects of AQ and CQ on co-regulator recruitment by Nurr1—beyond their distinctive modulation of Nurr1 dimerization—provide a basis for their different Nurr1 activation efficacies. Using a limited coverage of potential interactors, our results indicated strongly a similar ability of Nurr1 to other nuclear receptors for interactions with a number of co-regulators and we postulate that further biologically-relevant co-regulators might involve in Nurr1 regulation.Based on our findings in orthogonal cellular and cell-free assay systems, we conclude that bidirectional modulation of Nurr1 by small-molecule ligands results from two contributions. Agonists promote dimerization of Nurr1 as a homodimer or as a heterodimer with RXR and additionally stabilize interaction of the Nurr1 LBD with co-regulators. Inverse agonists favor monomeric Nurr1 and decrease Nurr1 co-regulator interactions Fig. . These cOverall our studies have demonstrated that Nurr1 activity can be modulated by small-molecule ligands in a bidirectional fashion and we report first-in-class inverse Nurr1 agonists that counter the receptor’s high constitutive transcriptional inducer activity. The opposite activities of Nurr1 agonists and inverse agonists are rationalized by their distinct effects on Nurr1’s interaction profile with co-regulators and dimerization state. Moreover, our results point to the existence of two binding sites within the Nurr1 LBD that can accommodate ligands, of which the binding can modulate Nurr1 activity both independently and simultaneously. This is demonstrated by pairs of modulators involving AQ and MFA or parecoxib, which achieve different Nurr1 activation efficacy when they are applied together compared with their individual effects, offering potentially another avenue for the design of selective Nurr1 modulators or agonists with enhanced efficacy. These results markedly contribute to molecular understanding of Nurr1’s activity and advocate the development of several types of potent Nurr1 modulator tool compounds that address the different binding sites and distinct receptor responses to enable in depth functional validation of Nurr1 as future drug target.Plasmids: The Gal4-fusion receptor plasmids pFA-CMV-hNur77-LBD, pFA-CMV-hNURR1-LBD, and pFA-CMV-hNOR1-LBD coding for the hinge region and LBD of the canonical isoforms of the human nuclear receptors Nur77 , Nurr1 , and NOR1  were constructed by integrating cDNA fragments obtained from PCR amplification using natural cDNA  or the pcDNA3.1 plasmid OHu22293D  as template between the BamHI cleavage site of the pFA-CMV vector  and an afore inserted KpnI cleavage site. Frame and sequence of the fusion plasmids were verified by sequencing. The Gal4-fusion receptor plasmids used for selectivity profiling were pFA-CMV-hPPARα-LBD, pFA-CMV-hPPARγ-LBD, pFA-CMV-hPPARδ-LBD, pFA-CMV-hRXRα-LBD, and pFA-CMV-hRARα-LBD coding for the hinge region and ligand-binding domain of the canonical isoform of the respective nuclear receptor have been reported previously29. pFR-Luc (Stratagene) was used as reporter plasmid and pRL-SV40  for normalization of transfection efficiency and test compound toxicity. The Gal4-VP1612 expressed from plasmid pECE-SV40-Gal4-VP1630  was used as ligand-independent transcriptional inducer for control experiments. The reporter plasmid pFR-Luc (Stratagene) used for the Gal4-hybrid assays contains a section between 176 to 83 base pairs upstream of the start codon of the firefly CDS that encompasses five copies of the Gal4 response element. To enable transactivation assays based on full-length NRs, this section was replaced with the human Nurr1 response elements DR5 , NBRE NL3 , or NurRE (from proopiomelanocortin (POMC); pFR-Luc-NurRE; TGATATTTACCTCCAAATGCCA), respectively. The human nuclear receptors Nurr1  and, for DR5, RXRα (pSG5-hRXR31) were overexpressed. Assay procedure: HEK293T cells (German Collection of Microorganisms and Cell Cultures (DSMZ), Braunschweig, Germany) were grown in DMEM high glucose, supplemented with 10% FCS, sodium pyruvate (1 mM), penicillin (100 U/mL), and streptomycin (100 µg/mL) at 37 °C and 5% CO2. The day before transfection, HEK293T cells were seeded in 96-well plates (3 × 104 cells/well). Before transfection, medium was changed to Opti-MEM without supplements. Transient transfection was performed using Lipofectamine LTX reagent  according to the manufacturer’s protocol with the corresponding plasmid mixture. For Gal4-hybrid assays, the plasmid mixtures comprised the respective Gal4-fusion nuclear receptor plasmid (pFA-CMV-NR-LBD), pFR-Luc, and pRL-SV40. For assays on full-length human Nurr1, the plasmid mixtures were pcDNA3.1-hNurr1-NE/pFR-Luc-NBRE/pRL-SV40 (NBRE), pcDNA3.1-hNurr1-NE/pFR-Luc-NurRE/pRL-SV40 (NurRE), and pcDNA3.1-hNurr1-NE/pSG5-RXR/pFR-Luc-DR5/pRL-SV40 (DR5). Five hours after transfection, medium was changed to Opti-MEM supplemented with penicillin (100 U/mL) and streptomycin (100 µg/mL), now additionally containing 0.1% DMSO and the respective test compound or 0.1% DMSO alone as untreated control. Each concentration was tested in duplicates and each experiment was performed independently at least three times. Following overnight (12–14 h) incubation with the test compounds, cells were assayed for luciferase activity using Dual-Glo™ Luciferase Assay System (Promega) according to the manufacturer’s protocol. Luminescence was measured with a Spark 10 M luminometer . Normalization of transfection efficiency and cell growth was done by division of firefly luciferase data by renilla luciferase data and multiplying the value by 1000 resulting in relative light units (RLU). Fold activation was obtained by dividing the mean RLU of a test compound at a respective concentration by the mean RLU of untreated control. Max. relative activation refers to fold reporter activation of a test compound divided by the fold activation of the respective reference agonist ). All hybrid assays were validated with the above mentioned reference agonists which yielded EC50 values in agreement with the literature.E. coli and purchased from Geneart , respectively. For expression of fusion proteins with N-terminal green fluorescent protein (GFP), an expression construct based on pET29b was prepared. For this, the entire section between the original NdeI site and the forth position following the His-Tag coding sequence of pET29b was replaced, hence, essentially leaving only the vector backbone unmodified. The section was replaced by a sequence encoding a restriction site for NcoI (overlapping with the start codon) and an open reading frame for Met-Gly-[His10-Tag]-Asp-Tyr-Asp-Ile-Pro-Thr-Thr-[TEV site]-superfolder GFP32 followed by restriction sites for BamHI (in frame) and XhoI. The sequences coding for the LBDs of RXRα  or Nurr1  each followed by a stop codon were then introduced in frame between the afore inserted restriction sites for BamHI and XhoI.The coding sequence for RXRα LBD and Nurr1 LBD was codon optimized for 10) and the SalI restriction site was replaced with a sequence encoding Leu-Gly-Ile-Glu-Leu-Val-[His8-Tag]-Asp-Tyr-Asp-Ile-Pro-Gly-Thr-Leu-[TEV site] followed by an Avi-Tag and restriction sites for BamHI and XhoI. The sequence encoding Nurr1 (aa 364–598) followed by two stop codons was cloned in frame between these restriction sites. From this construct, a fusion protein is expressed with N-terminal maltose-binding protein (MBP) followed by an Asn10 linker, a His8-Tag, a cleavage site for TEV protease, an Avi-Tag, and the Nurr1 LBD with unmodified C-terminus.For generation of biotinylated Nurr1 LBD, the pMal vector system  was used. In pMal-c2E, the section between the sequence encoding 10x Asparagine (AsnE. coli T7 express cells (NEB) were co-transformed with pGro7  and one of the Nurr1  or RXRα (pET) expression constructs and selected overnight at 37 °C on LB (Luria Broth) agar containing 34 µg/ml chloramphenicol and either 100 µg/ml ampicillin  or 35 µg/ml kanamycin (for pET). Culture in liquid LB was inoculated and grown at 37 °C with constant shaking at 180 rpm until optical density at 600 nm (OD600) reached 0.7. At this time point, expression of the chaperone GroEL/ES from pGro7 was induced with 1 g/L L(+)-Arabinose and the temperature was reduced to 20 °C. At OD600 = 1 expression of the target protein was induced by addition of 0.5 mM IPTG. After 12–16 h, cells were harvested by centrifugation and resuspended in buffer A  Glycerol, and 20 mM ß-mercaptoethanol). Cells were kept on ice and disrupted in presence of 1 mM ATP, DNAse I, RNAse A, 20 mM MgSO4, and EDTA-free cOmplete™ protease inhibitor cocktail  by addition of lysozyme and 10 passages through an Invensys APV-1000 homogenizer . Cell debris was removed by centrifugation at 16,500 × g for 20 min at 4 °C.For expression, 2, and E. coli biotin ligase birA at a molar ratio of ~1:10 for enzymatic conjugation of biotin to the lysine residue in the avitag. After overnight incubation at 4 °C, the solution was subjected to a column packed with 5 ml monomeric avidin UltraLink™ resin . Unlabeled protein and birA were removed by washing for 10 column volumes with buffer A before biotin-labeled Nurr1 LBD was eluted using buffer A supplemented with 2 mM biotin. The product was then concentrated and subjected to size exclusion chromatography using a 10/30 Superdex75™ column equilibrated and run in HTRF assay buffer.Purification was achieved by immobilized metal chromatography (IMAC) using columns packed with Ni Sepharose 6 Fast Flow resin on an ÄKTApurifier FPLC system . After washing with buffer supplemented with 50 mM imidazole the protein was eluted with 300 mM imidazole. Afterward, GFP fusion proteins were processed with His tagged TEV protease overnight while imidazole content was reduced to 10 mM by dialysis against buffer A in order to allow for reverse IMAC. The flow through was concentrated and applied to size exclusion chromatography using a 16/60 Superdex200™ column equilibrated and run in HTRF assay buffer . Following the initial IMAC purification step, the MBP fusion protein for generation of biotin-labeled Nurr1 LBD was processed with MBP-tagged TEV protease during overnight dialysis against buffer A. Afterward, uncleaved fusion protein, free MBP-Tag, and TEV protease were removed by passaging through a gravity flow column packed with Amylose High Flow resin (NEB). The flow through was then supplemented with 0.5 mM biotin, 0.5 mM ATP, 5 mM MgClRecruitment of co-regulator peptides to the Nurr1 LBD was studied in a homogeneous time-resolved fluorescence resonance energy transfer (HT-FRET) assay system. Terbium cryptate as streptavidin conjugate  was used as FRET donor for stable coupling to biotinylated recombinant Nurr1-LBD protein. Twenty-nine co-regulator peptides fused to fluorescein as FRET acceptor were purchased from ThermoFisher Scientific . Assay solutions were prepared in HTRF assay buffer supplemented with 0.1% (w/v) CHAPS and contained recombinant biotinylated Nurr1 LBD , Tb-SA (3 nM) and the respective fluorescein-labeled co-regulator peptide (100 nM) as well as 1% DMSO with test compounds at 100 µM or DMSO alone as negative control. All HTRF experiments were carried out in 384 well format using white flat bottom polystyrol microtiter plates . After 2 h incubation at RT, fluorescence intensities (FI) after excitation at 340 nm were recorded at 520 nm for fluorescein acceptor fluorescence and 620 nm for Tb-SA donor fluorescence on a SPARK plate reader (Tecan Group Ltd.). FI520nm was divided by FI620nm and multiplied with 10,000 to give a dimensionless HTRF signal. Dose–response experiments with varying concentrations of the test compounds amodiaquine, chloroquine, meclofenamic acid, parecoxib, and oxaprozin were conducted in the same manner and setting. The co-regulator peptides in this experiment were the following: steroid receptor co-activator (SRC) 1-1, Fluorescein-KYSQTSHKLVQLLTTTAEQQL-OH; SRC 1-2, Fluorescein-LTARHKILHRLLQEGSPSD-OH; SRC 1-3, Fluorescein-ESKDHQLLRYLLDKDEKDL-OH; SRC 1-4, Fluorescein-GPQTPQAQQKSLLQQLLTE-OH; SRC 2-1, Fluorescein-DSKGQTKLLQLLTTKSDQM-OH; SRC 2-2, Fluorescein-LKEKHKILHRLLQDSSSPV-OH; SRC 2-3, Fluorescein-KKKENALLRYLLDKDDTKD-OH; SRC 3-1, Fluorescein-ESKGHKKLLQLLTCSSDDR-OH; SRC 3-2, Fluorescein-LQEKHRILHKLLQNGNSPA-OH; SRC 3-3, Fluorescein-KKENNALLRYLLDRDDPSD-OH; nuclear receptor co-repressor (NCOR) ID1, Fluorescein-RTHRLITLADHICQIITQDFARN-OH; NCOR ID2, Fluorescein-DPASNLGLEDIIRKALMGSFDDK-OH; silencing mediator for retinoid and thyroid hormone receptor (SMRT) ID1, Fluorescein-GHQRVVTLAQHISEVITQDYTRH-OH; SMRT ID2, Fluorescein-HASTNMGLEAIIRKALMGKYDQW-OH; CREB-binding protein 1 (CBP-1), Fluorescein-AASKHKQLSELLRGGSGSS-OH; C33, Fluorescein-HVEMHPLLMGLLMESQWGA-OH; D11-FXXLF, Fluorescein-VESGSSRFMQLFMANDLLT-OH; D22, Fluorescein-LPYEGSLLLKLLRAPVEEV-OH; EAB1, Fluorescein-SSNHQSSRLIELLSR-OH; EA2, Fluorescein-SSKGVLWRMLAEPVSR-OH; androgen receptor-associated protein 70 (ARA70), Fluorescein-SRETSEKFKLLFQSYNVND-OH; N-terminal sequence of androgen receptor (AR N-term), Fluorescein-SKTYRGAFQNLFQSVREVI-OH; peroxisome proliferator-activated receptor gamma co-activator 1-alpha (PGC1a), Fluorescein-EAEEPSLLKKLLLAPANTQ-OH; nuclear receptor co-activator 6 , Fluorescein-VTLTSPLLVNLLQSDISAG-OH, nuclear receptor interacting protein 1 , Fluorescein-SHQKVTLLQLLLGHKNEEN-OH; RIP140L8, Fluorescein-SFSKNGLLSRLLRQNQDSY-OH; TB3, Fluorescein-SSVASREWWVRELSR-OH; thyroid hormone receptor-associated protein (TRAP) TRAP220/DRIP-1, Fluorescein-KVSQNPILTSLLQITGNGG-OH; TRAP220/DRIP-2, Fluorescein-NTKNHPMLMNLLKDNPAQD-OH.Strength and modulation of the formation of the heterodimer composed of the LBDs of Nurr1 and RXRα was investigated by titration of GFP-RXRα LBD against a fixed concentration of Nurr1 LBD. Assay solutions were prepared in HTRF assay buffer supplemented with 0.1% (w/v) CHAPS as well as 1% DMSO with test compounds at 100 µM or DMSO alone as negative control. The FRET donor complex formed from biotinylated Nurr1 LBD  and Tb-SA (0.75 nM) was kept constant while the concentration of GFP-RXRα LBD was varied starting with 4 µM as the highest concentration and titrated with a dilution factor of 0.7. Free GFP was added to keep the total GFP content stable at 4 µM throughout the entire series in order to suppress artefacts from changes in degree of diffusion enhanced FRET. Samples were equilibrated at RT for 2 h before FI520 and FI620 were recorded after excitation at 340 nm, and the HTRF signal was calculated as described above.Nurr1 homodimerization was studied by the same strategy using GFP-Nurr1 LBD instead of GFP-RXRα LBD. Since affinity observed for Nurr1 homodimer formation was higher, the maximum concentration for GFP-Nurr1 LBD and the total GFP concentration was reduced to 500 nM.The Nurr1–NCoR-1 interaction was studied by titrating GFP-Nurr1 LBD against biotinylated NCoR-1 copeptide (18 nM) and Tb-SA (12 nM) in presence of a fixed concentration  of the respective ligand or 1% DMSO. To maintain a constant GFP concentration, free GFP protein was added to the dilution series. The experiments were performed in HTRF assay buffer , 5% (w/v) Glycerol, supplemented with 0.1% (w/v) CHAPS and 5 mM DTT) with 1% DMSO in an assay volume of 20 µl. After 1 h incubation at RT, fluorescence intensities after excitation at 340 nm were recorded at 520 nm for GFP acceptor fluorescence and 620 nm for Tb-SA donor fluorescence and the HTRF signal was calculated as described above.General: Calculations were conducted in Molecular Operating Environment  using default settings for each tool/function unless stated otherwise. Crystal structure analysis: Alignment of the Nurr1 LBD in apo state (PDB: 1OVL1), bound to prostaglandin A1 (PDB: 5Y417) and to dopamine metabolite DHI (PDB: 6DDA9) was conducted using MOE sequence editor. The subunits B were used in all cases. The proposed binding region for amodiaquine type ligands was highlighted according to annotated amino acids from NMR perturbation experiments and mutational studies6. Distances of the salt bridge between Lys590 to Glu440 were measured in angstrom (Å) in MOE from nitrogen (NH3+) of Lys590 to oxygen (O−) of Glu440. Data shown are the mean ± SD corresponding to the different subunits of the X-ray structures.n ≥ 3), each in duplicates. The cell-free experiments were performed with three technical replicates (N = 3), whereas the cofactor screen was performed with four technical replicates (N = 4). All dose–response curves were calculated in GraphPad Prism using a nonlinear regression with variable slope . Statistical significance was evaluated by two-tailed student’s t-test  with n ≥ 4. Results were considered statistically significant with p values < 0.05; significance levels are denoted as *p < 0.05, **p < 0.01, ***p < 0.001. Boxplots were generated in GraphPad Prism and show: center line, median; box limits, upper and lower quartiles; whiskers, min/max; n ≥ 4.Calculations and graphical analysis of experimental data was conducted using GraphPad Prism version 7.00 for Windows  and Microsoft Excel 2016 . All cellular experiments were performed with at least three independent biological repeats (Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Eurytemora affinis, to elucidate the evolutionary genomic response to rapid salinity decline. Rapid declines in coastal salinity at high latitudes are a predicted consequence of global climate change. Based on time-resolved pooled whole-genome sequencing, we uncover a remarkably parallel, polygenic response across ten replicate selection lines, with 79.4% of selected alleles shared between lines by the tenth generation of natural selection. Using extensive computer simulations of our experiment conditions, we find that this polygenic parallelism is consistent with positive synergistic epistasis among alleles, far more so than other mechanisms tested. Our study provides experimental and theoretical support for a novel mechanism promoting repeatable polygenic adaptation, a phenomenon that may be common for selection on complex physiological traits.The role of epistasis in driving adaptation has remained an unresolved problem dating back to the Evolutionary Synthesis. In particular, whether epistatic interactions among genes could promote parallel evolution remains unexplored. To address this problem, we employ an Evolve and Resequence (E&R) experiment, using the copepod  Using time-series whole-genome sequencing data from a laboratory evolution experiment, along with extensive computer simulations, the authors show that synergistic epistasis could drive rapid parallel freshwater adaptation in a saline copepod. A longstanding debate in evolutionary biology regards the role of epistasis in polygenic adaptation. Epistasis refers to cases where the effects of alleles at different loci are non-additive with respect to their contribution to a quantitative phenotype, such that allelic effects are dependent on the presence of other alleles at other loci4. Dating back to the Evolutionary Synthesis, R.A. Fisher’s influential infinitesimal model assumed that many alleles contribute independent, small effects toward fitness6, whereas Sewell Wright’s shifting balance theory placed paramount importance on allelic combinations 9.An abundance of recent population genomic studies has found that adaptation is often highly polygenic, with hundreds or thousands of loci responding to environmental change11. Here we define parallel evolution as evolution driven by natural selection favoring the same loci or mutations in independent populations exposed to the same environmental challenge15. Under Fisher’s model, adopted by the field of quantitative genetics, adaptation is predicted to proceed with low levels of parallelism at the genetic level where different, effectively interchangeable, loci could contribute to adaptation through a diversity of alternative evolutionary pathways3. A key assumption made by this model is that contributing alleles are redundant in function, resulting in the expectation of non-parallelism18. With genetic redundancy, different populations can reach the same adaptive optimum through frequency changes of different sets of alleles3. Alternatively, if allelic effects are non-redundant, due to epistatic effects among specific alleles, then selection might favor the same combination of alleles across independent adaptive events. In such cases when alleles are functionally linked, polygenic adaptation could become highly parallel20.This debate is particularly important for our understanding of mechanisms of adaptation, including parallel evolution, and therefore the ability to predict future evolutionary genomic responses to global change22. Nevertheless, some recent theoretical studies have indicated that epistasis could have important impacts on long term responses to selection25. Synergistic fitness effects could arise among alleles  in cases such as co-adapted gene complexes, which could be common for physiological phenotypes26. For example, ion transport is achieved through a suite of cooperating proteins functioning in a coordinated fashion28, such that the effects of any given genetic variant are likely nonredundant and their specific effects depend strongly on the other alleles present. However, the role of epistasis remains largely overlooked in genomic studies30 and its contribution to producing patterns of parallel polygenic evolution is unknown.Despite Wright’s avid interest, epistasis has long been treated as statistical noise that does not contribute directly to adaptation34, especially for small-effect loci. While some genomic studies have found more genetic parallelism than expected by chance36, such studies tended to observe fewer than 50% of selected alleles in common between populations exposed to the same selection pressure14. In addition to epistasis, several factors have been proposed that could promote parallelism, including large distance to the new trait optimum, low divergence between populations, higher parallelism for large effect and high frequency alleles, pleiotropy, and others37. Yet, the relative impacts of these factors in promoting molecular parallelism remain unknown. Therefore, understanding the basis of molecular parallelism requires approaches that can accurately characterize the genomic architecture and evolutionary trajectory of polygenic adaptation to tease apart putatively important factors38.Genomic studies of adaptation in wild and experimentally evolved populations have often uncovered non-parallel molecular evolution6. Analyzing replicated evolutionary events combined with whole-genome sequencing could generate sufficient power to distinguish polygenic shifts from genetic drift and uncover the degree of parallel evolution. Achieving such replication is possible with laboratory evolution experiments38. Yet to date, such experiments in metazoans have been limited to a small number of taxa, providing limited examples for generalization. For instance, an experimental evolution study using Drosophila revealed that polygenic adaptation to a novel thermal environment proceeded in a non-parallel fashion, with alleles at different loci responding to selection across independent lines39. However, thermal adaptation involves widespread physiological processes, with temperature affecting all metabolic pathways in ectotherms41 and likely having many redundant genetic components39. Therefore, to determine the conditions under which polygenic adaptation can be repeatable, we require additional studies using alternate selection pressures and a variety of systems.A limitation to identifying mechanisms of parallel polygenic adaptation has been the challenge of detecting its signatures in genomes. Polygenic adaptation is predicted to result in subtle allele frequency shifts that could be indistinguishable from genetic drift using standard approachesEurytemora affinis, a key grazer in coastal ecosystems. Climate change is inducing rapid salinity transformations in coastal waters across the globe44, yet little is known regarding the extent and evolutionary trajectory of rapid genomic adaptation to declining salinity. As such, the specific goals of this study are to (1) characterize the evolutionary genomic response of salinity adaptation in terms of the number, genomic distribution, and fitness effects of contributing alleles, (2) evaluate the degree and genetic basis of parallel evolution across replicate selection lines, given the theoretical expectations for polygenic adaptation, and (3) determine whether our experimental results are replicated in wild populations found across a salinity gradient.Thus, we employ a laboratory experimental evolution approach, combined with time-resolved whole genome sequencing , to dissect the genomic basis of adaptation to salinity decline in the copepod To address our goals, we expose ten replicate lines to salinity decline for ten generations alongside four control lines maintained at constant salinity. These lines are then sampled for pooled whole-genome sequencing at three timepoints. Our replicated and controlled experimental design gives us the power to detect targets of selection characterized by subtle allele frequency shifts from standing genetic variants. Next, we perform computer simulations under different models of genetic architecture to evaluate the expectations for the degree of parallelism across replicate lines and explore the contribution of different factors in promoting parallel genomic evolution. Finally, we perform pooled-whole genome sequencing for eight wild populations collected from a range of salinities in the Baltic Sea to test whether the loci under selection in the laboratory experiment also exhibit signatures of selection across a natural salinity gradient in the wild.15.In this study, we uncover highly parallel polygenic adaptation in a laboratory natural selection experiment. Using extensive population and quantitative genetic simulations in conjunction with our experimental results, we find strong support for a potentially widespread mechanism, namely positive epistasis, in promoting this parallel response. Our findings provide unique insights into a longstanding question in population genomics regarding the prevalence and causes of parallel evolution. In addition, our results point to the direct applicability of experimental evolution for predicting future evolutionary responses to climate changeExposure to low salinity over ten generations resulted in a dramatic, genome-wide evolutionary response in the ten treatment (selection) lines Fig. . To dete45 , with a mean value of 0.133  enrichment analysis for genes containing or proximal to SNPs underlying our selected alleles. This analysis implicated ion transport gene function as a primary physiological target of selection (Supplementary Data P < 0.05) identified using Gowinda47, 29 (32%) were related to transmembrane transport , ion channel activity , the nervous system , and muscle contraction .The evolutionary trajectory of adaptation depends on the complexity of the trait(s) under selection48. Of particular interest was a selected allele 2.7 Mb long that spanned a region containing seven paralogues of the NHA gene family, which encode sodium-hydrogen antiporter proteins  and number of replicate lines in which the allele exhibited a significant frequency shift , with an average of 79.5% overlap in selected alleles between replicate lines in generation ten (based on the Jaccard indexNe) and selection coefficients (s), and with significant frequency shifts determined in the same manner as for the empirical data, resulted in mean Jaccard indices of only 0.33 ± 0.015 SD at generation six and 0.52 ± 0.016 SD at generation ten . In addition, our empirical Replicate Frequency Spectrum , the much greater degree of parallelism of allele frequency shifts in our experimental lines was quite exceptional  could reduce parallelism53. Although our selected alleles were putatively unlinked, we also explored the effect of physical linkage between alleles by simulating realistic recombination architectures for the 121 alleles (haplotype blocks). Surprisingly, we found that in fact physical linkage only slightly decreased the Jaccard index by ~0.003 on average across models . As populations in our simulations started from linkage equilibrium, future work should investigate the effect of starting LD structure on the degree of parallelism.Physical linkage could theoretically result in a high degree of parallelism between lines if selected alleles tend to be found close together on the same haplotype and are inherited together39 examined selection response to a changing temperature regime, which affects a broad range of physiological processes41, and found highly heterogeneous responses among replicate experimental lines with much lower parallelism than predicted under the “multiplicative fitness” model. The exceptionally high levels of parallelism found in our experiment, relative to the baseline multiplicative model  and ten , indicating that this level of positive epistasis was sufficient to explain the degree of parallelism observed in our data. By comparing the Jaccard index under simulations of the “multiplicative” model (α = 0) relative to the “positive epistasis” model (α = 36.5), we found that positive epistasis could account for an increase of up to 0.27 in Jaccard index of selected alleles among the replicate lines at generation ten , then these functionally linked alleles would behave like a smaller number of alleles contributing to the adaptive response and would tend to have a parallel selection response. We found that depending on the fitness function, our 121 selected haplotype blocks  responded as if they were 20 or fewer alleles in terms of the degree of parallelism . We found that SNPs characterizing selected haplotypes were at significantly higher starting frequencies than non-selected SNPs . This finding was not an artifact of selected SNPs with low starting frequencies being difficult to detect using the CMH test58. In fact, our CMH test results showed a negative correlation between the test statistic and starting frequency , suggesting that lower-frequency SNPs were in fact more detectable as targets of selection. Our LMM test showed no significant correlation between the test statistic and starting frequency , indicating no clear bias.As our previous study using this copepod system implicated selection on high-frequency alleles, potentially maintained by balancing selection, as a promoter of parallelismr = −0.260, t value = −2.94, DF = 119, P = 0.00399; Generation ten: r = −0.214, t value = −2.40, DF = 119, P = 0.0183). We also used theoretical model simulations to evaluate the potential contribution of selection on high-frequency alleles toward promoting parallelism. Using the same simulation schemes as above, we compared the degree of parallelism observed when (1) alleles started from mutation-drift balance  and (2) alleles started with a frequency of 0.5, meant to maximize the potential effect. We found that simulating selection from mutation-drift balance only decreased the Jaccard index by ~0.001 on average across models, relative to simulations from empirical frequencies  harbored segregating SNPs in the wild populations. Due to the polygenic nature of the selection response observed in the laboratory, we used the method of Berg and Coop (2014)59 to test for a population genomic signature of polygenic selection on the SNPs underlying experiment-selected alleles (haplotype blocks) in the Baltic Sea populations. Rather than analyzing each SNP separately, this method tests a set of trait-associated loci for a signature of selection, captured in the QX statistic, based on the degree to which SNPs at those loci vary across populations and covary with each other. We found that the QX statistic for experiment-selected loci was significantly higher than that of the null distribution of genomic background loci matched in minor allele frequency , indicating that alleles that responded to selection in our experiment were more common in Baltic Sea populations from lower-salinity habitats.Population genomic analyses revealed that experiment-selected alleles also exhibited signatures of selection in wild ons Fig.  spanningons Fig. . This stf4 statistic60, we found genomic evidence for considerable gene flow among geographically distant populations. The f4 statistic tests whether the possible phylogenetic trees relating populations are consistent with admixture among populations. Gene flow or admixture is inferred if all three possible trees for a group of four populations are rejected by the data60. Testing all 126 possible groupings of four populations, 47 groupings (37.3%) showed significant support for gene flow  locier Figs. , 2, in s80. Due to massive increases in ice melt and precipitation, salinity is predicted to decline at unprecedented rates in higher latitude coastal waters, by up to 5 PSU in the coming decades44. Salinity is arguably the strongest driver of aquatic biogeographic distributions83. Therefore, changes in this critical environmental stressor will force populations to adapt, migrate, or face extinction. Zooplankton populations, which constitute the largest animal biomass in water column habitats, may be particularly vulnerable to changes in salinity because of their limited ability to migrate and often narrow salinity tolerance ranges84. This study makes important contributions toward understanding the evolutionary mechanisms of how populations will respond to such rapid changes in environmental salinity.Among the most ecologically and economically impactful consequences of global climate change is the rapid change in ocean salinity throughout the globeE. affinis populations in the Baltic Sea have a high probability of adapting rapidly to decreasing salinity caused by climate change. The observation that the same alleles responded to laboratory selection across replicate lines suggests that the ability to adapt to decreasing salinity relies on a predictable set of genetic variants that we also found present in the wild populations. Although our laboratory selection experiment was performed on a population derived from a single location, potentially biasing our discovery of selected loci, we did find that the majority of experiment-selected genetic variants (74%) were segregating in wild Baltic Sea populations and that many were found at high and intermediate frequencies  in 2017 (~1000 copepods) and on May 30, 2018 . The two collections of copepods were mixed and maintained at 15 PSU to increase population size and acclimate to laboratory conditions. Two samples of adult copepods  from the mixed culture were collected for pooled whole-genome sequencing (Pool-seq) to represent the starting population for the laboratory natural selection experiment and capture variance in starting SNP frequency. The culture was then split into 14 equally sized beakers. Control lines (N = 4) were maintained for the duration of the experiment in 15 PSU water made with Instant Ocean, along with Primaxin (20 mg/L) to avoid bacterial infection. The control lines were fed the marine alga Rhodomonas salina every three to four days with water changed weekly. The ten treatment lines were exposed to decreasing salinity over the first six generations until they reached 0 PSU , and then maintained at 0 PSU for four additional generations.All copepod populations examined in this study, including that used in the laboratory evolution experiment and those surveyed in the wild are considered 88. Treatment lines were fed a 50:50 mixture of R. salina and the freshwater alga R. minuta at 5 PSU and only R. minuta at 1 PSU and below. These algal species are closely-related cryptophytes89 that are both are rich in long-chain polyunsaturated acids and are the preferred food source of Eurytemora populations91. As these algal cells are highly sensitive to osmotic shock during salinity change, changing the food source from saline to freshwater Rhodomonas was unavoidable during salinity decline in the experiment. Although population size remained fairly constant for most treatment lines, two treatment lines went extinct between generations six and ten (BSE-7 and BSE-10) and were therefore sequenced only at generation six.Beginning at generation two, salinity declination proceeded at each generation as follows: 5 PSU, 1 PSU, 0.1 PSU, 0.01 PSU, 0 PSU. Animals were not transferred individually to the next generation but instead were allowed to survive and reproduce undisturbed with overlapping generations. The generation number was monitored by assuming a generation time of approximately three weeksN = 50; 25 male and 25 female) were collected for sequencing from each laboratory selection line at generations six (after one generation at 0 PSU in the treatment lines) and ten (after five generations at 0 PSU in the treatment lines). Two of the control lines (BS3C and BS4C) were also sampled at generation 20 to increase the number of sampled timepoints for our LMM test for selection. Sampled copepods from each line , using data collected from 1995-2020. From each population, individual copepods (ranging from 50 to 200 in number) were pooled and their DNA was extracted using the DNeasy Blood and Tissue Extraction kit . Paired-end whole-genome sequencing libraries were prepared using the Illumina Nextera DNA kit  and sequenced on five lanes of an Illumina HiSeq 4000 sequencer at the University of Chicago Genomics Facility, generating an average of ~176 million paired-end (100 bp) reads per pool.Additionally, wild Sea Fig.  using boEurytemora affinis complex was constructed from long-read and long-range sequencing technology. To generate genomic data for assembly, an inbred line was generated from 30 generations of full-sibling mating of copepods derived from a saline population in Baie de L’Isle Verte, St. Lawrence marsh, Quebec, Canada . Prior to DNA extraction, the culture was treated with a series of antibiotics to reduce bacterial contamination, including Primaxin (20 mg/l), Voriconazole (0.5 mg/l for at least 2 weeks prior to DNA extraction), D-amino acids to reduce biofilm . DNA was extracted from pooled adult copepods and used to generate Pacific Biosciences (PacBio) long-read data , Dovetail Chicago® (213 million 150 bp read pairs) and Hi-C (171 million 150 bp read pairs) Illumina HiSeq X data.A draft genome for wtdbg v2.592 and polished with Racon v1.4.393. This procedure resulted in an assembly of size 575 Mb in 3013 contigs with an N50 of 951 kb, an N90 of 106 kb, and maximum contig length of 7.7 Mb. Contigs were scaffolded with HiRiseTM (Dovetail Genomics) using Dovetail Chicago® and Hi-C data. This procedure resulted in a highly contiguous assembly with 90% of the genome present in four scaffolds , likely corresponding to the four chromosomes of E. affinis observed in karyotype (data not shown). These four scaffolds consisted of 518 Mb of sequence. Gene annotations were generated by mapping annotations from the previously published, short-read-assembled genome of E. affinis complex94 using LiftOff v1.6.195.PacBio reads were assembled into contigs using E. affinis populations, the subject of the laboratory selection experiment, are highly genetically divergent from North American populations of the E. affinis complex86, from which the draft genome was derived. Therefore, to maximize the number and accuracy of SNP calls, a pseudo-reference genome was assembled for SNP calling following an approach used in a previous study96. This approach uses a reference transcriptome as an anchor to assemble Pool-seq data around coding regions, generating a reference assembly that includes coding sequences and surrounding sequence with putative regulatory function.European E. affinis laboratory culture derived from Kiel, Germany. Animals were pooled and subjected to a Trizol/Qiagen RNeasy hybrid total RNA extraction protocol. An mRNA sequencing library was prepared using the Illumina TruSeq Stranded mRNA kit and sequenced on an Illumina HiSeq 4000 sequencer, generating approximately 186 million 100 bp paired-end reads. Raw RNA-seq reads were processed using BBDuk in the BBtools v38.37 package (https://sourceforge.net/projects/bbmap/) to filter adapter sequences, low complexity sequences, and low quality (Q < 10) bases using a sliding window (ktrim = r k = 23 mink = 11 hdist = 1 qtrim = w trimq = 10 minlen = 36 entropy = 0.01 entropywindow = 50 entropyk = 5 tbo).To generate RNA-seq data for assembly of the reference transcriptome, approximately 100 individuals of all life stages were collected from the European 97. Cleaned reads were assembled using Trinity v2.6.6 in strand-specific mode98. mRNA expression estimates were made using RSEM v1.3.199 with cleaned reads mapped to the Trinity assembly with Bowtie 297. Only the most highly expressed Trinity “isoform” per ‘gene’ was retained to obtain the most highly supported transcriptome assembly. Transcript sequences were clustered to 95% similarity using CD-HIT v4.7100 to reduce redundancy associated with allelic variation and assembly errors. Transdecoder v5.5101 was used to predict open reading frames and coding sequences. Predictions included BLASTP102 (BLAST + v2.7.1) hits to annotated proteins of the E. affinis complex draft genome and HMMER v3.2.1 (http://hmmer.org) hits to the Pfam protein database103.To filter sequences that may have originated from microbial contamination prior to assembly, reads were mapped against a database of reference and representative bacterial, archaeal, and fungal genomes from the NCBI RefSeq using Bowtie 2 v2.3.5 and were removed if they mapped concordantly104, retaining reads with a mapping quality >20. The corresponding “right” pairs of the mapped reads were extracted and the mapped reads were assembled using Trinity v. 2.6.698, as Trinity was developed to assemble highly variable sequences with potentially uneven coverage. The resulting assembly was clustered to 95% similarity using CD-HIT v4.7100. The aforementioned mapping and assembly procedures were repeated one additional time, mapping reads to the Trinity-assembled Pool-seq data rather than to the transcriptome. In this way, the assembly was extended into genomic regions surrounding the transcriptome-derived coding sequences. Finally, only assembled contigs with a significant BLASTN hit  to the draft genome (see above) were retained to further eliminate assembly errors. The pseudo-reference genome ultimately spanned a greater portion of the genome with greater contiguity than the transcriptome . Quality-filtered Pool-seq reads were mapped to the pseudo-reference genome using BWA-MEM v0.7.17104 retaining only reads that mapped concordantly with a mapping quality >20. Duplicate reads were removed using Picard v2.18.27 (http://broadinstitute.github.io/picard) and regions around indels were realigned using GATK v3.8106. For the laboratory lines and wild populations separately, SAMtools v1.3.1 was used to convert BAM files into mpileup format after removing low-quality alignments and bases (Q < 20). VarScan v2.4.3107 mpileup2cns was used to call SNPs for each of the mpileup files using the following options: “–min-coverage 20 –min-avg-qual 20 –min-var-freq 0.0001 –variants –output-vcf”. The resultant VCF files were processed using the R package poolfstat v. 1.1.1108 retaining bi-allelic SNPs with 4 reads required for a base call, an overall minimum MAF of 0.01, and a minimum and maximum coverage of 10 and 200 reads, respectively. This procedure resulted in 367,846 SNPs called in the laboratory samples and 693,438 SNPs called in the wild samples.Raw Pool-seq reads collected from the laboratory selection lines and Baltic Sea wild populations were processed with Trimmomatic v0.39Ne) for each line with the R package poolSeq v0.3.5110 and WFABC v1.1111. Using the poolSeq model, we made estimates using both the “Plan I” (census-size dependent) and “Plan II” (census-size independent) methods, and, given the uncertainty in the census population size, assumed a range of reasonable census sizes for the “Plan I” calculations. We made Ne estimates in genomic windows of 1000 SNPs in size and took the median of those windows  was considered the response variable in the linear regressions with weights proportional to sequencing depth and number of individuals sampled (Neff)115. An angular transformation  was applied to SNP frequencies to normalize the percentage values and reduce bias due to different starting frequencies118.As the CMH and Chi-square tests did not take full advantage of our sampling design with control and treatment lines, SNPs were also tested for signatures of selection using LMMs in the R package treatment (2 levels) on SNP frequency change from the start of the experiment (the response), considering the fixed effect of generation (2 levels) and the random effect of line (14 levels). The following two models were compared: (1) yi ~ generation + (1|line) + εi and (2) yi ~ generation + treatment + generation:treatment + (1|line) + εi, where yi is the SNP frequency change from the starting population at the ith SNP and εi is the Gaussian error at the ith SNP, given the sequencing coverage and number of individuals sampled. LRT test statistics were compared to the Chi-square distribution with two degrees of freedom to obtain p values. P values were converted to q values to correct for multiple testing113 and SNPs with a q value < 0.05 were considered significant.Likelihood ratio tests (LRT) were used to test for the significance of the effect of haplovalidate119 considering the eight treatment lines with three sampled timepoints. This approach identifies and delimits selected haplotype blocks by clustering candidate SNPs with correlated frequency changes. The union of candidate SNPs from all three tests for selection  were used as input to haplovalidate, which was run with default settings. Identified selected haplotype blocks were considered as selected alleles. The median frequency of SNPs characterizing each haplotype block was used to estimate allele frequencies at each timepoint in each line. To determine whether an allele had responded to selection in each line and timepoint, we simulated 10,000 iterations of neutral evolution using the R package poolSeq109 for each allele’s starting frequency using an effective population size of 1750  were estimated by linear modeling with lme4114 from the slope of the linear regression of logit-transformed allele frequencies against generation, averaged across lines. The slope of this regression has been shown to accurately estimate s, assuming a continuous-time approximation to the Wright-Fisher model and codominance109. The factor line was considered a random effect in the linear model with weights proportional to sequencing depth and number of individuals sampled (Neff)115. We also calculated s using the poolSeq R package, which also uses lme4 to regress logit-transformed allele frequencies against generation, but without considering the random effect of line or variable sequencing depth. The two estimates of s were well correlated .Selection coefficients  indicates the strength and sign of the epistatic effect. Furthermore, this framework allows for mutations of variable effect sizes and for mutations with larger individual effect sizes to have larger epistatic effects  epistasis under a population genetic (multiplicative fitness) framework by extending Keightley and Otto’sis Table . The “mu50 and are also available in the simulation software MimicrEE2121. We simulated under multiple different quantitative fitness functions for two reasons: (1) our experiment allowed individuals to survive and replicate naturally , and therefore the exact mode of selection is unknown, and (2) our experimental design included a moving optimum , which was not perfectly captured by available fitness functions. Therefore, simulating under a range of different quantitative fitness functions could help capture the allele frequency dynamics under the quantitative genetic paradigm, regardless of the exact (and unknown) mode of selection.In addition, we included several quantitative fitness functions with epistatic effects, in which the positive fitness effects of alleles increase in the presence of additional alleles Table . The quaMimicrEE2121; however, we changed the name to avoid confusion, because negative epistasis also has diminishing fitness effects as the phenotype increases.The Shifted Optimum QT model is a standard QTL model with a Gaussian fitness curve around a phenotypic optimum Table . The DirOverall, parameter values under the QT models (Supplementary Table (1) 121 unlinked alleles (haplotype blocks), (2) 121 alleles with linkage and recombination, and (3) 4,977 SNPs on 121 unlinked haplotype blocks with linkage and recombination between SNPs on the same haplotype block. In simulations with linkage, alleles recombined based on their physical distance using the copepod Tigriopus californicus recombination rate of 1.6 cM/Mb122. Selected alleles started at observed starting frequencies unless otherwise specified. In all cases, whether an individual carried an allele at the start of the simulation was based solely on that allele’s starting frequency , the entclass1pt{minimaUnder a standard population genetic, multiplicative framework Table  an indivs) times. On the other hand, under our epistatic model the fitness benefit of the having a single allele isFor a concrete example, imagine a haploid individual with 121 loci and Detecting signatures of selection in the laboratory natural selection experiment]), and where every locus had a starting frequency of 0.5. In the cases where the number of loci contributing to a trait was varied . For this reason we introduced 47, which takes into account gene length and SNP density, using all SNPs underlying selected haplotypes. Genes within 10 kb of a selected SNP were included in the analysis, with each gene counted only once (“gene” mode). Gene ontology terms  associated with each gene model were derived from significant (E < 0.001) BLASTN hits to the UniProtKB Swiss-Prot database.Gene ontology enrichment analyses were performed using Gowinda v1.1259 was used to test for signatures of polygenic selection on experiment-selected SNPs in eight wild populations from the Baltic Sea  was estimated using three samples of 50,000 background SNPs. To test whether experiment-selected loci evolved faster than background loci, that is, exhibited a signature of selection, a null distribution of QX statistics was calculated using 10,000 samples of background SNPs matched in minor allele frequency to the experiment-selected loci. This null distribution was used to calculate a p value for whether the experiment-selected loci evolved significantly faster than the genomic background.The method of Berg and CoopSea Fig. . Using tf4 statistic60 implemented in TreeMix v. 1.13125 for every combination of four populations, calculating standard errors with groups of ten SNPs. We considered the f4 test significant if all three of the possible four-population trees were rejected with a |Z-score| > 2.To test for signatures of gene flow among geographically distant populations, we calculated the Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-8Reporting Summary


Bnt05G007759), two associated with FWPC , and two associated with leaf dry weight per plant (LDWP) and dry weight per plant (DWP) , were identified. We describe forage trait candidate genes that are highly correlated with FWP and FWPC; Bnt05G007759 may be involved in nitrogen metabolism, while Bnt13G018582 and Bnt13G018583 may encode TEOSINTE branch 1/CYCLOIDEA/proliferating cytokine 1 (TCP) domains. Bnt04G005779 and Bnt04G005780, which may regulate growth and development, are highly related to LDWP and DWP. These genomic resources will provide a basis for breeding varieties.Genome-wide association study (GWAS) of six forage traits using whole-genome sequencing data generated from 301 ramie accessions found that traits were continuously distributed; the maximum variant coefficient was fresh weight per clump (FWPC) (2019) and individual plant height (IPH) (2019) minimum. Correlation analysis demonstrated that 2019 and 2020 results were similar; all traits were correlated. GWAS analysis demonstrated that six traits exhibited consistent and precise association signals. Of the latter, 104 were significant and detected in 43 genomic regions. By screening forage trait-associated single nucleotide polymorphisms and combining Manhattan map with genome annotation, signals were categorized according to functional annotations. One loci associated with fresh weight per plant (FWP) (chromosome 5;  Ramie (China grass) is one of the oldest vegetable fiber crops in China, and its fibers have been used as textiles for over 4700 years in China . HoweverThe demand for animal products has increased markedly and protein-rich feed shortages have consequently increased sharply . To meetIndividual plant height (IPH) and fresh weight per plant (FWP) are important factors influencing ramie biomass yield. CP and fiber components are two critical indices of forage quality that affect digestibility and nutritional intake during animal feeding . In receVerticillium wilt in cotton using GWAS [Arabidopsis was determined by conducting GWAS [The aim of genome-wide association studies (GWAS) is to discern a large number of SNPs and identify trait-associated SNPs to reveal their biological mechanisms . Compareing GWAS . GWAS heing GWAS . Furthering GWAS . GWAS haing GWAS . HoweverA total of 301 ramie varieties were harvested on 10 June 2019, and 2020, and the traits related to forage were recorded for assessment. The results indicated that all traits showed tremendous phenotypic variation and continuous distribution in the population with the coefficient of variation ranging from 18.0% to 57.0% and were close to fitting a normal distribution . TherefoCorrelation analysis was performed to gain insight into the relationships between these traits . The resThe genetic structure of a population can markedly influence the power of a GWAS; thus, before performing the GWAS, we inferred the population structure using a subset of 1,433,616 SNPs without close linkages . Our pre10 p-value is 6, the statistical model conforms to the expected value. In total, 78 significant association signals were detected in 43 genomic regions (p = 1.5 × 10−8) among all association SNPs. In addition, there was another significant SNP on chromosome 4 , which was significantly correlated with the traits of LDWP and DWP, and was located between Bnt04G005779 and Bnt04G005780 genes.To identify potential candidate genes related to forage traits in natural populations of ramie accessions, according to 5,951,826 SNPs  identifi regions . Of the 10 p-value was > 6, SNPs above this threshold were deemed to be significantly associated with the target traits. We examined genes containing SNPs associated with forage traits and revealed that 78 forage trait-associated SNPs were located in 43 gene regions (including the genic region and upstream/downstream region within 20 kb) (When a −logn 20 kb) . We caten 20 kb) . Next, wn 20 kb) .Bnt05G007759  transcription factor family, which participates in cell growth, leaf tissue proliferation, and branch growth [Bnt13G018582 and Bnt13G018583 are potential candidate genes for the traits of FWPC.Among the signals associated with the FWPC trait, we found a particularly interesting association locus on chromosome 5 that was associated with the FWPC trait located between the genes h growth ,28. The h growth . FWPC isBnt04G005779 and Bnt04G005780 , FWP (fresh weight of each plant cut off from the ground), DWP (dry weight measured per plant), LDWP (dry weight of leaves per plant after drying), stem dry weight per plant (SDWP: dry weight of stem after drying), and FWPC , were recorded for assessment. According to the normal cultivation method, 301 accessions were harvested on 10 June 2019, and 10 June 2020. Afterwards, the traits were investigated in the second growing season of each year, when the ramie grew to about a meter high.Multiple comparisons of all parameters were conducted using one-way analysis of variance (ANOVA) and Tukey’s honest significant difference test.Descriptive statistics were computed using SPSS 22.0. Microsoft Excel 2010 was used to determine the frequency distribution for each trait.2 of each allele was estimated using PopLDdecay (version: 3.34) with the following parameters: −MAF 0.05 −Miss 0.2 −MaxDist 300 [2 values were calculated for each distance.All SNPs from ramie accessions were filtered using the PLINK software  , generatp-value threshold [https://github.com/YinLiLin/RCMplot (accessed on 8 December 2020).). According to genome annotation, SNPs were divided into five regions: exon, intron, upstream, downstream, and intergenic. After screening for counterfeit SNPs, high-quality and significantly correlated SNPs were obtained, and the threshold was log10 p-value ≥ 6.Genome sequence was performed for 301 accessions using the next-generation Illumina HiSeq platform. Sequencing and analysis were conducted by OE Biotech Co., Ltd. . Raw sequence reads were filtered to produce clean reads that were aligned to the ramie genome  using BWA (version 0.7.15), allowing no more than 4% mismatch and one gap . The alihreshold . ManhattBLAST analysis was conducted on the ramie genome using favorable SNP sequences , and det