Aedes aegypti vectors the pathogens that cause dengue, yellow fever, Zika virus, and chikungunya and is a serious threat to public health in tropical regions. Decades of work has illuminated many aspects of Ae. aegypti's biology and global population structure and has identified insecticide resistance genes; however, the size and repetitive nature of the Ae. aegypti genome have limited our ability to detect positive selection in this mosquito. Combining new whole genome sequences from Colombia with publicly available data from Africa and the Americas, we identify multiple strong candidate selective sweeps in Ae. aegypti, many of which overlap genes linked to or implicated in insecticide resistance. We examine the voltage-gated sodium channel gene in three American cohorts and find evidence for successive selective sweeps in Colombia. The most recent sweep encompasses an intermediate-frequency haplotype containing four candidate insecticide resistance mutations that are in near-perfect linkage disequilibrium with one another in the Colombian sample. We hypothesize that this haplotype may continue to rapidly increase in frequency and perhaps spread geographically in the coming years. These results extend our knowledge of how insecticide resistance has evolved in this species and add to a growing body of evidence suggesting that Ae. aegypti has an extensive genomic capacity to rapidly adapt to insecticide-based vector control. Aedes (Stegomyia) aegypti  vectors the arboviruses causing dengue, Zika, yellow fever, and chikungunya  identified in a sample from southern Mexico , first identified in houseflies, and conferring resistance to pyrethroids as well , has alosophila , anophelosophila , tobaccoosophila , cockroaosophila , and manosophila .Ae. aegypti.  Some, such as F1534C . We removed six specimens from the Colombian sample that had mean coverage below 15× or fewer than 70% of reads mapping to the reference genome, leaving a sample of 10 specimens from Cali and 24 from Río Claro (see n = 27) from n = 18), Franceville, Gabon (n = 13), Kaya Bomu, Kenya (n = 19), and Ngoye, Senegal (n = 20), from In addition, we examined publicly available sequence data from the United States (US)  to 21.10 (Colombia), with between 94.80% (Gabon) and 98.19% (United States) of reads mapping on average  online. Variant calling produced an initial data set of 247,108,978 variants in 131 specimens. After filtering, we retained 79,035,096 high-quality single nucleotide polymorphisms (SNPs) with a known location on one of the three autosomes and an additional 881,515 SNPs in regions of the genome that could not be confidently assigned to chromosomes. Our analysis focused on the SNPs on the three autosomes. The number of SNPs found in each cohort, before and after applying filters based on relatedness as discussed below, is shown in In keeping with previous work, substantially more SNPs were called per specimen in the African sample than in the American sample  online. Because the presence of related individuals can confound population genetic analyses that assume a random sample of the population was taken, we also filtered genomes based on kinship coefficient after variant calling using KING-robust , an algoTo investigate the extent and patterns of population structure of our data set, we used principal component analysis. The first principal component for each chromosome explained 9–10% of the variance and generally separated samples from Africa versus samples from North and South America  online. On each chromosome, the Senegal sample was closer in the first principal component to the North and South American samples than were the Gabon and Kenya samples. This is in keeping with previous findings  that havπ) and Tajima's D. Nucleotide diversity was lower in the American samples than in the African samples  within 500 kb windows and found that LD in the Brazilian population was noticeably elevated on all three chromosomes, while the other five populations overlapped more closely  online. r2  online.We scanned the genome for signatures of recent selective sweeps using SweepFinder2 , with thWe individually examined seven peaks or groups of peaks , from approximately 351 to 352 Mb, overlaps 27 protein-coding genes and four noncoding RNAs . Closer investigation shows this peak extending from approximately 314.5 to 318 Mb in the Brazilian sample  is overexpressed in response to pyrethroid (permethrin) selection , which has been extensively associated with insecticide resistance via nonsynonymous single nucleotide changes that cause resistance to pyrethroids, DDT, or both ; ankyrin proteins are associated with, and directly interact with, voltage-gated sodium channels  is well-studied in terms of mosquito immunity and facilitates infection of Ae. aegypti with West Nile virus (RpS21 (AAEL000529) is upregulated following dengue infection , a class B scavenger receptor. However, just downstream, the Gabon cohort shows another signal of a putative selective sweep, from approximately 229.5 to 231.25 Mb. This peak overlaps 39 genes, 19 of which are protein coding and 10 of which have a specific functional prediction (Ae. aegypti compared to sugar-fed (Ae. aegypti cell line results in a significant decrease in chikungunya RNA levels (notch (AAEL008069), another nearby gene, is silenced, in vitro viral load of DENV-2 (dengue serotype 2) is significantly increased 6 days postinfection compared to control mosquitoes (Aaea6 (AAEL008055) is upregulated in response to posteclosion application of exogeneous juvenile hormone -resistant and susceptible lab strains  is downregulated in late dengue infection and overexpressed throughout yellow fever expression in vivo  were identified as among the top 25 most differentiated genes between an urban population in Senegal versus a forest population from Senegal together with a population from Uganda ; AAEL012f and S7gFinally, the cohort from Senegal also shows several putative selective sweeps on chromosome 3 between 100 and 140 Mb , and AAEL005676. The difference in results between our two enrichment strategies highlights the need to account for the physical clustering of genes when examining signatures of selection, which are autocorrelated across the genome  available through VectorBase. We then manually added AAEL023266 (Vgsc) and AAEL023844 —we noteAs an alternative mode of characterizing selective sweep signatures, we used the multilocus genotype (MLG) statistics defined in b and supplementary fig. S10aVgsc; G123 is elevated in the Brazilian and Colombian samples, and G2/G1 is decreased (supplementary fig. S10bVgsc in Colombia (see Discussion).We reexamined the two most conspicuous selective sweep signatures in terms of their apparent involvement in insecticide resistance, regions “a” and “b” described above, using an alternative approach for detecting and characterizing selective sweeps: the multilocus genotype statistics described by figs. 2Vgsc and the surrounding region. We began by conducting principal component analysis of SNPs inside Vgsc , which showed specimens clustering into an approximate triangle (a). One of the points of the triangle, on the right side, is anchored by a cluster of Senegalese, Gabonese, and Kenyan specimens, and the x-axis  approximately divides African specimens from American specimens, with, however, much intermixing. The base of the triangle, on the left, comprises American specimens only; the y-axis  shows American specimens in three distinct and roughly equidistant clusters, with some spread between them. All Colombian and Brazilian specimens can be easily assigned to one of the clusters, and a subset of the specimens from the United States take intermediate positions.Given its important role in insecticide resistance in numerous species, we conducted a more thorough analysis of triangle a. One ofM. domestica nomenclature). F1534C was not called in any specimen in our filtered data set, although we were later able to genotype this locus as discussed below : F1534C , V1016I b), and specifically to the second principal component, along which many American specimens cluster into discrete groups. The loadings for this principal component show several clusters of SNPs with some of the highest contributions in the PCA. When we plot the location of five associated resistance SNPs, we see that, except for F1534C , they correlate with clusters of SNPs that are outliers in terms of their contribution to the second principal component. In addition to the clusters associated with the known resistance loci, there are additional clusters in the 5′ portion of the gene (note that Vgsc is transcribed on the reverse strand of the reference). Focusing on exonic SNPs beyond 316.1 Mb, we identified three  with a PCA loading of greater than 0.025 that overlap two adjacent exons; all three SNPs are found in the untranslated region. Vgsc is extensively alternatively spliced  between individual variants within Vgsc in the three populations from the Americas shows that regions of elevated LD are particularly prominent in the South American populations . The similarities between this scaffold and Vgsc have previously been noted . Using this method, we called putative genotypes at F1534C, revealing that every South American specimen is homozygous for the resistant allele ; glutathione S-transferases (Brazil and Colombia); a cytochrome P450 (Brazil); and a nicotinic acetylcholine receptor subunit gene (Gabon).In addition, we present the first genome-wide scans for positive selection in Ae. aegypti's agility in responding to these insecticides.We caution that genes associated with insecticide resistance may be disproportionately well-annotated and are likely to be disproportionately well-investigated, because of their obvious public health implications. Some sweep signatures overlap a large number of genes, many of whose functions are currently unannotated, making detailed conclusions about the targets of selection impossible; in addition, the peak of a sweep signature may not always correspond to the target of selection . HoweverVgsc. Examining this genomic region in Colombia reveals two observations: one, somewhat unexpectedly, the putative selective sweep in this population appears to be localized upstream of the gene, not within Vgsc itself, based on results from SweepFinder2 as well as elevated Tajima's D within Vgsc; two, the clustering of the Colombian sample into three tight groups along principal components suggests that two long-range haplotypes linking several loci associated with insecticide resistance are present in the population, with one haplotype containing the resistant alleles at at least four previously identified insecticide resistance SNPs and the other containing the wild-type alleles. Indeed, examining the PCA loadings shows that these polymorphisms line up well with clusters of SNPs driving the separation between PCA clusters. We note that this pattern of three equidistant clusters on a PCA is the characteristic signature of a polymorphic chromosomal inversion  hint at a possible origin for the haplotype carrying five known resistance alleles: regions of very low FST between the two haplotypes are interspersed with sharply delineated, discrete regions of very high FST, and two such regions overlap the four focal loci  that differ between the two haplotypes. While further work is needed to draw conclusions about the origin of the haplotype carrying resistance alleles at five loci, these patterns are not suggestive of recurrent de novo mutation of the focal loci on the F1534C background.Vgsc itself in the Colombian sample. Our proposed explanation for this apparent discrepancy involves the putative long-range haplotypes present in the Colombian population. The presence of two such haplotypes, both at intermediate frequency, would necessarily skew the SFS toward intermediate-frequency alleles, thereby increasing Tajima's D in this region. In effect, we suggest that in Colombia, a second, and ongoing, selective sweep confined to only part of the Vgsc gene has partially eroded the signal of an earlier sweep that encompassed the entire gene and some of the flanking regions. Alternatively, it could be that this haplotype is being maintained by some form of balancing selection in the Colombian sample, or its intermediate frequency has resulted from an influx of migrant haplotypes from another population, rather than ongoing positive selection. However, the high LD between V410L, S723T, I915K, or V1016I, as well as the lack of diversity among both the resistant and wild-type backgrounds , future work with broader sampling should test these possibilities explicitly. Perhaps our clearest contribution is the strong signatures of positive selection that we identify around numerous insecticide resistance genes. These results paint a compelling picture of strong selective pressures and a population that appears to be able to rapidly adapt to these pressures in part due to a fairly large number of genes that can potentially confer resistance phenotypes; these results are consistent with previous observations that a variety of loci and pathways underlie Ae. aegypti's response to insecticides in general and pyrethroids in particular  and a rural locality near the nature reserve area of Río Claro in the department of Antioquia. Medellín has an average altitude of 1,500 m and a mean annual temperature of 22.5 °C. Río Claro's average altitude and temperature are 1,750 m and 22 °C, respectively. We also sampled from Cali, capital of Valle del Cauca department . Cali's average altitude, temperature, and annual precipitation are 1,479 m, 24 °C, and ∼1,050 mm, respectively. The Cañón del Río Claro Nature Reserve is located between the municipalities of Sonsón, Puerto Triunfo, and San Luis, southwest of Antioquia .Ae. aegypti larval stages, and their main potential breeding grounds (pools and tanks used to store drinking water) were inspected. Larvae were then transported in Whirl-Pak plastic bags to the insectary of the Program for the Study and Control of Tropical Diseases (PECET) of the University of Antioquia, Medellín, Colombia , where stages III and IV were individualized and their breeding continued for entomological series, reference collection, and molecular processing of adults. Larvae, pupae, and adults were sorted following the protocols suggested by the Biosystematics Unit, Division of Entomology, Walter Reed 1997. To assign to species (Ae. aegypti), we used external morphological characters of larvae, pupae, and adult females. All specimen collections were performed following the guidelines of the Colombian decree nNo. 1376. No specific permits were required for this study. Mosquitoes were collected on a private property and permission was received from landowners before sampling.Our collection focused on Aedes in Colombia using Genetra Puregene Tissue Kits  and constructed barcoded libraries for sequencing using KAPA HyperPrep kits  with a target fragment size of 500 bp. We assessed the size of the library inserts using a Tapestation 4150  and dsDNA screentape . Finally, we pooled the samples and sequenced them in a NovaSeq6000 S2 (Illumina) machine, generating 2 × 150 bp reads. The target coverage was 15× per sample. This effort yielded 40 sequenced genomes. To these specimens, we added publicly available sequence data .Sites were genotyped with GATK HaplotypeCaller  independhttps://gatk.broadinstitute.org/hc/en-us/articles/360035890471-Hard-filtering-germline-short-variants), the following six hard filtering criteria were implemented, with sites required to pass five of the six filters: 1) variant quality by depth (QD) greater than or equal to 2; 2) variant strand bias as estimated by Fisher's exact test (FS) less than 40; 3) variant strand bias as estimated by the symmetric odds ratio test (SOR) less than 4; 4) mapping quality (MQ) greater than or equal to 40; 5) mapping quality rank sum test (MQRS) between −5 and 5, inclusive; and 6) site position within reads rank sum test (RPRS) between −3 and 3, inclusive.The merged VCFs were converted into zarr format using scikit-allel 1.3.2 (DOI:10.5281/zenodo.4759368). Variants were filtered in python using scikit-allel. For our focal sample from Colombia, we removed specimens with a mean coverage below 15× or fewer than 70% of reads mapping to the reference genome. In the combined sample, we also removed invariant sites and sites with indels. Based on GATK best practices for hard filtering . Any site that was part of a kmer that was not uniquely mappable, or any site denoted as repetitive, was noted for exclusion from the appropriate analyses as noted below.Finally, for some analyses, we removed regions of the genome previously identified as repetitive , as wellWe visualized the genetic structure in our sample using principal component analysis on each chromosome and on the three chromosomes together. SNPs were excluded if their minor allele frequency was 5% or less in the total sample. SNPs were also excluded if they overlapped repetitive regions or nonuniquely mapping regions, and genotypes with genotype qualities below 20 were masked. Genotypes were converted to the number of alternate genotypes present , and principal component analysis was done using scikit-allel. Plots were visualized using the first two principal components, and the percentage of variance explained by each of the first ten principal components was plotted.π) was calculated in nonoverlapping 500 kb windows for each of the six countries using the windowed_diversity function in scikit-allel. Tajima's D was calculated similarly, using the windowed_tajima_d function. For nucleotide diversity, we removed repetitive and nonuniquely mappable regions, and for both statistics, we masked genotypes with qualities less than 20. For visualization, both statistics were also calculated in sliding 5 Mb windows with a step size of 500 kb. Genome-wide FST between each pair of countries was calculated using scikit-allel's moving_weir_cockerham_fst function, using nonoverlapping windows of 500,000 variants, after removing repetitive and nonuniquely mapping regions.We also examined patterns of genetic diversity and divergence across the genome. Nucleotide diversity . In each region, linkage disequilibrium was calculated within each cohort in 100 kb nonoverlapping windows. Linkage disequilibrium was summarized by Putative selective sweeps were identified in each cohort using SweepFinder2 , using tAe. aegypti LVP_AGWG AaegL5.3 gene annotation file, available through VectorBase, by GO term.We used functional enrichment analysis with Gowinda version 1.12  to look q values were found in clusters, suggesting the potential for physical proximity and linkage disequilibrium to influence these results. Therefore, we implemented an additional enrichment test (https://github.com/SchriderLab/permEnrichmentTest) based on the approach of permuting the association between scores and tested loci. Specifically, this relationship was permuted by joining the three chromosomes into an artificial circular genome, then “rotating” all scores around the circle by a random interval. By preserving relationships between adjacent loci, this approach offers the ability to control for linkage. As before, we ran this enrichment analysis using the top 1% of all tested sites. For each cutoff, we ran 10,000 permutations of the SweepFinder2 scores before asking which GO terms were enriched in the sweep set relative to the permuted set. As in our analysis with Gowinda, we extended genes by 1,000 bp in either direction before determining which genes contained a putative sweep.Many of the genes in the gene sets with the lowest H statistics . In addition, we visualized the “loadings” of the first two principal components, plotted against each SNP's chromosomal position. We also calculated nucleotide diversity (π) and Tajima's D in the region spanning 310–320 Mb on chromosome 3, in 500 kb windows slid by 50 kb, by country, as well as in the top and bottom clusters of the Vgsc-specific PCA (for South American specimens only). Finally, we divided the California portion of the US sample into three clusters as per π and Tajima's D within each cluster, visualizing the values in conjunction with those from Brazil and Colombia.We performed principal component analysis as previously described on all SNPs within the bounds of FST between the haplotypes carried in the South American specimens in the top and bottom clusters of the Vgsc PCA, respectively, using the windowed_weir_cockerham_fst function in scikit-allel. FST was calculated in windows of 50 kb slid by 5 kb and in windows of 10 kb slid by 1 kb.We also calculated r2, within Vgsc and 100 kb up- and downstream after masking low-quality genotypes as described above. We calculated LD within all South American specimens, all North American specimens, and then all American specimens combined. We also calculated LD between the five focal loci; in this case, for reasons detailed previously, genotypes with qualities below 20 were masked at all loci except for 315,939,224, the position corresponding to the F1534C mutation.In addition, we calculated LD between individual variants, again using Molecular Biology and Evolution online.msad072_Supplementary_DataClick here for additional data file.


Small molecule compounds that activate transcription of Nurr1-retinoid X receptor alpha (RXRα) (NR4A2-NR2B1) nuclear receptor heterodimers are implicated in the treatment of neurodegenerative disorders, but function through poorly understood mechanisms. Here, we show that RXRα ligands activate Nurr1-RXRα through a mechanism that involves ligand-binding domain (LBD) heterodimer protein-protein interaction (PPI) inhibition, a paradigm distinct from classical pharmacological mechanisms of ligand-dependent nuclear receptor modulation. NMR spectroscopy, PPI, and cellular transcription assays show that Nurr1-RXRα transcriptional activation by RXRα ligands is not correlated with classical RXRα agonism but instead correlated with weakening Nurr1-RXRα LBD heterodimer affinity and heterodimer dissociation. Our data inform a model by which pharmacologically distinct RXRα ligands (RXRα homodimer agonists and Nurr1-RXRα heterodimer selective agonists that function as RXRα homodimer antagonists) operate as allosteric PPI inhibitors that release a transcriptionally active Nurr1 monomer from a repressive Nurr1-RXRα heterodimeric complex. These findings provide a molecular blueprint for ligand activation of Nurr1 transcription via small molecule targeting of Nurr1-RXRα. Nurr1  is a nuclear receptor (NR) transcription factor that is essential for the development, regulation, and maintenance of several important aspects of mammalian brain development and homeostasis. Nurr1 is critical in the development of dopaminergic neurons that are critical for control of movement that degenerates in Parkinson’s disease (PD) . Recent Although NRs are considered to be ligand-dependent transcription factors, targeting Nurr1 activity with small molecule compounds has remained challenging. Nurr1 contains the conserved NR domain architecture  includinAn alternative way to modulate Nurr1 transcription activity is through ligands that target its NR heterodimer-binding partner, retinoid X receptor alpha . Heterodimerizaton with RXRα represses Nurr1 transcription on monomeric DNA response elements called NBREs  that areHere, we tested a set of RXRα ligands in structure-function assays to determine their mechanism of action in regulating Nurr1-RXRα transcriptional activity. Unexpectedly, our studies show that ligand activation of Nurr1-RXRα transcription is not associated with classical pharmacological RXRα agonism, which is defined by a ligand-induced increase in coactivator binding to the RXRα LBD resulting in transcriptional activation. Instead, our data support a model whereby Nurr1-RXRα activating ligands weaken Nurr1-RXRα LBD heterodimer affinity via an allosteric protein-protein interaction (PPI) inhibition mechanism and release a transcriptionally active Nurr1 monomer from the repressive Nurr1-RXRα heterodimer.dim) that cannot heterodimerize with RXRα  and bexarotene, the latter of which was reported to display biased activation of Nurr1-RXR heterodimers over RXR homodimers  biochemical assay  to assesWe previously showed that ligands displaying graded pharmacological PPARγ agonism show a strong correlation between coactivator peptide recruitment to the LBD in a TR-FRET biochemical assay and cellular transcription of full-length PPARγ . HoweverThe two selective Nurr1-RXRα activating compounds, BRF110 and HX600, function as a pharmacological antagonist and weak/partial graded agonist in the RXRα homodimer TR-FRET and transcriptional reporter assay, respectively. These findings suggest that the mechanism by which the RXRα ligand set influences Nurr1-RXRα transcription may occur independent of coregulator recruitment to the RXRα LBD and RXRα-mediated transcription—indicating that ligand-dependent RXRα homodimer modulation and Nurr1-RXRα heterodimer modulation may function through distinct mechanisms.D values using ITC via the dimer dissociation dilution . The simulations show that weakening Nurr1-RXRα LBD heterodimerization affinity, for example upon binding an RXRα ligand, shifts the equilibrium from the Nurr1-RXRα LBD heterodimeric (hd) population to Nurr1 LBD monomer (m) population  and heterodimer (hd) 2D NMR peaks for Thr411 from two replicate measurements using different batches of protein (p = 0.77 (p=0.0021) and rs = 0.75 (p=0.0042).We calculated the relative population of monomeric Nurr1 LBD dissociated from the Nurr1-RXRα LBD heterodimer by measuring the peak intensity of the monomeric  experiments to determine how the compounds influence the oligomeric state of the Nurr1-RXRα LBD heterodimer . The NurThe SEC data provide insight into the potential allosteric mechanism(s) by which compounds in the RXRα ligand set influence Nurr1-RXRα heterodimerzation and transcription via effects on RXRα LBD. In the absence of ligand, RXRα exists as a mixture of homodimers and homotetramers , which cFinally, we performed principal component analysis (PCA) using all the experimental data collected as input to determine the data features most associated with transcriptional activation of the 3xNBRE-luciferase reporter by Nurr1-RXRα in an unbiased manner . Two of Although Nurr1 is a promising drug target for aging-associated neurodegenerative disorders, uncertainty in the druggability of its LBD has motivated research into alternative approaches to influence Nurr1 transcription. Nurr1-RXRα heterodimer activation via RXRα-binding ligands has emerged as a promising modality to develop neuroprotective therapeutic agents or adjuvants to other therapies. Several RXRα ligands that increase Nurr1-RXRα activation have progressed to clinical trials of PD and AD including bexarotene and IRX4204 . UnderstClassical pharmacological modulation of NR transcription by agonists, antagonists, or inverse agonists that activate, block, or repress transcription typically function by enhancing coactivator protein interaction, blocking coregulator interaction, or enhancing corepressor interaction, respectively . Our stuOur data in support of a Nurr1-RXRα LBD heterodimer dissociation activation mechanism along with other published studies inform a model for activation of Nurr1-RXRα transcription by RXRα ligands . Nurr1 dIn contrast to the ligand-induced heterodimer dissociation activation mechanism, ligands that stabilize/strengthen Nurr1-RXRα heterodimerization or do not significantly affect Nurr1-RXRα dissociation—such as the RXRα antagonists in our study—may show more complex regulatory mechanisms. In addition to influencing Nurr1-RXRα interaction, antagonist ligands bound to RXRα, which is itself tethered to Nurr1 on NBRE genomic elements, could influence transcription of NBRE-driven gene transcription via classical pharmacological modulation mechanisms. RXRα-bound ligands will influence the conformation of and coregulator protein binding affinity to the RXRα AF-2 surface, which will in turn regulate recruitment of coregulator (coactivator or corepressor) proteins via interaction with the RXRα AF-2 surface.In summary, our study shows that RXRα ligands can function as allosteric PPI inhibitors that bind to the canonical orthosteric ligand-binding pocket within the RXRα LBD and influence and Nurr1-mediated transcription via Nurr1-RXRα heterodimer dissociation. Our findings provide a molecular understanding into the mechanism of action of Nurr1-RXRα activation that may influence the design and development of new compounds with therapeutic efficacy in PD, AD, and other aging-associated neurodegenerative disorders.CAGCAGCGCCTAAGAGGACATG; reverse primer, CATGTCCTCTTAGGCGCTGCTG. To clone the ΔNTD RXRα construct (residues 127–462), site directed mutagenesis and PCR was used to add a XhoI cut site and an ATG before at the end of the NTD using the following primers: forward primer, CCACCCCTCGAGAAACATGG; reverse primer, CCATGTTTCTCGAGGGGTGG—then restriction enzymes (XhoI and HindIII) were used to cut out the ΔNTD construct (DBD-hinge-LBD) for insertion using T4 ligase into pcDNA3.1 empty vector that had been linearized with XhoI and HindIII. To clone the RXRα LBD construct (200–462), Xhol was used to cut pcDNA3.1 and subsequently Gibson assembly was used to clone the LBD into the linearized vector using the gBlock sequence provided in All ligands were obtained from commercial vendors including Cayman Chemicals, Sigma, Axon Medchem, MedChemExpress, or Tocris Bioscience; BRF110 (CAS 2095489-35-1), HX600 (CAS 172705-89-4), 9cRA (CAS 5300-03-8), Bexarotene (CAS 153559-49-0), LG100268 (CAS 153559-76-3), CD3254 (CAS 196961-43-0), SR11237 (CAS 146670-40-8), UVI3003 (CAS 847239-17-2), LG100754 (CAS 180713-37-5), IRX4204 (CAS 220619-73-8), Rhein (CAS 478-43-3), HX531 (CAS 188844-34-0), Danthron (CAS 117-10-2), and PA425 (CAS 457657-34-0). FITC-labeled LXXLL-containing peptide derived from human PGC-1α  was synthesized by LifeTein with an N-terminal FITC label and an amidated C-terminus for stability. Bacterial expression plasmids included human Nurr1 (NR4A2) LBD (residues 353–598)  and huma2 in DEME (Gibco) supplemented with 10% fetal bovine serum (Gibco) and 100 units/mL of penicillin, 100 μg/mL of streptomycin, and 0.292 mg/mL of glutamine (Gibco) until 90–95% confluence in T-75 flasks prior to subculture or use. SK-N-BE(2)-C were grown at 37°C and 5% CO2 in a media containing 1:1 mixture of EMEM (ATCC) and F12 medium (Gibco) supplemented with 10% fetal bovine serum (Gibco) until 90–95% confluence in T-75 flasks prior to subculture or use.All cells were obtained from and authenticated by ATCC and determined to be mycoplasma free. HEK293T (#CRL-11268) and SK-N-BE(2)-C (#CRL-2268) cells were cultured according to ATCC guidelines at low passage number . HEK293T cells were grown at 37°C and 5% COEscherichia coli BL21(DE3) cells in autoinduction ZY media (without NMR isotopes) or M9 minimal media (using 15NH4Cl for NMR isotopic labeling). For autoinduction expression, cells were grown for 5 hr at 37°C, then 18 hr at 22°C, then centrifuged for harvesting. For M9 expression, cells were grown at 37°C and induced with 1.0 mM isopropyl β-D-thiogalactoside at OD (600 nm) of 0.6, grown for an additional 18 hr at 22°C, and then centrifuged for harvesting. Cell pellets were lysed using sonication and proteins were purified using Ni-NTA affinity chromatography and gel filtration/SEC. TEV protease was used to cleave the 6xHis-tag for all experiments except protein used for TR-FRET. The purified proteins were verified by SDS-PAGE, then stored in a buffer consisting of 20 mM potassium phosphate (pH 7.4), 50 mM potassium chloride, and 0.5 mM EDTA. All studies that used RXRα LBD protein used pooled SEC fractions for the apo-homodimeric form except for analytical SEC of the apo-tetrameric form.Proteins were expressed in 2 cell culture well (Corning) at 0.5 million for transfection using Lipofectamine 2000 (Thermo Fisher Scientific) and Opti-MEM with an empty vector control or full-length Nurr1 expression plasmid (1 μg), with or without full-length or different RXRα truncation constructs (1 μg), and 3xNBRE-luc (1 μg). HEK293T cells were seeded in 9.5 cm2 cell culture well (Corning) at 0.5 million for transfection using Lipofectamine 2000 (Thermo Fisher Scientific) and Opti-MEM with empty vector control or full-length RXRα expression plasmid (1 μg), and 3xDR1-luc (1 μg). After incubation for 16–20 hr, cells were transferred to white 384-well cell culture plates (Thermo Fisher Scientific) at 0.5 million cells/mL in 20 μL total volume/well. After a 4 hr incubation, cells were treated with 20 μL of vehicle control (DMSO) or 1 μM ligand. After a final 16–20 hr incubation, cells were harvested with 20 μL Britelite Plus (PerkinElmer), and luminescence was measured on a BioTek Synergy Neo multimode plate reader. The luminescence readouts were normalized to cells transfected with the empty vector (truncation construct assay) or cells treated with DMSO (assays with or without ligand treatment). Data were plotted using GraphPad Prism. In SK-N-BE(2)-C cells were seeded in 9.5 cmAssays were performed in 384-well black plates (Greiner) using 22.5 μL final well volume. Each well contained 4 nM 6xHis-RXRα LBD, 1 nM LanthaScreen Elite Tb-anti-His antibody (Thermo Fisher #PV5895), and 400 nM FITC-labeled PGC1α peptide in a buffer containing 20 mM potassium phosphate (pH 7.4), 50 mM KCl, 5 mM TCEP, and 0.005% Tween 20. Ligand stocks were prepared via serial dilution in DMSO and added to wells  in triplicate. The plates were read using BioTek Synergy Neo multimode plate reader after incubation at 4°C for at least 2 hr. The Tb donor was excited at 340 nm, and its emission was measured at 495 nm; the emission of the acceptor FITC was measured at 520 nm. Data were plotted using GraphPad Prism as TR-FRET ratio (measurement at 520 nm/measurement at 495 nm) at a fixed ligand concentration (2–4 µM depending on the starting compound stock concentration). In D) and enthalpy were determined to be 16.3 µM and –13.10 kcal/mol, respectively; this information was used in analysis of the interactions between Nurr1 LBD and ligand-bound RXRα LBD to approximate the dissociation step required for Nurr1 LBD to heterodimerize with RXRα LBD. ITC experiments of Nurr1 LBD to RXRα LBD (apo or ligand-bound) was performed by titrating Nurr1 LBD to RXRα LBD in a 10:1 ratio  incubated with two equivalent of vehicle (DMSO) or ligand, and the run was performed using the same experimental design. Experiments were performed at 25°C in duplicate, except for two RXRα ligands (IRX4204 and PA425) where only one experiment was performed; and one experimental replicate for HX600 was performed at 15°C, which gave a similar KD value to the data collected at 25°C. NITPIC software  final concentration. RXRα LBD homodimerization affinity was measured using a dimer dissociation dilution method  by titrasoftware  was usedsoftware , which wsoftware . In Figu1H,15N]-TROSY-HSQC NMR experiments were performed at 298 K on a Bruker 700 MHz NMR instrument equipped with a QCI cryoprobe. Samples were prepared in a buffer containing 20 mM potassium phosphate (pH 7.4), 50 mM potassium chloride, 0.5 mM EDTA, and 10% D2O. Experiments were collected using 200 μM 15N-labeled Nurr1 LBD with or without two molar equivalents of unlabeled RXRα LBD in the absence or presence of RXRα ligands. Data were processed and analyzed using NMRFx (monomer) and heterodimer (Iheterodimer) species using the following equation:Two-dimensional -TROSY HSQC NMR data ; a receptor concentration of 200 µM ; and ligand added at ±2 molar equivalents (LRratio = 1e-3 and 2.0); and relative relaxation rates for the monomer and heterodimer estimated to give peak full width half height (FWHH) line widths in 2D -TROSY HSQC NMR data ; otherwise default parameters were used.NMR lineshape analysis was performed using NMR LineShapeKin version 4  and MATLTo prepare Nurr1-RXRα LBD heterodimer for analytical SEC analysis, purified Nurr1 LBD and RXRα LBD (each 700 μM in 2.5 mL) were incubated together at 4°C in their storage buffer containing 20 mM potassium phosphate (pH 7.4), 50 mM potassium chloride, and 0.5 mM EDTA for 16 hr, injected  into HiLoad 16/600 Superdex 75 pg (Cytiva) connected to an AKTA FPLC system . Gel filtration was performed using the same buffer to purify Nurr1-RXRα LBD heterodimer for analysis, collecting 2 mL fraction to isolate heterodimer population into 15 samples of 0.5 mL (at a protein concentration of 300 μM) for analytical gel filtration. Additionally, purified Nurr1 LBD and RXRα LBD (pooled gel filtration fractions consisting of the homodimeric and homotetrameric species) were also used. Samples containing RXRα LBD (homodimer or heterodimer with Nurr1 LBD) were incubated with two molar equivalents of each ligand for 16 hr at 4°C before injection onto Superdex 75 Increase 10/300 GL (Cytiva) connected to the same AKTA FPLC system. The UV chromatograms were exported in CSV format and plotted using an in-house Jupyter notebook Python script using matplotlib and seaborn packages.p) and Spearman (rs) correlation coefficients and two-tailed p-values between two experimental measurements per plot. Statistical testing of control to variable conditions was performed using one-way ANOVA testing as detailed above. PCA was performed in GraphPad Prism; all experimentally determined data were used as variables . p-Value statistical significance shorthand conforms to GraphPad Prism standards: n.s., p>0.05; *, p≤0.05; **, p≤0.01; ***, p≤0.001; and ****, p≤0.0001.Correlation plots were performed using GraphPad Prism to calculate Pearson  feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Volker Dötsch as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Sean W Fanning (Reviewer #2).Thank you for submitting your article "Molecular basis of ligand-dependent Nurr1-RXRα activation" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) The RXRɑ antagonist HX531 has little to no effect on Nurr1-RXRɑ reporter activity like the other RXRɑ antagonists Rhein and Danthron  but appears to selectively induce a >5-fold enhancement in the apparent affinity of LBD heterodimerization  that seems inconsistent with the proposed model. The authors address a potential caveat with their analysis of the ITC data on lines 68-77 of page 4 but don't comment on any specific ligands or data. As a result, the correlation analyses presented in Figure 5c-d, although largely compelling, may be oversimplified. This is also true in Figure 6, where the correlation between reporter activity and Nurr1 monomerization assessed by NMR  is much weaker than that with the ITC data . Can the authors comment on the overall rigor and suitability of this simple correlative approach to justify these apparent discrepancies?2) Related: In the correlation studies the authors include danthron and rhein as compounds in the Nurr1/RXR assay but not in the RXR PPI and RXR CTF assay. It is not clear if they are included in the correlation graphs in Figure 4 either; however, if they are excluded there should be a reason indicated. How do these compounds behave in the other assays?3) Can the authors clarify how many independent times ITC data were collected for each ligand? The legend for Figure 5 states n = 1, but the methods state that at least two independent experiments were done. If so, the KD values in Figures 5 and 6 should reflect all of the data unless this is justified explicitly.4) Related: Asterisks are used to indicate significance but the p-values assigned to them are not discussed in the figure legends nor in the methods. Also, in Figure 5b, error bars are not shown for the Kd values, and significance isn't shown between treatments. Adding in this statistical analysis would help judge whether dimerization differences are meaningful between ligands.5) Since ITC is being used, information on changes to the heats, Gibbs free energy, enthalpy, and entropy should be available. Some discussion on whether changes to the observed binding affinities are ethically or entropically driven would be of interest to the biothermodynamics community.6) Is there a way to estimate the error of measuring Nurr1 monomer populations by NMR in Figure 6a, b, c, and e? This could also be useful in making correlations in Figure 6.7) Response at 1 μM was shown but no dose-response studies were performed to show differences in potency. These could be potentially correlated with the later studies to see if there's a relationship between transcriptional activities and dimerization affinity. Are such dose-response curves available?8) Binding studies were only done for RXRalpha and the coregulator peptide. It would have been interesting to learn if the coregulator peptide binds the RXR-Nurr1 heterodimer. Comments?9) HX531 appears to significantly enhance heterodimerization, is there any speculation as to why this is?10) Should state in the cell culture section whether STR profiling was done to confirm cell line identity during the study, similar to mycoplasma testing.11) In Figure 1, one of the key constructs (the δ LBD) shows high variability in its activity. Although it is not statistically different than the FL-Nurr1 control the variability that ranges from the FL-Nurr1 control to the FL-Nurr1/Fl-RARa group. Clearly, the LBD is playing a role, but my concern is that it might not be quite so clean. Is it possible that there is a contribution from the RXR DBD in this assay?12) On page 4 line 43 the authors state,"…the two selective Nurr1-RXRa activating compounds BRF110 and HX600 function as pharmacological RXRa antagonists in these assays – a clear example that pharmacological RXRa modulation of Nurr-1/RXRa modulation is distinct". It isn't clear what assays the authors are referring to where these two compounds behaved as antagonists. This paragraph is prior to the presentation of the heterodimer assay where heterodimer formation is inhibited and in the previously presented assays these compounds were either activators (Nurr1/RXR), inactive (RXR coactivator PPI), or inactive/weak activators (RXR). Please clarify.Reviewer #1 (Recommendations for the authors):The RXRɑ antagonist HX531 has little to no effect on Nurr1-RXRɑ reporter activity like the other RXRɑ antagonists Rhein and Danthron  but appears to selectively induce a >5-fold enhancement in the apparent affinity of LBD heterodimerization  that seems inconsistent with the proposed model. The authors address a potential caveat with their analysis of the ITC data on lines 68-77 of page 4 but don't comment on any specific ligands or data. As a result, the correlation analyses presented in Figure 5c-d, although largely compelling, may be oversimplified. This is also true in Figure 6, where the correlation between reporter activity and Nurr1 monomerization assessed by NMR  is much weaker than that with the ITC data . Can the authors comment on the overall rigor and suitability of this simple correlative approach to justify these apparent discrepancies? I don't think more work necessarily needs to be done, but perhaps a more explicit description of caveats and possible implications for their model would be helpful.Can the authors clarify how many independent times ITC data were collected for each ligand? The legend for Figure 5 states n = 1, but the methods state that at least two independent experiments were done. If so, the KD values in Figures 5 and 6 should reflect all of the data unless this is justified explicitly.Is there a way to estimate the error of measuring Nurr1 monomer populations by NMR in Figure 6a, b, c, and e? This could also be useful in making correlations in Figure 6.Reviewer #2 (Recommendations for the authors):Congratulations on a compelling and high-impact study. I have a few recommendations that I believe will enhance the significance of this study.IntroductionThere's a typo in the first sentence of paragraph 2.* are used for significance but the p-values assigned to them are not discussed in the figure legends nor in the methods that I could find.ResultsThe second section discussing RXR ligands and Nurr1 transcriptional activity.– Response at 1 μM was shown but no dose-response studies were performed to show differences in potency. These could be potentially correlated with the later studies to see if there's a relationship between transcriptional activities and dimerization affinity.Third section looking at RXR coregulator binding.– Binding studies were only done for RXRalpha and the coregulator peptide. It would have been interesting to learn if the coregulator peptide binds the RXR-Nurr1 heterodimer.Fourth section using ITC to study heterodimerization.In figure 5b, error bars are not shown for the Kd values, and significance isn't shown between treatments. Adding in this statistical analysis would help judge whether dimerization differences are meaningful between ligands.– HX531 appears to significantly enhance heterodimerization, is there any speculation as to why this is?– Since ITC is being used, you should have information on changes to the heats, Gibbs free energy, enthalpy, and entropy. Some discussion on whether changes to the observed binding affinities are enthalpically or entropically driven would be of interest to the biothermodynamics community.Materials and methodsShould state in the cell culture section whether STR profiling was done to confirm cell line identity during your study, similar with mycoplasma testing.Overall: Are there any inferences for this allostery that could be made based on existing RXRalpha co-crystal structures? An analysis of RXR allostery at the end could point to the mechanism of Nurr1 specificity. This would be a very interesting future study.Reviewer #3 (Recommendations for the authors):In Figure 1, one of the key constructs (the δ LBD) shows high variability in its activity. Although it is not statistically different than the FL-Nurr1 control the variability that ranges from the FL-Nurr1 control to the FL-Nurr1/Fl-RARa group. Clearly, the LBD is playing a role, but my concern is that it might not be quite so clean. Is it possible that there is a contribution from the RXR DBD in this assay? Otherwise, what do the authors believe causes this variability in this particular group?In the correlation studies the authors include danthron and rhein as compounds in the Nurr1/RXR assay but not in the RXR PPI and RXR CTF assay. It is not clear if they are included in the correlation graphs in Figure 4 either; however, if they are excluded there should be a reason indicated. How do these compounds behave in the other assays?On page 4 line 43 the authors state,"…the two selective Nurr1-RXRa activating compounds BRF110 and HX600 function as pharmacological RXRa antagonists in these assays – a clear example that pharmacological RXRa modulation of Nurr-1/RXRa modulation is distinct". It isn't clear to me what assays the authors are referring to where these two compounds behaved as antagonists. This paragraph is prior to the presentation of the heterodimer assay where heterodimer formation is inhibited and in the previously presented assays these compounds were either activators (Nurr1/RXR), inactive (RXR coactivator PPI), or inactive/weak activators (RXR). Essential revisions:1) The RXRɑ antagonist HX531 has little to no effect on Nurr1-RXRɑ reporter activity like the other RXRɑ antagonists Rhein and Danthron  but appears to selectively induce a >5-fold enhancement in the apparent affinity of LBD heterodimerization  that seems inconsistent with the proposed model. The authors address a potential caveat with their analysis of the ITC data on lines 68-77 of page 4 but don't comment on any specific ligands or data. As a result, the correlation analyses presented in Figure 5c-d, although largely compelling, may be oversimplified. This is also true in Figure 6, where the correlation between reporter activity and Nurr1 monomerization assessed by NMR  is much weaker than that with the ITC data . Can the authors comment on the overall rigor and suitability of this simple correlative approach to justify these apparent discrepancies?PCA reveals data features associated with Nurr1-RXRα agonism” where we used principal component analysis (PCA) of all of the experimental data and a new discussion paragraph (second to last) describing how Nurr1-RXRα agonism/heterodimer dissociation and antagonism/heterodimer stabilization likely function via different regulatory mechanisms.We do not believe that the RXRα antagonist data is inconsistent with the ligand-induced activation model and have additional text to the manuscript discussion and a new Results section titled “2 values as a description of correlation. However, since linear fits are not appropriate in cases where both variables are experimentally measured, in this revised manuscript we instead performed Pearson (rp) and Spearman (rs) correlation analyses and report the associated p-values that report on whether the correlation coefficients (rp or rs) are statistically significant (see grey boxes above plots). Furthermore, the new PCA analysis we added also enhances the rigor of this analysis by analyzing all of the experimental data together in an unbiased manner.The reviewer also questions the significance of the correlation analysis performed in our study. In our original submission, we used a linear correlation analysis by plotting two sets of experimentally determined data, fitting the data via linear regression, and reporting R2) Related: In the correlation studies the authors include danthron and rhein as compounds in the Nurr1/RXR assay but not in the RXR PPI and RXR CTF assay. It is not clear if they are included in the correlation graphs in Figure 4 either; however, if they are excluded there should be a reason indicated. How do these compounds behave in the other assays?We did not include danthron and rhein in the RXRα LBD coactivator peptide interaction TR-FRET assay  because the compounds are colored and would interfere with the assay. We ran the RXRα/3xDR1-luciferase cellular transcriptional reporter assay  at the same time as the TR-FRET assay, and therefore we did not include danthron and rhein since no comparison to the TR-FRET data could be made. Danthron and rhein data from the Nurr1-RXRα/3xNBRE-luciferase cellular transcriptional reporter assay are not included in the correlation plots in Figure 4e-g.Danthron and rhein do not appear to cause interference in the 3xNBRE-luciferase cellular reporter assay we used to measure Nurr1-RXRα transcription, nor in the ITC, NMR, or SEC experiments that measure Nurr1-RXRα LBD interaction; and were therefore included in correlation plots among these experiments.3) Can the authors clarify how many independent times ITC data were collected for each ligand? The legend for Figure 5 states n = 1, but the methods state that at least two independent experiments were done. If so, the KD values in Figures 5 and 6 should reflect all of the data unless this is justified explicitly.In this revision the Figures now show data points for duplicate ITC measurements for all but two RXRα ligands (PA425 and IRX4204).4) Related: Asterisks are used to indicate significance but the p-values assigned to them are not discussed in the figure legends nor in the methods. Also, in Figure 5b, error bars are not shown for the Kd values, and significance isn't shown between treatments. Adding in this statistical analysis would help judge whether dimerization differences are meaningful between ligands.As requested by the reviewer, we performed statistical testing and p-value analysis for ligand-treated conditions to control conditions (apo/DMSO) for all experimentally measured values in all relevant figures and ensured that all plots include error bars.5) Since ITC is being used, information on changes to the heats, Gibbs free energy, enthalpy, and entropy should be available. Some discussion on whether changes to the observed binding affinities are ethically or entropically driven would be of interest to the biothermodynamics community.As requested by the reviewer, we included the measured and fitted ITC thermodynamic parameters to Table 1 (new in the revised manuscript), perform correlation analyses between binding affinity and enthalpy/entropy, and include a discussion of the data and analyses.6) Is there a way to estimate the error of measuring Nurr1 monomer populations by NMR in Figure 6a, b, c, and e? This could also be useful in making correlations in Figure 6.We updated Figure 6 to include peak intensity errors calculated by the software we used for NMR analysis; additional details can be found in the methods section.7) Response at 1 μM was shown but no dose-response studies were performed to show differences in potency. These could be potentially correlated with the later studies to see if there's a relationship between transcriptional activities and dimerization affinity. Are such dose-response curves available?In this study we are interested in the functional efficacy of the ligand when completely bound to RXRα and therefore performed studies using a single concentration of ligand that is near/above their reported RXRα LBD binding affinity. It is possible there could be a correlation between ligand potency and function, or heterodimerization with Nurr1 could influence ligand affinity/potency vs. RXRα homodimers, which could be apparent in ligand dose-response experiments; we plan to investigate this in future studies.8) Binding studies were only done for RXRalpha and the coregulator peptide. It would have been interesting to learn if the coregulator peptide binds the RXR-Nurr1 heterodimer. Comments?Nurr1-RXRα activation is not correlated with pharmacological RXRα agonism” and included three references to introduce this concept.The Nurr1 LBD contains a reversed “charge clamp” in its AF-2 surface—a proper charge clamp is important for binding LXXLL-containing motifs present within transcriptional coactivator proteins—and as such Nurr1 does not interact with canonical nuclear receptor coregulator proteins. We updated the first paragraph of the Results section titled “If were to perform the TR-FRET coactivator peptide interaction assay to determine how RXRα ligands influence coactivator peptide interaction with Nurr1-RXRα, we anticipate the results would be confounded by several different mechanisms including ligand-dependent Nurr1-RXRα dissociation; ligand-dependent changes in coactivator peptide interaction with RXRα; and ligand-dependent changes in RXRα oligomerization as our size exclusion chromatography (SEC) data indicate ligands differentially affect the hydrodynamic solution properties of RXRα LBD.9) HX531 appears to significantly enhance heterodimerization, is there any speculation as to why this is?Our size exclusion chromatography (SEC) data  shows that HX531 decreases the hydrodynamic radius of the RXRα LBD (shifts the SEC peak to the right compared to apo-RXRα LBD), which could indicate HX531 changes the RXRα LBD monomer-dimer equilibrium towards a monomeric state . Because Nurr1 heterodimerization with RXRα competes for RXRα LBD homodimerization, ligands that favor a monomeric RXRα LBD state would result in higher affinity heterodimerization with Nurr1 LBD. This is an interesting point that we plan to investigate in future studies.10) Should state in the cell culture section whether STR profiling was done to confirm cell line identity during the study, similar to mycoplasma testing.We updated the methods cell culture section to state that cells were authenticated by ATCC and cultured at low passage number (less than 10 passages).11) In Figure 1, one of the key constructs (the δ LBD) shows high variability in its activity. Although it is not statistically different than the FL-Nurr1 control the variability that ranges from the FL-Nurr1 control to the FL-Nurr1/Fl-RARa group. Clearly, the LBD is playing a role, but my concern is that it might not be quite so clean. Is it possible that there is a contribution from the RXR DBD in this assay?In the first paragraph of the Results section, we updated the description of these data in the Results section and added p-values for the statistical comparison of the RXRα constructs (full-length and truncation) vs. full-length Nurr1. We also added additional information and two more references that implicate a Nurr1 LBD-RXRα LBD interaction in the mechanism by which RXRα represses Nurr1 transcription.12) On page 4 line 43 the authors state,"…the two selective Nurr1-RXRa activating compounds BRF110 and HX600 function as pharmacological RXRa antagonists in these assays – a clear example that pharmacological RXRa modulation of Nurr-1/RXRa modulation is distinct". It isn't clear what assays the authors are referring to where these two compounds behaved as antagonists. This paragraph is prior to the presentation of the heterodimer assay where heterodimer formation is inhibited and in the previously presented assays these compounds were either activators (Nurr1/RXR), inactive (RXR coactivator PPI), or inactive/weak activators (RXR). Please clarify.Nurr1-RXRα activation is not correlated with pharmacological RXRα agonism” to more concisely make the point that ligand-dependent RXRα homodimer modulation and Nurr1-RXRα heterodimer modulation may function through distinct mechanisms.We revised this section of the manuscript removing this confusing statement and added a new section at the end of the Results section titled “Reviewer #1 (Recommendations for the authors):The RXRɑ antagonist HX531 has little to no effect on Nurr1-RXRɑ reporter activity like the other RXRɑ antagonists Rhein and Danthron  but appears to selectively induce a >5-fold enhancement in the apparent affinity of LBD heterodimerization  that seems inconsistent with the proposed model. The authors address a potential caveat with their analysis of the ITC data on lines 68-77 of page 4 but don't comment on any specific ligands or data. As a result, the correlation analyses presented in Figure 5c-d, although largely compelling, may be oversimplified. This is also true in Figure 6, where the correlation between reporter activity and Nurr1 monomerization assessed by NMR  is much weaker than that with the ITC data . Can the authors comment on the overall rigor and suitability of this simple correlative approach to justify these apparent discrepancies? I don't think more work necessarily needs to be done, but perhaps a more explicit description of caveats and possible implications for their model would be helpful.Addressed in our response to Essential revisions #1.Can the authors clarify how many independent times ITC data were collected for each ligand? The legend for Figure 5 states n = 1, but the methods state that at least two independent experiments were done. If so, the KD values in Figures 5 and 6 should reflect all of the data unless this is justified explicitly.Addressed in our response to Essential revisions #3.Is there a way to estimate the error of measuring Nurr1 monomer populations by NMR in Figure 6a, b, c, and e? This could also be useful in making correlations in Figure 6.Addressed in our response to Essential revisions #6.Reviewer #2 (Recommendations for the authors):Congratulations on a compelling and high-impact study. I have a few recommendations that I believe will enhance the significance of this study.IntroductionThere's a typo in the first sentence of paragraph 2.We fixed it, thanks!* are used for significance but the p-values assigned to them are not discussed in the figure legends nor in the methods that I could find.We added the p-values to the figures and described the statistical testing in the figure legends and Materials and methods section.ResultsThe second section discussing RXR ligands and Nurr1 transcriptional activity.– Response at 1 μM was shown but no dose-response studies were performed to show differences in potency. These could be potentially correlated with the later studies to see if there's a relationship between transcriptional activities and dimerization affinity.Addressed in our response to Essential revisions #7.Third section looking at RXR coregulator binding.– Binding studies were only done for RXRalpha and the coregulator peptide. It would have been interesting to learn if the coregulator peptide binds the RXR-Nurr1 heterodimer.Addressed in our response to Essential revisions #8.Fourth section using ITC to study heterodimerization.In figure 5b, error bars are not shown for the Kd values, and significance isn't shown between treatments. Adding in this statistical analysis would help judge whether dimerization differences are meaningful between ligands.p) and Spearman (rs) correlation coefficients and two-tailed p-values to all appropriate figure panels.We used GraphPad Prism to calculate Pearson :In Figure 1, one of the key constructs (the δ LBD) shows high variability in its activity. Although it is not statistically different than the FL-Nurr1 control the variability that ranges from the FL-Nurr1 control to the FL-Nurr1/Fl-RARa group. Clearly, the LBD is playing a role, but my concern is that it might not be quite so clean. Is it possible that there is a contribution from the RXR DBD in this assay? Otherwise, what do the authors believe causes this variability in this particular group?Addressed in our response to Essential revisions #11.In the correlation studies the authors include danthron and rhein as compounds in the Nurr1/RXR assay but not in the RXR PPI and RXR CTF assay. It is not clear if they are included in the correlation graphs in Figure 4 either; however, if they are excluded there should be a reason indicated. How do these compounds behave in the other assays?Addressed in our response to Essential revisions #2.On page 4 line 43 the authors state,"…the two selective Nurr1-RXRa activating compounds BRF110 and HX600 function as pharmacological RXRa antagonists in these assays – a clear example that pharmacological RXRa modulation of Nurr-1/RXRa modulation is distinct". It isn't clear to me what assays the authors are referring to where these two compounds behaved as antagonists. This paragraph is prior to the presentation of the heterodimer assay where heterodimer formation is inhibited and in the previously presented assays these compounds were either activators (Nurr1/RXR), inactive (RXR coactivator PPI), or inactive/weak activators (RXR).Addressed in our response to Essential revisions #12.


Helicobacter pylori strains and tracking SARS-CoV-2 diversification.Existing single nucleotide polymorphism (SNP) genotyping algorithms do not scale for species with thousands of sequenced strains, nor do they account for conspecific redundancy. Here we present a bioinformatics tool, Maast, which empowers population genetic meta-analysis of microbes at an unrivaled scale. Maast implements a novel algorithm to heuristically identify a minimal set of diverse conspecific genomes, then constructs a reliable SNP panel for each species, and enables rapid and accurate genotyping using a hybrid of whole-genome alignment and k-mer exact matching. We demonstrate Maast’s utility by genotyping thousands of The online version contains supplementary material available at 10.1186/s13059-023-03030-8. Many bacterial and viral species now have thousands of sequenced genomes in public databases, and these numbers are rapidly increasing, fueled by technologies such as metagenome assembly, high-throughput culturing, and single-cell genome sequencing. For many species, genome collections harbor immense genetic variation , 2. Singhttps://github.com/tseemann/snippy) identifies SNPs from the alignment of short reads to a reference genome and kSNP [SNPs are often identified from whole genome sequences. For example, Parsnp  construcand kSNP  identifiand kSNP . With alDespite the variety of SNP genotyping methods, a rapid increase in the number of sequenced microbial genomes presents a computational challenge for existing tools. Sequence alignment is the major obstacle to analyzing so many genomes, though kSNP also remain largely untested with thousands of strains. A second challenge is the fact that many species have a high level of genome redundancy , especiaHelicobacter pylori (H. pylori) strains and summarize the biogeographic patterns of this species across the globe. We also show that Maast can efficiently track the evolution of SARS-CoV-2 during the COVID-19 outbreak. Maast is available as open-source software with both source code and documentation freely accessible on GitHub (https://github.com/zjshi/Maast).To address this gap, we present Maast  for accurate genotyping of orders of magnitude more microbial strains than other state-of-the-art methods. Our key innovation is an algorithm to pick a minimal set of maximally diverse genomes. Only these genomes are used for SNP discovery which reduces genomic redundancy and computational cost. We also implement a hybrid method combining whole-genome alignment and optimized k-mer exact match for genotyping SNPs in either assembled genomes or unassembled whole-genome sequencing (WGS) libraries. Maast performs SNP genotyping faster and more accurately than existing methods but sometimes misses rare variants present in genomes not selected for SNP discovery. We apply Maast to a large collection of previously sequenced Maast is an open-source bioinformatics pipeline written in Python and C +  + that fully automates SNP calling and genotyping for microbial species. Maast has two components: 1) constructing a reference panel of SNPs for a microbial species using a reduced set of non-redundant genomes . Only SNP calling was benchmarked, and not downstream steps such as tree building. Surprisingly, both kSNP3 and Parsnp failed to run through all the A. rectalis genomes. kSNP3 was manually terminated after running for a maximum allowed time (48 h) and consuming more than 1 TB disk space, while Parsnp failed to run even for 1,000 genomes, apparently due to high intraspecific genetic diversity. To check that these results were not specific to A. rectalis, we ran both tools on two other human gut species  and Bacteroides_B dorei (n = 3170) and obtained the same result. We conclude that, to our knowledge, Maast is the only tool able to call SNPs in species with thousands of diverse genomes.We evaluated the computational performance of Maast with kSNP3 and Parsnp, two other commonly used methods for microbial SNP calling. All methods were run for a single bacterial species, A. rectalis by randomly introducing SNPs across a representative genome (“Methods”). While Parsnp ran to completion, kSNP3 again exceeded our 48-h maximum time window. Overall, Maast was 6.3 to > 127-fold faster than Parsnp and 31 to > 127 fold faster than kSNP3  study . Erroneous alignments can in theory produce both incorrect genotypes  and missing genotypes . False positives were only observed for kSNP  and Parsnp (n = 4) but much higher for kSNP (n = 5073). These results suggest that Maast is at least as accurate as currently available methods.To validate the accuracy of Maast SNP genotypes, we first compared Maast to Parsnp and kSNP on 1000 B. uniformis, Additional file B. uniformis, Additional file To evaluate the genotyping accuracy of Maast on short reads, we compared Maast to Snippy by running both tools with their default settings on Illumina short reads simulated from 45 isolate genomes of a single species , which was > 10 times more than a recent study [H. pylori genotypes and geography . Interestingly, we observed H. pylori strains from different host species but the same continent were more similar than those from human hosts living in different continents  between pairs of healthy human hosts compared to diseased pairs  strains, including 8734 isolate genome assemblies and 28,362 WGS read samples, collected from 60 countries over a span of ~ 200 days  and genome assemblies. In terms of SNP discovery, Maast is faster than existing methods due to our DynCC algorithm that identifies a subset of diverse genomes that can be used for the computationally intensive step of comparing all pairs of genomes without losing sensitivity. In fact, using these tag genomes improves our ability to identify SNPs at a given MAF and prevalence in the broader population by reducing bias due to uneven sampling of sequenced genomes. In terms of SNP genotyping, Maast uses less RAM and disk than existing methods while being about two orders of magnitude faster due to a combination of efficient data structures and novel genotyping algorithms for both genomes and unassembled reads.H. pylori, a bacterial species with strong biogeographic patterns. We also used the early months of the SARS-CoV-2 pandemic as a case study to illustrate that Maast could be applied to emerging viral pathogens. Maast’s ability to process thousands of genomes makes it a useful tool for tracking microbial evolution in real time. By constructing and maintaining a SNP panel and k-mer database for any medically important species over time, Maast can be applied to rapidly genotype thousands of new samples without the need for genome assembly, enabling new variants to be monitored as they spread. Beyond these applications, Maast is also ideally suited for population genetic investigations of any species with many assembled or unassembled genome sequences. Its genotypes can be utilized to measure rates and patterns of selection, recombination, drift, and migration among lineages of a species.The computational efficiency and accuracy of Maast enables a broad array of potential applications. In this study, we demonstrated how Maast could be used to study the genetic structure of Despite its advantages, Maast has several limitations. One challenge for SNP discovery is that Maast, like Parsnp and other methods requiring whole genome sequences, cannot be applied to species with very few high-quality assembled genomes. This is a common scenario for uncultured prokaryotic species plus many eukaryotes and viruses beyond the best-studied pathogens, although metagenome-assembled genomes and single-cell sequencing methods are closing this gap –22. The H. pylori and track SARS-CoV-2 variants during the COVID-19 outbreak, applications that involved thousands of strains across the globe. As the number of species with multiple high-quality, near-complete genome sequences continues to grow, Maast is poised to catalog this vast genetic variation.Maast is an open-source software tool that provides a highly efficient method for discovering and genotyping common, bi-allelic SNPs using either whole-genome sequences or unassembled sequencing reads. It includes a novel algorithm that dynamically collapses thousands of genomes into a diverse, representative set of tag genomes that can be used to discover the most common SNPs with reduced computational resources and less bias than using all genomes for a species. This SNP discovery method is combined with a genotyping strategy that is a hybrid of whole-genome alignment and k-mer exact matching. Maast’s genotyping method achieves higher speed and accuracy than existing tools, scaling up population genetic analysis to an unrivaled number of strains compared to state-of-the-art methods. We have demonstrated that Maast could efficiently reconstruct the genetic structure of Maast is an open-source bioinformatics tool for fast and accurate SNP genotyping from conspecific genome assemblies and sequencing reads. The development of Maast is motivated by the following observations: (1) redundant genomes are common in genome databases and these contribute little to SNP discovery while imposing a computational burden, (2) the level and pattern of redundancy vary across species, (3) the choice of reference genome affects SNP discovery, and (4) for sequencing reads, genotyping by assembly first or read alignment does not scale with increasing amounts of data.Maast implements a catalog-first-genotype-later scheme. It was designed in order to enable genotyping to be scaled up for very large populations. Maast also synchronizes microbial genotyping for both genome assemblies and sequencing reads. The key innovations of Maast are its efficiency and novel workflow. We note that the underlying algorithms in this workflow leverage previously existing methods for whole genome alignment and k-mer exact matching.Maast takes a set of intraspecific genomes as input and has the following major steps per species: (1) estimates the pairwise genomic distance between genomes and identifies genome clusters, (2) collapses clusters to a single tag genome per cluster, (3) picks a centroid genome as reference genome, (4) performs multiple whole-genome alignment, constructs consensus genome and calls SNPs to generate a SNP panel, and (5) leverages the SNP panel to perform in silico genotyping for genome assemblies or sequencing reads.Maast estimates the genomic distance between a pair of genomes using Mash   based on the user-defined minor allele frequency (MAF): n = 1/MAF. By default, SNPs are defined as genomic sites with MAF > 1%. In this case, at least 100 genomes are required to enable an effective resolution of 1%, that is 1 in 100 genomes has the minor allele. With higher MAF thresholds, fewer genomes are needed, and vice versa.We implemented a dynamic graph algorithm (Dynamic Connected Component search (DynaCC)) in Maast to identify tag genomes based on pairwise Mash distances. The goal is to identify a minimal set of genomes that captures as much genetic variation as possible through a heuristic search. DynaCC starts with a complete graph where nodes are genomes and edges are weighted by distance. The algorithm applies a distance cutoff (d-cut) strategy to prune edges, where pairs of genomes (edges) with a distance higher than a d-cut are deemed sufficiently dissimilar and removed. By applying a stringent d-cut, many edges are deleted, which reduces the complete genome graph to connected components and isolated nodes. By doing so, Maast avoids loading a complete connected graph into RAM as well as performing operations on it, which would be computationally expensive when the number of genomes is large. A connected component here is analogous to a genome cluster which is defined as a maximum set of nodes in which a path exists between every pair of nodes, and a path is a sequence of edges which joins a pair of nodes. The algorithm identifies tag genomes as a union of hub genomes from connected components and all isolated nodes, and by default, it chooses a hub genome as the genome with the most edges in a connected component . Maast also allows users to identify hub genomes using weighted edges through the flag “–edge-weighted” as well as more sophisticated node centrality estimation methods  through the flag “–tag-centrality”. Users can also run Maast using all genomes, rather than with tag genomes, as long as they have sufficient computing power given the number of genomes.Next, we sought to implement an algorithm that solves the problem of identifying ~ n tag genomes from N genomes, where N > n. Since the level and pattern of redundancy within species vary, an identical d-cut can result in detected n (n′) drastically different across species. When n′ is lower than the target n, SNPs are called with a de facto higher MAF, resulting in a lower sensitivity for detecting SNPs. When n′ is much higher, the benefit of computing efficiency will diminish. It is also unrealistic to predict a static d-cut per species, due to the unknown diversity of input genomes. Here DynaCC searches and determines an optimal d-cut per species dynamically with respect to the input genomes. It first uses a range factor  designating a critical range of n (n to n*rf) that is acceptable, since the exact n may never exist in the search space. It then applies an initial d-cut (default 0.01) and if n′ is lower than n, it searches the lower bound of d-cut with an exponential decay by a factor of 10 until an n′ higher than n is found or a hard bound (default 0.000001) is reached. Between the initial d-cut and lower bound, the algorithm performs a binary search until an n′ falling in the critical range is found or the maximum searching step (default 10) runs out. When no optimal n′ is found, the algorithm settles with a suboptimal n′ which is both higher than and nearest to n. The resulting n′ tag genomes are used for the downstream workflow.Alistipes putredinis (Species ID: 101302). All programs were run in an identical computing environment (AWS EC2 r5.16xlarge) for a maximum of 48 h. DynaCC successfully divided these genomes into 110 clusters in ~ 3 min, while the same task took MMSeqs2 ~ 4 h. Both CD-HIT and vsearch ultimately did not run through the task: the CD-HIT main program was terminated by signal 11 (reached 498 GB RAM cap) and vsearch did not complete within 48 h. Second, DynaCC is more efficient at dividing whole genome sequences into a fixed number of clusters. Other algorithms will usually take one single distance (or similarity) cutoff as input and use this same cutoff across all species to divide genome sequences into a smaller untargeted number of clusters. DynaCC automatically searches a large space of cutoffs around a target minimum number of clusters  and finds an  optimal cutoff per species to generate at least the targeted number of clusters. For example, DynaCC automatically divided genomes of A. putredinis (n = 3646) and A. rectalis (n = 5214), two example species in Additional file A. putredinis and 106 for A. rectalis), while using a static genomic distance cutoff of 0.01 resulted in 46 genome clusters for A. putredinis and 2650 clusters for A. rectalis. In contrast, MMSeqs2 by default generated 3646 A. putredinis clusters and 5214 A. rectalis clusters, barely collapsing any redundant genomes. Thus, DynaCC rapidly prunes a large set of genomes to a fairly consistent target size across diverse and heterogeneous species. We implemented a method with this behavior because we found that it enabled efficient downstream SNP calling.DynaCC presents two advantages for clustering whole genome sequences compared to other clustering tools, such as CD-HIT, vsearch, and MMSeqs2. First, DynaCC leverages a fast approximation of ANI, making it better suited for processing a large number of whole genome sequences. For example, we ran DynaCC, CD-HIT, vsearch , and MMSeqs2 on 3,646 whole genomes of The choice of reference genome is important to SNP discovery. Maast objectively picks a reference genome for each species as the centroid of tag genomes. Maast calculates all pairwise genomic distances between tag genomes using Mash. For simplicity, the tag genome with the lowest average L-1 distance to all other tag genomes is selected as the centroid genome for the species. Maast also allows users to select the centroid genome using L-2 and L-Inf distance. By default, Maast uses the centroid genome as the reference genome for multiple sequence alignments as well as SNP calling. Unless otherwise mentioned, we used the centroid genome for each SNP analysis in this study.For each species, we performed whole-genome alignment by aligning each intraspecific genome to the centroid or otherwise specified reference genome using MUMmer   with consistent quality control steps and parameters identical to those used in SNP calling. For each SNP in the panel, Maast then searches all best alignments to locate the genomic position of the SNP. If the genomic position of a SNP is found in an alignment, Maast reads and reports the allele on the query genome; otherwise, Maast reports the SNP as missing.For sequencing reads, Maast uses a k-mer exact matching algorithm for efficient and accurate genotyping. Maast leverages the SNP panel to extract short unique genomic regions (k-mers) as probes for detecting alleles that distinguish highly similar genomes and uses these k-mers to rapidly genotype reads. For k-mer extraction, Maast identifies any 31-base SNP-covering k-mers (sck-mers) that cover (in any of the 31 bases) each of the SNP alleles. We chose 31 for k based on analyses in our previous study. For each SNP, Maast first extracts all possible 31-mers containing the SNP site from the representative genome . Next, Maast extracts sck-mers by sliding a fixed-size window along the multiple sequence alignment. At any given position, the window allows extraction of n sck-mers, where n equals the number of rows in the multiple sequence alignment . Next, Maast reduces these n sck-mers to m unique sck-mers  look up query suffix in the l-mer index, if found, (2) examine all sck-mer entries identified by the l-mer index one by one and report exact matches. After generating all k-mers in each metagenomic sequencing read, Maast recruits an L-bit index  to locate a bucket of pre-sorted sck-mers in the database containing all possible exact matches to the full k-mer. The algorithm invokes a sequential search for exact matches between the full k-mer and only the sck-mers in this bucket. We note that both alleles may be detected for one SNP site due to the presence of more than one strain, de novo mutations, or sequencing errors in the sample. In the default output of Maast, if multiple alleles are detected, the counts of both alleles will be reported. Further processing of the alleles is delegated to downstream analysis. Throughout this study, we assumed there was only one dominant strain per sample and when multiple alleles were detected for a SNP site within one sample, we proceeded with the major allele  and ignored the minor allele for all analyses unless otherwise mentioned. The count of reads matching an allele will be incremented by one as long as at least one k-mer was detected in the read being processed.To facilitate efficient storage and match of sck-mers, we use 64-bit integers to represent the sck-mers with 00 for “A,” 01 for “C,” 10 for “G,”and 11 for “T”and discarded the sck-mers with wildcards (e.g. “N”). We sort sck-mers in colex order which is essentially a reflection of lexicographic order that reads a sequence from the right to the left (versus left to the right). For example, given a set of unsorted sequences, {“214”, “123”, “134”, “125”}, the corresponding lexicographic order is {“123”, “125”, “134”, “214”} and the colex order is {“123”, “214”, “134”, “125”}. Colex order enables fast matching of suffixes, because related suffixes are near each other in a list. Specifically, we build an The output format of in silico genotyping is similar for both genome assemblies and sequencing reads. It uses a concise table-shaped format for its output, in which every row represents a bi-allelic SNP site. Each row has exactly 8 fields: species, SNP ID, contig, contig position, allele 1, allele 2, and coverage of allele 1 and coverage of allele 2. Currently, the algorithm only supports bi-allelic SNPs which are the vast majority of the SNPs.http://ftp.ebi.ac.uk/pub/databases/metagenomics/mgnify_genomes as of September 2019. The UHGG database is a very large collection of gut microbial whole genome sequences, which are originally from both isolate assemblies and metagenome-assembled genomes (MAGs). The inclusion of MAGs from diverse human populations and geographic locations is critical for capturing natural genetic variation within human gut species. From the UHGG database, we selected 146 species, each with 200 or more high-quality (completeness >  = 90% and contamination rate <  = 5%) whole genome sequences, which accounted for a total of 109,365 genomes. Twenty-nine of them had more than 1000 genomes, and Escherichia coli_D (species id: 102506) had the most genomes (n = 6645).We downloaded genome sequences from the Unified Human Gastrointestinal Genomes  (UHGG) ahttps://github.com/tseemann/snippy; June 2020) uses BWA mem [We compared Maast to several other methods in a series of simulations and data analyses designed to evaluate different aspects of computational performance and accuracy. For SNP calling in whole genome sequences, we included two methods, Parsnp and kSNP, which represented two distinct methods. Parsnp reports SNPs in the coordinates of the specified reference genome, and kSNP does not. We selected ParSNP as opposed to other methods for calling SNPs through multiple whole genome alignment, such as Mugsy  and Mauv BWA mem   for the simplicity of comparison and analysis. We skipped a sample if it did not have a forward read sample as extracted from a SRA file using fastq-dump in the SRA toolkit. However, we note that using the reads of both directions can effectively increase the coverage, which thus should be recommended especially for species with low abundance.Agathobacter rectali, from UHGG genome colletion  were randomly selected to be non-reference alleles, which we inserted in silico. For simplicity, we only simulated bi-allelic SNPs. To evaluate the scalability of the methods, we used three levels of input size .We compared Maast with kSNP3 and Parsnp to evaluate its computing performance on SNP calling with whole genome sequences. We downloaded a total of 5214 high-quality genomes for one of the most well-sequenced bacterial species, Bacteroides uniformis) from the Culturable Genome Reference (CGR) study [To evaluate SNP genotyping in short reads, we downloaded a total of 63 samples of short reads for an arbitrary species (R) study . Each saWe evaluated the computing performance of Maast and other tools on an AWS EC2 instance with the following specifications: AWS r5.16xlarge, 32 physical CPU cores (64 vCPU), Intel 8175 M CPU @ 2.50 GHz, 512 GB RAM and EBS gp2 RAID array providing 13,600 Mbps bandwidth. We measured both speed and peak RAM consumption using the GNU time (version 1.7) command with option “-v”.To ensure a fair comparison between different methods, all methods were run on all of the input data with the same reference genome whenever one was used. Only the step of SNP calling was evaluated; if a method had downstream steps such as SNP tree-building we did not benchmark these. Each method was run using all cores of an environment whenever possible.We manually terminated the running of any tool after both tools had been running over a designated maximum running time window (48 h) and had not finished. When terminated, we estimated the speed of Parsnp and kSNP with the maximum time use of 48 h and projected peak RAM use linearly as a function of the number of genomes.Bacteroides uniformis, the end-to-end runtime breakdown of Maast is ~ 7 min for picking tag genomes and generating SNP panel, ~ 13 min for building sck-mer database, and ~ 19 min for genotyping 2746 whole-genome assemblies or ~ 90 s for genotyping 63 samples of whole-genome sequencing short reads.For the example species of Bacteroides uniformis) from the CGR study. These genomes were cultivated from fecal samples of healthy humans and characterized as non-redundant and high-quality draft genomes. We used InSilicoSeq [To evaluate the accuracy of SNP genotyping by Maast in short reads and to compare it to Snippy, we simulated reads from whole genome sequences. For these simulations, we downloaded a total of 45 isolate genomes for a species  which was arbitrarily selected. We did not use the centroid genome as the reference genome here to control possible biases. Our evaluations focused on the correct identification of SNPs and on the accuracy of the alleles in the typed SNPs. For each strain, ground truth genotypes were determined by aligning the whole sequence of that strain to the reference genome. We focused our evaluations on the SNPs that were potentially able to be genotyped by both Maast and Snippy, i.e., SNPs in the Maast panel. True positives (TP) and false positives (FP) were the correct and incorrect genotypes compared to ground truth; false negatives (FN) were sites with no genotype or an incorrect genotype. In this way, we conveniently calculated the Positive predictive value (PPV) as the ratio between the sum of TP sites and the sum of all reported sites and the sensitivity as TP/(TP + FN) or the ratio of TP sites to the total number of sites in the Maast SNP panel. These values were calculated for each method on non-reference sites only as Snippy only reported them.H. pylori from NCBI and PATRIC [To show Maast is powerful for exploring genetic structure within microbial species, we downloaded a total of 3524 whole genome sequences of d PATRIC  websitesH. pylori strains. To maximize the number of strains in this analysis, we downloaded an additional 1522 short-read samples from NCBI SRA as of April 2021, each representing a distinct strain. We then combined these short-read samples together with 1756 PATRIC genomes to form one of the largest H. pylori strain collections (n = 3178) to date with diverse host and geographic information  of each SNP, generated a binary matrix of major allele presence/absence per strain, and performed dimension reduction on this matrix with UMAP (umap package version 0.2.3.1) in R. We plotted all strains in the resulting UMAP coordinates. We visually identified 1,073 outliers  and removed them from the plot. To account for possible bias due to less covered SNPs, we applied two filters to both genomes and metagenomes: (1) we excluded a SNP in both genomes and metagenomes if it was present in fewer than 5 metagenomes, and (2) we excluded a metagenome if it had < 1000 genotyped SNPs or < 50% of total SNPs genotyped.Additional file 1. Supplementary Figures S1 to S22.Additional file 2: Supplementary Tables S1. Summary of intra-specific genomes and common SNPs for 146 microbial species associated with the human gut.Additional file 3: Supplementary Tables S2. List of UHGG genomes for 3 CGR species that were used for evaluating the computational performance of Maast.Additional file 4: Supplementary Tables S3. Detailed computational performance of Maast, Parsnp and kSNP based on running each tool on 500, 1000 and 5000 genomes.Additional file 5: Supplementary Tables S4. List of accession numbers for the whole genome assemblies and sequencing reads of B. uniformis.Additional file 6: Supplementary Tables S5. Detailed comparison of SNP calling accuracy between Maast, Maast + NRC, kSNP and Parsnp.Additional file 7: Supplementary Tables S6. List of accession numbers for 3,428 whole genome assemblies of H. pylori.Additional file 8: Supplementary Tables S7. Accession numbers and metadata of 3,178 H. pylori strains.Additional file 9: Supplementary Tables S8. List of 475 H. pylori strains with paired whole genome assemblies and sequencing reads.Additional file 10: Supplementary Tables S9. Accession numbers and metadata of 37,096 SARS-CoV-2 strains downloaded from the NCBI SARS-CoV-2 Data Hub and SRA.Additional file 11. Review history.