Arabidopsis thaliana, numerous heritable DNA methylation differences have been identified in greenhouse-grown isogenic lines, but it remains unknown how natural, highly variable environments affect the rate and spectrum of such changes. Here we present detailed methylome analyses in a geographically dispersed A. thaliana population that constitutes a collection of near-isogenic lines, diverged for at least a century from a common ancestor. Methylome variation largely reflected genetic distance, and was in many aspects similar to that of lines raised in uniform conditions. Thus, even when plants are grown in varying and diverse natural sites, genome-wide epigenetic variation accumulates mostly in a clock-like manner, and epigenetic divergence thus parallels the pattern of genome-wide DNA sequence divergence.There has been much excitement about the possibility that exposure to specific environments can induce an ecological memory in the form of whole-sale, genome-wide epigenetic changes that are maintained over many generations. In the model plant  Arabidopsis thaliana lines differ substantially in their methylation profiles. How much of this is independent of genetic changes remains, however, unclear, especially given that there is very little conservation of methylation between species, simply because the methylated sequences themselves, mostly repeats, are not conserved over millions of years. On the other hand, there is no doubt that artificially induced epialleles can contribute to phenotypic variation. To investigate whether epigenetic differentiation, at least in the short term, proceeds very differently from genetic variation, and whether genome-wide epigenetic fingerprints can be used to uncover local adaptation, we have taken advantage of a near-clonal North American A. thaliana population that has diverged under natural conditions for at least a century. We found that both patterns and rates of methylome variation were in many aspects similar to those of lines grown in stable environments, which suggests that environment-induced changes are only minor contributors to durable genome-wide heritable epigenetic variation.It continues to be hotly debated to what extent environmentally induced epigenetic change is stably inherited and thereby contributes to short-term adaptation. It has been shown before that natural  A. thaliana provides ample opportunities for linked epigenetic variation, and the genomes of A. thaliana accessions from around the globe are rife with differentially methylated regions (DMRs) Differences in DNA methylation and other epigenetic marks between individuals can be due to genetic variation, stochastic events or environmental factors. Epigenetic marks such as DNA methylation are dynamic; they can be turned over during mitosis and meiosis or altered by chromatin remodeling or upon gene silencing caused by RNA-directed DNA methylation (RdDM). Moreover, changes in DNA sequence or structure caused by, for instance, transposable element (TE) insertion, can induce secondary epigenetic effects at the concerned locus A. thaliana isogenic lines grown for several years in a stable greenhouse environment A. thalianaThe seemingly spontaneous occurrence of heritable DNA methylation differences has been documented for wild-type In addition to these spontaneous epigenetic changes, the environment can induce demethylation or de novo methylation in plants, for example after pathogen attack A. thaliana accessions collected across North America. Using a new technique for the identification of differential methylation, we found that in a population of thirteen accessions originating from eight different locations and diverged for more than one hundred generations, only 3% of the genome had undergone a change in methylation state. Notably, epimutations at the DNA methylation level did not accumulate at higher rates in the wild as they did in a benign greenhouse environment. Using genetic mutations as a timer, we demonstrate that accumulation of methylation differences was non-linear, corroborating our previous hypothesis that shifts in methylation states are generally only partially stable, and that reversions to the initial state are frequent To assess whether a variable and fluctuating environment is likely to have long-lasting effects in the absence of large-scale genetic variation, we have analyzed a lineage of recently diverged A. thaliana accessions collected across North America. Different from the native range of the species in Eurasia, where nearly isogenic individuals are generally only found at single sites, about half of all North American individuals appear to be identical when genotyped at 139 genome-wide markers Previous studies of isogenic mutation accumulation (MA) lines raised in uniform greenhouse conditions identified many apparently spontaneously occurring pure epimutations A. thaliana having been introduced to North America during colonization by European settlers A. thaliana specimens from the mid-19th century can be found, among these specimens from the Eastern Seaboard and the Upper Midwest. We conclude that the HPG1 accessions constitute a near-isogenic population that should be ideal for the study of heritable epigenetic variants that arise in the absence of large-scale genetic change under natural growth conditions. Because we observed only a weak positive correlation between genetic distance and phenotypic differences in the greenhouse  and 170,998 structural variants (SVs) relative to the Col-0 reference genome, which were then used to build a HPG1 pseudo reference genome . Our null hypothesis was that a variable and highly fluctuating natural environment increases the rate of heritable methylation changes. In contrast to this expectation, DMPs appear to have accumulated in sub-linear fashion in both the HPG1 and MA populations mCHH and 2% of mCHG positions were outside of methylated regions , were spaced much farther apart, and were separated by many more unmethylated loci  in the HPG1 population. A limitation of previous plant methylome studies using short read sequencing has been that these relied on integration over methylated or single differentially methylated sites, or on the analysis of fixed sliding windows along the genome to identify DMRs. What appears intuitively to be more appropriate is to first identify regions that are methylated in individual strains   regions , consistted loci .. This eUsing the unified set of MRs, we tested all pairs of accessions for differential methylation, identifying 4,821 DMRs with an average length of 159 bp . Of the DNA methylation in gene bodies has been proposed to exclude H2A.Z deposition and thereby stabilize gene expression levels mC site in the MA population of being variably methylated in the HPG1 population was only 7%, but it was 41% among sites that were also variably methylated in the MA population – a six-fold enrichment . HPG1 DMRs were four-fold more likely to coincide with MA DMRs than with a random methylated region from this set . We obseWe next wanted to know how this short-term variation compared to methylation variation across much deeper splits. To this end, we identified variably methylated regions between a randomly chosen MA line and a randomly chosen HPG1 line; these DMRs, which differentiate distantly related accessions, were also enriched in each of the two sets of within-population DMRs (MA or HPG1) . Finallymet1 single and the vim123 triple mutants , which means that genetic variance across the entire genome contributed less than half of methylation variance . hDMRs iTo identify genetic variants that potentially directly cause methylation changes in their local genomic neighborhood, we focused on variably methylated regions that were within 1 kb of segregating SNPs or indels. Of 191 such DMRs, only three showed a systematic correlation with nearby sequence polymorphisms. We noticed, however, that coding regions with structural variants larger than 20 bp that distinguished the MA and HPG1 populations were more likely to be methylated in both lineages than non-polymorphic coding regions . ConsequLastly, we asked whether the genome-wide methylation pattern reflected genetic relatedness, i.e., population structure. Hierarchical clustering by methylation rates of variable sites and regions grouped strains by sampling location . This reA. thaliana HPG1 lineage, which has likely diverged for at least a century throughout North America. Our analyses have revealed little evidence for broad-scale and durable epigenetic differentiation that might have been induced by the variable and fluctuating environmental conditions experienced by the HPG1 accessions since they separated from each other. While the exact conditions these plants have been subjected to since their separation from a common ancestor remain unknown, the time scale and diversity of geographic provenance are strong indicators of the variability of the environment between the different sampling sites, supported by temperature and precipitation data from nearby weather monitoring stations. The general analytical framework enabled by the HPG1 lineage – nearly isogenic lines grown for more than a century under variable and fluctuating conditions – could not have been achieved in a controlled greenhouse experiment.We have tested the hypothesis that accumulation of epigenetic variation under natural conditions proceeds over the short term in a very different manner than the clock-like behavior of genetic variation Studies of epiRIL populations have shown that pure epialleles can be stably transmitted across several generations We found that positions of differential methylation in the HPG1 population are more likely to overlap with DMPs detected between closely related MA lines than between more distantly related MA lines. This observation supports the hypothesis that there are different classes of polymorphic sites. One of these includes ‘high lability’ sites that are independent of the genetic background, that change with a high epimutation rate, and that are therefore more likely to appear in each population. Another class of DMPs comprises more stable sites that gain or lose methylation more slowly and that therefore are less likely to be shared between different populations.A. thaliana might be differently disposed to record environmental signals in its epigenome compared to more long-lived species. From an evolutionary perspective, in perennial species the advantage of epigenetically mediated local adaptation to changing conditions could be more pronounced, and future studies are warranted to address this question.Differences between accessions in terms of DNA methylation recapitulated their genetic relatedness, further corroborating our hypothesis that heritable epigenetic variants arise predominantly as a function of time rather than as a consequence of rapid local adaptation. Epigenetic divergence thus does not become uncoupled from genetic divergence when plants grow in varying environments, nor does the rate of epimutation noticeably increase. A minor fraction of heritable epigenetic variants may be related to habitat, which could be responsible for LISET-036 being epigenetically a slight outlier , even thBecause of the near-isogenic background of the HPG1 accessions, we were also able to gauge how much of epigenetic variation is either caused by, or stably co-segregates with genetic differences. HPG1-specific highly differentially methylated regions were more often linked to genotype variation than regions that were variably methylated in both the HPG1 and MA populations. This suggests that heritable hDMRs can, to a certain extent, be considered facilitated epigenetic changes Both differentially methylated regions and positions are over-represented in genes, but TEs and intergenic regions contain many variable regions and only very few variable single sites. Altogether our data indicate that both variably and constitutively methylated positions in genes are typically separated by many unmethylated sites and that a large fraction of these is therefore not classified as being  methylated. Variability of DNA methylation in plant genes thus mainly affects single, sparsely distributed cytosines, the biological relevance of which remains unclear.Our comparisons between MA laboratory strains and natural HPG1 accessions have revealed that loci of variable methylation overlapped much more between the two groups than expected by chance, despite these populations having experienced very different environments that also differ greatly in their uniformity, and despite completely different genetic backgrounds. The observation that changes at many sites and loci are independent of the genetic background and geographic provenance suggests that spontaneous switches in methylation predominantly reflect intrinsic properties of the DNA methylation and gene silencing machinery, with the CG maintenance system seemingly being the most error-prone. Our most important finding is probably that DNA methylation is highly stable across dozens, if not hundreds of generations of growth in natural habitats; 97% of the total methylated genome space was not contained in a DMR. The stark contrast to published data, which describes more than 90% of cytosines in the genome as variably methylated in a set of 140 divergent natural accessions http://1001genomes.org) and have been submitted to the stock center.Accessions g for 120 s. Genomic DNA was extracted using the Qiagen Plant DNeasy kit . Total RNA was extracted from rosette leaves pooled from eight to ten individual adult plants using the Qiagen Plant RNeasy Kit . Residual DNA was eliminated by DNaseI  treatment.DNA was extracted from rosette leaves pooled from eight to ten individual adult plants. Plant material was flash-frozen in liquid nitrogen and ground in a mortar. The ground tissue was resuspended in Nuclei Extraction Buffer . After cell lysis in nuclei extraction buffer containing 10% Triton-X-100, nuclei were pelleted by centrifugation at 2000 DNA libraries for genomic and bisulfite sequencing were generated as described previously A. thaliana DNA genomic libraries were analyzed in control lanes. Transcriptome libraries were sequenced with 101 bp single end reads. Four libraries with different indexing adapters were pooled in one lane; no control lane was used. For image analysis and base calling, we used the Illumina OLB software version 1.8.All sequencing was performed on an Illumina GAII instrument. Genomic and bisulfite-converted libraries were sequenced with 2×101 bp paired-end reads. For bisulfite sequencing, conventional The SHORE pipeline v0.9.0 Arabidopsis thaliana genome sequence version TAIR9 in iteration 1 and against updated “Haplogroup 1-like” genomes in further iterations. The mapping tool GenomeMapper v0.4.5s Reads were aligned against the Genetic variants were called in an iterative approach. In each step, SNPs and structural variants common to all strains were detected and incorporated into a new reference genome. The thus refined “HPG1-like” genomes served as the reference sequence in the subsequent iterations .. We perBase counts on all positions were retrieved by SHORE v0.9.0 For comparisons across lines, positions were accepted if at most one intermediate penalty on their score was applicable to at least one strain (score ≥32). In this case, the threshold for the other strains was lowered, accepting at most one high and two intermediate penalties (score ≥15). In this way, information from other strains was used to assess sites from the focal strain under the assumption of mostly conserved variation, allowing the analysis of additional sites.Only sites sufficiently covered (≥5x) and with accepted base calls in at least half of all strains (≥7 out of 13) were processed further. Variable alleles with a frequency of 100% were classified as "common" and variants with a lower frequency as "segregating".de novo assembly approach described below.Additional SNPs were called using the targeted de novo assembly pipeline targeted towards sequencing gaps (described below). We reported deletions and insertions from all tools, and additionally inversions from Pindel. DELLY combines split read alignments with the identification of discordant paired-end mappings. Thus, our SV calling made use of three out of four currently available methodologies.Although a plethora of SV detection tools have been developed, the predicted variants show little overlap between each other on the same data sets. Furthermore, the false positive rate of many methods can be drastic Reads for DELLY were mapped using BWA v0.6.2 The arguments for the command line calls of all tools are listed in de novo assembly method was developed.While using a re-sequencing strategy, there are regions without read coverage (“sequencing gaps”) because either the underlying sequence is being deleted in the newly sequenced strain, or highly divergent to the reference sequence, or present in the focal strain, but not represented in the read set. To access sequences in the first two classes, a local Insertion breakpoints or small deletions, however, can mostly not be detected by zero coverage due to reads ranging with a few base pairs into or beyond the structural variants. Therefore, we defined a “core read region” as the read sequence without the first and last 10 nucleotides. To be able to assemble the latter cases, the definition of “sequencing gaps” was extended from zero-covered regions to stretches not spanned by a single read's core region.All reads aligned to the surrounding 100 nucleotides of such newly defined sequencing gaps as well as the unmappable reads from the re-sequencing approach together with their potential mapped partners constituted the assembly read set. Two assembly tools were used to generate contigs, SOAPdenovo2 v2.04 All differences between contig and reference sequences were parsed  for each assembly tool. Only identical variants retrieved from both assemblers were selected.de novo assemblies were consolidated  are assumed to be low, since gaps would deteriorate the alignment capability towards the ends of the read.Therefore, we filtered out variants from the consolidated variant set spanning a genomic region already covered by at least one inner core region of a mapped read of the corresponding strain ., assumiVariants passing the core read filter in all strains were classified as common variants and were incorporated into the reference sequence of the previous iteration, thus replacing the reference allele. Segregating variants, which could not be detected in all strains, were additionally built into the reference in separate “haplotype regions” (or “branches” of the reference sequence) to eventually be able to assess whether reads preferentially mapped to the reference or the alternative haplotype sequence .. Linkedb) and at the corresponding site on the reference haplotype sequence (rref) . The null hypothesis of homozygosity was rejected after P value correction by Storey's method q values below 0.05.For each strain, all reads were re-mapped to this new reference sequence yielding read counts at the variant site on each branch (re (rref) .. Here, The same variant could be part of several different haplotypes and thus, could be included into different branch sequences. Reads supporting this variant would map at multiple locations in the reference. Therefore, we allowed all aligned rather than only unique reads to contribute to read counts and omitted the paired-end correction procedure.q value above 0.05, it was assumed to be present in the population. If the test on the same haplotype failed in another strain, but the absolute read count of the haplotype sequence exceeded the alternative haplotype read count by ≥2-fold, then this haplotype was considered present in the corresponding strain as well.We followed a similar “population-aware” approach to prefer commonalities among strains as was used for the SNP calling for labeling variants as being common or segregating. Here, variable sites with accumulated coverage over both branch and reference sequence of ≤3x were marked as “missing data”. If there was at least one haplotype in a strain with a We classified variants where at least 7 out of 13 strains did not show missing data as ‘common’ if the branched haplotype was present in all strains, as ‘not present’ if the reference haplotype was present in all strains, or into ‘segregating’ if there was support for both haplotypes.To combine common variants identified by the described stepwise algorithm into potentially less evolutionary events, we aligned 200 bp around each variant of the last iteration's genome back to the TAIR9 Col-0 reference genome using a global alignment strategy (‘needle’ from the EMBOSS v6.3.1 package).In total, we found 842,103 common and 2,017 segregating polymorphisms without removing linked loci compared to Col-0 after two iterations, to which the different tools contributed to different extent depending on the variant type ..Genomic and bisulfite sequencing were performed as described in ref. The procedure followed one described We performed whole methylome bisulfite sequencing to an average depth of 18x per strand  on two pP value correction scheme as in Becker et al., we identified 535,483 DMPs across all 13 strains.We performed the same methods as in ref Using the model developed in ref ind = 13 (the number of accessions in this study) and Δ(1)  = 1,046,892 , we estimated a total number of possible DMPs in the population of N = 59,770,415, which is close to the 43 million cytosines in the A. thaliana genome. Given such an estimate for N, the Δ function can be evaluated numerically to estimate the number of DMPs we would have detected had we analysed the same number of accessions as in ref For NA. thaliana genome, into hypo- and hypermethylated regions The value of an approach that defines methylated regions (MRs) before identifying differentially methylated regions (DMRs) has been demonstrated before with a Hidden Markov Model (HMM) method developed for the analysis of methylated-DNA-immunoprecipitation followed by array hybridization (MeDIP-chip) P values were computed by testing the scores against an empirical distribution of scores obtained by random permutation of all cytosines throughout the genome. After FDR calculation, consecutive stretches in high state with an FDR <0.05 are defined as methylated regions (MRs).Apart from these changes and some final filtering steps (see below), we followed the same computational steps as described by Molaro and colleagues The HMM was run on all genome-wide cytosines, independent of their coverage. Methylation rates were obtained using accumulated read counts from the strain replicates, resulting in one segmentation of the genome per strain. Gaps of at least 50 bp without a covered C position within a high methylation state automatically led to the end of the high methylation segment. Positions with a methylation rate below 10% at the start or end of highly methylated regions (until the first position with a rate larger than 10%), were assigned to the preceding or subsequent low methylation region, respectively.The method to identify MRs yielded 13 different segmentations of the genome, one for each strain. We selected regions being in different or highly methylated states between strains and statistically tested them for differential methylation . To obtain epiallele frequencies, we clustered strains into groups based on their pairwise comparisons and statistically tested the groupings against each other. Regions that showed statistically significant methylation differences between at least two sets of strains were identified as DMRs. Finally, because of the sensitivity of the statistical test, we empirically filtered DMRs for strong signals and defined highly differentially methylated regions (hDMRs). All these steps are described in depth in the following.We defined a breakpoint set containing the start and end coordinates of all predicted methylated regions. Each combination of coordinates in this set defined a segment to perform the test for differential methylation in all pairwise comparisons of the strains, if at least one strain was in a high methylation state throughout this whole segment .. To alsBecause of the sheer number of such regions, we applied the following greedy filter criteria: Regions were discarded from any pairwise comparison if less than 2 strains contained at least 10 cytosines covered by at least 3 reads each (accumulated over strain replicates) in this region . RegionsThese filters reduced the set of regions to test from ∼2.5 million to ∼230,000 per pairwise comparison.strain-specific beta binomial distributions, and b) for the read counts of both strains including their replicates together, resulting in (three) common beta binomial distributions. In this way, we obtained each distribution's mean µ and standard deviation σ. We selected only regions for potential DMRs, whose intervals  for strain 1 and  for strain 2 did not overlap.We designed a statistical test for differential methylation between two strains for a given region. The test assumes that the number of methylated and unmethylated read counts per position along a region follows a beta binomial distribution – similar to the HMM in MR calling. More precisely, there are 3 distributions for each sequence context and for each strain. Using gradient-based numerical maximum likelihood optimization, we fitted the parameters for each beta binomial distribution on the available read count data of the region in the respective strain. This was done a) for each of the two strains separately (while taking strain replicates into account), resulting in (two times three) P values by calculating the ratio of the strain-specific and the common log likelihoods of the available read count data using the corresponding beta binomial distributions and by testing it against a chi-squared distribution (with 6 degrees of freedom). Let sample S have ScN cytosines in context c in total and ScpC reads at position p in context c, from which Scpx are methylated, then we compute:To further corroborate statistical significance, we computed After correction for multiple testing using Storey's method P values as described above without summing over the contexts . We termed resulting DMRs CG-DMRs if the methylation at only CG sites within this region was statistically significantly different, and similarly CHG-DMRs and CHH-DMRs.Additionally, this method allowed calling differential methylation in a region for each context separately by computing For 13 strains there are at maximum 78 pairwise comparisons per region. To summarize pairwise comparisons and obtain epiallele frequencies, we assigned strains into differentially methylated groups. To achieve such clustering, we constructed a graph for each region where strains were represented as vertices and connected to other strains by an edge if the region was identified as a DMR between them .. We assWe set up a custom algorithm, which iteratively solves the “vertex coloring problem” for an increasing number of different colors, starting with two and quitting once all strains could be successfully assigned a color .. In eacstrain means from now on). We roughly approximated each group's mean methylation values (group means) as the mean values of all strain means within a group. The grouping diversity describes the accumulated absolute differences between the strain means and their respective group means divided by the number of strains. As an example, consider group mean for the blue strains in the example is (89+90+90+93+87)/5 = 89.8% and for the white strains 52%. The grouping diversity of the clustering shown here would be (from strains A to K): (|56–52|+|59–52|+|64–52|+|89–89.8|+|41–52|+|93–89.8|+|90–89.8|+|45–52|+|47–52|+|90–89.8|+|45–52|+|87–89.8|)/11 = 2.84.Each strain had 3 context-dependent means of its beta binomial distributions per region  was assigned into the group to which the accumulated absolute difference between its strain mean and the group mean was lowest. In the example of group mean (90%) than to the white one (52%).If there was more than one possible grouping of the strains, we chose the one with the lowest This procedure summarized the ∼221,000 DMRs of all pairwise strain comparisons into 11,323 DMRs between groups of strains.Once grouped, the same statistical test as for differential methylation between two strains was used to test groups of strains. Beta binomial distributions were approximated using the read counts of all strains in a group as if they were replicate data. This procedure identified 10,645 groups of regions showing significantly different methylation. Because the method used for the selection of the regions to perform the differential test can result in overlapping regions, DMRs can still overlap each other. From sets of overlapping DMRs, the non-overlapping DMR(s) with the lowest ‘grouping diversity’ was (were) retained, resulting in 4,821 final DMRs. For the vast majority of DMRs (98%), strains were classified into two groups, i.e. there are only two epialleles.Our sensitive statistical test classified as differential some regions with low variance and only subtle methylation difference; we therefore defined as highly differentially methylated regions (hDMRs) with potentially greater biological relevance all DMRs that were longer than 50 bp and that showed a more-than-three-fold difference in methylation rate in at least one sequence context, when considering at least five cytosines of that context .. In addThe data from Stroud and colleagues For each differentially methylated region, we considered a linear mixed model to estimate the proportion of variance that is attributable to genetic effects (heritability) and its standard error. The approach is similar to variance component models used in GWAS, e.g. refs. K = 2 to K = 9 with a burn-in of 50,000 and 200,000 chains for 10 repetitions and determined the best K value using the ΔK method We identified non-synonymous SNPs using SHOREmap_annotate We used the TAIR10 annotation for genes, exons, introns and untranslated regions; transposon annotation was done according to Positions were associated to the corresponding element when they were contained within the boundaries of that element. (D)MRs were associated to a class of element if they overlapped with that class of element; a (D)MR could only be associated to one class of element. When summing up basepairs of an element class covered by (D)MRs, the number of basepairs of a (D)MR overlapping with that class of element were considered. In that case the space covered by a (D)MR could be assigned to different classes of elements, while each basepair of the (D)MR could be assigned to only one class.We tested for significant overlap of DMRs using multovl version 1.2 (Campus Science Support Facilities GmbH (CSF), Vienna, Austria). We reduced the genome space to the basepair space covered by MRs identified in at least one HPG1 accession. DMRs were considered in the analysis if their start and end positions were contained within the MR space. DMRs that only partially overlapped with the MR space were trimmed to the overlapping part. Overlap between DMRs from different datasets was analyzed by running 100,000 permutations of both DMR sets within the MR basepair space. multovl commands are listed in http://www.arabidopsis.org) genome using Tophat version 2.0.8 with Bowtie version 2.1.0 Reads were processed in the same way as genomic reads, except that trimming was performed from both read ends. Filtered reads were then mapped to the TAIR9 version of the Arabidopsis thaliana (ftp://ftp.arabidopsis.org/home/tair/Genes/TAIR10_genome_release/TAIR10_gff3/) supplied with the most recent transposable element annotation For quantification of gene expression we used Cufflinks version 2.0.2www.r-project.org). Circular display of genomic information in When not mentioned otherwise in the corresponding paragraph, graphical displays were generated using R version 3.0.1  Global Summary of Day (GSOD) data. Collection locations were matched to the closest weather station with <5% missing data for five years prior to the collection date. Cumulative liquid precipitation was calculated each year starting from January 1.http://gbrowse.weigelworld.org/fgb2/gbrowse/ath_methyl_haplotype1/. DNA methylation data, MR coordinates and genetic variant information have also been uploaded to the genome browser of the EPIC consortium  and can be accessed at http://genomevolution.org/r/939v. The software of our methylation pipeline can be downloaded at http://sourceforge.net/projects/methpipeline.The DNA and RNA sequencing data have been deposited at the European Nucleotide Archive under accession number PRJEB5287 and PRJEB5331. A GBrowse instance for DNA methylation and transcriptome data is available at S1 FigRecent temperature histories of samples. Samples were matched to the closest weather stations (distance in km). Daily temperature range (dark bars) and means (white points) of 330 days prior to the collection date, such that late year data reflect temperature ranges of the previous year. Five-year temperature ranges (light bars) and means (white line) indicate longer-term temperature variability. Different collection dates and locations both contribute to different means and variance of recent temperatures experienced by samples.(EPS)Click here for additional data file.S2 FigRecent precipitation histories of samples. Cumulative (from January 1) liquid precipitation at the weather stations closest to each collection site (distance in km). Black lines show yearly histories for five years prior to collection, the thick black line indicates the cumulative history of the previous 330 days. Blue line shows the LOESS estimate of mean precipitation accumulation over a year.(EPS)Click here for additional data file.S3 Figde novo assembly pipeline were combined into a consolidated variant set. (2) Variants with core read coverage were filtered out, the remaining variants were classified into common and potentially segregating. Brown triangles symbolize insertions/deletions, the brown X represents a SNP. (3) All common variants were incorporated into the reference genome. All segregating variants were introduced in branches of the reference genome, which incorporated polymorphisms linked by less than 107 bp. (4) After mapping the reads against the branched reference, a binomial test was performed in each strain and for each variable site to call the allele, i.e., to determine whether there was statistical evidence for the presence of only one haplotype covering the variant's coordinates. Variants with the same non-reference allele call in all strains were considered as “common”; those with a reference call in at least one strain and a variant call in at least one other strain were classified as “segregating”. (5) All common variants from the previous step were incorporated into the new reference sequence, and a new iteration was started over from (1), or this new genome served as the HPG1 pseudo-reference genome after iteration 2. (B) Increase of detected variants and decrease of unsequenced genome space and unmappable reads by iterative read mapping. The legend on the right side denotes absolute values after iteration 2. The reference value (100%) derives from the mapping against the Columbia-0 genome sequence (TAIR9), and for common variants it is the number of variants leading to the genome of iteration 1. Thus, ∼842,000 common variants led to the genome of iteration 2, ∼864,000 to the genome of iteration 3. (C) Composition of common polymorphisms by variant type (top) and by detection tool (bottom). Variants found by more than one tool contributed to the count for all respective tools.Iterative re-alignment strategy and statistics. (A) Iterative re-alignment approach to evaluate predicted variants and to build a HPG1 pseudo-reference genome. (1) For each strain, variants called by diverse structural variant detection tools and a local (EPS)Click here for additional data file.S4 FigDistribution of genetic variants along the five chromosomes. Relative density of common variants in 100 kb sliding windows with a step size of 10 kb. SNP: single nucleotide polymorphism, SV: structural variant.(EPS)Click here for additional data file.S5 FigAnnotation of genetic variants. Polymorphisms were hierarchically assigned to CDS> intron> 5′ UTR> 3′ UTR> transposon> intergenic. HPG1: haplogroup-1 lines, SNP: single nucleotide polymorphism, SV: structural variant.(EPS)Click here for additional data file.S6 FigMagnification of the central area of the phylogenetic network in (EPS)Click here for additional data file.S7 FigPhenotypic analysis. (A) Leaf growth measured over time . Error b(EPS)Click here for additional data file.S8 FigNumber of DMPs in dependence of the number of sequenced accessions. (A) Fraction of all DMPs of different allele frequencies that can be detected by sequencing a given number of accessions. We assumed the methylation state in the Col-0 reference as the ancestral state. Vertical lines indicate the number of accessions in this study (13) and in ref (EPS)Click here for additional data file.S9 FigDifferentially methylated positions (DMPs). (A) Epiallele frequencies of DMPs for CG sites only (left), and comparison of all three sequence contexts (right). (B) Annotation of invariantly (N-DMPs) and differentially (DMPs) methylated sites. Cytosines were hierarchically assigned to CDS> intron> 5′ UTR> 3′ UTR> transposon> intergenic.(EPS)Click here for additional data file.S10 FigEffect of sample pooling on the number of identified DMPs. Small filled triangles and box-and-whisker plot indicate distribution of differentially methylated positions (DMPs) that were called by comparing individual plants of generations 31 and 32 of mutation accumulation (MA) lines 30–39 and 30–49 with individual plants of 0-4-26 and 0-8-87, which represent generation 3 of two independent lines (16 comparisons in each group). The large unfilled triangles indicate the number of DMPs that were called when data from four lines of generations 31 and 32 were pooled in silico and compared against all four lines from generation 3. On average, a substantially lower number of DMPs is called with pooled data.(EPS)Click here for additional data file.S11 FigmCGs) within genes in dependence of whether these sequences are inside or outside of MRs. (C) Distances in bp between methylated CG sites within genes in dependence of whether these sequences are inside and outside MRs . (D) Distances between methylated CG sites within body methylated (BM) genes identified with the method from ref. Characteristics of  methylated regions. (A) Histogram of the length of unified methylated regions (MRs), differentially and highly differentially methylated regions (DMRs and hDMRs). The red and green lines indicate mean and median length, respectively. For better visibility, regions larger than 2,000 bp were excluded from the representation. The minimum size of hDMRs was 50 bp. (B)-(D) are based on data of one HPG1 strain (LISET-036). (B) Number of unmethylated cytosines (Cs) in-between methylated CG sites ((EPS)Click here for additional data file.S12 Figreg1 to reg5) and applied filter criteria (a), (b) and (c). (2) If a region passed filters (a), (b) and (c) (in the example only reg1 and reg2), criteria (a), (c) and (d) were checked for each pairwise comparison between strains on that region. Note, the selection of a region in (1) must not necessarily lead to a differential test between any two strains . Refer to the Selecting parts of methylated regions to test for differential methylation. (A) Example illustrating the selection procedure of regions and pairwise strain comparisons to be tested for differential methylation. (*) For simplicity, the illustration uses a minimum number of covered sites of two reads per region . (1) We selected all possible regions where two strains presented different states of methylation ((EPS)Click here for additional data file.S13 FigMethylation rate differences in regions of differential methylation. Histograms of the absolute mean methylation rate differences of differentially methylated regions  and highly differentially methylated regions  of all different sequence contexts.(EPS)Click here for additional data file.S14 FigDifferential gene expression. (A) Hierarchical clustering of HPG1 accessions by expression of differentially expressed genes. (B) Differentially expressed genes per pairwise comparison. FPKM: fragments per kilobase per million mapped reads.(EPS)Click here for additional data file.S15 FigmCG/5mCHG/5mCHH: methylated cytosine of respective context.Differentially methylated regions (DMRs) and gene expression. Examples of DMRs (top panel) overlapping with a protein-coding gene , a non-coding RNA  and a transposable element . The expression of the corresponding locus is represented in the bottom panel. FPKM: fragments per kilobase per million mapped reads, 5(TIF)Click here for additional data file.S16 FigDistribution and annotation of single site epimutations in two populations. (A) Relative density of differentially methylated positions (DMPs) along the 5 Arabidopsis chromosomes according to their status of overlap between mutation accumulation (MA) and haplogroup-1 (HPG1) lineages. For each class and each chromosome, the window with the maximal density was set to 1. Sliding window; window size 100,000 bp; step size 10,000 bp. (B) Ratios between epimutation frequencies and sequencing depth along the 5 chromosomes for MA and HPG1 lines. Epimutation frequencies were determined as the number of DMPs per cytosine with at least threefold coverage per window. Coverage is represented as average coverage per window across all accessions of each population. Dashed lines mark the balanced coverage ratio of 1. Sliding window; window size 100,000; step size 10,000 bp. (C) Annotation of all cytosines (Cs), non-differentially methylated positions (N-DMPs) and DMPs according to their overlap between MA and HPG1 lineages. Cytosines were hierarchically assigned to CDS> intron> 5′ UTR> 3′ UTR> transposon> intergenic.(EPS)Click here for additional data file.S17 FigOverlap of MA and HPG1 DMPs according to MA generational distance. We computed differentially methylated positions (DMPs) between two randomly chosen mutation accumulation (MA) strains separated by specific numbers of generations and plotted the fraction of those DMPs shared with a randomly chosen haplogroup-1 (HPG1) strain. Each boxplot summarizes ten such random comparisons.(EPS)Click here for additional data file.S18 FigOverlap of MA and HPG1 DMRs per genomic feature and DMR sequence context. Differentially methylated regions (DMRs) were hierarchically assigned to CDS> intron> 5′ UTR> 3′ UTR> transposon> intergenic. CG-DMRs are differently methylated regions in the CG context only and C-DMRs in any other  context(s).(EPS)Click here for additional data file.S19 FigmCG) or in any additional or alternative context(s) (mC).Epigenetic variation in HPG1 lines and methylation-deficient mutants.  Fraction of sites that lost (A) or gained (B) methylation in mutant samples compared to two wild type (WT) Col-0 samples (EPS)Click here for additional data file.S20 FigHeritability of hDMRs by sequence context and status in MA lines. Distributions of heritability values of highly differentially methylated regions (hDMRs) according to significant sequence context and methylation status of overlapping regions in the mutation accumulation (MA) lines.(EPS)Click here for additional data file.S21 FigHierarchical clustering by differentially and invariantly methylated positions and regions. DMPs: differentially methylated positions, DMRs: differentially methylated regions, N-DMPs: not differentially methylated positions, N-DMRs: not differentially methylated regions.(EPS)Click here for additional data file.S22 FigAnalysis of hDMRs unique to LI-SET-036. (A) The number of strains sharing the same methylation status for highly differentially methylated regions (hDMRs) found in each strain is plotted . (B) Stacked bar plots showing the distributions of sequence contexts (bottom) and overlapping genomic features (top three plots) for hDMRs unique to each strain. ‘CG only’ exclusively considers CG-hDMRs whereas ‘CHG’ and ‘CHH’ might additionally include hDMRs of other contexts than CHG and CHH, respectively. The distribution across intergenic space, TEs and genes was similar for all strains. See section “Analysis of LI-SET-036 specific hDMRs” above for more details.(EPS)Click here for additional data file.S1 TableHaplogroup-1 (HPG1) accessions used in this study.(ODS)Click here for additional data file.S2 TableSummary statistics on genome sequencing.(ODS)Click here for additional data file.S3 TableCommon SNPs and SVs.(TXT)Click here for additional data file.S4 TableSegregating SNPs and SVs.(TXT)Click here for additional data file.S5 TableSummary statistics on methylome sequencing.(ODS)Click here for additional data file.S6 TableMethylated regions (MRs).(TXT)Click here for additional data file.S7 TableDifferentially methylated regions (DMRs).(TXT)Click here for additional data file.S8 TableHighly differentially methylated regions (hDMRs).(TXT)Click here for additional data file.S9 TableSummary statistics on transcriptome sequencing.(ODS)Click here for additional data file.S10 TableDifferentially expressed (DE) genes identified in pairwise comparisons between HPG1 accessions. All DE genes  between any two accessions are listed. If more than one gene name appears in column 1, the read counts could not be assigned to one gene in particular and/or a fused transcript was suggested by the read data.(ODS)Click here for additional data file.S11 TableStatistics of overlapping DE genes and hDMRs.(ODS)Click here for additional data file.S12 TableOverlap of SVs with MRs in HPG1 and Col-0 in different genomic features.(ODS)Click here for additional data file.S13 TableScoring matrices for SNP calling and assessing cytosine site statistics (for bisulfite sequencing).(ODS)Click here for additional data file.S1 FileCommand lines for software packages used in this study.(PDF)Click here for additional data file.


Late-onset Pompe disease (LOPD) is a rare treatable lysosomal storage disorder characterized by progressive lysosomal glycogen accumulation and muscle weakness, with often a limb-girdle pattern. Despite published guidelines, testing for LOPD is often overlooked or delayed in adults, owing to its low frequency compared to other muscle disorders with similar muscle patterns. Next-generation sequencing has the capability to test concurrently for several muscle disorders. This could potentially lead to increased diagnosis of LOPD, disorders with non-specific muscle weakness or atypical patients.GAA causing LOPD, along with 77 other genes causing muscle disorders with overlapping phenotypes.We developed a gene panel to further study its clinical utility in a cohort of patients with suspected muscle disorders. We designed a gene panel to analyze the coding sequences and splice site junctions of GAA exons were successfully covered with >20X and only 0.3 % of exons across all genes were <20X. The panel showed an excellent sensitivity (100 %) and specificity (98 %) across all selected genes, using known variations in Pompe patients and controls. We determined its clinical utility by analyzing 34 patients with suspected muscle disorders of undetermined etiology and various muscle patterns, who were referred or followed in neuromuscular and genetics clinics. A putative diagnosis was found in up to 32 % of patients. The gene panel was instrumental in reaching a diagnosis in atypical patients, including one LOPD case. Acid alpha-glucosidase activity was used to confirm the molecular results in all patients.At a median coverage of ~200X (sequences per base), all This work highlights the high clinical utility of gene panels in patients with suspected muscle disorders and its potential to facilitate the diagnosis of patients showing non-specific muscle weakness or atypical phenotypes. We propose that gene panels should be used as a first-tier test in patients with suspected muscle disorders of undetermined etiology, which could further increase overall diagnosis of muscle conditions, and potentially reduce diagnostic delay. Further studies are necessary to determine the impact of first-tier gene panels on diagnostic delay and on treatment outcome for LOPD.The online version of this article (doi:10.1186/s13023-016-0390-6) contains supplementary material, which is available to authorized users. GAA gene, leading to a deficiency in acid alpha-glucosidase  and accumulation of glycogen in all tissues, most notably in muscles and heart [Pompe disease OMIM 232300) is an autosomal recessive condition caused by mutation in the 32300 is nd heart . Classicnd heart . Withoutnd heart . Some pand heart . Diaphrand heart , 10. Hownd heart –13. LOPDSince early diagnosis, followed with prompt initiation of ERT, may significantly improve its clinical benefits, universal screening has been advocated as a way to improve outcomes in Pompe patients . For LOPNGS technologies make it possible to perform several millions of accurate sequence reactions in parallel on solid support, each producing a short sequence called a “read” . SequencWe developed a gene panel to simultaneously analyze genes associated with several muscle disorders with overlapping phenotypes in order to increase the diagnosis of LOPD in children and adults, of other disorders with non-specific muscle patterns and of patients with atypical presentations. We validated this gene panel using CEPH cell line NA12878 and samples from known Pompe patients and determined its clinical validity among a group of patients with undetermined muscle disorders. In parallel, we validated a method to determine acid alpha-glucosidase activity on dried blood spots (DBS) by tandem mass spectrometry, and correlated the enzyme activity data with results from the gene panel.We included genetic disorders that are part of the differential diagnosis of LOPD , 24: mushttp://genomeinabottle.org) [GAA Sanger sequencing available. Known mutations and polymorphisms were used for comparison with results from NGS  . DNA wasGAA exon 14, 66 % GC content) and 2 loci with average GC-content . Multiplexed samples were sequenced if they met a minimal acceptable enrichment factor of 800-1000X. Sequencing was done on a MiSeq® sequencer (Illumina) using paired-end protocol (150 pb) and V2 chemistry. On average, 12.4 millions of pass filter reads were produced per sequencing run with 95.4% of reads having an average Phred score quality above Q30 (1 error in 1000 bases).Sequencing of the gene panel by NGS was performed as follows: DNA from samples was fragmented to a mean size of ~350bp by sonication (Covaris M220). Then, sequencing libraries were prepared with TruSeq® library kit (Illumina) according to the manufacturer’s protocol, except for the following modifications at the library amplification step by LM-PCR: in order to minimize amplification bias related to GC content, we substituted the provided polymerase by the KAPA HIFI HotStart polymerase (KAPA biosystems), and we used the following amplification conditions with initial denaturation for 45 sec at 98 °C, followed by seven cycles with 15 sec at 98 °C, 30 sec at 60 °C and 30 sec at 72 °C, ending with final extension step 1 min at 72 °C. We then performed enrichment of exonic and flanking intronic sequences of selected genes in 12 samples in a multiplex reaction using our custom in-solution hybridization oligonucleotide probe library . We followed the manufacturer’s protocol, but decreased the number of post-capture amplification cycles to 13 to minimize amplification bias related to GC content. Enrichment efficiency was measured by quantitative PCR for one GC-rich locus (https://bitbucket.org/mugqic/mugqic_pipelines). Briefly, 1) raw reads were trimmed using Trimmomatic (version 0.32) [http://broadinstitute.github.io/picard/), 4) gene annotation was performed with SnpEff/SnpSift  [http://exac.broadinstitute.org), genotype quality less than Q30 or present in 3/20 or more local controls sequenced on the same platform (therefore considered as artefacts). In order to identify single exon deletions in the GAA gene, we manually inspected the read coverage depth across GAA for each patient compared to NA12878 cell line, using the Integrative Genomics Viewer  , 2) sequon 0.32) , 3) geneon 0.32) , 29 withictions)  with an ictions) , 5) a fi , 2) seqThe method for the acid alpha-glucosidase activity measurements in DBS was adapted from Dajnoki et al.  and is dWe recruited pediatric and adult patients with undetermined muscle disorders referred to or already followed at specialized neuromuscular or genetic clinics in different provinces in Canada . Inclusion and exclusion criteria are shown in Fig. GAA in all patients was manually reviewed for heterozygous exon deletions as described in 2.4, owing to the relatively high frequency of whole exon 18 deletion. Similarly, in patients with a single causal variation in recessive disorders, we searched the relevant gene for heterozygous exon deletion. Suspected heterozygous exon deletions were confirmed by qPCR analysis or Sanger sequencing if breakpoints were known. In patients who did not have causal variations identified, 6 exons with consistently insufficient coverage  were sequenced to exclude mutations by the Sanger method on a 3730XL DNA analyzer (Applied Biosystems) at McGill University and Genome Quebec Innovation Center. Putative causal variations were sequenced in parents when available and were classified accordingly to the ACMG recommendations [NGS followed by bioinformatic analysis was then performed on the DNA sample as described above. Coverage depth of exons of ndations .GAA along with 77 other genes causing muscle disorders that are considered in the differential diagnosis of LOPD [GAA, the gene responsible for Pompe disease. We sequenced the NA12878 CEPH cell line in two independent sequencing runs, among known Pompe patients and neuromuscular patients for a total of 12 samples per sequencing run.The gene panel comprises  of LOPD , 24. Thehttp://genomeinabottle.org) [GAA exons were successfully covered with >20X. In order to calculate the specificity, we restricted our analysis to variations located in exons and splice site junctions (+/- 5 bp), which are more relevant for disease causing variations. Deep intronic regions contain more frequently repetitive sequences that yield higher false positive rates. A total of 208 variations (204 SNV and 4 indels) were detected in exons and splice site junctions of selected genes. Among these, four variations (1 indel) were not found in the reference genotypes, which yielded a specificity of 98 % (95 % C.I.: 95–99 %).We determined the sensitivity and specificity across all selected genes by performing a comparison of observed genotypes with reference genotypes of the CEPH cell line NA12878. The genome of NA12878 has been analyzed by multiple sequencing platforms and high quality reliable genotypes are available to assess sensitivity and specificity (tle.org) . We obseGAA gene. Our gene panel showed a 100 % sensitivity (95 % C.I.: 98–100 %) based on the analysis of 197 known single nucleotide variations and indels  DBS at four different enzyme levels of activity (from low QC1 to high QC4) were supplied by the CDC and used to measure the intra- and interday assay precision of the method , the c.-32-13T > G mutation, associated with LOPD, and the recurrent exon 18 deletion. In four other patients, we identified pathogenic variations supportive of an inherited muscle disorder  [Firstly, gene panels offer a higher clinical utility than any single disorder test by enabling screening for several diseases with overlapping phenotypes at the same time. This is especially useful in the context of patients with non-specific muscle patterns, such as children with motor delay or adults with limb-girdle patterns which are frequently encountered in neuromuscular and genetics clinics. There are more than 25 different types of limb-girdle muscular dystrophies, which often could not be distinguished clinically . Moreoveent P25) . In thesent P25) . All theent P25) –45. EvenNEB mutations and biopsy showed features supporting a diagnosis of CAP myopathy. Most cases of NEB mutations present in the neonatal period or within the first years of life, although some late-onset presentations have been described [NEB was not a known cause of CAP myopathy at the time of clinical evaluation and had only been reported recently in a single case [DMD, presented unusual severe limb-girdle weakness of pediatric onset. In the two largest reported series of DMD mutation carriers, the proportion of manifesting carriers varied from 5 to 22 % and the pediatric cases remained rare [Secondly, use of gene panels in patients with muscle weakness could facilitate the diagnosis of atypical phenotypes, and reduce diagnostic delay. Delay in diagnosis varied from 1 year in pediatric patients to more than 20 years in adults included in the present study. Patients P25, P28, and P29 showed atypical or unusual characteristics that likely contributed to delayed diagnosis. P25 showed unusual late onset of symptoms for escribed –48. Moregle case . Patientned rare , 51. Howned rare , 53. In ned rare . AlthougGAA variants reported in ClinVar that were not classified as likely benign, are of uncertain clinical significance (June 2015). The Pompe disease mutation database  lists 71 variations with unknown effect among 426 variations (17 %) that are not classified as likely benign [GAA in our cohort of 34 patients, although we observed 0–5 VUS per patient across all genes analyzed. Pompe patients with a single heterozygous mutation identified are uncommon and represent 2–17 % of patients with enzyme-based diagnosis from several case series [GAA gene is critical to minimize this number. Currently, bioinformatic identification of deletions and duplications with targeted NGS-based data is of limited sensitivity, especially for less than three exons [GAA mutation was missed. Owing to these limitations, testing for acid alpha-glucosidase activity would appear the test of choice in the context of a strong clinical suspicion of Pompe disease.The pitfalls of using gene panels as first-tier analysis in the place of enzyme activity for identification of Pompe disease include the identification of variants of unknown significance (VUS), detection of a single heterozygous mutation, and limited performance of current bioinformatic tools to detect deletions and duplications from targeted NGS data –35, 41. y benign . Consequy benign –58. In te series –58. Asseee exons –35. HoweThe current state of NGS technology favors the use of gene panels over exome sequencing in the clinical setting, although this is likely to change in upcoming years. Beside the higher cost of exome sequencing, the main disadvantage is related to more incomplete coverage of exonic sequences (~5 %) owing to generally lower average coverage compared to panels –61. RaisOur work highlights the high clinical utility of gene panels in patients with muscle disorders and its potential to facilitate the diagnosis of patients showing non-specific muscle weakness or atypical phenotypes. This could potentially lead to earlier diagnosis, if performed as a first-tier test. It remains to be determined if this would have a significant impact on treatment outcome for LOPD.


The ATM (ataxia telangiectasia mutated) protein has recently been proposed to play critical roles in the response to mitochondrial dysfunction by initiating mitophagy. Here, we have used ATM-proficient GM00637 cells and ATM-deficient GM05849 cells to investigate the mitophagic effect of spermidine and to elucidate the role of ATM in spermdine-induced mitophagy. Our results indicate that spermidine induces mitophagy by eliciting mitochondrial depolarization, which triggers the formation of mitophagosomes and mitolysosomes, thereby promoting the accumulation of PINK1 and translocation of Parkin to damaged mitochondria, finally leading to the decreased mitochondrial mass in GM00637 cells. However, in GM05849 cells or GM00637 cells pretreated with the ATM kinase inhibitor KU55933, the expression of full-length PINK1 and the translocation of Parkin are blocked, and the colocalization of Parkin with either LC3 or PINK1 is disrupted. These results suggest that ATM drives the initiation of the mitophagic cascade. Our study demonstrates that spermidine induces mitophagy through ATM-dependent activation of the PINK1/Parkin pathway. These findings underscore the importance of a mitophagy regulatory network of ATM and PINK1/Parkin and elucidate a novel mechanism by which ATM influences spermidine-induced mitophagy. Spermidine is a natural polyamine involved in a number of biological processes, such as cell proliferation, the cell cycle and apoptosis12357891213The mechanism by which damaged mitochondria are targeted for mitophagy is poorly understood. However, recent studies have shown that mitochondrial priming for selective autophagic recognition is mediated mainly by the PTEN-induced putative kinase 1 (PINK1)/Parkin signaling pathway in most mammalian cells15161718202123242516The protein kinase ataxia-telangiectasia mutated (ATM), best known for its role as a prime mediator of the DNA damage response28293132333435363738404135The present study explores the effects of spermidine on PINK1/Parkin-mediated mitophagy in the SV40-immortalized normal human fibroblast strain GM00637, which contains wild-type ATM, and GM05849 fibroblasts, which are deficient in ATM, to reveal the function of ATM in spermidine-triggered mitophagy. Ultimately, we seek to characterize a novel pathway by which damaged mitochondria are targeted for mitophagy. Our study reveals herein that spermidine induces PINK1/Parkin-mediated mitophagy in ATM-proficient GM00637 cells but not ATM-deficient GM05849 cells. Furthermore, ATM affects the mitophagy process dramatically via regulating the accumulation of PINK1 and the translocation of Parkin. These findings complement the expanding evidence that activated ATM plays versatile functional roles beyond the well-characterized DNA damage response. It triggers a large number of proteins such as PINK1 and Parkin to facilitate mitophagy.The mitophagic effect of spermidine was determined using measurements of the mitochondrial membrane potential (MMP), analyzing the formation of mitophagosomes and mitolysosomes, and detecting the mitochondrial mass. To determine the proper exposure time and dose of spermidine, cells were exposed to different concentrations of spermidine  to detect cell viability. In addition, cells were exposed to spermidine for different durations  to evaluate the conversion of LC3 I to LC3 II, a well-established marker for autophagyTo examine the effect of spermidine on MMP, GM00637 and GM05849 cells were treated with 50 μM spermidine for 2 h, followed by incubation with TMRM and MTG (200 nM) simultaneously for 20 min in complete growth medium. Confocal microscopy images revealed an accumulation of MTG and TMRM in the mitochondria, and fluorescence intensity was quantified. In such MTG and TMRM co-loaded cells, depolarizing mitochondria turn from red to green, and the ratio of MTG/TMRM is indicative of the level of depolarization. The control GM00637 cells were in a highly polarized status, as indicated by the stronger fluorescence of TMRM and decreased fluorescence intensity ratio. In contrast, 50 μM spermidine significantly decreased the fluorescence of TMRM  and robuWe next examined the effect of spermidine on mitophagy by direct observation using electron microscopy. GM00637 cells cultured in the presence of 50 μM spermidine for 8 h resulted in a pronounced accumulation of swollen and malformed mitochondria, along with a package of autophagosomes, that formed mitophagosomes. We also observed the monolayer membrane lysosomes engulf the mitophagosomes to become mitolysosomes. Nevertheless, spermidine had no such dramatic effects on the mitochondrial structure in GM05849 cells as indicated by the intact mitochondria .A second method involving the combination of the mitochondria tracker dye MTG and the lysosome tracker dye LTR was used to locate the mitolysosomes. The lysosomes colocalized with mitochondria, denoting the formation of mitolysosomes in spermidine-treated GM00637 cells. Enlarged images showed the lysosome surrounding the mitochondrion (shown by the arrows). CCCP showed more pronounced effects as a positive control . HoweverBesides, immunoblotting for mitochondrial matrix protein Hsp60 and inner mitochondrial membrane protein COX IV, which are markers of mitochondrial mass, was used as a quantitative way to monitor the last step of the degradation process of mitophagy. Both Hsp60 and COX IV were dramatically decreased in spermidine-treated GM00637 cells compared with that of control , indicatPINK1 accumulates on the outer membranes of dysfunctional mitochondria when mitophagy is initiated. Under normal conditions, it is rapidly degraded. As shown in Spermidine-induced mitophagy is dependent on ATM and mediated by the PINK1/Parkin pathway. Therefore, it is critical to determine how this process is regulated. To confirm the role of ATM in spermidine-induced mitophagy, we first examined the phosphorylation of ATM Ser-1981, a general marker of ATM pathway activation. Immunofluorescence analysis showed that p-ATM Ser-1981 levels increased substantially in both the cytoplasm and the nucleus of GM00637 cells . The ATMTo further study the involvement of ATM kinase activation in spermidine-induced mitophagy, we determined whether ATM targets the PINK1/Parkin pathway. We show that the expression of PINK1 was inhibited in GM00637 cells pretreated with KU55933  and not Driven by the discovery that an exogenous supply of spermidine induces autophagy in cultured yeast, nematodes and flies, as well as in a myriad of mammalian cells63346The mechanisms through which spermidine might exert its mitophagic effect and the exact processes of mitophagy are still not completely known. PINK1 and Parkin, whose mutations are implicated in the onset of Parkinson disease, could be one targeting mechanism because dysfunctional mitochondria specifically accumulate PINK1 and selectively recruit Parkin to promote their autophagy15171823Given that spermidine exerted its mitophagic effect through the PINK1/Parkin pathway, we investigated how the pathway was initiated. A possible component of the targeting process is a signal from the mitochondria that will undergo autophagy. Previous studies and our current study propose that ATM activation is one such signal for mitophagy. Although ATM kinase is best known for its role in orchestrating nuclear DNA damage responses, there is now a growing body of evidence showing that ATM is involved in other important signaling pathways, such as the maintenance of mitochondrial homeostasis434647Because ATM plays such an important role, it is critical to determine how it is activated. Previously, ATM was shown to act as a redox sensor and was activated by oxidative stress in human cells3434In summary, this study demonstrates that the activation of ATM is responsible for PINK1 accumulation and Parkin translocation and subsequent mitophagy after spermidine stimulation . These f® Green FM (MTG) and LysoTracker® Red DND-99 (LTR) were obtained from Life Technologies .Spermidine, CCCP and 25% glutaraldehyde were obtained from Sigma-Aldrich Inc. . KU55933 (an ATM kinase inhibitor) was obtained from Tocris . Tetramethylrhodamine methylester (TMRM), MitoTracker2 incubator.GM00637 and GM05849 cells, a kind gift from Dr. William Bonner’s laboratory , were cultured in Dulbecco’s Modified Eagle’s Medium  and Eagle’s Minimum Essential Medium with non-essential amino acids , both of which were supplemented with 10% fetal bovine serum , 100 IU/ml of penicillin and streptomycin at 37 °C in a 5% COSO4, dehydrated in ethanol and acetone and embedded in resin. Ultrathin sections (60–70 nm) were cut and mounted on pioloform-coated copper grids (Plano). Sections were stained with lead citrate and uranyl acetate and viewed with a transmission electron microscope  operated at 80 kV. Micrographs were taken using a Gatan Erlangshen ES500W camera.GM00637 and GM05849 cells were plated in a 9 cm culture plate and treated with 50 μM spermidine for 8 h. The cells were then collected and fixed with 1 ml of 2.5% glutaraldehyde. The fixative was replaced with fresh liquid every 24 h until the samples were sent to the electron microscope facility of Lanzhou University. The samples were then post-fixed in OGM00637 and GM05849 cells were treated with 50 μM spermidine for 2 h and 50 μM CCCP (positive control) for 30 min. To identify depolarizing mitochondria, the cells were simultaneously stained with 200 nM TMRM and 200 nM MTG for 20 min at 37 °C as described previouslyImmunofluorescence analysis was performed as in our previous study56Cells were fractionated into cytoplasmic and mitochondrial fractions using Cytoplasmic and Mitochondrial Protein Extraction Kit . Purity of the fractions was assessed by blotting against GAPDH (cytoplasmic), COX IV and Hsp60  proteins. After fractionation, the expression pattern of Parkin was analyzed by western blotting.NP-40 Lysis Buffer  was used to extract total proteins as in our previous studyThe data were analyzed using GraphPad Prism 5.0 software . Statistical comparisons were made using a one-way analysis of variance (ANOVA) followed by the Least Significant Difference (LSD) post hoc test. Unless indicated otherwise, the level of significance was set at p < 0.05.How to cite this article: Qi, Y. et al. ATM mediates spermidine-induced mitophagy via PINK1 and Parkin regulation in human fibroblasts. Sci. Rep.6, 24700; doi: 10.1038/srep24700 (2016).


For our understanding of the pathogenesis of rheumatoid arthritis (RA), it is important to elucidate the mechanisms underlying early stages of synovitis. Here, synovial cytokine production was investigated in patients with very early arthritis.Synovial biopsies were obtained from patients with at least one clinically swollen joint within 12 weeks of symptom onset. At an 18-month follow-up visit, patients who went on to develop RA, or whose arthritis spontaneously resolved, were identified. Biopsies were also obtained from patients with RA with longer symptom duration (>12 weeks) and individuals with no clinically apparent inflammation. Synovial mRNA expression of 117 cytokines was quantified using PCR techniques and analysed using standard and novel methods of data analysis. Synovial tissue sections were stained for CXCL4, CXCL7, CD41, CD68 and von Willebrand factor.+ macrophages. Extravascular CXCL7 expression was significantly higher in patients with very early RA compared with longer duration RA or resolving arthritisA machine learning approach identified expression of mRNA for CXCL4 and CXCL7 as potentially important in the classification of early RA versus resolving arthritis. mRNA levels for these chemokines were significantly elevated in patients with early RA compared with uninflamed controls. Significantly increased CXCL4 and CXCL7 protein expression was observed in patients with early RA compared with those with resolving arthritis or longer established disease. CXCL4 and CXCL7 co-localised with blood vessels, platelets and CD68Taken together, these observations suggest a transient increase in synovial CXCL4 and CXCL7 levels in early RA. The rheumatoid synovium is characterised by a complex inflammatory infiltrate, which can either be highly structured with distinct features of lymphoid neogenesis or comprise a more diffuse infiltrate. There is considerable evidence that cytokines produced by the synovial infiltrate play an important role in the orchestration of both the development and the resolution of synovial inflammation.In order to capture a population of patients in a time frame close to the onset of clinically apparent joint inflammation, patients with at least one clinically swollen joint were seen within 12 weeks of the onset of any symptom attributed, by the assessing rheumatologist, to an inflammatory arthritis. At this time, among a range of other investigations performed within the Birmingham Early Inflammatory Arthritis Cohort (BEACON), ultrasound-guided biopsies were taken. At an 18-month follow-up visit, patients who had progressed to RA as classified according to the 1987 American College of Rheumatology (ACR) criteriaProfiling of cytokine mRNA expression led to the finding that the chemokines CXCL4 and CXCL7 are expressed during the earliest phase of RA, but not in patients with resolving arthritis or established RA. We found that both chemokines, which are classically regarded as platelet-derived chemokines, were also expressed on macrophages in the synovium, implicating a previously undescribed role for this cell type in the earliest clinically evident stages of RA.Patients with early arthritis were seen in the BEACON cohort; details of this clinic have been reported previously.et al.10–12Prior to biopsy, joints were assessed using a Siemens Acuson Antares scanner  and multifrequency (5–13 MHz) linear array transducers; for details, see Filer −ΔCt, where ΔCt represents the difference in Ct between glyceraldehyde-3-phosphate dehydrogenase and the target gene. Validation experiments established the reproducibility of quantitation and a combination of positive controls from anti-CD3/anti-CD28-activated lymphocytes and commercially available control mRNA from human cell lines representing different tissues (Stratagene) were used to ascertain that all cytokines listed could be detected.TaqMan low-density real-time PCR arrays  were designed to determine expression of 117 cytokines and cytokine-related molecules . RNA was extracted from synovial tissue sections using an RNeasy RNA extraction kit . RNA from synovial tissue used in the low-density array for genes with non-intron spanning primers was treated with DNase (Qiagen). A reaction mixture containing RNA, QuantiTect-RT Master Mix (Qiagen) and QuantiTect Reverse Transcriptase  was added to a TaqMan low-density array microfluidic card. Six times more RNA was loaded into microfluidic cards designed for weakly expressed genes and genes with non-intron spanning primers (see online supplementary table S1). Reverse transcription and real-time PCR was performed in a 7900HT Real-Time PCR System (Applied Biosystems). Relative gene expression (RQ) was expressed as 2Cytokine mRNA data were log-transformed and all zero values were replaced by 0.00002, the smallest non-zero expression value in the data. This yielded 117 log-transformed expression values per patient. These data were analysed by applying a combination of principal component analysis and learning vector quantisation (LVQ), a distance-based classification technique, which determines class representatives from a set of example data in an iterative training process. Example data corresponded to the individual cytokine profiles observed within the four classes of patients. Matrix relevance LVQ identifies a suitable distance measure, which is discriminative with respect to the different classes. The specific technique used was generalised matrix relevance LVQ (GMLVQ), a variant that optimises the distance measure with respect to its discriminative power.Frozen sections from synovial biopsies taken from patients with resolving arthritis, early RA and established RA were stained with antibodies specific for CXCL4  or CXCL7 , and CD41 , CD68  and von Willebrand factor (vWF) . Staining with isotype-matched, species-matched and concentration-matched negative controls was performed in parallel. Secondary antibodies used were goat antirabbit Chromeo 494 , Cy3-conjugated streptavidin , goat antimouse Alexa Fluor 488  and goat antimouse Cy5 . Immunofluorescence was visualised using a Zeiss LSM 780 Zen Confocal and analysed using Zeiss imaging software . Five to six 2×2 tile scans were taken from each section at ×400 total magnification. Regions were drawn around tissue; areas with folding or bleeding into tissue caused during biopsy collection/processing were excluded from analysis. As readout, pixels per unit area were calculated.Details of study participants used for cytokine mRNA profiling and immunofluorescence studies are shown in The mRNA expression of a panel of 117 cytokines and related molecules was assessed in synovial biopsies using low-density real-time PCR arrays. Subject groups investigated were patients with resolving arthritis (n=9), patients with very early RA (n=17), patients with established RA  and uninflamed control subjects (n=10). Twenty-two genes were identified as being differentially expressed between subject groups by Kruskal–Wallis and Dunn's post-test analysis, shown in We applied a novel strategy of multivariate analysis to test whether combinations of gene expression signals rather than individual cytokine mRNA signals could distinguish synovium from patients with self-limiting arthritis from those with early-stage RA. Matrix relevance GMLVQ analysis was applied to the cytokine mRNA data to achieve classification of samples by determining a discriminative distance measure that characterises differences between subject groups. For classification of the established RA and uninflamed groups, analysis of the obtained relevance matrix revealed that the 10 most informative genes in discriminating patients with established RA from uninflamed controls were CXCL7, CXCL4, IL1B, MST1, CCL20, IL8, LGALS12, LTA, CXCL13 and OSM A. ReceivSince cytokine mRNA profiling suggested an upregulation of CXCL4 and CXCL7 expression in patients with early RA, we next sought to test whether expression of these chemokines was also elevated at the protein level. CXCL4 and CXCL7 were stained in synovial tissue sections from patients with resolving arthritis (n=9), early RA (n=10) and established RA (n=11), and staining was visualised by immunofluorescence. Representative images of staining of CXCL4 and CXCL7 in the synovium are shown in In the synovium of all subject groups studied, CXCL4 and CXCL7 staining was found to co-localise with CD68 staining, indicating an association of both of these chemokines with macrophages A, B. As In this study, we have identified two chemokines, CXCL4 and CXCL7, which are expressed in the earliest clinically apparent stage of RA. They were predominantly detected on macrophages infiltrating the synovium, indicating a previously undescribed role for this cell type in contributing to RA pathogenesis in the very early phase of disease.Macrophages are found in large numbers throughout the rheumatoid synovium. Type A synoviocytes are macrophage-derived cells that constitute part of the healthy synovial lining while large numbers of activated macrophages are found in the inflammatory infiltrate in the sublining and at the pannus–cartilage interface. The origin of these cells is a matter of debate; they could derive from the proliferation of tissue-based macrophages or from circulating monocytes entering from the blood stream.18–20et al,CXCL4 is chemotactic for neutrophils, fibroblasts and monocytes, prevents monocyte apoptosis, induces differentiation of monocytes into macrophages and enhances monocyte phagocytosis and oxygen radical production.Previous studies have suggested roles for CXCL4 and CXCL7 in RA. Elevated levels of CXCL7 have been reported in the serum, synovial fluid and synovial tissue of patients with RA.et alIn our study, CXCL4 and CXCL7 expression in the synovium was found to be predominantly localised to CD68+ macrophages, with less co-localisation observed with platelets. Where CXCL4 and CXCL7 expression was seen to co-localise with platelets in the synovium, this was mainly confined to vessel thrombi. The genes for CXCL4 and CXCL7 are located in a gene cluster comprising several CXC chemokines in a locus on chromosome 4,Interest in chemokines in RA has been revived by findings such as detection of citrullinated chemokines in RA synovial fluid that have enhanced chemotactic activity


FGFR2) as a candidate gene for breast cancer with single nucleotide polymorphisms (SNPs) located in intron 2 region as the susceptibility loci strongly associated with the risk. However, replicate studies have often failed to extrapolate the association to diverse ethnic regions. This hints towards the existing heterogeneity among different populations, arising due to differential linkage disequilibrium (LD) structures and frequencies of SNPs within the associated regions of the genome. It is therefore important to revisit the previously linked candidates in varied population groups to unravel the extent of heterogeneity. In an attempt to investigate the role of FGFR2 polymorphisms in susceptibility to the risk of breast cancer among North Indian women, we genotyped rs2981582, rs1219648, rs2981578 and rs7895676 polymorphisms in 368 breast cancer patients and 484 healthy controls by Polymerase chain reaction-Restriction fragment length polymorphism (PCR-RFLP) assay. We observed a statistically significant association with breast cancer risk for all the four genetic variants (P<0.05). In per-allele model for rs2981582, rs1219648, rs7895676 and in dominant model for rs2981578, association remained significant after bonferroni correction (P<0.0125). On performing stratified analysis, significant correlations with various clinicopathological as well as environmental and lifestyle characteristics were observed. It was evident that rs1219648 and rs2981578 interacted with exogenous hormone use and advanced clinical stage III , respectively. Furthermore, combined analysis on these four loci revealed that compared to women with 0–1 risk loci, those with 2–4 risk loci had increased risk . In haplotype analysis, for rs2981578, rs2981582 and rs1219648, risk haplotype (GTG) was associated with a significantly increased risk compared to the common (ACA) haplotype . Our results suggest that intron 2 SNPs of FGFR2 may contribute to genetic susceptibility of breast cancer in North India population.Genome-Wide Association Studies (GWAS) have identified Fibroblast growth factor receptor 2 ( Worldwide, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer mortality among women BRCA1, BRCA2) account for less than 25% of the familial breast cancer risk, and less than 5% of the overall risk Breast carcinogenesis involves a complex combination of genetic, environmental as well as lifestyle factors. Inherited susceptibility makes an important contribution to breast cancer development and the risk is around two times more in first degree relatives of women with the disease FGFR2 gene, is located at chromosome 10q26, and contains 22 exons FGFR2 to be associated with breast cancer risk, rs2981582 and rs1219648 were the most strongly associated marker SNPs in the two studies respectively FGFR2Fibroblast Growth Factor Receptor 2 (FGFR2) belongs to the FGFR family of tyrosine kinase receptors and contributes to the process of tumorigenesis through cell growth, invasiveness, motility and angiogenesis FGFR2 intron 2 polymorphisms  on sporadic breast cancer and determined their association with the risk for North Indian women in a case control approach, including combined effect of these variants, LD structure measurement, haplotype analysis, as well as relation with patients' clinical, environmental and lifestyle characteristics. We observed significant association of these variants with breast cancer susceptibility for North Indian women.Wide variations in genetic architecture, including differential allele frequencies of SNPs and differently evolved LD structure for the GWAS-identified genetic variants reflect differences among ethnicities and may contribute to disparities in the incidence and characteristics of breast cancer. Thus, variants identified in one study may not have the same impact on risk in other populations. Therefore, there is a need to replicate previously associated loci in multiple populations worldwide. This will help in determining the genetic heterogeneity among different population groups for these loci, particularly in India, which witnesses a rapidly rising breast cancer burden but relatively fewer studies to identify the common breast cancer associated variations. Such studies will assist in evaluating the generalizability of initial findings and to identify the causal variants. Therefore, we tried to assess the impact of The study was approved by Institution Ethics Committee of All India Institute of Medical Sciences (AIIMS), New Delhi and the Institutional Human Ethical Committee of Jamia Millia Islamia, New Delhi. All the participants provided their written informed consent to be included in the study.This hospital-based case control study included a total of 852 genetically unrelated women subjects of North Indian ethnicity comprising 368 sporadic breast cancer cases and 484 healthy controls. Controls were frequency- matched to cases on age (±2 years) and geographical location. The study participation response rates for cases and controls were 88.46% and 81.07%, respectively. All breast cancer cases (aged 24–80 years) were newly diagnosed, histopathologically confirmed with primary breast cancer and were recruited from the Department of Surgical Oncology, AIIMS. Classification of breast cancer has been done according to TNM staging system by American Joint Committee on Cancer (AJCC) and Nottingham grading system for histological grading. Exclusion criteria included in the study were reported previous cancer history, metastasized cancer from other organs and previous exposure to radiotherapy or chemotherapy.2), geographical location, education level and economic independence (employed/unemployed)].Detailed information on clinical profiles for cases and controls were collected from their medical records and are presented in Participating women provided 3–5 ml of venous blood samples used for isolating genomic DNA based on standard phenol–chloroform extraction method FGFR2 SNPs showing association with breast cancer in one or more GWAS and candidate gene studies including the two proposed functional variants  Previously reported P<0.05 was considered statistically significant. Further, all P-values were corrected for multiple comparisons according to Bonferroni method. LD pattern and population haplotype frequencies for the SNPs were estimated using HaploView v4.2 To compare the overall distribution of genotypes between patients and healthy controls 3×2 Chi-square (χ2) test was performed. Hardy–Weinberg equilibrium (HWE) was evaluated by a goodness-of-fit χ2 test. For estimating associations between individual genotypes and breast cancer risk, and for cumulative risk analysis, odds ratios (ORs) and their 95% confidence intervals (CIs) were computed using unconditional logistic regression analysis with adjustment for age. One-way ANOVA  was carried out for estimating the contribution of different number of risk loci to breast cancer risk. These statistical analysis were performed using Statistical Package for the Social Sciences, version 17  and FGFR2 intronic variants  were available from 368 breast cancer cases/484 healthy controls and a notably significant association with breast cancer susceptibility was observed.Results of genotype analysis on the four selected P>0.05) with Hardy–Weinberg equilibrium in both cases  and controls  for the SNPs rs2981582, rs1219648, rs2981578 and rs7895676 respectively.The observed genotype frequencies were found to be in agreement . Logistic regression analysis (age adjusted) further confirmed this association which remained significant in per-allele model for rs2981582/T, rs1219648/G, rs7895676/C and in dominant model for rs2981578 (AG+GG) even after Bonferroni correction (P<0.0125).Distribution of genotype and allele frequencies of the four P = 0.006) compared to those with 0–1 risk loci. Further a progressively increased risk was noted from 1 risk loci  to 2 risk loci  to 3–4 risk loci  in comparison to 0 risk loci (P<0.05) differences in the contribution of various risk loci to diseased condition for the four SNPs taken together as well as in different combinations , and BCD . On conducting multiple comparison analysis in ANOVA, similar trends were observed (P = 0.026) and 4 risk loci (P = 0.002) compared to 0 risk loci. Also for the four different SNP combinations, significant P values for all the risk loci were observed for combination BCD . For determining the contribution of different risk loci overall as well as in four different trio combinations of these SNPs, logistic regression and one-way ANOVA tests were performed . Logistiisk loci . ANOVA dobserved . SignifiP value cut-off after bonferroni correction was set as (P<0.000694).Further we analyzed association of these variants with various clinicopathological characteristics including several reproductive and environmental risk factors of breast cancer in a stratified analytical approach  The corrrs2981582(C/T)/FGFR2, genotype CT vs. CC (vs.  =  in comparison to) and combined CT+TT vs. CC significantly correlated with premenopausal status . T allele displayed stronger association with ER-positive women  and with PR-positive women . Association with histologically less malignant grade I+II , early age at menarche  and employed status  was also observed.For rs1219648 (A/G)/FGFR2, AG genotype presented a significantly higher distribution in premenopausal patients exhibiting higher risk . Furthermore, G-carriers were more significantly linked to tumors with ER-positive status , LN positive status , and more malignant histological grade III . Also, interaction of the risk allele with positive breastfeeding status  and strongly with exogenous hormone exposure  was noted.For rs2981578 (A/G)/FGFR2, G allele carriers were more likely to bear tumors of greater aggressiveness with advanced clinical stage III+IV  and LN metastasis . Association with late age at menarche  and parous status  was also observed.For rs7895676 (T/C)/FGFR2, TC genotype exhibited significantly greater risk in PR-positive women . Further association of the risk allele with LN-positive status , pathologically less malignant grade I+II tumors  and with negative breastfeeding status  was evidenced.For 2 ≥0.80, D′ = 1, in control group of our study population, the four studied SNPs were found to be in moderate to weak LD . However, contrary to the combined risk analysis, predominant contribution towards the risk in terms of higher odds ratio was observed for trio combinations ABC  and ACD . However, SNP combination BCD , seems to be relevant as pair wise D′>0.80 for these three SNPs (P = 0.008). While among duo SNP combinations, AC (rs7895676 and rs2981582) displayed highest odds ratio , Setting measure of high LD between two genetic markers cut off to a value rree SNPs  and carrFGFR2 were all significantly associated with increased breast cancer risk. Recent identification of these intron 2 SNPs FGFR2 as a candidate gene for breast cancer. At present, much effort is focussed into targeting additional genetic alterations that drive breast cancer and FGFR2 which has been implicated in different types of human malignancies, including breast cancer In this case–control study of sporadic breast cancer in North Indian women we found that the variant genotypes rs2981582C/T, rs1219648A/G, rs2981578A/G and rs7895676T/C of FGFR2 variant with breast cancer, as was observed in Europeans and other Asian populations Since a previous report from South India FGFR2 and it has been shown that haplotype marked by this allele associates with a higher level of FGFR2 transcription both in breast cancer cell lines and tumors FGFR2 variants contribute to breast cancer and confer their effect primarily in ER-positive and PR-positive tumor subtypes Present study reports significant association of rs2981582 and rs1219648 with breast cancer, consistent with previous observations from two Asian studies by Liang et al. For rs1219648, we observed a strong association of G allele with more invasive tumors with higher chance of LN metastasis, consistent with a previous report from China FGFR2 variants to ER-positive and PR-positive tumors suggests that these SNPs affect the reproductive hormone-related pathway in the development of breast cancer in North Indian women. But these observations need to be confirmed in larger sample size studies from our population.Breast cancer tends to be diagnosed at an earlier age in developing countries than in European and American populations and a rapid rate of increase in incidence has been observed before menopause FGFR2 SNPs rs2981578 and rs7895676 within intron 2 region alter the DNA binding affinity of transcription factors octamer-binding transcription factor 1 (Oct-1)/runt-related transcription factor 2 (Runx2) and CCAAT/enhancer binding protein β (C/EBPβ) respectively, resulting in an increased FGFR2 gene expression both in cell lines and in breast tissues in patients homozygous for the risk allele as compared to those homozygous for the wild type allele Recently done analysis by Meyer et al. have shown that two P<0.000694), while other clinical features lost statistical significance, suggesting the importance of these SNPs in sub-categorized breast cancers in our population. But these observations need to be confirmed in further studies with larger sample size, to rule out false positive results and to establish intron 2 FGFR2 SNPs as breast cancer susceptibility loci.Association with exogenous hormone exposure and higher stage for SNPs rs1219648 and rs2981578 respectively, achieved statistical significance even after Bonferroni correction . Moreover a progressively augmented risk with increasing number of risk loci was also noted demonstrating that a combination of these variants cumulatively increases risk . In haplFGFR2 variants taken together along with LD measurement, haplotype analysis and stratified analysis for possible correlation with patients' clinical parameters in susceptibility to breast cancer.To the best of our knowledge, we are reporting for the first time, a case control study on these four intronic FGFR2 variants in intronic region suggests the probable explanation for their association with the risk through differential expression. Aberrant expression of alternatively spliced isoforms of FGFR2 has been shown to activate signal transduction leading to transformation in breast cancer cells FGFR2 in relation to intron 2 SNPs has been supported by the analysis carried out by Meyer et al. FGFR2 intron 2 shows a high degree of conservation in mammals, and number of conserved putative transcription-factor binding sites have been identified in it FGFR2 upregulation remains unclear.Location of these FGFR2 could be targeted in search for newer and efficient biomarkers in the future. Several altered FGFR2 characteristics have been linked with breast tumorigenesis and have shown promising results in studies on breast cancer cell lines and tumors, like amplification and overexpression, mutations, alternative splicing and isoform switching Besides SNPs, other features of FGFR2 intron 2 SNPs with breast cancer risk, as well as their interaction with various clinical parameters revealing their contribution to breast cancer susceptibility among North Indian women. Although, findings of the present study by themselves are unlikely to have any immediate clinical implications, however, such studies may play a key role in elucidating the biological mechanism that underline breast tumor heterogeneity, which may ultimately lead to improved treatment and prevention. These findings suggest that genetic variants of FGFR2 might be used as candidate potential biomarkers for breast cancer risk. Further epidemiological and experimental studies of larger data sets along with sub-categorization by clinical parameters and expression studies are warranted to explore and confirm the role of these variants in increasing breast cancer risk, particularly from India, that will help us better understand the genetic heterogeneity in complex diseases like breast cancer.In conclusion, our study revealed a significant association of Figure S12 value} of the four intronic Linkage disequilibrium analysis {a) D′ value and b) rFGFR2 SNPs in the studied population of North India.(TIF)Click here for additional data file.Table S1Clinical, lifestyle and other demographic details of cases (N = 368) and controls (N = 484) in the present study.(DOC)Click here for additional data file.Table S2Details of the SNPs selected for present study.(DOC)Click here for additional data file.Table S3Distribution of risk loci for the studied FGFR2 SNPs  in cases and controls of North India.(DOC)Click here for additional data file.Table S4One Way ANOVA for relative risk assessment of different number of risk loci for the studied FGFR2 SNP combinations.(DOC)Click here for additional data file.Table S5Frequencies of inferred haplotypes of various FGFR2 SNPs combinations taken two at a time in breast cancer cases and controls.(DOC)Click here for additional data file.