Phoremia sp. and Mellopsis doucasae, from which we prepared 70 samples per species, divided among seven treatments: (i) freshly processed (control); preserved in fuel ethanol for (ii) 15 and (iii) 60 days; preserved in commercial ethanol for (iv) 15 and (v) 60 days; fresh material frozen for (vi) 15 and (vii) 60 days. After oven drying, samples were analyzed for δ15N, δ13C values, N(%), C(%) and C/N atomic values using continuous flow isotope ratio mass spectrometry. All preservation methods tested, significantly impacted δ13C and δ15N and C/N atomic values. Chemical preservatives caused δ13C enrichment as great as 1.5‰, and δ15N enrichment as great as 0.9‰; the one exception was M. doucasae stored in ethanol for 15 days, which had δ15N depletion up to 1.8‰. Freezing depleted δ13C and δ15N by up to 0.7 and 2.2‰, respectively. C/N atomic values decreased when stored in ethanol, and increased when frozen for 60 days for both cricket species. Our results indicate that all preservation methods tested in this study altered at least one of the tested isotope values when compared to fresh material (controls). We conclude that only freshly processed material provides adequate SIA results for litter-dwelling crickets.Stable isotope analysis (SIA) is an important tool for investigation of animal dietary habits for determination of feeding niche. Ideally, fresh samples should be used for isotopic analysis, but logistics frequently demands preservation of organisms for analysis at a later time. The goal of this study was to establish the best methodology for preserving forest litter-dwelling crickets for later SIA analysis without altering results. We collected two cricket species,  The rg niches . Stable ood webs –18. Stabood webs , to seawood webs , 21, higood webs , and sevood webs , grasshoood webs , termiteood webs , ants [2ood webs , and verood webs , and turood webs , among ostratum in these forests [Crickets are the most common Orthoptera in neotropical forest litter , which i forests . Knowled forests , 30, and forests  is needeStable isotopes may only be used as indicators of an organism’s diet if the isotope composition of analyzed samples corresponds exactly to that of the organism in the field, without aggregation of isotopes from any other source. Because sample processing for SIA relies on access to laboratory facilities, samples often cannot be analyzed in the field , 21, 25,Because litter crickets are easily startled and flee in response to substrate vibration , live  [13C/12C sample values, thus amplifying the 13C signal. Carbon from ethanol preservatives might also be incorporated into the cricket bodies, altering the isotopic signals. Fuel ethanol is distinct from commercial ethanol because it is a complex mixture of flammable liquid and volatile hydrocarbons derived from petroleum, with carbonic chains varying from 4 to 12 carbon atoms as well as oxygenates and nitrogen compounds [δ15N isotope signals. THus, a priori knowledge of how preservation methodologies interact with different sample types and the resulting impact on isotopic values is essential.Samples subjected to organic solvents may have altered carbon isotopic signals due to loss of dissolved lipids and gain of solvent constituent carbon. By removing lipids, which are naturally highly depleted of  in 12C) , ethanolompounds ; these aViable alternatives to ethanol preservation may include freezing when immediate drying is not possible, but not all studies have considered the potential impacts of this method on stable isotope ratios. Among the studies that have tested effects of freezing, some found significant and even strong impacts on stable carbon and nitrogen isotope values , 45, 46,δ13C and δ15N values, ranging from no impact to over 2‰ difference between stored samples and controls [Studies of the effects of different preservation methods (including freezing) on stable carbon and nitrogen values have produced highly variable results for both controls , 45, 48.controls , and thecontrols  related δ13C and δ15N isotope values of freshly dried crickets with those preserved via freezing or chemical storage, for either 15 or 60 days prior to analysis. If the preservation methods are appropriate for these organisms, then SIA results should not differ between freshly dried and preserved cricket samples.The aim of this study was to identify the best methodology for preservation of forest litter-dwelling crickets for later stable isotope evaluation. We compared the We expected that samples preserved in ethanol  would vary in isotopic carbon and nitrogen values compared to controls. Further, we anticipated that the distance of carbon and nitrogen isotope values in ethanol, compared to control samples, should increase with storage time. Finally, we expected that frozen samples, which are not subjected to chemical compounds, should not differ from freshly processed material.Phoremia sp. (Trigonidiidae: Nemobiinae)  . They are also phylogenetically distant [We collected two species of forest litter cricket (Orthoptera: Grylloidea), obiinae)  and Mellzarinae) . These s distant , 52, thuCollections were carried out from the 4 th to 19 th July 2013. Crickets were collected manually (live capture) on leaf litter using wide-mouth (15 cm diameter) jars. Collections were done daily for about four hours per day, until we gathered approximately 1000 individuals. All collections were done in a 60 ha semideciduous Atlantic forest remnant, called “Mata da Biologia”, on the Campus of the Federal University of Viçosa, Viçosa, MG (S 20°45’30”—W 42°51’50”). No specific permissions were required to collect insects in the area at the time of the study. Our group has general authorization to collect insects in the entire Brazilian territory via a Permanent License for Collection of Zoological Material, provided by the Brazilian Institute of the Environment and Natural Resources (Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis—IBAMA) for CFS, number 553948. Species determination was done with the aid of a specialist (Marcelo R. Pereira); voucher specimens were deposited in the Orthopteran collection of the Orthoptera Laboratory in the General Biology Department, , affiliated with the Regional Museum of Entomology at the Federal University of Viçosa , Viçosa, Minas Gerais, Brazil. Crickets were brought to the laboratory, provided with water on a dampened piece of cotton, and kept alive and unfed for two days in order to empty the digestive tract.Phoremia sp. adults or four M. doucasae immatures, totaling 70 samples for each species. Life stages were selected as available in the field. These samples were divided equally among seven treatments for a total of 10 sample replicates per treatment, as follows: (i) freshly processed (controls); (ii) preserved in fuel ethanol for 15 days; (iii)preserved in fuel ethanol for 60 days; (iv) preserved in 92.8% (96° GL) commercial ethanol for 15 days; (v) preserved in 92.8% (96° GL) commercial ethanol for 60 days; (vi) fresh material frozen for 15 days; (vii) fresh material frozen for 60 days. All glass vials used in the ethanol treatments were properly closed and stored at room temperature, and protected from light and heat. Immediately after killing (controls) or after preservation period, the samples were oven-dried in open glass vials at 60°C for 72 hours. After drying, each pooled sample was ground and sieved (100 μm mesh) separately, prior to isotope analysis, so as to achieve complete homogenization of each sample. Therefore, individual insects of each sample were ground together and analyzed as a homogenized batch. After finishing homogenization of each sample, all laboratory tools where detergent-washed, rinsed and dried, before processing the next sample.After two days of food deprivation, crickets were killed by exposure to −20°C for up to 20 min. As soon as the crickets stopped moving, they were taken out of the refrigerator, so as to prevent freezing them. This killing method aimed to prevent suffering while avoiding freezing of the samples. After killing, each cricket was rinsed with distilled water, to wash dirt and particles attached to the cricket’s body surface, and stored in glass vials in pooled samples of eight 2 and CO2, which were then purified in the elemental analyzer through a chromatographic separation column in ultrapure helium carrier, and sequentially admitted to the mass spectrometer by means of an interface . The 15N/14N and 13C/12C isotope ratios were evaluated after separation of molecules according to isotope mass, and finally compared to the calibrated gas ratios using Vienna PeeDee Belemnite (VPDB) limestone and atmospheric nitrogen as international standards for δ13C and δ15N, respectively. These working standards were calibrated using NBS-19 and NBS-22, and AIEA-N1 and IAEA-N2 as reference materials and the estimated analytical precision of these measurements was 0.1‰ for carbon and 0.2‰ for nitrogen based on the standard deviation of WS replicates during the runs. Results are expressed in “delta” notation  in parts per thousand (‰) as relative deviations of the isotope ratios compared to standards, using the following formula:Rsample and Rstandard are the 13C/12C and 15N/14N ratios for the sample and international standards, respectively. X represents the “heavy” isotopes 13C or 15N. The δX values denote isotopic enrichment or depletion relative to the standard; a more positive “δ” value is isotopically enriched, meaning that the sample contains proportionally more of the “heavy” stable isotope (13C or 15N). The weight percentages of carbon and nitrogen output from the the instrument mentioned above were converted to C/N atomic values.We performed continuous flow isotope ratio mass spectrometry (CF-IRMS) for sample analysis using a Delta Plus mass spectrometer (Thermo Scientific) coupled to a Carlo Erba CHN 1110 elemental analyzer (Thermo Scientific). Analyses were performed at the Isotope Ecology Laboratory at the Center for Nuclear Energy in Agriculture, CENA, University of São Paulo , São Paulo state, Brazil. Briefly, organic matter  was converted into gas by fully dry combustion, generating Nδ13C, δ15N, %C, %N and C/N) by adjusting generalized linear models (GLM). Modeling was performed using R [We used analyses of variance (ANOVA) with preservation method as an explanatory factor. We ran an independent analysis for each response variable  by stepwise omission of non-significant terms.We evaluated which factor levels (preservation methods) were significantly different through contrast analysis, by aggregating levels and comparing change in deviance . If the Adequate preservation methods should result in stable isotope signatures that do not differ from freshly processed material. We interpreted any differences between preserved and freshly processed material as an indication of altered isotope signature due to inadequate preservation methods. We evaluated storage time effects on SIA values by comparing samples preserved in the same solution types, independent of control results.δ15N, δ13C and C/N values in both species compared to controls  revealed no signs of decomposition by the end of the experiment (60 days). However, most preservation methods altered ols Figs . Althougols Figs , total cols Figs .δ15N values of freshly processed samples (controls) for Phoremia sp. did not differ from samples submitted to four of the preservation methods  and samples preserved for 60 days in fuel or commercial ethanol) , with the exception of samples frozen for 60 days , but did differ from all other preservation methods , however all other preservation methods did differ from controls   . All prePhoremia sp. showed no significant differences in C or N isotope values between different chemical solutions  when storage time was equal  did not differ from frozen or control samples  when storage time was equal . There were no differences in δ13C values between chemical solutions  and controls when storage time was equal  and δ13C  (For ethanol) ; Table 1Phoremia sp.– C/N: Com.Et.15 = Fuel Et.15, F1,63 = 0.0, P = 1; Com.Et.60 = Fuel Et.60, F1,65 = 0.33, P = 0.56, M. doucasae—C/N: Com.Et.15 = Fuel Et.15, F1,63 = 0.09, P = 0.93; Com.Et.60 = Fuel Et.60, F1,65 = 1.69, P = 0.2, There were no differences in C/N atomic values between ethanol preservation solutions in either cricket species when storage time was equal , our re or both –61.δ13C values and C/N atomic values, with chemical preservatives generally producing depletion and freezing producing enrichment (after 60 days) in both cricket species. δ13C was enriched over time in both cricket species, with M. doucasae (1.5‰) becoming more enriched than Phoremia sp. (0.6‰). The range of enrichment values (0.6-1.5‰), irrespective of preservation time, was similar to the ranges found for ants [δ15N values also varied between species, with depletion of up to 2.2‰ in M. doucasae and enrichment of 0.9‰ in Phoremia sp. preserved in ethanol for 15 days. Different mechanisms are proposed to explain how preservation techniques may affect 13C/12C and 15N/14N ratios in samples. One hypothesis attributes enrichment in both to assimilation of heavier isotopes present in the preserving agents [δ13C signatures, and once preserved samples are immersed, their signature may shift toward that of the preservative. Another hypothesis suggests the loss of molecules carrying the “lighter” isotope  [13C (and rich in 12C) [13C/12C ratio in sample, thus amplifying its 13C signal. According to Post et al. [δ13C values [The two preservation methods tested in this study similarly affected for ants , fish, ofor ants , fish mufor ants , zooplanfor ants , and birfor ants . δ15N vg agents , 25. Bot in 12C) , ethanolt et al. , the C/Nt et al. ), then lC values –65. The δ13C in all ethanol samples, which were greater in Phoremia sp. ; B: male Mellopsis doucasae .A: Male (PDF)Click here for additional data file.S1 TableModel simplification was performed by backward term extraction, removing one term at a time, performing consecutive one degree of freedom contrast analyses. Minimum Adequate Model’s significance was evaluated contrasting it with the null model.(PDF)Click here for additional data file.S2 TableModel simplification was performed by backward term extraction, removing one term at a time, performing consecutive one degree of freedom contrast analyses. Minimum Adequate Model’s significance was evaluated contrasting it with the null model.(PDF)Click here for additional data file.S1 Dataset(CSV)Click here for additional data file.


High density genotyping data are indispensable for genomic analyses of complex traits in animal and crop species. Maize is one of the most important crop plants worldwide, however a high density SNP genotyping array for analysis of its large and highly dynamic genome was not available so far.We developed a high density maize SNP array composed of 616,201 variants . Initially, 57 M variants were discovered by sequencing 30 representative temperate maize lines and then stringently filtered for sequence quality scores and predicted conversion performance on the array resulting in the selection of 1.2 M polymorphic variants assayed on two screening arrays. To identify high-confidence variants, 285 DNA samples from a broad genetic diversity panel of worldwide maize lines including the samples used for sequencing, important founder lines for European maize breeding, hybrids, and proprietary samples with European, US, semi-tropical, and tropical origin were used for experimental validation. We selected 616 k variants according to their performance during validation, support of genotype calls through sequencing data, and physical distribution for further analysis and for the design of the commercially available Affymetrix® Axiom® Maize Genotyping Array. This array is composed of 609,442 SNPs and 6,759 indels. Among these are 116,224 variants in coding regions and 45,655 SNPs of the Illumina® MaizeSNP50 BeadChip for study comparison. In a subset of 45,974 variants, apart from the target SNP additional off-target variants are detected, which show only a minor bias towards intermediate allele frequencies. We performed principal coordinate and admixture analyses to determine the ability of the array to detect and resolve population structure and investigated the extent of LD within a worldwide validation panel.The high density Affymetrix® Axiom® Maize Genotyping Array is optimized for European and American temperate maize and was developed based on a diverse sample panel by applying stringent quality filter criteria to ensure its suitability for a broad range of applications. With 600 k variants it is the largest currently publically available genotyping array in crop species.The online version of this article (doi:10.1186/1471-2164-15-823) contains supplementary material, which is available to authorized users. High-throughput genotyping has revolutionized genetic analyses in humans, livestock species, crop and model plants in the past decade –3. CoverMaize is one of the most important crops worldwide serving as food, livestock feed, and component of industrial products. A key step in corn production was the establishment of divergent heterotic patterns for hybrid breeding . Most woGenotyping arrays offer an efficient alternative to whole genome sequence data for gaining genomic information in high-throughput. However, the establishment of a high density genotyping array requires the identification of a large number of variants polymorphic in a representative discovery panel to ensure its utility for a wide range of approaches and study designs. In maize, the identification of sequence variants for genomic analyses faces specific challenges due to its evolutionary history and high variability of its genome. As an ancient polyploid species, the maize genome is characterized by numerous duplicated chromosomal regions giving rise to paralogous sequences –16. A reThe highest resolution of a commercially available genotyping array for maize has been achieved by the Illumina® MaizeSNP50 BeadChip . It has in silico variant calls from sequencing data. Here, we describe the design of the high density Affymetrix® Axiom® Maize Genotyping Array which represents a powerful tool for fine-mapping of genomic regions, genome-wide studies, and detection of marker-trait associations. We also demonstrate its application for investigating subpopulation structure and LD in diverse maize germplasm.We selected sequence variants for the design of a high density 600 k SNP genotyping array for maize based on 57 M SNPs and small indels that were discovered by mapping whole genome sequencing reads of 30 representative temperate maize lines against B73 AGP_v2. For experimental validation, we selected 1.2 M variants by applying stringent filtering criteria. This 1.2 M subset was used to genotype 285 maize samples representing the genetic diversity of European (EU) and American (US) temperate maize as well as a sample of tropical maize lines. We created a final selection of 616,201 high quality variants based on their assay performance, physical distribution, and concordance with For variant (i.e. SNP and indel) discovery we sequenced 30 maize inbreds composed of 17 European Flint lines as well as nine European and four US Dent lines  over- or under-representation of the corresponding bin. To ensure a high performance on the final array, the highest weight was assigned to the first criterion. We focussed on clearly separated genotype clusters with little variance that were not influenced by information regarding the inbreeding level  their performance on the screening arrays, (ii) concordance of array genotyping calls with The 600 k genotyping array is composed of 616,201 variants , corresponding to an average density of one variant per ~ 3.4 kb  were monomorphic across all samples of the validation panel. After excluding three genotypic samples without clear germplasm group assignment, 95.6% of the 600 k variants were polymorphic within Dent (N = 73), 98.7% in Flint (N = 79), and 97.2% within F1 hybrids (N = 23), respectively  was of special interest since these 45,974 variants detect previously uncharacterized variants in the flanking region of the target variant. Due to a reduced hybridization efficiency OTVs are characterized by cluster splits or additional relatively low signal intensity clusters compared to expected homozygous and heterozygous genotypes  and admixture analyses of 155 public inbred lines. The first principal coordinate revealed a clear separation of Dent and Flint with a small group of samples located in the center Figure A. This cth century, when Columbus brought subtropical maize from the Caribbean Islands to Southern Spain, later followed by travelers importing so called Northern Flint [In Central Europe, Flint plays an important role for hybrid breeding programs relying on the Dent x Flint heterotic pattern. PCoA of 76 Flint lines as well as one popcorn and two sweetcorn accessions resulted in the separation of European Flint lines adapted to more Northern or Mediterranean climate, respectively Figure C. This srn Flint  from Canrn Flint , 50–52. Cross-validation results obtained by ADMIXTURE  suggesteWe conclude that the variants represented on the 600 k array are well suited for dissecting the diversity and genetic composition of temperate maize lines. Performance of the array with regard to the analysis of tropical material or wild maize relatives will need further investigation.2 ≤ 0.2) could be observed within 158 kb on average with some chromosomal differences  that showed almost no bias towards intermediate allele frequencies, thus are potentially of interest for population genetic analyses. Finally, we performed principal coordinate, admixture, and LD analyses to illustrate the potential of the array to analyse population substructure and LD decay with high resolution.This paper describes the establishment of the currently largest publicly available SNP array in crop species composed of 616,201 SNPs and small indels. The Affymetrix® Axiom® Maize Array is optimized for European and American temperate maize. It is well suited for fine-mapping of genomic regions, genome-wide studies, and detection of marker-trait associations. Important aspects in the development of the maize 600 k genotyping array were: (i) identification of polymorphic, high-confidence variants based on whole genome sequence data of 30 representative temperate maize lines, (ii) selection of physically equally distributed variants for validation, taking predicted variant performance and subgroup specific segregation into account, (iii) experimental variant validation by genotyping 285 DNA samples originating from diverse subgroups of maize, and (iv) final selection of variants upon stringent filtering based on cluster metrics, concordance with For whole genome sequencing of 30 maize inbred lines .Sequence reads were mapped against reference sequence B73 AGP_v2 using BWA (version 0.7.5) . Alignmeftp://ftp.gramene.org/pub/gramene/maizesequence.org/release-5b/filtered-set/) which contains 39,656 gene models. Variant effects were predicted using SNPeff (version 3.2) [Briefly, initial variants were called independently using two algorithms to obtain a more robust SNP set, as recommended by . We usedFor all variants from the 5.6 M list p-convert values were calculated per probe according to the Affymetrix® Axiom myDesign GW bioinformatics pipeline and categorized as “recommended”, “neutral”, “not recommended”, and “not possible”, respectively. The p-convert value can take a value between 0 and 1 and describes the predicted probability to convert on the array by taking its sequence, binding energies, expected degree of non-specific binding and hybridization to multiple genomic regions into account. Two probe sets (forward and reverse) for each SNP from the Illumina® MaizeSNP50 BeadChip (GenTrain score > 0) were directly included in the list of variants for the screening arrays without further filtering unless they were classified as “not possible”. For the newly identified variants only probe sets categorized as “recommended” or “neutral” were further analyzed. For coding variants, the probe with the higher p-convert value was chosen based on this classification, whereas for all remaining variants probe sets were further filtered according to the following multi step approach. Based on the reference genome size of 2.066 Gb, first, the maize genome was partitioned in 20,660 bins of size 100 kb, aiming at an equal physical distribution of variants. Assuming up to 1.23 M possible variants, which could be tested on two screening arrays, after substracting the fixed variants (150 k coding variants and 2*48 k Illumina® MaizeSNP50 BeadChip SNPs), each 100 kb bin would contain on average 48 variants. Three cases were distinguished to fill the physical bins: (i) all possible variants of a bin were included if less than 48 “recommended” or “neutral” variants were identified in the corresponding bin, (ii) “recommended” variants were considered as fixed, if their number did not exceed 48 and remaining “neutral” variants were subjected to another filtering step, and (iii) “recommended” variants were further filtered, if ≥ 48 were observed in the corresponding bin. For this filtering step to fill up underrepresented bins, allele frequencies were determined for Dent (N = 13) and Flint (N = 17) lines separately by calculating the ratio of homozygous non-reference allele calls in relation to all available calls per variant. Variants were classified according to their pool-specific allele frequency as class “A”, corresponding to intermediate (between 0.2 and 0.8), or “B” to extreme non-reference allele frequencies (< 0.2 or > 0.8). One third of the variants, which filled up the bins, were chosen to be specific for Flint (category Dent “A” | Flint “B”) and one third specific for Dent (Dent “B” | Flint “A”), respectively. Further, one sixth each had to be either common (category Dent “A” | Flint “A”) or rare for both groups (Dent “B” | Flint “B”), respectively.In a final step, 50 kb bins were considered if the original 100 kb bin had been filled with less than 48 variants. Additional variants were selected if there were less than 8 variants per 50 kb bin to avoid underrepresentation of genomic regions by choosing variants randomly (i) from “recommended” variants with at least six, but less than 22 homozygous reference allele calls in at least 28 of the 30 lines of the discovery panel to avoid extreme allele frequencies , and (ii) if further variants were required, from all remaining variants. Altogether, a final list of 1,228,506 variants was established for validation with a diverse panel of maize lines on two customized 675 k Affymetrix® Axiom® myDesign GW screening arrays.The selected set of 1.2 M variants was used to genotype 285 DNA samples from genetically diverse maize germplasm to evaluate their assay performance. The validation panel was composed of 224 Dent and Flint inbred lines of which 92 were proprietary lines. From those, line B37 was included three times as technical replicate and three lines  were represented by two biological replicates each. In addition, 13 tropical lines , ten doubled haploid lines from three European Flint landraces, four lines with no available pool assignment, and two Teosinte accessions were analysed. Finally, we included 27 hybrids, among which there were 23 F1 hybrids from Mendelian trios with both parental lines present in the public elite line panel, and four proprietary hybrids , Universität Hohenheim , USDA-ARS , CIAM , CRA-MAC Maize Research Unit , and CSIC .The 30 sequenced lines  for the variant call frequency instead of the default value (0.97) to account for the high amount of PAVs in the maize genome [Raw hybridization intensity data processing, clustering, genotype calling , off-target variant  calling, and variant categorization according to genotype cluster metrics  the weight of the variant class that was multiplied with 35 to ensure a high performance on the final array (range: 0 to 750), (ii) the value of the sequence match multiplied with 90 to minimize false-positives (range: 0 to 90), and (iii) the weight of the bin representation, which was multiplied by 10 for lowest impact (range: −10 to 10). For the 48,324 Illumina® MaizeSNP50 SNPs which were tiled from both sides, the probe with the higher rank was included in the final set in case of varying ranks. If both probes of a variant exhibited the same rank, one probe was chosen randomly. Due to an erroneous mapping of 2,669 Illumina® MaizeSNP50 BeadChip SNPs to the B73 reference sequence a wrong (non-polymorphic) position was assayed on the screening arrays and these non-validated SNPs were not included on the final array. The top 616,201 variants were selected for the final array design among which 45,655 originated from the Illumina® MaizeSNP50 BeadChip. Information on SNP IDs, genome positions, probe sets, and alleles are available at NCBI GEO as platform GPL18778 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL18778) or from the product information of manufacturer Affymetrix.Remaining variants were ranked by applying a voting system. First, variants were ranked according to their classification with and without inbred correction. Variants, which were classified as “OTVstable” or changed their category from “NoMinorHom” (only one homozygous and one heterozygous genotype cluster) without inbred correction to PHR after inbred correction, were assigned a weight of 10. Variants, which belonged to any other class without inbred correction, but changed to PHR after inbred correction received a weight of 5, and all remaining variants not fulfilling the previous criteria obtained a weight of 0. Second, variants were weighted regarding the concordance of their calls with the 2 threshold of 0.8. PCoA based on Rogers´ distances was performed using R with the packages “synbreed” [For all analyses indels were treated as bi-allelic SNPs. In PCoA and ADMIXTURE analyses OTVs were included with their genotype calls of the target variant as well as information on presence or absence of a flanking variant, resulting in 616 k plus 46 k variants. Variants with ≥ 10% of missing data were excluded. Remaining missing data were imputed using Beagle  via the ynbreed” , “adegenynbreed” , and “apynbreed” . Analysiynbreed”  running ynbreed”  and LD dynbreed” .http://www.ncbi.nlm.nih.gov/bioproject/PRJNA260788). Information on SNP IDs, genome positions, probe sets, and alleles can be retrieved from NCBI GEO, platform GPL18778 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL18778).Supporting sequence data are available in the NCBI Sequence Read Archive (SRA) repository under BioProject accession number PRJNA260788 , source of the material , inbred penalty, and number of replicates. Table S5. Call rates of validation samples (N = 281) on the two screening arrays, without and with inbred correction. Samples are sorted according to mean call rate with inbred correction across arrays (last column). Table S6. Number and percentage of variants per category with and without inbred correction for new identified and Illumina® MaizeSNP50 BeadChip variants, respectively, on the screening arrays and on the 600 k array. Table S7. Annotation and prediction of variant effects for the 616,201 variants of the maize 600 k array. Predictions were obtained with SNPeff [Table S8. Overview of replicates included in the validation panel and corresponding percentage of genotype call concordance calculcated from 570 k SNPs (omitting variants with flanking OTVs). (XLSX 96 KB)Additional file 1: Table S1: Description of the sequence variant discovery panel with group assignment, origin (Europe or USA), raw sequence coverage, number of replicates per line, and assigned inbred penalty . h SNPeff . MultiplFigure S2. Representative cluster plots for the six categories according to SNPolisher. Figure S3. Variant density shown for the screening arrays (light grey) and for the variants of the Affymetrix® Axiom® Maize Array (black) across the 10 maize chromosomes. Centromere positions are indicated by a black horizontal bar. Figure S4. Cross-validation errors from ADMIXTURE for different values of K for 155 maize lines based on 251,152 variants including OTVs (markers in LD with r2 > 0.8 were excluded). Figure S5. Subgroups identified in 155 maize lines of the validation panel as revealed by ADMIXTURE for K = 7 based on 251,152 variants including OTVs (markers in LD with r2 > 0.8 were excluded). Figure S6. Cross-validation errors from ADMIXTURE for different values of K for 155 maize lines based on 27,099 flanking OTVs (markers in LD with r2 > 0.8 were excluded). Figure S7. Subgroups identified in the 155 public lines of the validation panel as revealed by admixture for K = 3 based on 27,099 flanking OTVs (markers in LD with r2 > 0.8 were excluded). (PDF 1 MB)Additional file 2: Figure S1: Effects of inbred correction on genotype calling in predominantly homozygous inbred lines shown for two variants.


Escherichia coli swim along circular trajectories adjacent tosurfaces. Thereby, the orientation  and the curvaturedepend on the surface properties. We employ mesoscale hydrodynamic simulations of amechano-elastic model of E. coli, with a spherocylindrical body propelled bya bundle of rotating helical flagella, to study quantitatively the curvature of theappearing circular trajectories. We demonstrate that the cell is sensitive tonanoscale changes in the surface slip length. The results are employed to propose anovel approach to directing bacterial motion on striped surfaces with different sliplengths, which implies a transformation of the circular motion into a snaking motionalong the stripe boundaries. The feasibility of this approach is demonstrated by asimulation of active Brownian rods, which also reveals a dependence of directionalmotion on the stripe width.Bacteria such as  Escherichia coli, Salmonellatyphimurium, Rhizobium lupini, or Proteus mirabilis to name just a few.In a bulk fluid, the bacteria move in a straight manner (run), with all flagella forminga bundle, interrupted by abrupt changes of the swimming direction (tumble) induced bydisintegration of the bundle.E. coliswims in a clockwise (CW) circular trajectory close to a solid boundary245Motility is a particular property of flagellated prokaryotic microorganisms, which areubiquitous in nature. Most bacteria exploit helical filaments for propulsion, driven byrotary motors located in their cell membrane. Prominent examples of peritrichousbacteria, which possess numerous flagella, are E. colihave been explained in terms of hydrodynamic interactions.28b as the extrapolated distance below the surface, where the fluid velocityvanishes.b = 0 for no-slip and b =∞ for perfect-slip surfaces. Intuitively, a trajectory can be expected toswitch from CW to CCW (or vice versa) when b reaches some characteristic valueb0. Such a transition has been observed experimentally for E.coli swimming near glass surfaces upon addition of alginate, and has beenattributed to changes in the slip length.The swimming behavior of bacteria near surfaces is governed by hydrodynamic forces6E. coli-type bacterium tostudy its motion near surfaces, and present results for its circular dynamics atsurfaces of various slip lengths, see E. coli senses thenanoscale slip length of the surface and responds with a circular trajectory of aparticular radius. The obtained dependence of the curvature on the slip length is welldescribed by a simple derived theoretical expression. Moreover, we employ these insightsto suggest a novel route to direct bacterial motion by patterning a surface with stripesof different slip lengths and corresponding CW and CCW trajectories, respectively. Thisleads to an preferential snaking motion along the stripe boundaries for sufficientlywide stripes. We demonstrate the viability of this approach by a simulation of activeBrownian rods, and elucidate the dependence of the diffusion anisotropy on the stripewidth.Here, we exploit a mesoscale model of a non-tumbling E. coliby a spherocylindrical body with four attached helical flagella, see 14d = 0.9 µm. Rotation of theflagella by applied torques leads to bundle formation16SI). Further details areprovided in the Methods section.In our mesoscale hydrodynamic simulations, we model the bacterium E. coli model for different slip lengths b. The trajectorychanges from a CW circle (SI) to a noisy straight line (b increases. h of the body tothe surface fluctuates around the average p(h) is presented in h > 500 nm are well described by the far-fieldexpression, which provides the length scale Near surfaces, bacteria generically swim on circular trajectories due to thecounter-rotation of the body and the flagellar bundle. W circle  and Movight line ) and aCght line ) as b inE. coli. Thus, theresulting height distribution is determined by near-field hydrodynamics, ratherthan far-field effects. This conclusion is supported by a study of bacterialmotion near slip surfaces,SI) with increasing θ between the bacterial swimming direction and thesurface in Our results demonstrate that the far-field approximation fails to quantitativelydescribe the swimming behavior of bacteria near surfaces. The average distanceκ foreach slip length is an average over up to 10 independent trajectories fromextensive hydrodynamic simulations. For each trajectory, the average curvatureis the inverse of the circle radius obtained from a least-square fit. Thecurvature exhibits a smooth crossover from CW trajectories (κ< 0) for no-slip surface to CCW trajectories (κ> 0). The data are very well described by the analytical expressionκ0 < 0is the curvature for a no-slip surface (b = 0),κ∞ > 0 for aperfect-slip surface (b = ∞), and heff theeffective gap width between the cell and the surface. The values of the fitparameters κ0,κ∞, and heff arelisted in heff is comparable to the mean gap width κ0|−1 and|κ∞|−1obtained from the fits agree with the values directly measured from oursimulations as well as the experimental results.2heff at different κ0 −κ)/(κ −κ∞) =b/heff, which is equivalent to Bacillussubtilis, Salmonella typhimurium and Rhodobactersphaeroides.The quantitative dependence of swimming trajectories on the slip length isdisplayed in κ isfound to be a complex function of the dipole strength q, the fluid gapwidth h, and the bacterial aspect ratio γ; see Eq.(71) in q/[pN µm2],h/[nm], γ) = , , and  for cell-body length h are consistent with the mean gap widths inγare about three times less than the bacterial aspect ratio q is q ≈ 1.5 pN µmd = 0.9µm. However, this value of q is about two to three orders ofmagnitude larger than the fitted values in the far-field expression! Note thatthe above torque is close to the experimental value of 500 pN nm.The dependence of the swimming-trajectory curvature on the slip length has beenanalysed very recently within the far-field approximation for a rotlet dipole, asimplified model for flagellated bacteria with counter-rotation of cell body andflagella.n q/pN µm,h/[nm],b0, which separates CW and CCW trajectories, i.e., yieldsa trajectory with vanishing average curvature. By setting κ =0, we find E. colimodel with body length b0 is about 40 nm and nearly independent of E. coli. Therefore, wepropose that E. coli can be employed as a natural sensor for the sliplength of surfaces.E. coli swims in circles with opposite signof curvature on neighboring stripes. This should result in an alternatingtrajectory along a stripe border, i.e., a SI. The dynamical behavior depends on the trajectory curvatures on thetwo types of stripes and the stripe width, as discussed in the following.Motivated by the results for homogeneous surfaces, we propose that stripedsurfaces with different slip lengths can be used to direct bacterial motion. Thealternating slip lengths of the stripes are chosen according to L and infinite length parallel to thex-axis, with curvatures |κ| = 1/R of equalmagnitude. We model the bacterium as a self-propelled rod33U along its axis e =  with the orientation angle 0= U/R when it swims within CCW stripes and Ω =–Ω0 within CW stripes. When the bacteriumcrosses a CW-CCW border , we assume a linear profileΩ = Ω0y/λ with the widthD||,rD. Furthersimulation details are described in the Methods section.For the demonstration of the emergence of directed motion, we first considerstripes of equal width L/R = 0.25), but becomes highly anisotropic along thex-axis for broad stripes (L/R = 6). In the latter case,we observe an oscillatory motion along stripe borders. To understand thisbehavior, we approximate the width λ by its average φ around φ =π  at CW-CCWborders. Similarly, at CCW-CW borders oscillations around φ = 0  are stable. The oscillatory motion is also seen with otherchoices of Ω(y), e.g., where t < rτ =1/rD and diffusive for yD convergeswith increasing L/R to the diffusion coefficient D0 within a stripe; once it hits thestripe border, it performs a directed snaking motion at the velocity Ualong x; the average duration of snaking isrτ, because there is no limit-cycle andsnaking is neutrally stable; therefore, after rτ(on average) the bacterium starts to diffuse again within a stripe; as soon asthe particle reach the neighboring border, motions along the positive andnegative x-directions are equally probable and the transport alongx gets diffusive. This becomes apparent from the distribution of theturning angles ϑ between successive displacements of durationΔt (see Methods section), shown in L/R = 6, which characterizes the geometricalproperties of a trajectory on various time scales. At short timesΔt < rτ, thecharacteristic angle with maximal probability drifts fromϑmax≈0 toϑmax≈π due tofree circular motion. On larger time scales, the motion gets firstunidirectional (ϑmax≈0) and finallybidirectional . We calculate the crossover timecτ between the ballistic and diffusive regimealong x as the time where the exponent α ≈1.4 in τFPM, i.e., themean time to cross the stripe. As can be seen from cτ andτFPM are proportional toL2/D0 andcτ >τFPM, consistent with ourconjecture; furthermore, cτ corresponds to theappearance of bidirectional motion, see the vertical white lines in ρ(y) in L at Δt =rτ and Δt →∞. At the short time scale Δt =rτ, the anisotropy assumes a maximum at L, exponentially forL/R ≤ 2 and then saturates for The swimming trajectories in R on both stripes, is very special, because it wouldrequire a fine-tuning of slip lengths on the two kinds of stripes, and thereforea very particular selection of wall materials. Therefore, we study howasymmetrically striped surfaces modify the behavior. More precisely, we setR− = 40 µm on CW stripes andR+ = –R/3 on CCW stripes. Thetrajectories in x-axis for broad stripes.However, the anisotropy of the diffusion increases nearly five-fold, see xD decreases with decreasing L, seeyD converges toD−D+/(D−+ D+) with increasing L.The crossover time cτ ~L2/D− and meanfirst-passage time τFPM ~L2/D− are dominated bythe faster diffusion process on the CW stripes with larger trajectory radius. Inaddition, the density distribution now displays different values in the twokinds of stripes because The setup of a symmetrically striped surface, with equal magnitude of thetrajectory radius E. coli bacteria are able to sense surface slip on thenanoscale. An implication of our results is to use E. coli as a biosensor ofsurface slip. Moreover, an increasing slip length, which can be achievedexperimentally by addition of grafted polymers, implies CCW circles of decreasingradius. Such tighter circles may enhance bacteria surface adsorption due to anincreased local residence time; compare We have presented a theoretical and numerical investigation of the effect of partialslip at surfaces on the curvature of swimming trajectories of flagellated bacteria.From a combination of mesoscale hydrodynamic simulations and scaling arguments, wepredict that We further demonstrate that striped surfaces can be designed to direct bacterialmotion along the stripe boundaries. The proposed striped surfaces with slip lengthsmaller and larger than about 30 nm and width of order of 100 µm areaccessible in laboratory experiments. Very importantly, the directed motion andanisotropic diffusion does not require a fine-tuning of slip lengths on thealternating stripe patterns, but is very robust and seems to only require differenttrajectory curvatures. Anisotropic transport even exists for stripes yielding thesame sign of trajectory curvature (both CW or CCW) but different magnitudes. Theresulting trajectory along the stripe border resembles a prolate cycloid. Therefore,transport along stripe boundaries is a very generic phenomenon.y- and x-directions, respectively, at some distance from theinitial location of the drop. In addition, a bacterial motion rectifier could beconstructed from a transversally confined two-stripe system. In this case, there isonly a single stripe boundary, so that all bacteria would move in the same directionin the center of the channel. Since there would also be directed motion along thewall,First, it is important to emphasize that the anisotropic diffusion will persist atfinite bacterial densities. In this case, a persistent multi-lane motion of bacteriacan be predicted, in which individual bacteria occasionally change lanes, verysimilar as predicted above. Second, since the curvature radius of the swimmingtrajectory depends on the geometric parameters of the bacterium shape, this approachcould also be employed for sorting bacteria according to size by an appropriatechoice of stripe width. Bacteria with a smaller trajectory curvature would diffusemore isotropically, whereas bacteria with a larger trajectory curvature woulddiffuse more anisotropically along the stripe boundaries. Thus, after placing amixture of both bacteria types on a small spot on the striped surface, bacteria withsmall and large trajectory curvatures will be found preferentially in a location inthe E. coli (SI). Each flagellum is driven by a motortorque Bk and temperature T = 300 K, smaller than thestall torque of approximately 4500 pN nm27The mesoscale hydrodynamic simulations combine coarse-grained molecular dynamicssimulations for the bacterium and multiparticle collision dynamics method1424V(z) = 0 otherwise, where σ = 100 nm is set toavoid close contact of the bacterium with the surface. This short-rangerepulsion automatically takes care of possible bacterium-wall collision, whichhas been shown to be a possible cause of the accumulation of bacteria nearwalls.2032x- and y-directions and two walls at z = 0 and The surface is modeled by a hard wall with tunable boundary conditions. No-slipcondition is implemented by applying bounce-back collision and virtual wallparticles,830U parallel to thesurface at a distance h, see d rotates CCWaround the y-axis at a rate ω and experiences twoforces of hydrodynamic origin along the x-axis: (i) a pressuredifferenceη is thefluid viscosity, and the dimensionless function f has the propertiesf(h/d) → 0 for f(h/d) →constant for Fs. By assuming a linear velocity profilezux∂ ≈(−ωd/2)/(heff +b), where heff is the effective gap width andb the slip length of the surface, we obtain SI).To derive an analytical expression for the curvature of the bacterial swimmingtrajectory, we consider a bacterium of length z-axis at a rate i.e.κ = Ω/U, is then give by b = 0) and b =∞). Here, κ < 0 denotes CW andκ > 0 CCW trajectories. We note thatκ is independent of fluid viscosity ηand swimming velocity U, since ω ∝U.Since the swimming bacterium is force-free, a force equal and opposite to h of the bacterium to the surface from ourhydrodynamic simulations is smaller than its length r =  is the center of mass position, U is the swimming velocityalong the orientation of the rod e =  with the orientation angle ξ andτ are zero-mean Gaussian white noises of variance D|| and rD is the rotationaldiffusion coefficient. Using experimental results for E. coli,220U = 20 µm/s, R = U/|Ω| = 40µm, D|| = 0.14 µm2/s,D||/2 and rD = 0.057s−1.We consider a self-propelled rod swimming in two dimensions over a patternedsurface and neglect the fluctuations of its distance from the surface, since thevariation in the distance r(t) on various time scales, we examine the probabilitydistribution function P ofturning angles ϑ between successive trajectory segments defined asr = r(t + Δt) −r(t) is the vector between two successive positions separatedby the lag time Δt;t controlsthe degree of temporal coarse graining. The distribution functionP is flat for a homogenousdiffusive motion due to the scale invariance of this process.In order to characterize the geometrical properties of a swimming trajectoryJ.H., A.W., R.G.W. and G.G. designed the research, analyzed the data, and wrote thepaper; J.H. and A.W. performed the research.How to cite this article: Hu, J., Wysocki, A., Winkler, R.G. & Gompper, G. Physical Sensing of Surface Properties by Microswimmers – Directing Bacterial Motion via Wall Slip. Sci. Rep.5, 9586; doi: 10.1038/srep09586 (2015).Supplementary InformationSupplementary InformationSupplementary InformationSupplementary Information


DNA methylation is a frequent epigenetic mechanism that participates in transcriptional repression. Variations in DNA methylation with respect to gene expression are constant, and, for unknown reasons, some genes with highly methylated promoters are sometimes overexpressed. In this study we have analyzed the expression and methylation patterns of thousands of genes in five groups of cancer and normal tissue samples in order to determine local and genome-wide differences. We observed significant changes in global methylation-expression correlation in all the neoplasms, which suggests that differential correlation events are frequent in cancer. A focused analysis in the breast cancer cohort identified 1662 genes whose correlation varies significantly between normal and cancerous breast, but whose DNA methylation and gene expression patterns do not change substantially. These genes were enriched in cancer-related pathways and repressive chromatin features across various model cell lines, such as PRC2 binding and H3K27me3 marks. Substantial changes in methylation-expression correlation indicate that these genes are subject to epigenetic remodeling, where the differential activity of other factors break the expected relationship between both variables. Our findings suggest a complex regulatory landscape where a redistribution of local and large-scale chromatin repressive domains at differentially correlated genes (DCGs) creates epigenetic hotspots that modulate cancer-specific gene expression. CpG island shores) are enriched in tissue and cancer-specific differentially methylated regions (cDMRs) when compared with CpG islands . Functional annotation and comparison of this list of genes with pathways, ontology and transcription factor (TF) binding regions reported by the Encode Project Consortium    we calculated differences in absolute correlations as though they were signed correlations. Although about 300 genes from the X chromosome were included, we observed that excluding them from the analysis did not change the results at all. In order to explore the biological plausibility of the analysis, we also tested the significance in correlation differences at the gene level between tumor and normal samples in three datasets, namely lung adenocarcinoma, head and HNSCC and breast cancer. For this, we applied Fisher's transformation to Spearman's Rank correlations. P-values were adjusted for the FDR.We calculated Pearson's and Spearman's correlations between methylation and gene expression intensity separately in each tumor-normal dataset and in both cases and controls. The P-values were calculated accordingly . This method discovered a much larger number of DCGs.DCGs are those genes whose expression and DNA methylation patterns significantly differ in cancer when compared with normal tissues. We calculated DCGs in two datasets (namely lung adenocarcinoma and HNSCC) based on the expression-average methylation correlation values for each gene. Fisher's transformation was applied to Pearson's and Spearman's correlation values and the WEB GESTALT tool , B-lymphocytes (Gm12787), hepatocellular carcinoma (HepG2), and human mammary epithelial cells (HMEC) cell lines and uploaded it to The Genomic Hyperbrowser  a minimal number of Monte Carlo samples of 1000, (2) a global FDR value of 0.001, (3) an alternative hypothesis of an overlap greater than expected, and (4) a null model that preserves segments of both tracks and permutes case and control assignment sequences on chip-seq tracks. Duplicate and triplicate tracks  were handled by combining their P-values using the Stouffer's Z-score method. This test is a meta-analytic method closely related to the Fisher's combined probability test used to combine the result of several independent tests bearing the sample null hypothesis. Stouffer's test is sensitive to consistent departures from the null hypothesis, while Fisher's method is more sensitive to occasional departures from the null hypothesis   was calculated with the Generalized Cross Validation (GCV) method. Penalized regression models gain computational efficiency by choosing a relatively small basis, known as k. By default, we set this value to 20. Although variations in the basis have a small impact on the model, we ensured that k-values were not so small to cause over-smoothing by using the “gam.check” function. P-values were computed by randomly re-shuffling 20,000 times in order to calculate the null distribution of the differencing variance estimator. Low P-values may indicate that the basis dimension is too low. We confirmed that all models had approximately normal residuals and that the values of the estimates divided by the residual variance (a.k.a k-index) were close to 1. Plots were created with the ggtools function implemented in the ggplot2 R package  Wood,  with corge Wood, . The thiWickham, .PubMed , whose Spearman's correlation was much more negative in adenocarcinoma samples (rho = −0.66) than in matched normal controls (rho = 0.25), and whose expression was significantly higher in normal samples . Remarkably, the other top findings were CEACAM5 , MYEOV, SLMAP, and TM4SF18 , CD300LG , and ENOSF1, MFAP5, PKIA, SFRP1, and ZFP3 . In the case of HNSCC we found 15 genes significant at a 10% FDR , SERPINB7 , and CRAT . Of particular interest is the fact that FGF11 was present in the two DCGs lists , which is an important finding when considering that the methylation array platform in these two cases was the same (Illumina 27K). Moreover, this gene remained nearly significantly differentially correlated in breast cancer at a 5% FDR . Overall, the correlation tendency of DCGs was more negative in cancer than in normal samples. About 67% and 80% of these genes had significantly lower correlations in lung adenocarcinoma and HNSCC; respectively.By applying Fisher's transformation to Spearman's correlations in the lung adenocarcinoma and HNSCC cohorts we discovered genes whose average methylation-gene expression correlation varies significantly between tumor and normal samples (a.k.a. DCGs). In the case of lung adenocarcinoma, we found 43 DCGs at a 10% FDR , “convergent extension involved in axis elongation” , “negative regulation of non-canonical Wnt receptor signaling pathway” , “negative regulation of planar cell polarity pathway involved in axis elongation” , “negative regulation of endopeptidase activity” , “negative regulation of B cell apoptotic process” , and “negative regulation of peptidyl-tyrosine phosphorylation”   and is related to “fatty acid beta-oxidation using acyl-CoA oxidase,” “peroxisomal matrix” and “C-acetyltransferase activity” processes. The second one contains five DCGs  and is related to “regulation of interferon-beta production,” “CD40 receptor complex,” and “tumor necrosis factor receptor superfamily” processes.We combined all the significant genes in the two lists 10% FDR) and performed gene ontology analysis versus its own background. This revealed significant enrichments of the selected 58 genes in the following biological processes.: “0% FDR anP-values for differences in correlations. The Stouffer test was used to combine all P-values into a single, gene-level P-value. The presence of significant autocorrelation between CpG correlations can increase the Type I error of the Stouffer test. Using the Mantel test, we observed no significant autocorrelation within 200 and 1000 base pairs from the closest TSS (10% FDR). Similarly, no significant autocorrelation was observed between CpGs falling further than 1000 base pairs upstream and downstream from the closest TSS (10% FDR). Thus, we can reasonably assume that the Type I error is not artificially inflated in this study.In order to further determine the potential impact that DCGs have in cancer, we decided to investigate the distribution of expression-methylation Spearman's correlations in the breast cancer dataset, since this dataset is based on a high-density array of CpG methylation probes. For this, we used Fisher's transformed Spearman's correlation values, which are distributed normally and allow calculating T-test confirmed a significantly more positive correlation in normal samples , whilst outside this 5000 bp region the correlations did not show such a strong effect . We defined the correlation ratio as the ratio of correlation Z scores . We observed that within the first 5000 base pairs the ratio is significantly more negative than outside this region , indicating that correlation values are more divergent and that more correlation sign shifts exist within the 5000 bp than outside (42.00% vs. 39.88%).We created a GAM model to inspect the relationships between CpG methylation intensity and proximal gene expression as a function of the distance to the closest TSS. The model showed that CpGs in the immediate vicinity of the closest TSS showed negative correlations with gene expression in both cancer and normal samples, but this pattern was the inverse in more distant points Figure , 3. For P-values at the gene level and identified groups of genes whose correlation patterns varied between cancer and normal cases significantly. We observed 1662 DCGs at a 5% FDR . However, a significant enrichment of DCGs vs. background genes in GC content was observed , which is an inherent bias due to the Stouffer test and the characteristics of the methylation arrays. The top DCGs can be consulted in Tables In order to characterize those genes with changing expression-methylation correlations, we combined one-tailed Fisher's We considered it interesting to analyze the patterns of expression-methylation correlation of the DCGs as a function of their distance to the closest TSS in normal and neoplastic breast tissues. GAM regression of the DCGs more positively correlated in cancer provided evidence for a general difference, which is more abrupt in the TSS region Figure , 7. A siP-value < 2.2 × 10−16, 95% CI ; Wilcoxon signed rank test (a.k.a Wilcox)) and in DCGs . Interestingly, the average methylation intensity within DCGs was substantially higher compared to the rest of the genome in both cancer  and normal tissues . Standard deviation was substantially higher inside DCGs compared to the rest of the analyzed genome both in normal  and in cancer tissues . In order to compare possible differences in variability of DCGs and background genes we calculated the CVs on methylation β-values. The data indicate significantly higher variability in cancer both at DCGs and background genes , and although the difference is higher among DCGs, the median difference in CVs is only 2.1% higher . Thus, there is no clear reason to believe that increased variability affects the determination of DCGs.In order to inspect possible differences in methylation and expression between cancer and normal samples, we calculated the mean and standard deviation for each CpG, with a special focus on CpGs corresponding to DCGs. The mean methylation intensity was significantly lower in normal samples , whilst average methylation was lower in non-promoter CpGs compared with their genomic homologs in both cases . Interestingly, promoter associated and non-promoter associated CpGs were significantly hypomethylated in normal compared to cancer breast . Using SAM we identified a general trend of DCGs promoter hypermethylation in cancer and 138 genes whose average promoter methylation was significantly higher in cancer at a 5% FDR .To determine possible promoter-dependent differences in methylation, we split the data into promoter-associated CpGs (at less than 1 Kb from the TSS) and in non-promoter associated CpGs. Average methylation of CpGs was found to be significantly higher in promoter-associated CpGs of DCGs with respect to their counterparts in the rest of the genome in both normal and cancer data . However, among all the DCGs no significant difference in expression could be observed at a 5% FDR using SAM; and only 50 genes ranked significant at a 10% FDR, with a clear trend of these to be more expressed in normal breast . Nevertheless, the expression of DCGs is not significantly more variable than the rest of the genome neither among normal samples  nor among cancer tissues .Average gene expression was significantly higher in cancer genome-wide and in DCGs . Although CpG average methylation was greater in cancer in both groups of DCGs , this measure was more significant in those genes with more positive correlations in cancer than in those with more negative correlations in cancer . Average expression was greater among genes with significantly higher correlation values in cancer than in the rest of the genome , but no difference was observed for those genes with lower correlation in cancer . However, no significant difference in expression was detected for both groups of DCGs between cancer and normal breast tissue .Since the 1662 DCGs contain genes that vary in different directions we analyzed those genes whose correlation was more negative in cancer  separately from the rest (661 genes). By comparing the average methylation for each CpG, we observed that both groups were hypermethylated with respect to the rest of the genome in normal and cancer tissues , “system development” , “regulation of cell proliferation” , “organ morphogenesis” , “neuron development” , and “embryonic skeletal system morphogenesis” . Similarly, DCGs were enriched in “phospholipid binding” , “HMG box domain binding” , “neurotrophin receptor binding” , “regulatory region DNA binding” , “sequence-specific DNA binding” , “nucleic acid binding transcription factor activity” , and in “transmembrane signaling receptor activity” . DCGs are also enriched in proteins located at the extracellular region  and in the plasma membrane . Fifty-five DCGs contain Homeobox domains, which implies a highly significant enrichment ; and 44 genes contain Pleckstrin homology domains   pathway,” “EGF receptor (ErbB1) signaling pathway,” “Regulation of CDC42 activity,” “IFN-γ pathway,” “Arf6 trafficking events,” “β1 integrin cell surface interactions,” “Urokinase-type plasminogen activator (uPA) and uPAR-mediated signaling,” “ErbB receptor signaling network,” “IL5-mediated signaling events,” “PDGFR-β signaling pathway,” “Signaling events mediated by focal adhesion kinase,” “S1P1 pathway,” “Hemostasis,” “Signaling events mediated by VEGFR1 and VEGFR2,” “Insulin Pathway,” “Thromboxane A2 receptor signaling,” “PDGF receptor signaling network,” “IL3-mediated signaling events,” “GMCSF-mediated signaling events,” “Platelet activation, signaling and aggregation,” “Validated transcriptional targets of AP1 family members Fra1 and Fra2,” “Glypican 1 network,” “Class I PI3K signaling events,” “Trk receptor signaling mediated by PI3K and PLC- γ,” “Arf6 downstream pathway,” “LKB1 signaling events,” “Sphingosine 1-phosphate (S1P) pathway,” “IGF1 pathway,” “Syndecan-1-mediated signaling events,” “Signaling events mediated by Hepatocyte Growth Factor Receptor (c-Met),” “Neurotrophic factor-mediated Trk receptor signaling,” “Endothelins,” “α9β1 integrin signaling events,” “Signaling by GPCR,” “TRAIL signaling pathway,” “Signaling events mediated by PTP1B,” and “Plasma membrane estrogen receptor signaling.”We searched for the enrichment of the DCGs in gene ontology terms . Similarly, we found a significant enrichment in miR-574-5p, miR-637, and miR-663 target genes . We searched for enrichment of all significant miRNAs-target genes in KEGG pathways , “TGF-β signaling pathway” , “p53 signaling pathway” , “Pathways in cancer” , “Renin-angiotensin system” , “MAPK signaling pathway” , “GABAergic synapse” , “Sphingolipid metabolism” , “Prostate cancer” , “Renal cell carcinoma” , “PI3K-Akt signaling pathway” , “Focal adhesion” , “Adipocytokine signaling pathway” , “Transcriptional misregulation in cancer” , “Maturity onset diabetes of the young” , “Small cell lung cancer” , “Ubiquitin mediated proteolysis” , “Circadian rhythm” , “mTOR signaling pathway” , “Hedgehog signaling pathway” , “Neurotrophin signaling pathway” , “Acute Myeloid Leukemia” , and “Lysine degradation” .Enrichment in TFBS was significant for 62 TFs  in EZH2 in four cell lines, namely Gm12878, H1hESC, HEPG2, and HMEC. Notoriously, the cell line that presented more significant overlaps was H1hESC, in which 11 TF tracks ranked significant . These were BCL11A, CTBP2, CTCF, EGR1, EZH2, GABPA, NANOG, RAD21, SUZ12, TCF2, and ZNF143. Only one track was available for SUZ12, but the enrichment was highly significant , which along with the EZH2 tracks makes plausible a preferential binding of PRC2 in these genomic regions. Twenty-seven tracks were available for H1hESC, HEPG2, and Gm12787 cell lines. Stouffer's corrected values revealed significant overlaps of DCGs with CTCF and EZH2 tracks . H1hESC were also enriched in H2A.Z, H3K27me3, H3K4me1, and H3K4me2 tracks. Stouffer merged P-values across Gm12878, HMEC, HEPG2, and H1hESC revealed significant enrichments in H3K27me3 and H3K9me3 chromatin marks . A similar procedure was followed with Chromatin State Segmentation tracks, which revealed significant enrichments in “Repressed” , “Heterochromatin” , “Poised Promoter” , and “Insulator”  chromatin domains. Finally, DNA Hypersensibility regions preferentially overlapped with DCGs in H1hESC and HMEC cell lines , but not in Gm12878 and HEPG2. Results of these experiments can be consulted in Supplementary Table By comparing uniformly processed genomic tracks for a preferential overlap of DCGs in various TF peaks we found marked enrichments  but no significant overlap was observed with HMDs . The average expression of those DCGs overlapping with known PMDs was nearly significantly lower than the rest of the DCGs in normal breast , but not in breast cancer . DCGs overlapping known PMDs are hypermethylated compared with non-overlapping DCGs in normal and cancerous breast . Indeed, PMD-overlapping DCGs tend to be hypermethylated in cancer compared with normal breast . Although non-PMD DCGs are also hypermethylated in cancer, PMD-overlapping DCGs are more strongly methylated than non-PMD DCGs .Since hypermethylation of promoters and hypomethylation of non-promoter associated regions is a common feature of Partially Methylated Domains . Feinberg et al. . DCGs are enriched in Homeobox genes and HOTAIR is a non-coding RNA derived from a HOX gene and involved in the aberrant gene targeting of PRC2 during cancer progression .Similarly, we obtained a list of 93 imprinted human genes from the  Jirtle, , of whicP-value = 9.99 × 10−5 and 2.2 × 10−16, Wilcox). Due to the fact that DNA G Quadruplexes (a.k.a. G4) are known to influence DNA methylation . Nevertheless, this can be a statistical artifact due to the enrichment of DCGs in GC content and must be interpreted with caution. Finally, we also compared DCGs loci with regions of focal Somatic Copy Number Aberrations (SCNAs) enriched in cancer. These SCNAs were discovered after analyzing 3131 and 1480 cancer and normal tissue samples, respectively .Finally, DCGs are also enriched in CpG islands and GC content compared with their background genes  and chronic lymphocytic leukemia (CLL)  associated with breast cancer prognosis . We also observed that DCGs are significantly enriched in EZH2 and SUZ12 binding in different cell lines, along with significant enrichments in repressive and bivalent chromatin marks. EZH2 and SUZ12 are the major components of the Polycomb Repressive Complex 2 (PRC2), which mediates transcriptional repression partially through histone methylation  savings. Information requests and comments can be sent to his email or his LinkedIn profile.AMO designed the study, carried out all the research and wrote the manuscript.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


AbstractPseudoeupodes Khaustov, gen. n. and new species Pseudoeupodes porosussp. n. are described from moss in Crimea. The taxonomy of the Eupodidae and some other families and genera of Eupodoidea is reviewed. The genus Turanopenthalodes Barilo, 1988 is transferred from Penthalodidae to Penthaleidae. The family Cocceupodidae Jesionowska, 2010 and the genus Filieupodes Jesionowska, 2010 are considered as junior synonyms of Eupodidae Koch, 1842 and Cocceupodes Thor, 1934, respectively. A key to genera of the family Eupodidae is provided.A new genus  Eupodoidea Koch, 1842 are fungivorous, phytophagous and predatory. The classification of the superfamily lacks stability  and a pair of filiform trichobothria (sc1). Naso (epivertex) folded downward onto ventral surface of prodorsum, setae v1 situated on dorsal part of naso near anterior margin of prodorsum; naso defined by different pattern of striation from surrounding prodorsum  and three pairs of large round lyrifissures . Hysterosoma dorsally with two transverse furrows, between setae c1 and d1, and between e1 and f1. Setae f1 not trichobothrium-like.l dorsum , 11–13. rodorsum . HysteroIdiosomal venter (ih), same form as dorsals.l venter , 14–15. PageBreakGnathosoma  meaning false.The genus name is derived from the related genus Caleupodes Baker, 1987. Both genera are characterized by the naso folded down to the ventral surface of the prodorsum, the same palpal chaetotaxy, six pairs of eugenital and five pairs of aggenital setae, the presence of only two pairs of pseudanal setae, femur IV not enlarged, trochanteral setal formula 1–1–1–1, and legs I-IV shorter than idiosoma. The new genus differs from Caleupodes by having striated dorsal cuticle , the absence of a transverse furrow between segments D and E , coxisternal setal formula 3–1–4–2 , and genua I and II with a solenidion . The new genus is also closely related to Niveupodes Barilo, 1991 in the naso folded down to the ventral surface of the prodorsum, femur IV not enlarged, trochanteral setal formula 1–1–1–1, legs I-IV shorter than idiosoma, and only two pairs of pseudanal setae. It differs from Niveupodes by the presence of dorsal transverse furrows between segments C – D and E – F , coxisternal setal formula 3–1–4–2 (3–1–4–3 in Niveupodes), five pairs of aggenital and six pairs of eugenital setae , the absence of scapular lyrifissure isc . The new genus differs from all other known eupodoid genera by the presence of pore-like structures of unknown origin and function situated on the soft cuticle between the coxisternal plates and the trochanters of all legs. These pore-like structures are unknown in any other group of trombidiform mites.The new genus is most similar to Taxon classificationAnimaliaTrombidiformesEupodidaeKhaustovsp. n.http://zoobank.org/3897531B-758D-4AC5-B0F0-F9D85048964CFemale. Length of idiosoma 288 (280–300), width 163 (157–180).PageBreakIdiosomal dorsum , v2 16 (15–19), sc1 50 (47–55), sc2 15 (14–17), c1 13 (12–16), c2 21 (19–25), d1 16 (14–19), e1 15 (14–18), f1 40 (37–45), f2 25 (24–28), h1 23 (21–26), h2 17 (15–21). Setae f1 longest of dorsal hysterosomal setae but not of trichobothrial form.l dorsum , 11–13. l dorsum . All dors of sc1 . LyrifisIdiosomal venter  slightly widened distally; setae 1c the shortest on coxal fields. Genital setae arranged in one longitudinal row, anterior five pairs (g1–g5) situated on non-striated genital covers bearing only microtubercles, posterior pair (g6) situated outside genital covers on striated cuticle (ag5 (7) to ag1 (10–11). Eugenital setae situated on protuberances and arranged in three groups: two anterior , one medial (eu3) and three posterior (eu4-eu6) (ps1 15 (14–18) distinctly longer than ps3 11 (10–14). Lyrifissure ih located anterolaterally to bases of setae ps3.l venter , 14–15.  cuticle , 15. Anteu4-eu6) . PseudanPageBreakGnathosoma , located subapically; subcapitular setae sbc2 densely pilose, sbc1 smooth, located laterally at level of proximal margin of palp trochanters, about one-third as long as sbc2, sbc2 inserted ventrally one third to one quarter of distance between sbc1 and tip of subcapitulum, labrum acuminate. Chelicera , Ti – 7(2φ), Ta – 18 . Famulus ε (stellate seta) located in a shallow depression clearly posterolaterally to basal part of rhagidial solenidion ω1 and anterior to seta ft”. Rhagidial solenidion φ1 obliquely oriented toward anterior lateral margin of leg, located anterodorsally and situated in shallow depression; solenidion φ2 located posterodorsally in the space between setae l’1 and l”1; solenidion σ located anterodorsally, about 1.5 times as long as φ2. All setae on tarsus, d and l’ on tibiae are eupathidia , (p), (it) and tc’ are eupathidia. Leg III (d on tibia are eupathidia. Leg IV (u), p’, ft’ and d on tibia are eupathidia.Legs . RelativLegs : Supraco Leg III : Setal f. Leg IV : Setal fCRIMEA: Yalta mountain-forest Nature Reserve, moss on soil, 21 April 1994, coll. A.A. Khaustov. Paratypes: five females, same data as holotype; seven female paratypes, CRIMEA: Yalta, moss on soil, 5 March 1994, coll. A.A. Khaustov.Holotype female, slide № AK210494, The name of the new species refers to the presence of unusual pore-like structures in the soft cuticle between the coxisternal plates and the trochanters of all legs.PageBreakEupodidae and the superfamily Eupodoidea is highly unstable. Preliminary morphological cladistic analyses  are monophyletic. Of the other four, the Rhagidiidae plus Strandtmanniidae formed a monophyletic group, the Penthaleidae was paraphyletic, and the status of the Eupodidae was not resolved  is the presence of an additional transverse furrow at the level of setae v1. Dendrochaetes acarus  needs to be restudied and redescribed to clarify the status of the family Dendrochaetidae.The monotypic family haetidae , 2009, wCocceupodidae, in which she included three genera: Cocceupodes Thor, 1934, Linopodes Koch, 1835 and Filieupodes Jesionowska, 2010. According to Cocceupodidae differs from Eupodidae by two main characters: setae v1 situated posterior to naso and the presence of only two pairs of circumanal setae (ps1 and ps3). In my opinion, the decision to create the family Cocceupodidae is groundless. The similar location of setae v1 on dorsal part of naso near anterior margin of prodorsum is also found in the eupodid genera Caleupodes, Niveupodes and Pseudoeupodes, but in these genera the naso is not directed anteriorly, but folded to the ventral side of the prodorsum. The presence of only two pairs of pseudanal setae . On the other hand, Cocceupodes and Eupodes Koch, 1842. Both genera have characteristic swollen femora IV adapted for jumping. Another synapomorphic attribute is the relatively long and thin legs I, which are usually subequal to or longer than the idiosoma, and much longer than legs II. In early derivative genera of Eupodidae, such as Neoprotereunetes Fain & Camerik, 1994, Caleupodes and Niveupodes, femora IV are not swollen and legs I are not so long and thin. Thus, the characters separating Cocceupodidae and Eupodidae proposed by Eupodidae. I therefore consider the family Cocceupodidae as a junior synonym of Eupodidae.Filieupodes, which differs from Cocceupodes by a single character, the filiform setae v1 (clavate or capitate in Cocceupodes). Filiform setae v1 is a plesiomorphic character state and should not be used for recognition of a new taxon. I therefore consider Filieupodes as a junior synonym of Cocceupodes.Eupodidae: Xerophiles Jesionowska, 2003, Benoinyssus, Claveupodes Strandtmann & Prasse, 1976, Eupodes, Aethosolenia Baker and Lindquist, 2002, Neoprotereunetes, Linopodes, Cocceupodes, Niveupodes, Caleupodes, and Pseudoeupodes gen. n.Currently I recognize 11 genera in the family PageBreak