Mapping of allele-specific DNA methylation (ASM) can be a post-GWAS strategy for localizing regulatory sequence polymorphisms (rSNPs). The advantages of this approach, and the mechanisms underlying ASM in normal and neoplastic cells, remain to be clarified.We perform whole genome methyl-seq on diverse normal cells and tissues and three cancer types. After excluding imprinting, the data pinpoint 15,112 high-confidence ASM differentially methylated regions, of which 1838 contain SNPs in strong linkage disequilibrium or coinciding with GWAS peaks. ASM frequencies are increased in cancers versus matched normal tissues, due to widespread allele-specific hypomethylation and focal allele-specific hypermethylation in poised chromatin. Cancer cells show increased allele switching at ASM loci, but disruptive SNPs in specific classes of CTCF and transcription factor binding motifs are similarly correlated with ASM in cancer and non-cancer. Rare somatic mutations affecting these same motif classes track with de novo ASM. Allele-specific transcription factor binding from ChIP-seq is enriched among ASM loci, but most ASM differentially methylated regions lack such annotations, and some are found in otherwise uninformative “chromatin deserts.”ASM is increased in cancers but occurs by a shared mechanism involving disruptive SNPs in CTCF and transcription factor binding sites in both normal and neoplastic cells. Dense ASM mapping in normal plus cancer samples reveals candidate rSNPs that are difficult to find by other approaches. Together with GWAS data, these rSNPs can nominate specific transcriptional pathways in susceptibility to autoimmune, cardiometabolic, neuropsychiatric, and neoplastic diseases. To estimate the random expectation of polymorphic CTCF motif occurrences in the genome (the background frequency), we ran atSNP on a random sample of 40,000 non-ASM informative SNPs (1:3 ASM vs non-ASM SNP ratio) and found that 8.4% of these non-ASM informative SNPs significantly disrupted a CTCF motif, corresponding to a substantial enrichment for disrupted CTCF motifs among ASM SNPs .The hypothesis that allele-specific TF binding site occupancy (ASB) due to sequence variants in regulatory elements could be a mechanism leading to ASM has been supported by previous data from us and others , 10, 11.p = 6.3 × 10−34). When testing enrichment separately for each of the 14 distinct ENCODE/JASPAR-defined CTCF motifs, we found significant enrichment for 13 of them  among ASM DMRs , all but two of the TF motifs that were correlated with ASM show inversely correlated behavior, such that a relatively higher binding likelihood (stronger PWM score) correlates with CpG hypomethylation  vs those found in the total group of non-cancer samples. We also analyzed the full set of ASM loci using a multivariate mixed model to test for interactions of normal vs cancer status with the TF binding site affinity to ASM strength correlations. The results showed that ASM loci in cancer and non-cancer samples have similar directions of the correlations of ASM with destructive SNPs in the top-ranked classes of polymorphic TF binding motifs  to assess allele-specific binding of at least one ENCODE-queried TF. We found that 16.6% (131) of these ASM index SNPs showed ASB for at least one TF that could be assessed using available ENCODE data. As predicted from the binding site occupancy hypothesis for ASM, at 100 (76%) of these sites, considering both CTCF and TF motifs, the hypomethylated allele showed significantly greater occupancy. This percentage far exceeds random expectation . Confirming this pooled analysis, among 9 TFs with more than 10 ASB occurrences associated with ASM, 7 examples, including the ELF1 (ETS-family) motif and others, showed a significant enrichment in ASM occurrences with an inverse correlation of predicted binding affinity with allelic CpG methylation .As a crucial validation, using our GM12878 SureSelect and WGBS data and the large number of ENCODE ChIP-seq experiments available for this cell line, we could directly ask whether ASM regions with or without polymorphic CTCF and classical TF binding sites exhibit allele-specific binding of the cognate factors. Among the 2102 high-confidence ASM index SNPs from our GM12878 data, 787 overlapped at least one ChIP-seq peak in this cell line and had enough ChIP-seq reads (p < 0.05) for 54 TF binding motifs among the de novo ASM occurrences, compared to the representation of these motifs among all the somatic mutations that were not associated with ASM. Even more convincingly, we found that a majority (71%) of the TF binding site motif classes that were enriched among instances of de novo ASM belonged to the same motif classes that were enriched among the much larger set of ASM loci that were tagged by germline SNPs  which served as germline reference sequences. We found somatic mutations at frequencies of 499 to 1023 per case, and among these mutations from 6 to 17% were associated with gains of ASM  and/or lie within regions of ASB. Such efforts are aided by databases such as AlleleDB for allele-specific marks –42, and To further assess the unique value of ASM mapping, we defined “chromatin desert” ASM regions as 1 kb genomic windows, centered on ASM index SNPs, that contained no DNAse peaks or only one DNAse peak among the 122 ENCODE cell lines and tissues, and no strong active promoter/enhancer, poised, or insulator chromatin state in any ENCODE sample. Less than 55% of such regions have SNPs listed in RegulomeDB, and when they are in that database, they almost always 93%) have weak scores equal to or greater than 5  with the TF binding site affinity to ASM strength correlations. Some motifs, such as CTCF binding sites, were highly depleted in deserts and therefore excluded from the analysis, which was performed on the subset of 74 TF motifs that had at least three occurrences per ASM class. The correlations, when significant (FDR < 0.05), were in the same direction  in all ASM classes. As expected from the findings above, we observed a slightly weaker correlation for cancer-only ASM loci compared to ASM loci in non-cancer samples. However, no differences in the strength of the correlations were found when comparing ASM occurrences in desert versus non-desert locations, both for normal and cancer-associated ASM loci. The simplest hypothesis to explain these results is that ASM DMRs in desert regions are footprints left by rSNPs that disrupt cryptic TF binding sites that were active at some stage of normal or neoplastic cell differentiation (or de-differentiation) but are no longer active in available cells or tissue types. Additional file Most of the ASM DMRs passed statistical cutoffs for ASM in less than half of the informative samples  values, both with standard settings utilizing high D′ and R-squared (R2) values to define “stringent” blocks (median size 5 kb) and with relaxed R2 criteria to define larger “lenient” blocks with a median size of 46 kb  R2 can fail to identify SNPs in perfect LD when rare mutations have occurred over time on pre-existing common alleles in the population—a situation that can have a high D′, and (ii) for some loci, the combined effect of multiple regulatory SNPs, some with weak R2 values but high D′, might be responsible for net effects on disease susceptibility. Using our complete list of ASM DMRs and GWAS data from NHGRI-EBI, including both supra-threshold and suggestive peaks (p < 10− 6), we identified 1842 ASM SNPs in strong LD (R2 > 0.8) or precisely coinciding with GWAS peak SNPs  with GWAS peak SNPs for autoimmune and inflammatory diseases, or related traits such as leukocyte counts . Of these 305 index SNPs, 8 were in HLA genes and the remainder were in non-HLA loci. About half of these regions showed ASM in immune system cell types and/or B cell tumors , rs840015 in strong LD with GWAS peak SNPs for celiac disease, rheumatoid arthritis, and hypothyroidism (nearest genes POU2F1 and CD247), rs10411630 linked to multiple sclerosis (MS) via LD with GWAS peak SNP rs2303759 , rs2664280 linked to inflammatory bowel disease, systemic lupus erythematosus (SLE) and psoriasis via GWAS SNPs rs2675662 and rs2633310  with GWAS peak SNPs for cancer susceptibility or response to treatment . Among these loci, a large majority showed ASM in cancers or cell types that approximate cancer precursor cells  and/or in T cells, which are relevant to cancer via immune surveillance. In these DMRs, 60 of the ASM index SNPs precisely coincided with the GWAS peak SNPs, supporting the candidacy of these statistically identified SNPs as functional rSNPs. Among the 268 ASM index SNPs, overlapping groups of 40 and 207 index SNPs altered ASM-correlated or enriched TF binding motifs, respectively, providing mechanistic leads to disease-associated transcriptional pathways , rs4487645 precisely coinciding with a GWAS peak SNP for multiple myeloma and immunoglobulin light chain amyloidosis  with GWAS peak SNPs for neurodegenerative, neuropsychiatric, or behavioral phenotypes ; and rs667897 linked to Alzheimer’s disease via GWAS peak SNP rs610932  and rs13294100, which coincides with a GWAS peak SNP for Parkinson’s disease (nearest gene SH3GL2). Each of these index SNPs disrupts one or more strongly ASM-enriched and/or correlated TF binding motifs . For harvesting these candidate ASM loci from their dataset, we did not require multiple CpGs in each sequence contig to show ASM, since although in our criteria this is a requirement, it was not utilized as a criterion by Onuchic et al. Overall, 50% of our informative SNPs were also informative in the HEP dataset and 31% of our ASM index SNPs passed the above criteria for ASM in the HEP WGBS data. Given the differences in analytical methods and the differences in numbers and tissue types of the individuals analyzed, this is an encouraging convergence of findings. At the same time, this comparison indicates that our dataset adds substantial new information. With even greater numbers of individuals (informative heterozygotes at more SNPs), additional cell and tissue types, and greater depth of WGBS, additional loci with ASM will be identified. Our data already reveal a large component of rare or “private” ASM with a substantial subset showing a strong ASM magnitude. Indeed, some of the ASM loci identified and validated by targeted bis-seq in our previous smaller study [A recent study by Onuchic et al. using Human Epigenome Project (HEP) data provided a map of ASM SNPs based on 49 WGBS from 11 donors (non-cancer tissues) and 2 cell lines [er study  are not Allele-specific binding of TFs and CTCF has been detected at up to 5% of assessed genomic sites , and theThe set of CTCF and TF binding motifs that we find to be strongly correlated with ASM when they contain disruptive SNPs overlaps only partly with the ASM-correlated motifs identified in the HEP study . EncouraTEAD1 gene, which is known to be over-expressed in aggressive and treatment-refractory cases of multiple myeloma [An advance in the current study is our ability to test and compare mechanisms of ASM in normal and neoplastic cells. We observed a dramatic increase in per sample ASM frequencies, on average, in the primary cancers compared to cell lineage-matched normal cells and to non-cancer samples overall. This increase was paralleled by a more modest but still significant increase in ASM frequency in whole placental tissue and in purified trophoblast, which, as shown here and in other studies , 21, 23, myeloma  and whicBased on the shared general mechanism of ASM in cancer and normal cells, an important practical conclusion is that analyzing combined series of cancer cases plus non-cancer samples increases the power of ASM mapping for finding mechanistically informative rSNPs. In conjunction with GWAS data, these rSNPs can point to genetically regulated transcriptional pathways that underlie inter-individual differences in susceptibility not only to cancers but also to nearly all common human non-neoplastic diseases. Due to the LD structure of the genome, GWAS peaks by themselves can only point to disease-associated haplotype blocks, with all SNPs in strong LD with the causal SNP(s) showing similar correlations to the phenotype. Therefore, additional types of evidence are needed before causal roles can be attributed to GWAS peak SNPs or to other SNPs in strong LD with them. ASM mapping can pinpoint candidate rSNPs that declare their presence by conferring the observed physical asymmetry in CpG methylation between the two alleles. The key finding that supports such mapping for biologically meaningful rSNP discovery is the one above, namely that ASM is caused by disruptive SNPs in TF and CTCF binding sites.ETS2 gene, where it disrupts an ASM-enriched ETS1_3 TF binding motif  is further highlighted by our observation of ASM DMRs in chromatin deserts. Our finding of similar correlations of ASM with destructive SNPs in specific TF binding motifs in both non-desert and desert regions suggests that mapping ASM in deserts can pinpoint candidate rSNPs in cryptic TF binding sites, which were presumably active at earlier stages of cell differentiation and have left “methylation footprints” that can be detected as ASM but cannot be found using other mapping methods.We mapped ASM genome-wide in DNA samples including diverse normal tissues and cell types from multiple individuals, plus three types of cancers. The data reveal 15,115 high-confidence ASM regions, of which 1842 contain SNPs in strong LD or precisely coinciding with GWAS peaks for human diseases and traits. We find that ASM is increased in cancers, due to widespread allele-specific hypomethylation and focal allele-specific hypermethylation in regions of poised chromatin, with cancer-associated epigenetic variability manifesting as increased allele switching. We also report rare but informative de novo ASM due to somatic mutations in TF binding sites in cancers. Despite these cancer-specific phenomena, enrichment and correlation analyses indicate that destructive SNPs in specific classes of CTCF and TF binding motifs are a shared mechanism of ASM in normal and cancer cells and that this mechanism also underlies ASM in “chromatin deserts,” where other post-GWAS mapping methods have not been informative. We provide our dense ASM maps as genome browser tracks and show examples of ASM index SNPs that are in LD with GWAS peaks and disrupt TF binding motifs, thereby nominating specific transcriptional pathways in the pathogenesis of autoimmune and cardiometabolic diseases, neuropsychiatric disorders, and cancers.+ and TE-7−) were purchased from A.T.C.C. and Cell Applications, Inc., and primary cultures of non-neoplastic human mammary epithelial cells (cytokeratin 18+) were purchased from Cell Applications, Inc. and ScienCell Research Laboratories.Human tissues and cell types analyzed in this study are listed in Additional file We used the Agilent SureSelect methyl-seq DNA hybrid capture kit according to the manufacturer’s protocol to analyze methylomes in a total of 27 non-neoplastic cell and tissue samples , MNG Genetics (MNG), and the Genomics Shared Resource of the Roswell Park Cancer Institute (RPCI), as indicated in Additional file Our analytical pipeline is diagrammed in Additional file ASM calling was performed after separating the SNP-containing reads by allele. For each heterozygous SNP, all reads overlapping the 2 kb window centered on the SNP were extracted using Samtools. Given the median insert size of our libraries (~ 200 bp), the use of a 2 kb window instead of the SNP coordinate allows extraction, in most cases, of both paired ends even if the SNP is only covered at one of the ends. SNP calling is performed on each paired read and read IDs are separated into two files as reference (REF) and alternate (ALT) alleles using R. After Bismark methylation extractor is applied, CpG methylation calls by allele are retrieved using allele tagged read IDs. Paired reads with ambiguous SNP calling  were discarded. For Nextera WGBS, due to the fill-in reaction using 5mC following DNA tagmentation which affects the 10 first base pairs (bp) on 5′ of read 2, methylation calling for Cs mapping to these bp was not considered. In addition, a slight methylation bias due to random priming and specific to each library kit was observed within the last 2 bp on 3′ of both paired ends for Nextera WGBS, within the first 10 bp on 5′ of both paired ends and the last 2 bp on 3′ of read 2 for TruSeq WGBS, and within the first 10 bp on 5′ of read 2 for ACCEL-NGS WGBS. Therefore, methylation calls in these windows were ignored.p value < 0.05 and ASM DMRs were defined as regions with > 20% methylation difference after averaging all CpGs covered between the first and last CpGs showing ASM in the region, a Wilcoxon p value corrected for multiple testing by the B-H method < 0.05 (FDR at 5%), and more than 3 ASM CpGs including at least 2 consecutive ASM CpGs. CpGs destroyed by common SNPs (maf > 0.05) were filtered out from both CpG and DMR level analyses. Very close or overlapping DMRs (< 250 intervening bp) were merged into one unique DMR.To further increase the stringency and accuracy of ASM calling, only regions with at least 3 CpGs covered by more than 5 reads per allele were considered. ASM CpGs were then defined as CpGs with Fisher’s exact test VTRNA2-1 gene, which we have previously shown to be subject to parental imprinting in trio samples [We ranked the ASM SNPs using two approaches, one based on confidence/recurrence criteria and the other on percent difference in methylation of the two alleles (ASM strength). For the confidence rank, we used the geometric mean of the average coverage of each allele, the number of samples showing ASM, and the percentage of these samples among all heterozygous (informative) samples. For the strength rank, we used the geometric mean of the methylation difference, number of ASM CpGs, and percentage of ASM CpGs among all covered CpGs. An overall rank was calculated using the geometric mean of these two ranks. ASM DMRs dictated by multiple index SNPs were ranked by the top-scoring SNP. ASM calling and ranking were performed using R and Stata 15. We used the GeneImprint database to flag and exclude from downstream analyses all ASM DMRs that mapped within 150 Kb windows centered on the transcription starting site of all known high confidence imprinted genes, including in this list the  samples  and whicN = 16) than in the non-neuronal samples , which is consistent with findings from another laboratory [Lastly, although varying levels of non-CpG methylation (mCH) have been observed in human and mouse tissues, and this non-canonical methylation appears to have unique sub-chromosomal distributions and biological functions , for claboratory , 65.Somatic mutation calling was performed on the 4 multiple myeloma samples for which paired normal peripheral blood B cells from the same individuals had been bis-sequenced using the same library preparation (ACCEL-NGS WGBS). We used BisSNP (with the same setting as for SNPs but without providing reference SNP dataset) to call all heterozygous variants for both myeloma and normal B cell samples. We then filtered out any variants reported as germline SNPs by DbSNP147. Variants mapping to ENCODE blacklisted regions were removed, and we next filtered out any variants that were present in the paired B cell samples. We used a sequencing coverage requirement for candidate mutations in the myeloma cases of 10× per allele .p values for these differences tend to zero as the number of reads increases. Therefore, to avoid artificially low p values, we carried out bootstrapping , followed by Wilcoxon tests for significance. Samplings and bootstrapping were performed using R. The tested ASM loci and amplicon coordinates are in Additional file Targeted bis-seq was utilized for validation of ASM regions. PCR primers were designed in MethPrimer , and PCRTo annotate ASM and informative SNPs, we defined small (1000 bp) and large (150 kb) windows centered on each index SNP. The small windows were used to assess mechanistic hypotheses involving local sequence elements and chromatin states and the large windows were used for functional annotation (genes and GWAS associated SNPs). We used BedTools to intersect the genomic coordinates of ASM windows to the coordinates of the annotation sets. From the UCSC Genome Browser (PMID: 12045153) (GRCh37 assembly), we downloaded RefSeq annotations, DNase hypersensitive sites, TF peaks by ChIP-seq, and chromatin state segmentation by HMM in ENCODE cell lines (https://www.encodeproject.org/). Chromatin state segmentation for relevant human primary cells and tissues  were downloaded from the Roadmap Epigenomics project (PMID: 25693563). We allowed multiple chromatin states at a single location when different states were present in different cell lines. Distances between ASM loci and genes were calculated from the transcription start sites. Regulome scores were downloaded from RegulomeDB . For eacp values  of the affinity scores for each allele and q value of the affinity score differences between alleles. Motifs that contained SNPs affecting allele-specific TF binding affinity were defined as motifs with a significant difference in binding affinity scores of the two alleles  and a significant binding affinity score in at least one allele . For each TF occurrence, the binding scores per allele were estimated using PWM scores calculated as described in our earlier study [To test for correlations of ASM with destructive SNPs in TF binding motifs, we used position weight matrices (PWMs) of TF motifs from ENCODE ChIP-seq data ,  to estimate the random expectation of each TF motif occurrence. To test whether the disruption of TF binding sites could be a mechanism of ASM, we correlated the difference in PWM scores between alleles of each occurrence of a given TF motif disrupted by an ASM index SNP to the differences in methylation levels between the two alleles, using linear regression. Only TF motifs with more than 10 disrupted occurrences in ASM regions were analyzed. For index SNPs showing ASM in multiple samples, we used the average methylation difference between the two alleles. For each TF motif, a significant correlation of ASM with predicted TF binding affinity differences between the two alleles was defined as FDR < 0.05 and R2 > 0.4.To test whether ASM index SNPs are enriched in variants that disrupt polymorphic TF binding motifs, we used logistic regressions to calculate ORs for each disrupted polymorphic motif. Enrichment was defined as an OR > 1.5 and B-H corrected R2 > 0.4). To not bias the analysis toward TF motifs without any ASM class effect , we also screened each TF motif, including CTCF motifs, using separated multivariate linear fixed models to include any motifs showing no correlation overall but a correlation trend only in one of the ASM classes .To ask whether the correlations between ASM and predicted TF binding affinity differences between alleles might be similar for ASM loci found only in cancers compared to ASM loci that were observed in at least one normal sample, and to ask this same question for chromatin desert ASM vs non-desert ASM regions, we used a multivariate mixed model with random slope and intercept, with pooling of TF motifs to reach sufficient power (number of occurrences used for the regression). TF motifs with less than 10 occurrences total, or less than 3 occurrences in any ASM class, were filtered out. TF motifs included in the final mixed models for the four classes of ASM loci were pre-selected from the bivariate model  were then pooled to be tested in the final mixed model, such that the model was run using a total of 178 TF motifs with 16,609 motif occurrences disrupted by 3394 ASM SNPs for the 2 ASM-class analysis and a total of 62 TF motifs with 10,709 motif occurrences disrupted by 1967 ASM SNPs for the 4 ASM-class analysis. To assess ASB in the GM12878 cell line, ChiP-seq data for 154 TFs available for this cell line were downloaded from ENCODE (PMID: 29126249). For each TF, SNP genotyping and allele-specific read count were performed using the ChiP-seq alignment data for the set of high confidence ASM SNPs found in our GM12878 data and compared to data from WGBS. ASB SNPs were defined as SNPs showing homozygous genotype in the ChiP-seq data (but heterozygous in WGBS) with a significant allele-specific occupancy bias . All analyses were performed using R and STATA statistical software.We defined chromatin deserts as 1 kb genomic windows, centered on ASM index SNPs, which contained no DNAse peaks or only one DNAse peak among the 122 ENCODE cell lines and tissues, and no strong active promoter/enhancer, poised, or insulator chromatin state in any ENCODE sample. The multivariate mixed model accounts for both intra- and inter-TF motif error terms and includes the predicted TF binding affinity difference, either for two classes of ASM loci (non-cancer ASM and cancer ASM) or 4 classes of ASM loci , the interaction between ASM class and binding affinity as fixed explanatory covariates for the methylation difference, and the TF motif as a random covariate. Marginal effects from predictions of the mixed model and Bonferroni-corrected p < 10−6) were downloaded from the NHGRI GWAS catalog [D-prime values [D-prime confidence intervals (0.60–0.84) and historical recombination (0.55) but set the maximum haplotype block size at 200 kb to minimize large block calling in genomic regions lacking haplotype block structure. The blocks so defined have a median size of 46 kb. To identify ASM SNPs in strong LD with GWAS peak SNPs, we utilized the default parameters of Gabriel et al. for haplotype block calling [R2 between our ASM SNPs and all GWAS SNPs within 200 kb. SNPs with high R2 represent a subtype of SNPs in high LD where not only a non-random association (high D′) is observed but where these SNPs can essentially be considered as proxies of each other. Statistical association between a GWAS SNP and trait can be directly imputed to any SNPs with very high R2, so such SNPs are obvious candidates for post-GWAS analyses. However, SNPs showing high D′ but low R2 with the GWAS SNP (which occurs when a rare SNP is in high LD with a more frequent SNP) might also contribute biologically to disease associations. We annotated each ASM index SNP for localization within these haplotype blocks, and for precise co-localization with a GWAS peak SNP or high R2 (> 0.8), and tested for enrichment of ASM SNPs within these blocks, as well as among GWAS peak SNPs, using the same approach as described above for other genomic features.GWAS traits and associated supra and subthreshold SNPs ( catalog , 68. We e values  in PLINKe values . To idenAdditional file 1: Table S1. Biological samples analyzed in this study.Additional file 2: Figure S1. Flow charts and diagram of computational and analytical approaches in this study. Figure S2. Summary of sample types and numbers and yield of informative SNPs. Figure S3. PCA of the combined WGBS and SureSelect methyl-seq data and series-wide overlap between ASM loci detected by the two methods. Figure S4. Distribution of ASM shows a high proportion of rare or private ASM in both cancer and normal samples and a significant increase in per-sample ASM in the cancers. Figure S5. Comparison of results from individual DNA samples analyzed by two WGBS library construction kits at two sequencing facilities, or by SureSelect and WGBS. Figure S6. Example of a chromosome region illustrating consistency between SureSelect methyl-seq, WGBS, and targeted bisulfite sequencing. Figure S7. Validations of ASM DMRs in disease-associated chromosomal regions: rs10411630 and multiple sclerosis. Figure S8. Validation of ASM DMRs in disease-associated chromosomal regions: rs2427290 and colorectal cancer. Figure S9. Validation of ASM DMRs in disease-associated chromosomal regions: rs2283639 and non-small cell lung carcinoma. Figure S10. Validations of ASM DMRs spanning a range of ASM ranks. Figure S11. Kernel density plots of methylation levels showing global hypomethylation and decrease in the percentage of highly methylated CpGs in cancers. Figure S12. Replication of the findings using WGBS from a single facility. Figure S13. Allele-specific losses of methylation leading to ASM in cancers. Figure S14. Kernel density plots of methylation level distributions showing statistically enriched instances of allele-specific gains of methylation in cancers. Figure S15. Shared ASM loci in cancer and non-cancer have similar ASM magnitude. Figure S16. Correlations between allelic TF binding affinity scores and ASM magnitude in the 4 classes of ASM loci. Figure S17. Examples of ASM DMRs in chromatin deserts. Figure S18. Models for inter-individual variability and allele-switching at ASM loci. Figure S19. The percentage of ASM loci that show switching behavior in cancers is smaller when considering only loci for which ASM is also detected in non-cancer samples. Figure S20. Examples of haplotype blocks defined by stringent and lenient parameters. Figure S21. Utility of D’ and R-square parameters for assessing candidate disease-associated rSNPs. Figure S22. Additional examples of mechanistically informative disease associated ASM index SNPs: autoimmune and neuropsychiatric. Figure S23. Additional examples of mechanistically informative disease associated ASM index SNPs: breast cancer and lymphoma. Figure S24. ASM loci displayed as annotated genome browser tracks.Additional file 3: Table S2. ASM index SNPs and DMRs identified in this study and annotated for multiple relevant parameters.Additional file 4: Table S3. Definitions of the terms in Table S2.Additional file 5: Table S4. Known imprinted regions with ASM detected in this study.Additional file 6: Table S5. New candidate imprinted regions and previously provisional imprinted loci with ASM detected in this study.Additional file 7: Table S6. ASM loci tested for validations by targeted bisulfite sequencing.Additional file 8: Table S7. Complete list of polymorphic CTCF and TF binding motifs found to be significantly enriched among ASM loci, requiring that the motif be disrupted by the ASM index SNP.Additional file 9: Table S8. Complete list of CTCF and TF binding motifs that show significant correlations between allelic PWM scores and magnitude of ASM.Additional file 10: Table S9. CTCF and TF binding motifs that show strong correlations of PWM scores with ASM and are also significantly enriched among ASM loci.Additional file 11: Table S10. ASM index SNPs in strong LD or precisely coinciding with GWAS peak SNPs for immune-related diseases and phenotypes.Additional file 12: Table S11. ASM index SNPs in strong LD or precisely coinciding with GWAS peak SNPs for cancer susceptibility.Additional file 13: Table S12. ASM index SNPs in strong LD or precisely coinciding with GWAS peak SNPs for brain-related diseases and phenotypes.Additional file 14. Review history.


The evaluation of circulating tumour DNA (ctDNA) from clinical blood samples, liquid biopsy, offers several diagnostic advantages compared with traditional tissue biopsy, such as shorter processing time, reduced patient risk and the opportunity to assess tumour heterogeneity. The historically poor sensitivity of ctDNA testing, has restricted its integration into routine clinical practice for non-metastatic disease. The early kinetics of ctDNA during radical radiotherapy for localised NSCLC have not been described with ultra-deep next generation sequencing previously.Patients with CT/PET-staged locally advanced, NSCLC prospectively consented to undergo serial venepuncture during the first week of radical radiotherapy alone. All patients received 55Gy in 20 fractions. Plasma samples were processed using the commercially available Roche AVENIO Expanded kit  which targets 77 genes.p = 0.4627).Tumour-specific mutations were found in all patients . The variant allele frequency of these mutations ranged from 0.05–3.35%. In 2 patients there was a transient increase in ctDNA levels at the 72 h timepoint compared to baseline. In all patients there was a non-significant decrease in ctDNA levels at the 7-day timepoint in comparison to baseline (This study demonstrates the feasibility of applying ctDNA-optimised NGS protocols through specified time-points in a small homogenous cohort of patients with localised lung cancer treated with radiotherapy. Studies are required to assess ctDNA kinetics as a predictive biomarker in radiotherapy. Priming tumours for liquid biopsy using radiation warrants further exploration. Circulating tumour DNA (ctDNA) describes tumour-derived DNA fragments released into peripheral blood through necrosis, apoptosis and spontaneous release . The terThe half-life of ctDNA is estimated to be up to 2 hours, and is dependent on factors including cell turnover, tumour size, excretion in bodily fluids and degradation rate by circulating nucleases . TherefoThe emerging potential clinical utilities of ctDNA in lung cancer management include screening , histoloAlthough approximately 45% stage I-III NSCLC cases receive radical radiotherapy as multi- or single modality treatment , there iPatients receiving radical radiotherapy alone for locally advanced, histologically confirmed NSCLC provided consent for serial venepuncture during the first week of treatment. All patients were deemed unsuitable for concurrent chemoradiation. Routine diagnostic investigations included an 18-FDG-PET-CT for all patients, and TNM8 staging was applied . PatientAll patients received 55Gy in 20 fractions over 4 weeks planned with the intensity modulated radiotherapy technique and delivered as 6MV arc therapy, with daily online cone beam-CT image guidance, treating Monday to Friday for 4 weeks. All target volumes were subject to peer review . PatientThe time, date and location of blood draws were agreed with each patient in consideration with their radiotherapy appointments and no additional hospital visits were required as part of the study. Patients provided 20 mL of blood at 3 different time periods i) immediately prior to fraction 1, ii) 72 h after fraction 1 and iii) 7 days after fraction 1, as illustrated in the study schema in Fig. Plasma samples were processed using the commercially available Roche AVENIO Expanded kit  as per the manufacturer’s protocol. Briefly, DNA was extracted using the AVENIO cfDNA Isolation Kit. Libraries were prepared, hybridised and analysed according to the AVENIO ctDNA Analysis Kits Reagent Workflow User Guide . Following extraction, DNA was washed using the AVENIO cfDNA Isolation Kit and quantified using the Qubit High Sensitivity assay kit and Qubit 2.0 Fluorometer . In order to assess average fragment size, the 4200 TapeStation System using the D1000 ScreenTape with D1000 Reagents  was used. Library preparation involved an adapter ligation, a bead clean up, PCR amplification and final bead clean up step and incubated overnight with the AVENIO ctDNA Expanded Panel . Hybridised libraries underwent a streptavidin bead clean up, PCR amplification and a final bead clean up step before DNA quantification. The pooled libraries were diluted to 4 nM, sequenced on the Illumina NextSeq 500  and data was analysed using Roche AVENIO ctDNA Analysis Software Version 1.0.0 . This software includes bioinformatics methods from CAPP-Seq and integrated digital error suppression providing analytical sensitivity and specificity of > 99% at allele frequencies down to 0.5–1% . Roche AStatistical significance of ctDNA levels between timepoints was calculated using the non-parametric Friedman test. Whilst day 3 and day 7 measurements follow normal distribution (parametric data), baseline measurements do not (non-parametric data).Samples in this study were acquired from the local Biobank who have ethical approval (REC reference 16/NI/0030) for the collection and release of human tissue samples and de-identified data from consented patients .Five patients aged 63–85 with histologically confirmed NSCLC stage II-III disease were enrolled onto this study between February and June 2019. Standard molecular analysis for our centre was complete in 1 of 3 applicable patients, in a case of adenocarcinoma , and the median time from processing to freezing was 30 mins (20–50). Potential tumour-specific mutations were found in all patients at baseline  and then tracked 3 days and 7 days post radiation as detailed in Table At 3 months post-treatment 4 of 5 patients were alive  at 7 days from commencing radiotherapy (5 fractions delivered), although the difference was not statistically significant. This is in keeping with an absolute reduction in viable tumour cells available to release DNA, or impaired DNA release processes in remaining tumour cells. Other possibilities include cellular senescence in response to sub-lethal DNA damage, and differential lethality in tumour clones prone to DNA secretion.n = 55) presented at the ASTRO Conference 2017 examining ctDNA in stage I-III NSCLC managed with surgery, radiotherapy and chemoradiation demonstrated a decrease with treatment generally [In keeping with this feasibility analysis, unpublished data  affected the VAF values produced. However, EDTA tubes processed within 6 h have been found to have similar performance than cell-stabilising tubes for ctDNA analysis .IDH2 and TP53 genes, both identified in this study, are associated with CHIP. However, given the variation in levels observed over the short study period we believe that these mutations are unlikely to reflect to CHIP in these patients. Furthermore, the frequency of TP53 mutations in this study (60%) is more in line with NSCLC (expected frequency of 60–70%) than with CHIP (expected frequency 2–5%).A key weakness of our study was the inability to differentiate between clonal haematopoiesis of indeterminate potential (CHIP)  and tumoFurther investigation is required to address the many questions surrounding the interaction of radiotherapy with ctDNA kinetics if the full clinical utility of this technology-enabled assessment is to be realised. If ctDNA kinetics prove to have prognostic capability, surveillance strategies could be individualised for following each patient’s treatment. Early trends may be predictive for response to radiation therapy, and such a predictive biomarker could inform discussions with patients about dose-escalation/acceleration and addition of concurrent drugs. Whether the low dose bath effect of VMAT radiotherapy affects total cfDNA is not known and investigation of this is warranted. It is worth noting that cfDNA increases reduce VAF of ctDNA, given that this is a proportion, although this was not measured in our small sample set. Future investigations of ctDNA kinetics during radical radiotherapy should involve large patient cohorts powered to ensure the interpretation of their results can be justified.In the oligometastatic setting, transient elevations in ctDNA following local consolidative radiotherapy could provide opportunity for understanding the uncontrolled tumour cell clones. In this way, a radiation-primed liquid biopsy would enable a non-invasive method of understanding mechanisms of resistance. Similarly, ongoing exploration of tumour evolution in lung cancer by the TRACERx group may create further opportunities for application of ctDNA in the future .In summary this was a prospective observational pilot study of ultra-deep NGS ctDNA analysis in a cohort of stage II-III NSCLC undergoing the first week of state-of-the-art curative-intent radiotherapy. The feasibility of ctDNA analysis was shown in this small patient cohort with high-quality NGS and a larger study with a range of dose-fractionations, time-points and disease stages, including matched tissue analysis, will help addressing the clinical relevance of ctDNA monitoring during radiotherapy.This pilot study of ultra-deep NGS ctDNA analysis in non-metastatic NSCLC in the first week of radical radiotherapy demonstrated the feasibility of this approach. All included patients had detectable ctDNA at baseline, and had reduced levels at 7 days. A non-significant transient ctDNA increase at 72 h preceded the decrease observed at 7 days in 2 patients, in keeping with trends in the other limited data. Such temporary increases may represent a surge of cellular lethality very early (< 1 week) in courses of radiotherapy.


KLLN has been found in diverse heritable and sporadic cancers, respectively. KLLN has many identified tumor suppressor functions, and when first reported, was thought to be exclusively nuclear. Here, we report on KLLN localization in both the nucleus and cytoplasm and the identification of a putative nuclear export signal (NES) sequence. KLLN overexpression in colon and breast cancer cells showed both nuclear and cytoplasmic presence. Inhibition of the CRM1 export pathway increased nuclear sequestration of KLLN, confirming the prediction of an NES sequence. Point mutations introduced in the predicted NES sequence decreased the strength of the NES and increased the nuclear sequestration of KLLN. Contrary to expectations, the transcription regulation and cellular proliferation functions of KLLN were unaffected by increased KLLN nuclear sequestration. Instead, increased nuclear KLLN correlated with increased nuclear sequestration of TRIM25 and decreased inhibitory phosphorylation of MDM2. Computational analysis of The Cancer Genome Atlas (TCGA) dataset showed positive correlation among KLLN, TRIM25 and MDM2 expression; pathway analysis of the common genes downstream of these three genes revealed protein degradation as one of the top canonical pathways. Together, our observations suggest that CRM1 pathway-based nuclear export of KLLN may impact proteasomal degradation.Germline and somatic promoter hypermethylation of  KLLN gene localizes to 10q23 and shares a transcription start site with PTEN, also a tumor suppressor gene [PTEN are seen in approximately 25% of Cowden syndrome (CS) and CS-like (CSL) patients. CS is an autosomal dominant cancer predisposition syndrome with increased risks for breast, thyroid, endometrial, kidney and colon carcinomas [PTEN mutation negative CS/CSL, KLLN germline promoter hypermethylation is observed in up to 35% of patients and is associated with three-fold increased prevalence of breast carcinomas and two-fold increased prevalence of renal cell carcinomas [in situ of the male breast have reported the presence of KLLN in a cluster of genes that are frequently methylated [KLLN promoter methylation was found to be associated with relapse or development of metastasis in clinical follow-up [KLLN deletions have also been observed in 21% of breast carcinomas in The Cancer Genome Atlas (TCGA) and there has been an established association between increased tumor grade and decreased KLLN expression in breast carcinomas versus adjacent normal tissue [KLLN is a tumor suppressor protein discovered in 2008 when researchers were searching for potential new targets of p53 that were involved in S-phase cell cycle checkpoint regulation. This study described KLLN as both necessary and sufficient for p53-mediated apoptosis in colon cancer cell lines . The KLLrcinomas . In PTENrcinomas . Recent thylated . In orall tissue , 9. TherKLLN is transcriptionally regulated by p53 and has known p53-binding sites on its promoter, that when masked by the promoter hypermethylation, results in decreased KLLN expression [TP53 and CHK1 by directly binding to each of its promoters [KLLN mutations or epimutations have been reported to lead to G2 checkpoint dysfunction [pression , 6, 9. Cromoters . The genromoters . KLLN exromoters –13. Someromoters , 13. KLLfunction . These sKLLN plasmid in one colon cancer cell line. However, unpublished data from our lab of mass spectrometry analysis performed to identify interacting partners of KLLN revealed that a vast majority of KLLN-interacting partners and related pathways were cytoplasmic (see Results). These observations, taken together with the various putative and established functions of KLLN, suggest that KLLN should have both nuclear and cytoplasmic presence. However, since KLLN is a relatively small protein (20 kDa), it likely diffuses between the different intracellular compartments. In this study, we show that KLLN possesses a nuclear export signal (NES) that helps it exit the nucleus and that the likely role KLLN plays in trafficking proteins between the nuclear and cytoplasmic compartments may relate to proteasomal degradation.The 2008 KLLN discovery study suggested that KLLN is exclusively nuclear in its cellular localization . These rKLLN plasmids followed by immunoblotting to show that KLLN localizes to both the nucleus and cytoplasm in breast and colon cancer cell lines . To investigate whether KLLN movement from the nucleus to the cytoplasm was regulated through an export pathway, we treated KLLN overexpressing cells with a known nuclear export inhibitor, Leptomycin B. Leptomycin B is a highly specific and potent inhibitor of the CRM-1 nuclear export pathway [Since  pathway , 16. MosKLLN plasmids were created with the L73F, L78F and L73_78F mutations. When these KLLN plasmids were used to overexpress mutant forms of KLLN in the colon and breast cell lines, we were able to show, using immunoblotting, that mutation of the NES resulted in increased sequestration of KLLN to the nucleus . We also tested expression of other genes that are known to be KLLN targets based on our previous RNA-seq and ChIP-seq data and found no significant differences in gene expression between cells with wildtype KLLN and those with NES mutant protein overexpression.Previous data from our lab using direct and omics-based approaches demonstrated that KLLN plays a significant role as a transcription factor because of its DNA-binding ability , 9, 11. To assess if KLLN nuclear sequestration affected cell viability or cell proliferation, we performed live cell counts and MTT assays, respectively, in all our colon and breast cancer cells. We found that KLLN nuclear sequestration did not alter cell viability or cellular proliferation in any of the cell lines tested . There was no observable pattern in the minimal changes that we observed between KLLN wildtype and KLLN-NES mutant protein-expressing cells. These observations suggest that KLLN nuclear sequestration does not affect KLLN’s role in transcriptional regulation or cell viability but may play non-canonical roles.TP53 gene expression is directly regulated by KLLN [A mass spectrometry analysis done on immunoprecipated samples after KLLN overexpression identified TRIM25, an ubiquitin E3 ligase, as one of 767 unique potential interacting partners of KLLN. TRIM25 is known to regulate p53 abundance and transcriptional activity as well as MDM2 abundance . Since T by KLLN , 11, and by KLLN , we explMass spectrometry-based proteome analysis with IPA revealed KLLN-interacting proteins predicted to play roles in protein degradation pathways . Our datThe subcellular localization of KLLN as a nuclear protein that tends to concentrate in the nucleolar region was established in the discovery study observing overexpressing GFP-tagged KLLN in the colon cancer cell line HCT116. This was considered conventional wisdom given that subsequent studies revealed KLLN’s role in transcription and genome stability –11. Howe3– Φ2-X2– Φ3-X– Φ4. This consensus sequence has a predominance of leucine residues in the hydrophobic regions marked by Φ as does the identified sequence on KLLN suggesting that the NES identified might be a true sequence.Since KLLN interacts with proteins that were known to be involved in active trafficking between the cytoplasm and the nucleus, we hypothesized that even though KLLN is a small protein, it may have a nuclear export signal (NES) or nuclear localization signal (NLS). Conventional wisdom suggests proteins of small size diffuse between the different subcellular compartments but there are examples of proteins of small size with either a NES or NLS sequence that use the export or import complexes to move between the various subcellular compartments such as RPS3. Standard experimental evidence for CRM1-mediated nuclear export of a protein includes sensitivity of the cargo protein to nuclear export inhibition mediated by leptomycin B . Here, wIt is accepted that the computational identification of a NES sequence has to be experimentally validated either through cellular mislocalization after leptomycin B treatment or nuclear export assays in cells. So, based on our results showing KLLN nuclear export was sensitive to leptomycin B and the predicted NES conforming to a general consensus sequence, we believe that the sequence identified in KLLN is a strong candidate for NES. Another interesting observation is that the identified NES sequence falls within one of two phylogenetically highly conserved regions. Based on mutations observed in patients, we mutated residues 73 and 78 that are also part of our NES consensus sequence. The prediction software confirmed that these mutations did reduce the strength of the NES. Experimental evidence also confirmed the reduced strength of the NES based on the observation that the overexpressed mutant KLLN protein sequestered in the nucleus more as compared to the wildtype KLLN. Taken together, these findings and observations gave us confidence that even though the predicted strength of the NES in KLLN was weak, this was a true NES sequence.Given our confidence in KLLN’s NES, we wished to explore its role. The logical path of reasoning would be affecting KLLN’s canonical function in regulation of transcription and cellular proliferation. Surprisingly, the increased nuclear sequestration of KLLN did not affect either of these functions. One speculation of this paradox would suggest that KLLN’s canonical nuclear function of transcription with subsequent cellular proliferation is based on threshold, and not on increasing gradients. By turning to our previous mass spectrometry data, we were able to identify TRIM25 as a direct interactor of KLLN. TRIM25 is a known regulator of p53 and MDM2 abundance and inhibits the transcriptional activity of p53 in response to DNA damage . OverexpIn silico analysis of the TCGA dataset using the UALCAN web resource identified a KLLN-TRIM25-MDM2 regulatory axis in renal cell carcinoma. There was an overabundance in overlap of genes whose expression was positively correlated with the expression of each of the three genes, KLLN, TRIM25 and MDM2. Pathway analysis using IPA revealed that one of the top canonical pathways was protein degradation, as expected. Our previous mass spectrometry results for direct interactors of KLLN had also revealed an overabundance of proteins involved in proteasomal degradation. Taken together, the in vitro and in silico data suggest that the nuclear export of KLLN has a potential role in protein degradation. Unfortunately, similar analysis performed on breast and colon adenocarcinoma TCGA datasets did not show comparable correlation results. This was attributable to the incomplete or short list of MDM2 correlated genes in both breast and colon cancer. Therefore, the number of representative genes in the shared gene lists was not sufficient to do a pathway analysis. However, for the same two cancers, when a pathway analysis was run on the shared list of genes whose expression correlates positively with KLLN and TRIM25, the protein ubiquitination pathway was observed to be the top canonical pathway. Since renal cell carcinoma is a component cancer of Cowden syndrome and KLLN is a known cancer predisposition marker for CS/CSL patients, we used the renal cell carcinoma data as a surrogate to identify the KLLN-TRIM25-MDM2 axis in the regulation of protein degradation.A technical limitation of our study is the inability to detect native KLLN protein due to the lack of reliable KLLN antibodies , 26. NonIn conclusion, our results identifying the putative NES sequence of KLLN establishes KLLN as a cargo protein for the CRM1 export pathway complex. Further, the association between KLLN localization and proteasomal degradation establishes a potential new function for KLLN. Even though TRIM25 is a known regulator of p53 and MDM2 expression and activity, our study establishes a correlative role for KLLN in this pathway. In our previous study, we had suggested the closing of a regulatory loop between p53 and KLLN in response to DNA damage and this study has contributed the missing pieces to that regulatory loop.2 and passaged using Trypsin-EDTA. All cell lines were purchased from ATCC  after 2010 and authenticity was documented by standard STRS analysis per ATCC routine. All cell lines were used during passage 3 to 20 and routinely tested for mycoplasma.Cell culture was done according to the protocol standardized by our lab . HCT116 KLLN sequence cloned in either the C-terminal or the N-terminal side of eGFP sequence. Plasmids with predicted KLLN-NES mutations were created using the QuikChange II kit . For transfection of the different plasmids, cells were seeded at 60–70% confluence in appropriate dishes and allowed to attach overnight. For overexpression of KLLN, cells were transfected with either the wildtype KLLN plasmid or the KLLN-NES mutant plasmids using Lipofectamine LTX (Life Technologies) or Lipofectamine 3000 (Life Technologies) according to the manufacturers protocol. Empty eGFP vector transfected cells were used as a control. Cells were collected for analysis 48–72 h after transfection. qRT-PCR and Western blotting were used to confirm transcript and protein overexpression of KLLN.The eGFP plasmid backbone was sourced from Clontech . The wildtype KLLN plasmids were created in house using the eGFP backbone with Leptomycin B  was used to inhibit nuclear export through the CRM1 export pathway in the breast and colon cancer cell lines. A 10 nM dose of leptomycin B was considered as the appropriate dose for our studies. Leptomycin B addition 48–72 h post-transfection with empty vector or KLLN plasmid was considered appropriate. Multiple timepoints for leptomycin B treatment was tested, and 4 and 16 h were chosen for our studies.-ΔΔCT method.RNA collection, reverse transcription and quantitative PCR was done according to the protocol standardized by our lab , 14. TotFor whole cell lysate preparation, cells harvested after transfection and/or doxorubicin treatment were lysed using Mammalian Protein Extraction Reagent  supplemented with protease and phosphatase inhibitors (Sigma-Aldrich) at 4°C for 30 min - 1 h. Lysates were collected from cell debris by centrifugation at 16,000 × g for 5 mins.For nuclear and cytoplasmic fractionation, we used a modified protocol from Gagnon et al. . Cells wProtein concentrations were quantified using the BCA Protein Assay (Pierce) according to manufacturer’s instructions. Concentration of the lysates were normalized to 1–2 mg/ml using 2X Laemmli dye (Sigma-Aldrich) and was heated at 100°C for 10 min and prepared for western blotting.Western blotting was done according to the protocol standardized by our lab . ApproxiPrimary antibodies commonly used in this study were against p53 (1:1000) (cat# sc126) GFP-B2 (1:1000) (cat# sc9996) and TRIM25 (1:1000) (cat# sc166926) from Santa Cruz Biotechnology, Santa Cruz, CA, USA, and FLAG (1:1000)  and H3K9me3 (1:1000) . Antibodies against pMDM2 (Ser166) (1:1000) (cat# 3521), GAPDH (1:20000) (cat# 2118), PARP (1:1000) (cat# 9542), HSP90 (1:1000) (cat# 4874) were sourced from Cell Signaling, Danvers, MA, USA. Alpha-actinin (1:1000)  and alpha-tubulin (1:10000)  were used as loading controls. Secondary antibodies used were Anti-mouse HRP and anti-rabbit HRP (1:2500) .KLLN plasmids. Seventy-two hours after transfection, cells were treated with the appropriate dose of leptomycin B or left untreated. After specific intervals of times with treatment, cells on the coverslips were washed with 1X PBS and fixed with 3.7% formaldehyde for 15 minutes at room temperature. After washing with 1X PBS, cells were permeabilized in 0.3% Triton-X in 1X PBS for 5 minutes at room temperature. After washing, coverslips were mounted onto slides with DAPI-containing mounting media . Slides were blinded and images were analyzed using upright confocal microscopy  and Leica Confocal Software for image analysis.Immunofluorescence was done according to the protocol standardized by our lab . For immKLLN and KLLN-NES mutant plasmids. Cells were harvested 48–72 h after transfection and counted on a Countess™ automated cell counter (Thermo Fisher Scientific) according to the manufacturers protocol. The number of viable cells were enumerated and plotted on a graph.To assess cell viability, cells were grown in 6-well plates, and transfected with empty vector, and wildtype Cells were grown in 6-well plates, and transfections were done as described for cell viability assay. After the appropriate intervals of times with treatment, 100 μl of MTT reagent (5 mg/ml) was added to each well containing 1 ml of fresh media. After incubation at 37°C for 1 h, the media was removed and 1 ml of DMSO was added to each well. After another incubation at 37°C for 10 min, the DMSO was mixed and three 200 μl aliquots per well were transferred to a 96-well plate. Absorbance is read at 560 nm and average absorbance values were plotted on a graph.in vitro studies were analyzed by two-tailed Student’s t-tests. Significance of results was determined based on calculated p-values and a p-value less than 0.05 was considered statistically significant.All experiments were performed in duplicate or triplicate. All data were represented with error bars signifying standard error of mean for population mean and standard deviation for sample mean. If relevant, data collected from these


Trichoderma reesei) production strain expressing a biosynthetic variant of a consensus bacterial phytase gene assembled via ancestral reconstruction with sequence bias for the phytase from Buttiauxella sp. Rats were administered PhyG daily via oral gavage at dose-levels of 0 (distilled water), 250, 500 or 1000 mg total organic solids (TOS)/kg bodyweight (bw)/day /kg bw/day, respectively). No test article-related adverse effects were observed. A no-observed-adverse-effect level (NOAEL) for PhyG was established as 1000 mg TOS/kg bw/day, the highest test concentration. Based on this NOAEL and an estimate of broiler consumption determined from the proposed inclusion of the phytase in feed at the maximum recommended level (4000 FTU/kg), a margin of safety value of 1613 was calculated. Results of in vitro genotoxicity testing and in silico protein toxin evaluation further confirmed PhyG to be non-genotoxic and not likely to be a protein toxin upon consumption. These data support the safety of PhyG as an animal feed additive.A 90-day subchronic oral toxicity study was conducted to evaluate the safety of a consensus bacterial phytase variant 6-phytase (PhyG) for use as an animal feed additive. This phytase is produced by fermentation with a fungal ( PhytateButtiauxella sp. and dosed at 1000 FTU/kg can achieve 76–92 % ileal phytate degradation in broilers .es assay .22.1via ancestral reconstruction based on available bacterial phytase sequences, with sequence bias for Buttiauxella sp phytase. This resulted in a consensus biosynthetic bacterial phytase sequence with high- melting temperature, which was subjected to further protein engineering with multiple amino acid substitutions and screening for improved enzymatic performance. Phytase variants were produced in a fungal expression system using the filamentous fungus Trichoderma reesei as a production organism. A synthetic, codon optimized gene encoding PhyG phytase under the control of the strong cbhI promoter was integrated into the fungal genome and transformants were screened for phytase activity. After the fermentation process, followed by removal of the production strain and cell debris using depth filtration with filter aids, the purified enzyme in liquid form was concentrated and desalted using ultrafiltration with a MWCO of 10,000 Daltons to produce the test article which was provided as a frozen (-20 °C) clear brown liquid. The same manufacturing lot was used in all studies. The test article met the established product specifications based on analytical testing and was determined as free from microbial and heavy metal contamination. The UFC (ultra-filtered concentrate) test article contained 206.47 mg/g total protein, a specific gravity of 1.063 g/ml and a total organic solids (TOS) content of 21.75 % ], with an activity of 97,885 phytase units (FTU) per gram of test material, equivalent to 450 FTU per mg TOS or 474 FTU per mg total protein . One phytase unit (FTU) was defined as the amount of enzyme that released 1 μmol of inorganic orthophosphate from a sodium phytate substrate per minute at pH 5.5 and 37 °C  is gene5In vitro genotoxicity testing and in silico protein toxin evaluation additionally confirmed PhyG to be non-genotoxic and not likely to pose a protein toxin risk upon consumption. A safety margin of 1613 was based on the established NOAEL, together with an estimate of broiler consumption and assuming incorporation of the phytase into animal feed at the maximum recommended level of 4000 FTU/kg. These findings support the safety of PhyG for the intended use as an animal feed additive.The results of this study have demonstrated that a novel consensus bacterial phytase, PhyG, was not associated with any adverse effects in Crl:CD(SD) rats at dose-levels up to 1000 mg TOS/kg bw/day in a 90-day repeated dose subchronic oral toxicity study conducted in accordance with current OECD guidelines. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Faecal tests are widely applied for colorectal cancer (CRC) screening and considered for triaging symptomatic patients with suspected CRC. However, faecal tests can be inconvenient, complex and expensive. Colorectal mucus (CM) sampled using our new patient-friendly non-invasive technique is rich in CRC biomarkers. This study aimed to evaluate diagnostic accuracy of CRC detection by measuring protein biomarkers in CM.Colorectal mucus samples were provided by 35 healthy controls, 62 CRC-free symptomatic patients and 40 CRC patients. Biomarkers were quantified by ELISA. Diagnostic performances of haemoglobin, C-reactive protein, tissue inhibitor of metalloproteinases-1, M2-pyruvate kinase, matrix metalloproteinase-9, peptidyl arginine deiminase-4, epidermal growth factor receptor, calprotectin and eosinophil-derived neurotoxin were assessed using receiver operating characteristic (ROC) curve analysis.Colorectal mucus haemoglobin was superior compared to other biomarkers. For haemoglobin, the areas under the curve for discriminating between CRC and healthy groups (‘screening’) and between CRC and symptomatic patients (‘triage’) were 0.921 and 0.854 respectively. The sensitivity of 80.0% and specificities of 94.3% and 85.5% for the two settings respectively were obtained.Haemoglobin quantification in CM reliably detects CRC. This patient-friendly approach presents an attractive alternative to faecal immunochemical test; however, the two methods need to be directly compared in larger studies. In these circumstances, only effective CRC screening can substantially reduce mortality from CRC.5Colorectal cancer (CRC) is a global health problem, being the third most common cancer with 1,801,000 new CRC cases and 861,700 deaths worldwide in 2018.5 this diagnostic technique is not perfect, being invasive, expensive and sometimes causing complications. For this reason, two-step screening employing primary non-invasive testing followed by secondary colonoscopies only in positive cases remains the most popular CRC screening strategy all over the world.4Although full colonoscopy is widely regarded as the reference standard for CRC detection and is often employed for primary CRC screening,6 and is currently being replaced with a more sensitive faecal immunochemical test (FIT), diagnostic sensitivity and specificity of which for CRC reach 74% and 95% respectively.7 In the USA, a recently introduced multitarget molecular stool test (‘Cologuard’)8 demonstrates an even higher sensitivity of 92.3% (at 89.8% specificity). However, the ‘Cologuard’ test requires whole stool collection9 and a complex multistep analytical procedure.10 At an extremely high cost of over $600 per assay, this test cannot be seriously considered for CRC screening. It appears that the combination of the highly cost-effective11 non-invasive FIT and confirmatory colonoscopies following positive FIT results can be regarded as the current strategy of choice for CRC screening.12The traditional guaiac faecal occult blood test (gFOBT) had been used for non-invasive CRC detection for decades, but it has a very low sensitivity,13 The number of CRC cases diagnosed in England through the fast-track (2-week) referral pathway is considerably higher,14 but most colonoscopies performed in these symptomatic patients find neither CRC nor other serious colorectal conditions.13 The ‘lack of effective triage systems for invasive investigations’ is now highlighted as a critical research gap in the area of CRC.15 The introduction of the FIT for CRC triage in symptomatic patients is currently considered as a possible solution for this problem.16Population screening is a very important way of both detecting asymptomatic CRC cases and also preventing this cancer by pre-malignant polyp removal; however, it detects less than 20% of CRC cases diagnosed annually in the UK.17 often made it inconvenient and unpopular among screening participants,19 thus negatively affecting CRC screening uptake.22 The presently introduced FIT requires only a single faecal sample and was shown to improve screening uptake to 66.4%.17 However, this figure might be increased further if the necessity of collecting faeces could be eliminated.Although faecal occult blood testing was successfully applied for several decades, the necessity of repeatedly collecting faeces for gFOBT24 overlaying the mucosal surface.25 CM therefore presents the main repository of diagnostically informative biomarkers released from the colonic mucosa.24 Although its fragments are usually excreted with faeces, they are not uniformly distributed throughout the faecal matter; hence, diagnostic test performance may be compromised, especially when samples for the FIT are prepared by patients. Moreover, the bulk of the mucosa-associated CM permanently moves distally without being incorporated in the faeces and remaining on the mucosal surface.26 We have recently developed and clinically evaluated a new simple and patient-friendly technique for non-invasive CM collection27 suitable for cytological examination28 and diagnostic biomarker detection.29 This new approach was successfully used for inflammatory bowel disease (IBD) detection and monitoring.29 Recently it has also been tried as a CRC detection method in a small pilot project30 that allowed us to test 24 potential protein biomarkers of CRC and then to select nine best performers for further evaluation. The present study aimed to comparatively evaluate CRC detection efficiency by quantifying the selected protein biomarkers in CM for discriminating CRC cases from either healthy controls (‘screening’ setting) or cancer-free patients with abdominal symptoms (‘triage’ setting).It is now convincingly proven that all host cells and biomolecules that can be detected in human faeces are initially released from the normal or neoplastic mucosa and incorporated into the well-structured layer of colorectal mucus (CM)The clinical part of this study included symptomatic patients with suspected CRC referred to have diagnostic colonoscopies and healthy volunteers, who were recruited at three clinical centres participating in the study: Department of Gastroenterology of St George’s Hospital , Department of Surgery of Yeovil District Hospital  and Department of Surgery of Southend University Hospital . The protocol of the study was approved by London-South East Research Ethics Committee (16/LO/2273) in accordance with the Declaration of Helsinki. All recruited patients and healthy volunteers provided written informed consent. The project was supported by the NIHR Clinical Research Network and included in its Central Portfolio Management System (CPMS) under CPMS ID 33369. It was also registered as ISRCTN16782445.31 Clinical study flowchart is presented in Fig. In this retrospective study, conducted between January 2017 and June 2018, clinically healthy volunteers and consecutive symptomatic patients were recruited. Symptomatic patients were enrolled following endoscopic investigation (often on the day of colonoscopy), and patients with diagnosed CRC provided samples before surgical intervention. Colonoscopy outcome was accepted as the diagnostic reference standard. Patients with concomitant inflammatory conditions  or major colorectal surgery in the past were not considered for recruitment. Likewise, patients with colorectal polyps detected and removed during colonoscopy were not recruited. The required study size was estimated as at least 35 subjects per group required for detecting AUC difference of 0.150 at higher AUC = 0.850 with 95% confidence level and 80% power.30 was employed in this study. Briefly, samples were taken by swabbing the external anal area immediately following bowel opening using a swab coated with flocked nylon. The swabs were manufactured by Copan Flock Technologies  according to the specifications defined by DiagNodus Ltd.An original kit for non-invasive CM sampling (self-sampling) designed by DiagNodus Ltd and described in our previous publications27 and (b) cell lysis buffer,27 two microscope slides enveloped in a ‘slide card’, a small spray bottle containing cytology fixative, a set of instructions with a simple questionnaire and a pre-paid mailing envelope. All kit components were labelled using number codes. All samples were collected by study participants at home.Each sampling kit comprised two swabs for sample collection, two polypropylene laboratory tubes containing 3 ml of (a) cell-preserving buffer30 Each study participant was requested to complete a simple brief questionnaire evaluating sampling procedure acceptability according to a 5-grade scale with answer options varying from 1—unacceptable to 5—convenient and comfortable.30 The collected samples and completed questionnaires were enclosed in the provided envelopes mailed directly to the laboratory of DiagNodus Ltd.Two consecutive CM samples were obtained following one defaecation and prepared as follows: (1) the samples taken for biomarker analysis were immediately placed to the cell-preserving buffer; (2) two smears were prepared for cytological examination, and the residual material on the swabs was placed in the lysis buffer as described in our previous papers.30 Sample anonymity for blinded analysis was assured by the coded labelling. The samples initially placed in the cell lysis buffer were kept at 4 °C without any further preparation.Before the analysis, the samples initially placed in the cell-preserving buffer were prepared, aliquoted and kept at −80 °C until use as previously described.The microscope slides with smears were removed from the ‘slide cards’, and one slide from each pair was subjected to the conventional staining with haematoxylin and eosin. The remaining fixed smears were stored for possible further investigation. Stained smears were assessed microscopically by a highly experienced cytopathologist (T.B.) blinded to the clinical details. Descriptions of the stained smears reflecting the presence of different types of cells were prepared for all study participants.30 and comprising haemoglobin, C-reactive protein (CRP), tissue inhibitor of metalloproteinases-1 (TIMP1), M2-pyruvate kinase (M2-PK), matrix metalloproteinase-9 (MMP9), peptidyl arginine deiminase-4 (PADI4), epidermal growth factor receptor (EGFR), calprotectin and eosinophil-derived neurotoxin (EDN) were quantified in the collected CM samples using commercially available ELISA kits. ELISA kits for haemoglobin, CRP, TIMP1, MMP9 and EGFR were provided by Abcam . M2-PK and PADI4 were detected using kits from MyBioSource . Calprotectin was measured by kits from Calpro AS . Kits manufactured by MBL  were applied for the quantification of EDN. All ELISA assays were carried out according to the protocols provided by kit manufacturers, and calibration standards as well as quality controls supplied with the kits were used for assay calibration and examination quality testing. All standards and samples were analysed in duplicates. Sample dilutions used for obtaining optimal measurement results were as follows: M2-PK and PADI4—no dilution; TIMP1—1/4; EDN—1/5; haemoglobin and EGFR—1/10; calprotectin—1/50; MMP9—1/100; CRP—1/250. These optimal dilutions were determined in our previous study.30 Assay results were quantitatively measured using Multiscan FC plate reader , at absorbance wavelengths recommended by the ELISA kit manufacturers.Nine protein biomarkers selected in our preliminary studyDiagnostic information for all patients was collected from their clinical records. For CRC cases tumours were defined as proximal or distal according to their localisation proximally or distally from the splenic flexure, and tumour staging according to the TNM classification was recorded for each case.ELISA result evaluation included the generation of calibration curves and absorbance measurement transformations into biomarker concentrations, which were then re-calculated for undiluted samples.32 was employed for determining biomarker-specific cut-off points . Sensitivity and specificity values for CRC detection were then determined. Quantitative data handling and statistical analyses were performed using IBM SPSS 19.0 statistical package . Diagnostic Accuracy Studies (STARD 2015) guidelines34 were followed for manuscript preparation.Statistical analyses were focused on assessing result distributions; however, descriptive statistics  were also calculated. Two-tailed Mann−Whitney test was applied to determine biomarker concentration differences between CRC patients, CRC-free symptomatic patients and healthy controls. Receiver operating characteristic (ROC) curve analyses were applied to evaluate biomarker performance for discriminating CRC cases from CRC-free symptomatic patients (‘triage’ setting) and CRC patients from healthy controls (‘screening’ setting). Areas under the curve (AUCs) were determined, and a recently proposed AUC-based method of optimal cut-off point determinationFigure Completed questionnaires were returned by 31 healthy volunteers and 108 symptomatic patients (comprising 38 CRC cases). None of the study participants reported any problems related to sample collection. Comparison between study groups has not revealed significant differences in sampling quality assessment; therefore, total estimate for the 139 respondents was calculated. The resulting average grade of 4.45 (95% CI between 4.32 and 4.57) was between the predetermined grades 5 (convenient & comfortable) and 4 (acceptable—OK). The average time required for sampling procedure completion was 6.2 min (95% CI between 5.3 and 7.1 min).All 35 CM smears from healthy volunteers could be analysed cytologically. These smears usually contained very few cells .In the group of 62 symptomatic CRC-free patients, samples from 51 subjects were suitable for cytology . A range of cytological manifestations was observed in this diverse group. Neutrophils were present in 11 cases, eosinophils in 6 cases, erythrocytes in 12 cases and apoptotic bodies in 6 cases. Haematoxylin-positive fibre-like structures interpreted as manifestations of extracellular DNA trap formation (ET-osis) were noted in 12 smears. Exfoliated normal colonocytes were present in 40 cases.Cytological analysis of 35 CM samples from 40 eligible CRC patients could be performed. In five cases smears were poorly prepared and unreadable. Distinctly identifiable tumour cells could be seen in smears from 11 CRC patients see Fig. . NeutropResults of protein biomarker quantification in non-invasively collected CM samples are presented in Table 15 Our recent work focused on CM has demonstrated that CM sample collection is a simple procedure very well accepted by patients.30 In our previous preliminary study we tested 24 proteins detectable in CM and regarded as potential CRC biomarkers and selected nine best performers for further evaluation.30 The outcome of the present study confirms our preliminary findings and clearly identifies haemoglobin as the most reliable CRC marker present in CM samples. The values of CM haemoglobin test sensitivity of 80.0% (95% CI between 63.4% and 90.4%) and specificity of 94.3% (95% CI between 79.5% and 99.0%) for the ‘screening’ setting obtained in this study were similar or slightly higher compared to those reported for CRC screening by FIT.7 It is also important to stress that in the ‘triage’ setting the 85.5% (95% CI between 73.7% and 92.7%) specificity of testing haemoglobin in CM was only slightly lower than for our ‘screening’ setting. This point is important since none of the other biomarkers evaluated in this study could reach 80% specificity in the ‘triage’ setting.The introduction of non-invasive biomarker-based tests to triage patients with abdominal symptoms for selecting those requiring immediate endoscopic investigations is an unmet clinical need.11In addition to showing an impressive diagnostic performance of CM haemoglobin for CRC detection, the present study has reproduced our earlier findings, further confirming the non-invasive CM sampling procedure to be very patient-friendly, as feedback from study participants convincingly demonstrated. Even in its more complex version that comprised preparation of smears for cytology, which will not be needed for the clinical use of the test, the average duration of the sampling procedure was only 6.2 min. Moreover, several study participants apparently had experience of collecting material for stool testing in the past and typically commented that the new test was ‘much simpler and easier than the old one’. These observations indicate that non-invasive testing of CM samples for haemoglobin can potentially present an attractive alternative to the FIT. CM sampling kit is very simple, and immunochemical CM sample testing for haemoglobin differs very little from faecal sample testing. It is therefore obvious that the cost of the CM test will be similar to that of the FIT, which is currently regarded as the optimal method for non-invasive CRC screening in terms of cost-effectiveness.35 and were reported to correlate with CRC-caused mortality,36 but the presence of this protein in the faeces of CRC patients has not been investigated so far. Our results on CRC-associated increase of this inflammation-related protein in the CM look intriguing, especially in view of poor diagnostic performance of other inflammation-related proteins . However, it should be noted that for reliable CRP quantification CM samples need to be diluted 1/250, which makes this assay less convenient practically. TIMP1 diagnostic performance appeared to be slightly inferior compared to that of CRP. This protein closely involved in extracellular matrix remodelling is known to be upregulated in colorectal tumours39 and was demonstrated to be involved in metastasis-associated angiogenesis.38 In addition, TIMP1 is regarded as a plasma or serum biomarker of CRC42 and was shown to be frequently present in stool samples from CRC patients.43 Elevated concentrations of another extracellular matrix-associated protein, MMP9, were also reported to be present in faeces of patients with this disease.44 However, in our present study, these markers were clearly less efficient for CRC detection than haemoglobin, especially in the ‘triage’ setting (see Fig. 46 performed modestly in this setting.In the beginning of this project we did not expect to identify haemoglobin as the best CRC biomarker amongst proteins present in the CM, but the presented results clearly show that all other tested biomarkers were less efficient in detecting CRC. Surprisingly, CRP has emerged as the second-best performing biomarker in the ‘triage’ setting. Elevated serum CRP concentrations are known to be associated with active IBD30, we highlighted PADI4 as an intriguing CRC biomarker possibly reflecting an increased probability of extracellular DNA trap formation48 in the mucus overlaying colorectal malignancies.49 The present study has, however, shown that  PADI4 allowed reliably distinguishing CRC patients from healthy volunteers, but failed to do so in the ‘triage’ setting. This suggests that the presence of elevated PADI4 levels in CM can be provoked by a wide range of gastrointestinal disorders, possibly including IBS, diverticulosis and small polyps.In the report on our previous study19 In addition, study results suggest that this approach may present a very useful tool for triage of patients with abdominal symptoms to determine those who will benefit most from invasive colonoscopies.The comparison of CM-associated biomarkers performed in this study allows concluding that haemoglobin measurement in CM provides high sensitivity and specificity values for CRC detection. Increased concentrations of CRP, TIMP1, M2-PK, MMP9 and PADI4 in CM samples could also serve as CRC biomarkers, but were less efficient in comparison with haemoglobin. In contrast, EGFR, calprotectin and EDN could not be recommended as reliable diagnostic markers. The presented results indicate that non-invasive CM self-sampling was very well accepted by the study participants and can be regarded as an attractive alternative to the collection of faeces. This point should be stressed, given that the necessity of collecting faecal samples may negatively affect compliance in CRC screening.The study had obvious limitations due to its relatively small size and the absence of direct comparison between our CM tests and FIT. Volunteers of the control group were considerably younger than the CRC patients; thus, our ‘screening setting’ results could potentially be biased. Moreover, we could not assess CM biomarker performance for colorectal polyp detection since sampling was performed post-colonoscopy, when the detected high-risk polyps were removed and only patients with very small polyps were included in the CRC-free symptomatic group. Finally, all analytical procedures were laboratory-based, but it is apparent that CM sample testing for haemoglobin can be easily presented as an inexpensive rapid point of care test, which remains to be developed and clinically evaluated.All these points need to be addressed in our future work that could include larger prospective clinical studies addressing CRC screening and triage separately.