Open Access

The BisPCR2 method for targeted bisulfite sequencing

  • Diana L Bernstein1,
  • Vasumathi Kameswaran1,
  • John E Le Lay1,
  • Karyn L Sheaffer1 and
  • Klaus H Kaestner1Email author
Epigenetics & Chromatin20158:27

https://doi.org/10.1186/s13072-015-0020-x

Received: 16 June 2015

Accepted: 23 July 2015

Published: 1 August 2015

Abstract

Background

DNA methylation has emerged as an important regulator of development and disease, necessitating the design of more efficient and cost-effective methods for detecting and quantifying this epigenetic modification. Next-generation sequencing (NGS) techniques offer single base resolution of CpG methylation levels with high statistical significance, but are also high cost if performed genome-wide. Here, we describe a simplified targeted bisulfite sequencing approach in which DNA sequencing libraries are prepared following sodium bisulfite conversion and two rounds of PCR for target enrichment and sample barcoding, termed BisPCR2.

Results

We have applied the BisPCR2 technique to validate differential methylation at several type 2 diabetes risk loci identified in genome-wide studies of human islets. We confirmed some previous findings while not others, in addition to identifying novel differentially methylated CpGs at these genes of interest, due to the much higher depth of sequencing coverage in BisPCR2 compared to prior array-based approaches.

Conclusion

This study presents a robust, efficient, and cost-effective technique for targeted bisulfite NGS, and illustrates its utility by reanalysis of prior findings from genome-wide studies.

Keywords

Targeted bisulfite sequencing DNA methylation Next-generation sequencing

Background

DNA methylation refers to the addition of a methyl group to the 5-carbon position of cytosine residues, and in mammalian genomes occurs most commonly in the context of CpG dinucleotides. As an epigenetic mark, this chemical modification does not alter the DNA sequence, but rather regulates transcriptional programs to direct processes such as cellular differentiation, genomic imprinting, and X-chromosome inactivation, while promoting genomic stability [14]. The majority of CpGs throughout the mammalian genome are fully methylated, while the remainder exists in an unmethylated or lowly methylated state, corresponding to active regulatory elements such as promoters and enhancers [48]. Aberrant DNA methylation has been implicated in an increasing number of morbidities, particularly cancer and aging-associated diseases such as type 2 diabetes, neurological disorders, and cardiovascular disease [911]. Many of the studies linking DNA methylation to disease have been prompted by the observation that only a small fraction of the inherited risk of these complex disorders can be explained by genetic variation, as determined by genome-wide association studies (GWAS) [12, 13]. DNA methylation, along with other epigenetic alterations, may provide the link between environmental factors or intrauterine exposure and complex disorders.

A key challenge in the epigenetics field has been achieving high-resolution genome-wide detection of these modifications in sufficient sample sizes to make claims about disease association. In mapping DNA methylation, the most advanced technologies include array-based techniques such as the Infinium HumanMethylation450 BeadChip, which assays 450,000 individual CpGs among 99% of RefSeq genes, and whole genome shotgun bisulfite sequencing (WGBS), which maps cytosine methylation across the entire genome at single base resolution, covering approximately 30 million CpGs. While array-based approaches are more cost-effective and higher throughput, the restrictive sampling of CpGs provides an incomplete landscape of the methylome [14]. However, WGBS experiments are extremely resource-intensive, because exhaustive sequencing is required to achieve sufficient coverage to accurately determine the percentage of methylation at all CpGs. Therefore, it is only practical to conduct WGBS on a limited number of samples, and coverage is usually in the range of 5–15X per CpG, limiting statistical significance of findings. In both instances, novel findings need to be validated in larger populations through targeted methylation analyses. Thus, there is an increasing need for targeted sequencing techniques that are high-throughput, cost-effective, and provide single base resolution.

Next-generation sequencing (NGS) strategies have been developed as an alternative to fluorescence-based pyrosequencing, which is limited by the number of samples that can be processed, and the fact that its short read lengths cover only a few CpGs at a time. These protocols entail PCR amplification of target regions from bisulfite-converted genomic DNA, followed by DNA sequencing library preparation using techniques such as standard Illumina protocols or transposase-based Nextera XT technology [15, 16]. While providing precise and accurate DNA methylation data with high statistical significance, DNA sequencing library preparation is quite expensive and cumbersome when evaluating large numbers of samples or target regions.

Therefore, we have developed a novel approach for constructing targeted bisulfite NGS libraries that are prepared by bisulfite conversion of genomic DNA followed by two rounds of PCR, termed BisPCR2, eliminating the need for traditional DNA library preparation procedures (Fig. 1). In the BisPCR2 method, the entire library preparation process has been reduced to a single 50-min PCR reaction. We have validated the usefulness of this method in the context of type 2 diabetes, first confirming reported differences in DNA methylation at the imprinted MEG3 locus, and by validation of previous genome-wide findings of CpG risk loci identified in type 2 diabetic human islets [17, 18].
Fig. 1

Schema of BisPCR2 method for targeted bisulfite sequencing. DNA sequencing libraries are prepared by bisulfite conversion of genomic DNA followed by two rounds of PCR for target enrichment (PCR#1) and subsequent sample barcoding (PCR#2). Partial adapter overhangs are added to target enrichment primers to permit simplified library preparation by PCR. PCR#1 amplicons are pooled prior to the PCR#2 reaction for each biological sample. Due to the presence of the unique barcodes, all PCR#2 amplicons can be pooled for a single next-generation sequencing run.

Results

The BisPCR2 method for targeted bisulfite sequencing

In order to simplify targeted bisulfite NGS, we developed a PCR-based method for library preparation, termed BisPCR2 (Fig. 1). The first step in this procedure is sodium bisulfite treatment of genomic DNA (gDNA), which deaminates unmethylated cytosines to uracils, while methylated cytosines are protected. In subsequent PCR amplification reactions, uracils are amplified and ultimately sequenced as thymine residues. The goal of the first PCR (PCR#1) is target enrichment to amplify regions of interest from bisulfite-converted gDNA. The target enrichment primers have overhangs with partial adapter sequences that are subsequently used to amplify barcoded libraries in the second round of PCR (PCR#2). Target enrichment PCR products (PCR#1) for each sample are pooled prior to PCR#2 to simultaneously add the same multiplexing indices to all amplicons of interest. A detailed diagram of BisPCR2 primer design is provided in Additional file 1: Figure S1. Following sample barcoding, all PCR#2 reactions are purified and pooled for sequencing on the Illumina Miseq with 150 base pair paired-end reads. We found that purification of final libraries with AMPure XP beads efficiently removed primer dimers in comparison to column based PCR purification (data not shown). To prove that BisPCR2 is comparable to traditional targeted bisulfite NGS approaches, we measured DNA methylation at the H19 locus in mouse genomic DNA using both methods and found nearly identical results (Additional file 2: Figure S2).

BisPCR2 library construction and sequencing

In this study, we selected five target loci for evaluation, as described below, and compared their DNA methylation profile in five non-diabetic and five type 2 diabetic human islet samples. Pancreatic islet donor information is provided in Table 1. Thus, for each of these ten biological samples, five PCR#1 amplicons were pooled, purified, and then used as template for the PCR#2 barcoding reaction. Target regions ranged in size from 171 to 298 bps (Table 2), and PCR#2 conditions were optimized to prevent amplification bias, particularly of smaller fragments, with the goal of balancing each library with roughly equivalent amounts of each amplicon (Fig. 2a, b).
Table 1

Human pancreatic islet donor information

Donor

Gender

Age (years)

BMI (kg/m2)

Non-diabetic 1

M

50

29.1

Non-diabetic 2

F

59

28.3

Non-diabetic 3

M

49

31.3

Non-diabetic 4

M

60

22.5

Non-diabetic 5

M

51

38.9

Type 2 diabetic 1

M

58

29.3

Type 2 diabetic 2

M

43

37

Type 2 diabetic 3

F

40

33.9

Type 2 diabetic 4

F

57

48.4

Type 2 diabetic 5

M

47

NA

Table 2

Description of PCR products assessed for DNA methylation analysis

Locus

Coordinates

Region length (bp)

Final amplicon (bp)

MEG3

chr14: 101,291,952-101,292,257

298

420

INS

chr11: 2,182,551-2,182,775

225

347

IRS1

chr2: 227,659,611-227,659,781

171

293

CDKN1A

chr6: 36,645,462-36,645,696

235

357

PDE7B

chr6: 136,172,765-136,172,917

153

275

Fig. 2

BisPCR2 DNA sequencing libraries. a Bioanalyzer gel visualizing the five amplicon fragments of a representative sample, ND1, following PCR#2. b Bioanalyzer electropherogram quantifying the amount of each fragment in ND1, illustrating the roughly equivalent amounts of all five amplicons. c Average reads per amplicon for each sample. ND non-diabetic, T2D type 2 diabetic.

The MiSeq sequencing run produced 14.15 million reads, with 12.75 million passing filter. The sample was spiked with 10% PhiX control, and 8.5% of total reads were aligned to the PhiX genome. Approximately 85% of remaining reads, or 10 million, were aligned to the human genome. Therefore, the expected number of reads per amplicon per sample was approximately 200,000 reads. The percentage of reads allocated to each of the ten samples ranged from 7.01 to 12.45% (Fig. 2c). The slight deviation from the expected 10% per sample is likely due to small pipetting errors when preparing the sequencing pool. Across all samples, the average read number per locus was 206,411, ranging from 78,000 to 358,000 reads (Fig. 2c). The range in sequencing depth is likely due to imprecise pooling of PCR#1 products. The amount of each PCR#1 product pooled was based on relative band intensity of one representative sample, non-diabetic 1 (ND1), run on a 1.5% agarose gel, and does not account for sample to sample variability, which we anticipated to be low. This approximation is suitable for many applications of the BisPCR2 method, although samples could be assessed for pooling independently if so desired. Nevertheless, even the minimal read depth of 78,000 allows for exceedingly precise determination of methylation levels.

Validation of type 2 diabetes differentially methylated loci

We tested our BisPCR2 targeted bisulfite sequencing approach by measuring DNA methylation of the promoter region of MEG3 in human pancreatic islets. MEG3 is a complex imprinted locus that produces 54 microRNAs, the MEG3 lncRNA, and multiple additional small RNA species. MEG3 was shown to be down-regulated with corresponding promoter hypermethylation in type 2 diabetic (T2D) human islets [17]. As an imprinted locus, the MEG3 promoter is expected to be approximately 50% methylated in normal human islets, and thus it is an ideal target for validating the BisPCR2 strategy.

Target enrichment primers were designed to amplify a 298 base pair region within the MEG3 promoter at position −188 to −493 relative to the transcription start site. This amplicon covered 19 CpGs, and the average CpG methylation across the region was significantly increased from 43% in non-diabetic to 61% in type 2 diabetic human islets (p < 0.0001), confirming the report by Kameswaran and colleagues. Of the 19 CpGs covered, 14 had significantly increased CpG methylation in type 2 diabetics (p < 0.05) (Fig. 3a). To further corroborate our findings, using primers directed to the same target region, we technically validated our results with pyrosequencing. Although the same target region was analyzed, the fluorescence-based pyrosequencing reaction covered only 2 of the 19 CpGs within the amplicon, #15 and #16. These 2 CpGs showed comparable levels of CpG methylation in the non-diabetic and type 2 diabetic samples as the BisPCR2 method (Fig. 3b). Thus, we were able to technically validate our results with pyrosequencing, and analyze ten times as many CpGs with the BisPCR2 method.
Fig. 3

BisPCR2 DNA methylation analysis confirms increased CpG methylation in type 2 diabetic human islets at the MEG3 locus. a Average percent CpG methylation at the MEG3 locus for five non-diabetic and five type 2 diabetic human islet samples measured by BisPCR2. p values calculated by a two-tailed t test. *p < 0.05. Error bars indicate SEM. b Quantification of average percent CpG methylation by pyrosequencing using the same samples and same MEG3 PCR primer sequences as in a. Only 2 of 19 CpGs are covered in the pyrosequencing assay. Data displayed as in a.

We next sought to employ the BisPCR2 strategy to validate published differentially methylated loci in islets from type 2 diabetics [18]. We selected four genes, INS, IRS1, CDKN1A, and PDE7B, for validation. These loci were among those determined by Dayeh and colleagues to be differentially methylated in type 2 diabetic human islets in a genome-wide screen conducted with the Infinium HumanMethylation450 BeadChip array [18]. The insulin gene has also been described as differentially methylated in type 2 diabetic human islets through a candidate gene approach [19]. We designed PCR#1 primers targeting the region −112 to −336 base pairs upstream of the insulin transcription start site capturing four CpGs, three of which were previously reported to have increased DNA methylation in type 2 diabetic human islets [18]. We found all four CpGs measured had significantly increased DNA methylation (p < 0.05) with an average of 24% in non-diabetic compared to to 46% in type 2 diabetic samples (p < 0.0005) (Fig. 4a). This includes one CpG from the Infinium array that was not previously identified as differentially methylated, INS CpG #4 (cg24338752).
Fig. 4

Validation of CpG loci differentially methylated in type 2 diabetic pancreatic islets by BisPCR2. Average percent CpG methylation in 5 non-diabetic and 5 type 2 diabetic human islet samples at loci previously shown to be differentially methylated in type 2 diabetic human islets, including a INS, b IRS1, c CDKN1A, and d PDE7B. Black arrows indicate CpGs analyzed previously by pyrosequencing by Dayeh and colleagues [18]. p value calculated by two-tailed t tests. *p < 0.05. Error bars indicate SEM.

Individual CpGs within the other three loci had previously been re-analyzed by pyrosequencing, and we modified these pyrosequencing primers with PCR#1 adapter overhangs to adapt these amplicons to the BisPCR2 technology [18]. By using the BisPCR2 method with the same amplicons we were able to directly quantify DNA methylation at all CpGs within the target region. At the IRS1 locus, we measured a comparable decrease of 10% CpG methylation in type 2 diabetic human islets, from 47 to 37%, at IRS1 CpG #1 (cg04751089), as had been reported previously (Fig. 4b) [18]. In this amplicon we also determined DNA methylation at two adjacent CpGs that were not included in the Infinium array, and while the changes trended in a similar direction, there was no significant difference between non-diabetic and type 2 diabetic samples. At the CDKN1A locus, we did not find a significant difference in DNA methylation at the CpG previously analyzed by pyrosequencing, CDKN1A CpG #1 (cg21091547), but did observe a significant decrease of 10% at two adjacent CpGs captured in this amplicon (Fig. 4c). CDKN1A CpG #3 (cg24425727) was also identified as differentially methylated to a similar extent in the genome-wide study by Dayeh and colleagues, while CDKN1A CpG #2 (cg11920449) was not previously identified as differentially methylated [18]. Lastly, we did not observe a change in DNA methylation at any of the three CpGs assayed at the PDE7B locus (Fig. 4d). Thus, in our validation study using BisPCR2, we were able to confirm some previous genome-wide findings and not others, while making novel observations about additional nearby CpGs not covered in prior assays.

Discussion

We were prompted to develop the BisPCR2 method by our need for a high-throughput, cost-effective method for interrogating multiple CpGs at base resolution within multiple target loci of interest. Fluorescence-based approaches to targeted bisulfite sequencing are limited by the number of CpGs that can be measured at one time, the inability to multiplex, and the reliance of measurements on a secondary enzymatic reaction. Next-generation sequencing techniques for targeted bisulfite sequencing employ the same strategy of bisulfite conversion and amplification of target loci, but result in a far more robust output by directly measuring base content of each CpG within an amplicon. Further, the ability to multiplex means that a single sequencing reaction can yield information about multiple target loci for multiple biological samples. One impediment of NGS approaches is the additional step of DNA sequencing library preparation following target enrichment, which can be expensive and time consuming. We have circumvented this challenge by modifying the target enrichment PCR primers with overhangs such that DNA sequencing libraries can be directly amplified from target enrichment amplicons. This modification dramatically decreases time and expense required for this NGS approach.

We technically validated the BisPCR2 method at the imprinted MEG3 locus, which was previously shown to be hypermethylated in type 2 diabetic human islets [17]. We confirmed previous findings by both BisPCR2 and pyrosequencing and demonstrated that BisPCR2 measures DNA methylation at 19 CpGs, while pyrosequencing measures only two despite identical target sequence inputs. Further, our validation study of type 2 diabetes CpG risk loci highlights several important considerations about implementing targeted DNA methylation analysis, particularly as a diagnostic marker. In some instances, such as IRS1, our results were nearly identical to those reported by Dayeh and colleagues [18]. We found similar levels of DNA methylation in normal and type 2 diabetic human islets at IRS CpG #1 (cg04751089) and found no significant changes at two adjacent CpGs that were not probed for on the Infinium HumanMethylation450 BeadChip. However, in contrast to genome-wide findings, we did not observe a change in DNA methylation at PDE7B CpG #2 (cg27306443), or at adjacent CpGs. While this discrepancy may be due to our limited sample size, it stresses the point that CpG methylation at this locus is not a sufficient diagnostic indicator of type 2 diabetes. Our findings at the CDKN1A locus highlight a third point, as we did not measure a change in CpG methylation at the reported cg21091547 (CDKN1A CpG #1), but did find a significant decrease in methylation at two adjacent CpGs, one of which was also identified by Dayeh and colleagues [18]. These results again stress the danger of relying on a single CpG for reporting methylation changes, and also emphasize the value in incorporating multiple CpGs for the understanding of how DNA methylation is implicated in disease risk.

While we have demonstrated the utility of BisPCR2 in validating findings from genome-wide DNA methylation analyses, this technology is also suitable for other applications including candidate gene approaches and clinical diagnostic assays. In candidate gene approaches, where genome-wide analyses may not be possible, many regions can be surveyed simultaneously by pooling multiple PCR#1 products across larger sample sizes than would be feasible for fluorescence-based assays. Clinical applications can benefit as well by reducing the costs of NGS approaches while collecting high-resolution data about neighboring CpGs, the value of which was discussed earlier. It should also be noted that our particular study does not take full advantage of the sequencing capacity of the MiSeq, as 200,000 reads per amplicon is orders of magnitude beyond what would be sufficient to draw statistically significant conclusions. Considering a sequencing threshold of 1,000 reads per amplicon, the capacity of the number of amplicons and samples that can be analyzed in one run could be increased by two hundred-fold. This excess capacity could be distributed to additional samples or additional target loci, or a combination of both. We provide 48 single index barcoding primers based on widely used Illumina sequencing technology, which would accommodate 250 target loci per sample. Further, the barcoding primers could easily be modified with dual indices to increase multiplexing capacity, which may be of particular utility in a clinical assay.

Conclusion

BisPCR2 is an efficient, cost-effective, and robust high-throughput technique for assessing DNA methylation at targeted loci of interest. Replacing DNA sequencing library preparation with a single round of PCR represents a significant improvement over other targeted bisulfite NGS approaches by reducing time and cost. This method is easily adaptable to different experimental setups to address a wide variety of biological questions relevant to DNA methylation.

Methods

Traditional targeted bisulfite NGS library preparation (BisPCRSeq)

100 ng of mouse genomic DNA, isolated from the intestinal epithelium of 3 month old C57BL/6 J-ApcMin/J mice (Jackson Laboratories), was bisulfite converted using the Epitect bisulfite kit (Qiagen). Template DNA was amplified using KAPA HIFI Uracel + (KAPA) with primers directed to the H19 locus (Forward: 5′-ATTAGTTAGTGTGGTTTATTATAGGAAG-3′ and Reverse: 5′-AACCATTCCAAAAATACACACATCTTA-3′). Sequencing libraries were made using the NEBNext Multiplex Sample Kit (NEB). These primers were also modified for incorporation into the BisPCR2 library preparation protocol, as described below.

Genomic DNA isolation and sodium bisulfite conversion

Primary human islets were obtained from the Integrated Islet Distribution Program (IIDP). 10,000 islet equivalents were obtained from five non-diabetic and five type 2 diabetic donors. Genomic DNA (gDNA) was extracted using the Qiagen® AllPrep DNA/RNA mini kit (Cat. No. 80204) following the manufacturer’s instructions. 500 ng of gDNA was treated with sodium bisulfite to convert unmethylated cytosines using the Qiagen® EpiTect® Bisulfite Kit (Cat. No. 59104). Reactions were carried out per the manufacturer’s protocol.

Target enrichment (PCR#1)

Bisulfite-converted gDNA was PCR amplified to enrich for regions of interest for DNA methylation analysis. Primers directed to target regions were modified with the following partial adapter overhangs: PCR#1 Left Primer Overhang: 5′-ACACTCTTTCCCTACACGACGCTCTTCCGATCT-3′; PCR#1 Right Primer Overhang: 5′-GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT-3′. Primers directed to the MEG3 and INS loci were designed using the Qiagen® PyroMark assay Design software. Primers for CDKN1A, PED7B and IRS1 were adapted from a recent study by Dayeh and colleagues [18]. Primer sequences and genomic coordinates are provided in Additional file 3: Table S1. PCR reactions were prepared with the Qiagen® PyroMark PCR Kit (Cat. No. 978703) per the manufacturer’s recommendations using 2.8 ng of bisulfite-converted gDNA template per reaction and the suggested optimized cycling protocol. All PCR#1 products for individual biological samples were pooled based on relative band intensity when 5 μl of PCR#1 reaction from a representative sample, non-diabetic 1, was analyzed on a 1.5% agarose gel. Final amounts were combined as follows: MEG3: 6 μl, CDKN1A: 8 μl, PED7B: 4 μl, IRS1: 4 μl, INS: 4 μl, for a total of 26 μl per biological sample. Each pool of PCR products was purified with the Qiagen® QIAquick PCR Purification Kit (Cat. No. 28104) per the manufacturer’s instructions.

Sample barcoding (PCR#2)

Unique DNA sequencing barcodes were incorporated into each sample by a subsequent round of PCR amplification. Barcoding primers are provided in Additional file 4: Table S2. The Qiagen® PyroMark PCR kit was used to amplify 1 ng of pooled PCR#1 template. Thermocycling conditions were modified to ensure consistent amplification of PCR products of different sizes and were as follows: 95°C—15 min; 10 cycles: 94°C—30 s, touchdown 68 to 56°C—30 s, 72°C—1 min; 72°C—10 min. PCR products were purified with Agencourt® AMPure® XP beads (Beckman Coulter, Cat. No. A63881). Sample concentrations were measured using the Qubit® fluorometer (Life Technologies) dsDNA high sensitivity assay. Fragment length was determined by separating 1 ng of sample on an Agilent high sensitivity DNA assay using the 2100 Bioanalyzer (Agilent Technologies). The molarity of the libraries was quantified using the KAPA library quantification assay (Kapa Biosystems, Cat. No. KK4873).

Next-generation sequencing

Next-generation sequencing was carried out on the Illumina MiSeq using Reagent Kit v2 following the manufacturer’s instructions. Briefly, a 2 nM pool of BisPCR2 libraries and 2 nM PhiX control were each denatured for 5 min with 0.2 N NaOH and diluted to final concentrations of 6 and 8 pM, respectively. The denatured pool was spiked with 10% denatured PhiX control and 600 μl of the prepared sample was loaded into the reagent cartridge. The sequencing reaction was carried out with 150 base pair paired-end sequencing. Sequences were aligned to an in silico bisulfite-converted human genome using the BS Seeker program, and any CpGs covered by the first sequencing read were ignored in the second sequencing read in paired-end sequencing [20]. The fraction of methylated cytosines was calculated as the merged frequency of cytosines for CpGs divided by total reads. Sequencing and DNA methylation analysis were carried out by the Next Generation Sequencing Core at the University of Pennsylvania (Philadelphia, PA, USA) [GEO: GSE69595].

Pyrosequencing

Pyrosequencing was performed to technically validate BisPCR2 at the MEG3 locus. Forward and reverse primers designed with Qiagen® PyroMark Assay Design software were used for both methods, and for pyrosequencing the reverse primer was biotinylated. Pyrosequencing primer sequences were as follows: Forward: 5′-GGGGTGATAGTTTTTGGTTTATATT-3′, Reverse: 5′-CCATAACCAACACCCTATAAT-3′, Sequencing: 5′-TTTTTATATATTGTGTTTGAATTTA-3′. Bisulfite-converted genomic DNA from human islets, processed as described above, was amplified with the Qiagen® PyroMark PCR Kit (Cat. No. 978703) per the manufacturer’s protocol. The pyrosequencing reaction was carried out using Qiagen® PyroMark Gold Q96 CDT Reagents on the PyroMark Q96 MD (QIAGEN) according to the manufacturer’s instructions.

Statistics

Data are shown as average ± SEM (n = 5). Average percent CpG methylation was compared by two-tailed t tests, and significance was defined as p < 0.05.

Abbreviations

NGS: 

next-generation sequencing

GWAS: 

genome-wide association study

WGBS: 

whole genome bisulfite sequencing

PCR: 

polymerase chain reaction

gDNA: 

genomic DNA

ND: 

non-diabetic

T2D: 

type 2 diabetic

SEM: 

standard error of the mean

Declarations

Authors’ contributions

DB developed and optimized the BisPCR2 strategy, designed the study, prepared the next-generation sequencing libraries and drafted the manuscript. VK isolated genomic DNA from human islet samples, conducted the sodium bisulfite treatment and performed pyrosequencing. JLL made significant contributions to development of the BisPCR2 methodology, including primer design. KS designed and executed the portion of the study comparing BisPCR2 to traditional targeted bisulfite NGS. KK supported the design and execution of the study and is the corresponding author. All authors read and approved the final manuscript.

Acknowledgements

This work was supported by the National Institutes of Health (R01DK088383 and UC4DK104119). Funding for open access charge: National Institutes of Health.

Compliance with ethical guidelines

Competing interests The authors declare that they have no competing interests.

Statement of ethical approval All animal experiments were conducted in accordance with protocols approved by the Institutional Animal Care and Use Committee of the University of Pennsylvania.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Genetics and Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania

References

  1. Callinan PA, Feinberg AP. The emerging science of epigenomics. Hum Mol Genet. 2006;15:R95–101.PubMedView ArticleGoogle Scholar
  2. Reik W, Dean W, Walter J. Epigenetic reprogramming in mammalian development. Science. 2001;293(5532):1089–93.PubMedView ArticleGoogle Scholar
  3. Calvanese V, Lara E, Kahn A, Fraga MF. The role of epigenetics in aging and age-related diseases. Ageing Res Rev. 2009;8(4):268–76.PubMedView ArticleGoogle Scholar
  4. Sheaffer KL, Kim R, Aoki R, Elliott EN, Schug J, Burger L, et al. DNA methylation is required for the control of stem cell differentiation in the small intestine. Genes Dev. 2014;28(6):652–64.PubMed CentralPubMedView ArticleGoogle Scholar
  5. Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature. 2009;462(7271):315–22.PubMed CentralPubMedView ArticleGoogle Scholar
  6. Stadler MB, Murr R, Burger L, Ivanek R, Lienert F, Schoeler A, et al. DNA-binding factors shape the mouse methylome at distal regulatory regions. Nature. 2011;480(7378):490–5.PubMedGoogle Scholar
  7. Ziller MJ, Gu H, Mueller F, Donaghey J, Tsai LT, Kohlbacher O. Charting a dynamic DNA methylation landscape of the human genome. Nature. 2013;500(7463):477–81.PubMedView ArticleGoogle Scholar
  8. Baubec T, Schuebeler D. Genomic patterns and context specific interpretation of DNA methylation. Curr Opin Genet Dev. 2014;25:85–92.PubMedView ArticleGoogle Scholar
  9. Bergman Y, Cedar H. DNA methylation dynamics in health and disease. Nat Struct Mol Biol. 2013;20(3):274–81.PubMedView ArticleGoogle Scholar
  10. Heerboth S, Lapinska K, Snyder N, Leary M, Rollinson S, Sarkar S. Use of epigenetic drugs in disease: an overview. Genet Epigenetics. 2014;6:9–19.Google Scholar
  11. Hamm CA, Costa FF. Epigenomes as therapeutic targets. Pharmacol Ther. 2015;. doi:10.1016/j.pharmthera.2015.03.003.PubMedGoogle Scholar
  12. Petronis A. Epigenetics as a unifying principle in the aetiology of complex traits and diseases. Nature. 2010;465(7299):721–7.PubMedView ArticleGoogle Scholar
  13. Rakyan VK, Down TA, Balding DJ, Beck S. Epigenome-wide association studies for common human diseases. Nat Rev Genet. 2011;12(8):529–41.PubMed CentralPubMedView ArticleGoogle Scholar
  14. Lee E, Luo J, Wilson JM, Shi H. Analyzing the cancer methylome through targeted bisulfite sequencing. Cancer Lett. 2013;340(2):171–8.PubMedView ArticleGoogle Scholar
  15. Morrill BH, Cox L, Ward A, Heywood S, Prather RS, Isom SC. Targeted DNA methylation analysis by high throughput sequencing in porcine peri-attachment embryos. J Reprod Dev. 2013;59(3):314–20.PubMed CentralPubMedView ArticleGoogle Scholar
  16. Masser DR, Berg AS, Freeman WM. Focused, high accuracy 5-methylcytosine quantitation with base resolution by benchtop next-generation sequencing. Epigenetics Chromatin. 2013;6:33.PubMed CentralPubMedView ArticleGoogle Scholar
  17. Kameswaran V, Bramswig NC, McKenna LB, Penn M, Schug J, Hand NJ, et al. Epigenetic regulation of the DLK1-MEG3 MicroRNA cluster in human type 2 diabetic islets. Cell Metab. 2014;19(1):135–45.PubMed CentralPubMedView ArticleGoogle Scholar
  18. Dayeh T, Volkov P, Salo S, Hall E, Nilsson E, Olsson AH, et al. Genome-wide DNA methylation analysis of human pancreatic islets from type 2 diabetic and non-diabetic donors identifies candidate genes that influence insulin secretion. PLoS Genet. 2014;10(3):e1004160.PubMed CentralPubMedView ArticleGoogle Scholar
  19. Yang BT, Dayeh TA, Kirkpatrick CL, Taneera J, Kumar R, Groop L, et al. Insulin promoter DNA methylation correlates negatively with insulin gene expression and positively with HbA(1c) levels in human pancreatic islets. Diabetologia. 2011;54(2):360–7.PubMed CentralPubMedView ArticleGoogle Scholar
  20. Chen P, Cokus SJ, Pellegrini M. BS Seeker: precise mapping for bisulfite sequencing. BMC Bioinformatics. 2010;11:203.PubMed CentralPubMedView ArticleGoogle Scholar

Copyright

© Bernstein et al. 2015

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