Open Access SOFTWARE ChIA-PET tool for comprehensive chromatin interaction analysis with paired-end tag sequencing
2010

Guoliang Li
Computational and Mathematical Biology
Genome Institute of Singapore
60 Biopolis Street138672SingaporeRepublic of Singapore

Melissa J Fullwood
Genomic Technologies
Genome Institute of Singapore
60 Biopolis Street138672SingaporeRepublic of Singapore

Han Xu
Computational and Mathematical Biology
Genome Institute of Singapore
60 Biopolis Street138672SingaporeRepublic of Singapore

FabianusHendriyan Mulawadi
Computational and Mathematical Biology
Genome Institute of Singapore
60 Biopolis Street138672SingaporeRepublic of Singapore

Stoyan Velkov
Computational and Mathematical Biology
Genome Institute of Singapore
60 Biopolis Street138672SingaporeRepublic of Singapore

Vinsensius Vega
Computational and Mathematical Biology
Genome Institute of Singapore
60 Biopolis Street138672SingaporeRepublic of Singapore

Pramila Nuwantha Ariyaratne
Computational and Mathematical Biology
Genome Institute of Singapore
60 Biopolis Street138672SingaporeRepublic of Singapore

Yusoff Bin
Computational and Mathematical Biology
Genome Institute of Singapore
60 Biopolis Street138672SingaporeRepublic of Singapore

Mohamed
Hong-Sain Ooi
Computational and Mathematical Biology
Genome Institute of Singapore
60 Biopolis Street138672SingaporeRepublic of Singapore

Chandana Tennakoon
Chia-Lin Wei
Genomic Technologies
Genome Institute of Singapore
60 Biopolis Street138672SingaporeRepublic of Singapore

Yijun Ruan firstname.lastname@example.com
Genomic Technologies
Genome Institute of Singapore
60 Biopolis Street138672SingaporeRepublic of Singapore

Wing-Kin Sung email@example.com
Computational and Mathematical Biology
Genome Institute of Singapore
60 Biopolis Street138672SingaporeRepublic of Singapore

Open Access SOFTWARE ChIA-PET tool for comprehensive chromatin interaction analysis with paired-end tag sequencing

Genome Biology
11222010* Correspondence:
ChIA-PET ToolChIA-PET Tool can be used to process long-range chromatin interaction data. Results are visualized on a user-friendly genome browser.AbstractChromatin interaction analysis with paired-end tag sequencing (ChIA-PET) is a new technology to study genome-wide long-range chromatin interactions bound by protein factors. Here we present ChIA-PET Tool, a software package for automatic processing of ChIA-PET sequence data, including linker filtering, mapping tags to reference genomes, identifying protein binding sites and chromatin interactions, and displaying the results on a graphical genome browser. ChIA-PET Tool is fast, accurate, comprehensive, user-friendly, and open source (available at http://chiapet.gis.a-

Rationale

Transcription factors and their three-dimensional interactions are crucial to gene regulation [1,2]. Many distal transcription factor binding sites have been identified by genome-wide chromatin experiments, such as chromatin immunoprecipitation (ChIP)-chip [3], ChIP-paired-end tag (PET) [4], and ChIP-Seq [5], but it is not clear which of these distal transcription factor binding sites are real and functional in gene regulation, and which are non-functional 'parking spots'. Three-dimensional chromatin interactions have been shown to bring distal transcription factor binding sites into close spatial proximity to gene promoters [6], but global analysis of three-dimensional chromatin interactions has been limited by the lack of techniques for high-resolution and whole-genome analysis.

Recently, we developed a global, de novo, high-throughput method, Chromatin interaction analysis with paired-end tag sequencing (ChIA-PET), for characterizing the threedimensional structures of long-range chromatin interactions in the nucleus [7][8][9], which makes it possible to identify transcriptional binding sites involved in long-range interactions at a genome-wide level. The key features in ChIA-PET analysis (Figure 1a) are that the cross-linked chromatin interaction nodes bound by protein factors are enriched by ChIP, and remote DNA elements tethered together in close spatial distance in these chromatin interaction nodes are connected through proximity ligation with oligonucleotide DNA linkers. We designed linker sequences that not only contain MmeI restriction sites for PET extraction, but also include specific nucleotide barcodes to assess the noise level in ChIA-PET data from random ligation. Upon MmeI digestion, the resulting PET construct contains a 20 bp head tag, a 38 bp linker sequence, and a 20 bp tail tag, which is the template for next generation paired-end sequencing, for example, Illumina paired-end sequencing from the two ends in opposite directions (Figure 1b). Each of the paired sequencing reads uncovers the 20 nucleotide tag sequence and the 16 nucleotide sequence from the attached linker sequence including the nucleotide barcodes. When PETs are mapped to the corresponding reference genome sequences, the genomic distance between the two mapped tags will reveal whether a PET is derived from a self-ligation product of a single DNA fragment (short genomic distance) or an inter-ligation product of two DNA fragments (long genomic distance, or inter-chromosomal) (Figure 1c). The overlapping ChIP fragments inferred by PET The ChIA-PET constructs prepared for sequencing analysis. Each PET construct involves a pair of tags (20 bp each) and a linker (38 bp) between the tag pairs. This full-length linker is derived from ligation of two half-linkers, A or B, each with a unique barcode nucleotide (CG for half-linker A and AT for half-linker B). The barcode nucleotides are highlighted as red letters. Linkers with AB barcodes are considered to be non-specific chimeric proximity ligation products. (c) Mapping tags of PET sequences to reference genome. The categories of 'self-ligation PETs' and 'inter-ligation PETs' were assigned. (d) Clustering of overlapping PET sequences in the same genomic regions to identify enriched protein binding sites by overlapping 'self-ligation PETs' and long-range chromatin interactions by overlapping 'inter-ligation PETs'. Intra chromosomal inter ligation PET cluster sequences will reveal true binding sites and long-range chromatin interactions bound by such protein factors, whereas the singletons mostly reflect the random background noise (Figure 1d).

Inter chromosomal inter ligation PET cluster

The ChIA-PET approach is very efficient in generating large volumes of PET sequence data for long-range chromatin interactions with different protein factors in complex genomes. Since the detection of long-range chromatin interactions involves high levels of background noise due to the complexity of chromatin structures in nuclear space and the nature of proximity ligation [7,8], a meaningful analysis requires a comprehensive, efficient pipeline. The immense challenges in the setup of an efficient pipeline to process the huge body of ChIA-PET sequence data include: how to accurately filter the linker sequences from the raw reads; how to accurately and efficiently map the tag sequences to reference genomes; how to evaluate the noise level in the data; how to identify bona fide binding sites and chromatin interactions; how to organize the datasets; and how to effectively visualize the long-range chromatin interactions identified by ChIA-PET analysis. Many of the bioinformatics challenges faced in the ChIA-PET analysis are unprecedented.

In developing the ChIA-PET data analysis algorithms, we assembled a package of sophisticated bioinformatics solutions called 'ChIA-PET Tool' for processing, analyzing, visualizing, and managing ChIA-PET data quickly, accurately, and automatically. In this report, we describe the design and implementation of ChIA-PET Tool, and demonstrate its efficiency and effectiveness through processing and analyzing an estrogen receptor α (ERα) ChIA-PET library dataset from the MCF-7 cell-line.

The architecture design of ChIA-PET Tool

The architecture of ChIA-PET Tool includes six modules: Linker filtering, PET mapping, PET classification, Binding site calling, Chromatin interaction calling, and ChIA-PET visualization ( Figure 2). First, in the Linker filtering module, the linkers from the input sequence reads are determined, and the PETs are sorted by the presence of readable linker barcodes. The PETs without readable linker barcodes are assigned as 'ambiguous PETs', whereas the PETs with readable barcodes are further assigned into chimeric PETs if they have heterogeneous linker barcode compositions (AB linker) or non-chimeric PETs if they have homogenous barcode compositions (AA or BB) ( Figure 1b). Next, in the PET mapping module, the PET sequences are mapped to the corresponding reference genome. The mapped PETs are then classified based on the genomic spans of the two tag mapping locations into 'self-ligation PETs' (short genomic spans) and 'inter-ligation PETs' (long genomic spans or inter-chromosomal). The self-ligation PETs are used for calling putative binding sites, and the inter-ligation PETs are used for chromatin interaction analysis. The processed results are uploaded to a mySQL database for organization and visualization in ChIA-PET browser and the generic genome browser (G-browser) [10].

To demonstrate the analysis procedure of ChIA-PET Tool, we used a real ChIA-PET library, IHH015A, for illustration. IHH015A is a part of the datasets of an ERα ChIA-PET study reported previously [9]. This ChIA-PET library consists of 13,866,893 PETs generated by Illumina GAII paired-end sequencing, and was separated into chimeric PETs (IHH015C) and non-chimeric PETs (IHH015M) through the linker filtering procedure described below. The analysis results are summarized in Table 1 and the remain-   ing data analysis flow is mainly illustrated with the non-chimeric library IHH015M ( Figure 3).

Linker filtering

A ChIA-PET input sequence has a pair of reads from the two opposite directions of the same ChIA-PET template. Both reads are 36 nucleotides long, and contain 20-nucleotide tag information and 16-nucleotide linker information ( Figure 1b). To determine the linker type, we aligned the linker part of the reads (the last 16 nucleotide sequence of the 36 nucleotides) to the half-linker nucleotide sequence A (or B). If both the paired reads have good alignment with half-linker A (or B) and the specific nucleotide positions 9 and 10 (the nucleotide barcode) are CG (or AT), we classify the PET as having a homogenous full linker composition with AA (or BB). If one read in a PET has a good alignment  PET overlap clustering PET overlap clustering with the half-linker A while the other one has a good alignment with the half-linker B, we classify this PET as having a heterogeneous full linker composition with AB (or BA). If there is no good alignment with any of the half-linkers (could also indicate low sequence quality), the PETs are classified as ambiguous PETs and will be discarded from further analyses. A PET sequence with a full linker AB indicates that this PET is derived from a ligation product formed between two different ChIP complexes from the two separate half-linker aliquots ( Figure 1a). Therefore, the corresponding PETs with linker composition AB are most likely derived from random and non-specific ligations between two different ChIP complexes. Hence, we classified the PETs with linker AB as the chimeric PET dataset, and the PETs with linkers AA and BB as non-chimeric PET dataset (note that the PETs with linkers AA and BB may still have certain chimeric PETs). After the linker alignment, the linker parts in the short sequence reads will be trimmed and the tag sequences will proceed for further analyses.

With linker filtering, ChIA-PET library IHH015A data were divided into two pools: IHH015M (mix of PETs with AA and BB linkers) and IHH015C (chimeric PETs with AB linkers). The IHH015M dataset has 4,269,610 PETs (30.8% of total input PET sequences) and IHH015C has 6,157,038 PETs (44.4%). The remaining PETs were classified as PETs with ambiguous linkers.

PET mapping to reference genome

After linker trimming, the tags are mapped to the corresponding reference genome using the Batman package (C Tennakoon et al., manuscript submitted) with at most one mismatch. Batman is a Burrows-Wheeler-transform-based method [11] that maps short sequences to a genome with very high speed. For each tag, Batman first considers the exact matches to the reference genome. If a single exact match is obtained, that location is taken as the mapping location of the tag, and the tag is classified as 'unique mapping'. If multiple exact matches are obtained, the tag is classified as 'multiple mappings'. If no exact match is obtained for the tag, a mapping is done with one mismatch allowed, and the result is similarly labeled as 'unique mapping' or 'multiple mappings'. If there is still no mapping for a tag with one mismatch, the tag is finally classified as 'non-mappable'.

After mapping the tags to the human reference genome (hg18) with Batman, only those PETs from IHH015M and IHH015C with both tags uniquely mapped to the reference

PET classification

The ChIA-PET sequences can be classified into two categories: self-ligation and inter-ligation PETs (Figure 1c). 'Selfligation PETs' are obtained from self-circularization ligation of the same chromatin fragments, and result in ChIA-PET sequences with both tags mapped within a short genomic distance of each other on the same chromosome in a head-to-tail orientation. 'Inter-ligation PETs' are derived from inter-ligation between two different DNA fragments, and can be partitioned into three different sub-categories: 'inter-chromosomal inter-ligation PETs', 'intra-chromosomal inter-ligation PETs', and 'different-orientation ligation PETs'. 'Inter-chromosomal inter-ligation PETs' are PETs with two tags mapped to two different chromosomes. 'Intra-chromosomal inter-ligation PETs' are PETs with both tags mapped to the same chromosome with a long genomic span, since PETs with long genomic span cannot arise from individual short chromatin fragments. 'Different-orientation ligation PETs' are PETs with both tags mapped to the same chromosome within a short genomic span, but with the wrong orientation or on different strands.

To determine the span cutoff between self-ligation and intra-chromosomal inter-ligation PETs, we plotted the genomic spans of the PETs mapped on the same chromosomes of the IHH015A dataset. The histogram shows that the vast majority of these PETs do not have genomic span over 2 kb (Figure 4a). Similarly, using log-log plot analysis of the same data (logarithm frequency against the logarithm span), we observed a mixture model with two straight distribution lines (Figure 4b), clearly representing two distinctive PET populations. The chromatin DNA was randomly chopped into short fragments (represented by self-ligation PETs) by sonication, which can be modeled by a 'stickbreaking process' [12] for breaking long chromatin fibers. Our analysis and simulation suggest that the size distribution of the chromatin DNA fragments represented by selfligation PETs follows a power-law distribution, which is a straight line in a log-log plot and corresponds to the left line in Figure 4b. By contrast, the right line in Figure 4b represents a PET population clearly different from the self-liga-tion PETs, which follows another power-law distribution and is an approximation for the intra-chromosomal chromatin interaction model as suggested by Dekker et al. [13]. Therefore, the span cutoff between self-ligations and intrachromosomal inter-ligations can be determined by the intersection of the two lines in the log-log plot. In our analysis for the IHH015M library data, the span cutoff called by our method is 4,595 bp. This estimation for DNA fragment size is consistent with agarose gel electrophoresis of the chromatin DNA sonication profile (Figure 4c) in the ChIA-PET protocol. The agarose gel result in Figure 4c clearly shows that most DNA fragments are below the 1,650 bp mark and it is hard to see any DNA smear above 5,000 bp.

Accordingly, the PET datasets in IHH015M and IHH015C were classified into different categories. In IHH015M, 491,750 PETs were classified as self-ligation PETs. By contrast, IHH015C only yielded 12,155 self-ligation PETs. IHH015M contains 91,519 intra-chromosomal inter-ligation PETs and 893,213 inter-chromosomal interligation PETs. IHH015C contains 94,743 intra-chromosomal inter-ligation PETs and 1,602,889 inter-chromosomal inter-ligation PETs. We note that the number of self-ligation PETs in IHH015M (with homogenous linker AA and BB; non-chimeric) is more than 40 times (491,750/12,155 = 40.5) that of the self-ligation PETs in IHH015C (with heterogeneous linker AB; chimeric). This is expected because self-ligations of the same DNA fragments are supposed to have the same linker types based on the experimental design (Figure 1a, b).

To check whether the inter-ligation PETs in IHH015M and IHH015C arise due to random ligation, the ratio of the intra-chromosomal inter-ligation PETs and the inter-chromosomal inter-ligation PETs was analyzed with a random model. From the PET datasets IHH015M and IHH015C, the numbers of DNA fragments from each chromosome were counted. Assuming that the ligation of the fragments occurs in a random manner and one fragment has an equal chance of ligating to any other fragments, the expected rate of finding an intra-chromosomal inter-ligation PET in a specific chromosome is proportional to the square of the number of DNA fragments in this chromosome. The total rate of intra-chromosomal inter-ligation PETs is proportional to the sum of the square of the numbers of DNA fragments over all individual chromosomes. Therefore, based on a random model, the expected rate of intra-chromosomal inter-ligation PETs is 0.0552 for IHH015C and 0.0546 for IHH015M. By contrast, the observed rate was 0.0558 for IHH015C and 0.0929 for IHH015M. The fold change between the observed rate and the expected rate for IHH015C (0.0558/0.0552 = 1.01; P-value 5.2E-4 from binomial test) was insignificant, validating the notion that the chimeric inter-ligation PETs are derived from random ligation. By contrast, the difference between the observed rate and the expected rate for IHH015M (0.0929/0.0546 = 1.70; P-value < 2.97E-323) was very significant, suggesting that the non-chimeric inter-ligation PETs are not random and probably enriched for specific chromatin interactions.

To visually illustrate the differences between the ChIA-PET libraries IHH015M and IHH015C, we represented every PET in these two datasets as a point (x, y) on a twodimensional map where x and y axes are the genome loci of the two tags in a PET. Figure 5. shows the density map of all the PETs in the two ChIA-PET libraries, by normalizing the maximum density to 1. The darker the rectangle, the higher is the PET density between two corresponding chromosomes. For the chimeric PET dataset (IHH015C), there was no particular distribution pattern of PET density. By contrast, for the non-chimeric PET dataset (IHH015M), the PET density of the intra-chromosomal inter-ligations was much higher than that of the inter-chromosomal inter-ligations, suggesting that most potential chromatin interactions detected by ChIA-PET are intra-chromosomal, not interchromosomal.

In summary, our analyses showed that non-chimeric PET dataset IHH015M is significantly different from the random model and the data can be used for further analyses.

Binding site analysis

Binding sites can be identified by looking for clusters of overlapping self-ligation PETs. As background noise is random, noisy PETs should distribute randomly throughout the genome. Only ChIP-enriched fragments would form clus-ters of overlapping self-ligation PETs, and are considered putative binding sites (Figure 1d). The probability that a cluster contains k self-ligation PETs by random chance can be calculated by a Monte Carlo simulation, allowing false discovery rates to be assigned to different clusters. The clusters with false discovery rates below a particular threshold, such as 0.01, are putative binding sites. A similar method was previously applied in ChIP-PET data analysis [4]. In considering the ChIP enrichment score of a binding site, the number of ChIP fragments at a binding site is counted. This is similar to the existing ChIP enrichment calculation protocols in ChIP-Seq data [5].

After predicting binding sites, a post-processing step can be applied to remove suspicious binding sites, which can arise from different sources. For example, a library from female cells, such as MCF-7, is not expected to have any binding sites in chromosome Y. Binding sites in satellite repeat regions are also likely to be attributable to non-specific mapping and should be dropped. Further, certain binding sites in cancer genomes may be the results of genome amplifications [8,14], and should be flagged, such that caution can be exercised in using them.

In our analysis, after calling putative binding sites and removing binding sites likely to be due to non-specific mapping, 4,035 binding sites were called from the IHH015M dataset at a false discovery rate <0.01, which covered 47,735 (9.7%) of the 491,750 self-ligation PETs in IHH015M. By contrast, only 24 binding sites were called from the chimeric PET dataset IHH015C at the same false Indeed, many of the ERα binding sites identified in IHH015 library were validated using ChIP-qPCR [9].

Chromatin interaction analysis

Chromatin interaction identification is done in two steps: first, define the ChIP enriched interaction anchor regions from inter-ligation PETs and then quantify the interaction frequency by counting the number of inter-ligation PETs between the two connected anchor regions. The identification of ChIP-enriched interaction anchors from inter-ligation PETs is performed by finding peaks and valleys from the overlapped tags from the inter-ligation PETs, in a similar manner employed by ChIP-Seq [15]. The tag length of each inter-ligation PET is extended in a 5' to 3' manner by the 'tag extension length' to represent a ChIP DNA fragment. The 'tag extension length' is equivalent to the most frequently detected span of the self-ligation PETs, which is around 200 bp for the IHH015A library. Most of the interaction anchor regions identified from inter-ligation PETs should also be overlapped with the protein binding sites identified from self-ligation PETs. After defining the enriched anchor regions from inter-ligation PETs, the number of overlapping inter-ligation PETs between any two anchors is counted to reflect the relative interaction frequency. As each interaction involves two anchors and one loop, it is called a 'duplex interaction'. Similar to the binding site analysis, a real interaction is expected to involve multiple overlapping inter-ligation PETs connecting two anchors.

To determine if an interaction PET cluster between two anchors is a real chromatin interaction and not by random chance, a simple method is to count the number of interligation PETs in the interaction cluster. If the cluster has a higher PET count, it has a higher probability to be a real chromatin interaction. This model, however, does not take into account the fact that, when the ChIP enrichments of two anchors are high, there is also a higher probability to form more inter-ligation PETs by random chance between these two anchors. To address such concerns, we formulated a statistical analysis framework to account for the random formation of any inter-ligation PETs between two anchors. The null hypothesis assumes that, in the ChIPenriched chromatin fragment population, each chromatin fragment has an equal chance to ligate to any other fragments in a random manner, and the interactions between different anchors are independent of each other. Under this random model, the number of inter-ligation PETs that link two anchors follows a hyper-geometric distribution. The formula is provided in Equation 1: The expected interaction frequencies between any two genomic loci and the false discovery rate for each interaction were calculated. Hence, both inter-ligation PET frequency and ChIP enrichment of the anchors are taken into account by this analysis.
Pr( | , , ) , , , I N c c c A I A B N c A c B I A B N c B A B A B = ⎛ ⎝ ⎜ ⎜ ⎞ ⎠ ⎟ ⎟ − − ⎛ ⎝ ⎜ ⎜ ⎞ ⎠ ⎟ ⎟ ⎛ ⎝ ⎜ 2 2 ⎞ ⎞ ⎠ ⎟(1)
Equation 1 considers a library with N inter-ligation PETs. R A and R B represent two anchor regions with c A and c B PETs, respectively, where c A , c B <<N. Equation 1 shows that, when c B ends are randomly chosen from 2N ends as ends in region R B , what is the probability of choosing I A, B ends from c A ends of region R A to form I A, B interactions between region R A and region R B . By this, we are able to compute a P-value to test if I A, B , the number of inter-ligation PETs between R A and R B , is over-represented. Given a cut-off threshold, T, of hypergeometric P-value, we are able to calculate the false discovery rate, which is the fraction of the clusters with P-value below T under the empirical random model generated by randomly permuting the ends of PETs. As c A and c B reflect the ChIP enrichment of two DNA anchors, any ChIP enrichment bias is accounted for by this model. An illustration of the random model is shown in Figure 6.

From the predicted interactions with three or more interligation PETs between anchors and false discovery rate < 0.05, we filtered the interactions that could be a result of mis-mapping or noise, as we did with the binding sites. We filtered out any interactions wherein the anchors were present in chromosome Y or the mitochondria, and satellite repeat regions. We also filtered out a specific kind of noise in the interaction clusters from genome structural variations in cancer cell-lines. The cancer cell-lines like MCF-7 have intensive genome rearrangements: genome insertion, deletion, inversion, and translocation, which can be identified with DNA-PET data [8,14]. Self-ligation PETs around the breakpoints of genome rearrangement from cancer celllines will be mapped as inter-ligation PETs in the reference genome, and interaction clusters related to such genome rearrangements should be removed.

Using the above analysis procedure and a threshold of 3 or more for the number of overlapping inter-ligation PETs, we identified 1,945 putative intra-chromosomal interactions and 12 putative inter-chromosomal interactions in the IHH015M dataset. Our validations, including 3C [13], ChIP-3C [16], and 4C [17], as well as fluorescent in situ hybridization (FISH) and small interfering RNA experiments, suggest that the majority of the intra-chromosomal chromatin interactions identified in this analysis are bona fide loci bound by ERα as reported in Fullwood et al. [9]. By contrast, the chimeric inter-ligation PETs in IHH015C yielded only 12 intra-chromosomal and zero inter-chromosomal inter-ligation PET clusters. Detailed manual curation verified that none of them constitute real interactions. Our comparison of a non-chimeric ChIA-PET dataset (IHH015M) with a similarly sized chimeric ChIA-PET dataset also indicates that the non-chimeric dataset generates statistically significant binding sites and interactions, whereas the chimeric dataset does not. This means that chimerism is not an issue in obtaining bona fide binding sites and chromatin interactions in the ChIA-PET library. As an example, abundant non-chimeric inter-ligation PETs in the IHH015M dataset identified the ERα-bound chromatin interaction event at the KRT8/18 locus in the human genome (Figure 7.), but no chimeric PETs in the IHH015C dataset were found at the KRT8/18 locus. This interaction at the KRT8/18 locus has been validated by 4C experiments [9].

ChIA-PET data visualization

All ChIA-PET data, including the called binding sites and chromatin interaction clusters, are uploaded to a mySQL database. A centralized ChIA-PET browser is built to organize data reporting and visualization. The ChIA-PET browser consists of two components: a tabular browser and a graphic genome browser. The tabular browser provides an organization of all ChIA-PET experimental datasets (libraries) from a variety of projects. It reports the unique PETs with unique mapping, the binding sites and the interaction clusters in tabular forms, and the users can download the data for further analysis. Example screenshots of binding sites, interaction clusters and the whole genome interactions from IHH015M are provided in Figures 8, 9 and 10. The graphical genome browser (G-browser) is created by adopting the 'generic genome browser system' [10], which allows users to view and manually curate the data. A screenshot of the G-browser is shown in Figure 7 More details of the browsers can be found from the ChIA-PET website [18] (username, guest; password, guest).

Implementation and performance

ChIA-PET Tool is implemented with various software written in C, Java and scripting languages (PERL and Python), and has been tested with the Linux operating system. The  components in the pipeline are linked up together to behave as a singular processing pipeline. The pipeline requires hardware support; for example, 32 Gbytes RAM and 1,024 Gigabytes hard disk are recommended on a duo-core server. mySQL (5.0.67 or higher) is used as the database engine. Other required software packages are Apache, Perl and Bioperl modules, and PHP (refer to the ChIA-PET Tool installation guide on the ChIA-PET website [18] for details).

We have tested the ChIA-PET Tool pipeline with two hardware configurations: one with 32 Gbytes RAM and 8 CPUs, and another with 64 Gbytes RAM and 16 CPUs. The input to the pipeline is the original paired-end sequence data from the Illumina sequencing output file. A ChIA-PET library from a single lane requires approximately 1 Gigabyte space for storage, which includes intermediate files generated throughout the processing. On average, it takes 1 hour for ChIA-PET Tool to process a single lane ChIA-PET dataset and 1 hour to upload the GFF file to the G-browser database (the upload time is subject to existing database size and server load).

Conclusions

We developed a comprehensive computational package, ChIA-PET Tool, to accommodate large amounts of ChIA-PET data, process the linkers, map the short tags to the reference genomes, classify the PETs into different categories, and identify statistically significant binding sites and chromatin interactions. We demonstrate the effectiveness of ChIA-PET Tool by analyzing a ChIA-PET library IHH015A with statistical results, and show that ChIA-PET Tool is a convenient, user-friendly, accurate bioinformatics solution, and an integral component of the ChIA-PET process for chromatin interaction analysis. Although we have only reported the use of ChIA-PET Tool in ERα ChIA-PET analysis, it is obvious that this tool can be applied to different ChIA-PET libraries bound by different transcription factors in different genomes for chromatin interaction analysis.

Availability

ChIA-PET Tool is open-source and free for non-commercial use. The complete package of ChIA-PET Tool is downloadable from the ChIA-PET website [18], together with

Figure 1
1Schematic of ChIA-PET analysis. (a) The ChIA-PET experimental protocol, which includes chromatin preparation, ChIP, linker ligation, proximity ligation, MmeI restriction digestion, and DNA sequencing. (b)

Figure 2
2Architecture of ChIA-PET Tool. The six main modules in ChIA-PET Tool (labeled) connect the input sequence data, the intermediate data, and the final results. The details of the processing are referred to in the main text.

BatMan

Figure 3
3ChIA-PET data analysis flow of library IHH015A. A library, IHH015A, is used to demonstrate the ChIA-PET Tool data analysis flow, and the non-chimeric data IHH015M from IHH015A is used to show the analysis results.

Figure 4
4Span distribution of intra-chromosomal PETs and the cutoff between self-ligation PETs and inter-ligation PETs. (a) The distribution of the intra-chromosomal PET genomic spans. (b) The log-log plot of interaction frequencies against PET span, and the cutoff between self-ligation PETs and inter-ligation PETs. (c) Agarose gel of chromatin DNA fragments prepared for ChIA-PET analysis. Selected DNA sizes are marked. rate, which covered only 130 (1.1%) of 12,155 self-ligation PETs in IHH015C. This indicates that most of the binding sites of IHH015M are bona fide.

Figure 5
5Genome-wide ChIA-PET interaction density plots from the IHH015M and IHH015C PET datasets The square density plot in each graph is normalized to [0, 1]. Darker squares indicate higher interaction densities.

Figure 6
6An illustration of the statistical model for probability analysis of ChIA-PET interactions. R A and R B represent two DNA regions ('anchors') with c A and c B PETs (here 9 and 7, respectively). I A, B is the number of inter-ligation PETs between R A and R B and here I A, B is equal to 5.

Figure 7 Figure 8
78An example of ChIA-PET mapping display in G-browser. A screenshot of the ChIA-PET G-browser is shown in human chromosome 12 at the keratin gene family region. The tracks are (from top to bottom): UCSC known genes track, which shows the KRT8 and KRT18 genes in blue and green; density histogram of IHH015M self-ligation PETs, which consists of AA and BB PET sequences, wherein peaks indicate enriched ERα-binding sites; intra-chromosomal inter-ligation PETs of IHH015M, wherein multiple overlapping inter-ligation PETs indicate interactions and singleton interligation PETs indicate noise; density histogram of IHH015C self-ligation PETs that consists of only chimeric (AB) PET sequences; intra-chromosomal inter-ligation PET tracks of IHH015C. From the graph, we know that IHH015M contains binding sites and interactions, and IHH015C does not, although both libraries have similar sequencing depth. Screen shot of binding site table view in ChIA-PET browser.

Figure 9
9Screen shot of interaction cluster table view in ChIA-PET browser.the ChIA-PET Tool file format, the ChIA-PET Tool installation guide, the ChIA-PET Tool user manual, and the ChIA-PET browser user manual. The raw sequences and the processed data are also available from ChIA-PET website[18] (username, guest; password, guest). More related data for ChIA-PET analysis are accessible from NCBI's Gene Expression Omnibus with accession number [GEO:GSE18046]. Abbreviations bp: base pair; ChIP: chromatin immunoprecipitation; ChIA-PET: chromatin interaction analysis with paired-end tag sequencing; ERα: estrogen receptor α; G-browser: generic genome browser; PET: paired-end tag.

Figure 10
10Screen shot of the whole genome interaction view in ChIA-PET browser.

Table 1 :
1Statistics of ChIA-PET data analysisThe original ChIA-PET dataset without linker filtering. b The dataset containing non-chimeric linker compositions AA and BB. c The dataset containing chimeric linker composition AB, considered as non-specific proximity ligation noise. d The binding sites and interactions after filtering those from chromosome Y, the mitochondria, and satellite repeat regions, and interactions from genome structural variations in the MCF-7 cell-line. FDR, false discovery rate. genome were considered for further analyses. The remaining PETs with tags multiply mapped or unmapped to the reference genome were filtered out. We obtained 2,016,907 PETs in IHH015M and 2,707,860 PETs in IHH015C with unique mappings. To further avoid miscalling of clonal amplifications by PCR involved in sample preparation as enrichment of ChIP fragments, we merged all similarly mapped PETs (within ± 1 bp) into one unique PET. In this way, we reduced false positive calls resulting from the sameIHH015A a
IHH015M b
IHH015C c

Total PETs
13,866,893
4,269,610 (30.8% of IHH015A)
6,157,038 (44.4% of IHH015A)

Unique PETs with unique
mapping

3,826,699
1,501,442
1,710,335

Self-ligation PETs
611,622
491,750
12,155

Different orientation PETs
31,543
24,960
548

Intra-chromosomal inter-
ligation PETs (excludes
different orientation PETs)

223,170
91,519
94,743

Inter-chromosomal inter-
ligation PETs

2,960,364
893,213
1,602,889

Binding sites (FDR <0.01,
filtered d )

5223
4,035
24

Intra-chromosomal
interactions (FDR <0.05; PET
cluster size ≥ 3; filtered d )

2221
1,945
12

Inter-chromosomal
interactions (FDR <0.05; PET
cluster size ≥ 3; filtered d )

18
12
0

a
AcknowledgementsThe authors thank the anonymous reviewers for their valuable comments. We acknowledge the Genome Technology and Biology Group at the Genome Institute of Singapore for technical support; Mr Atif Shahab and Mr Chan Chee Seng for computing support. GL is supported by a GIS post-doctoral fellowship. MJF is supported by an A*STAR scholarship and a L'Oreal-UNESCO For Women In Science National Fellowship. YR and C-L W are supported by A*STAR of Singapore and NIH ENCODE grants (R01 HG004456-01, R01HG003521-01, and part of 1U54HG004557-01).Author DetailsAuthors' contributions WKS, GL, MJF and YR conceived the project and wrote the paper. GL and HX designed the algorithm and performed the research. GL, HX, FHM, SV, YBM, HSO, CT and PNA implemented the software. GL, FHM, MJF, YBM and SV tested the pipeline. All authors read and approved the final manuscripts.
Insights from genomic profiling of transcription factors. P J Farnham, Nat Rev Genet. 10Farnham PJ: Insights from genomic profiling of transcription factors. Nat Rev Genet 2009, 10:605-616.

Dynamic genome architecture in the nuclear space: regulation of gene expression in three dimensions. C Lanctot, T Cheutin, M Cremer, G Cavalli, T Cremer, Nat Rev Genet. 8Lanctot C, Cheutin T, Cremer M, Cavalli G, Cremer T: Dynamic genome architecture in the nuclear space: regulation of gene expression in three dimensions. Nat Rev Genet 2007, 8:104-115.

Genome-wide location and function of DNA binding proteins. B Ren, F Robert, J J Wyrick, O Aparicio, E G Jennings, I Simon, J Zeitlinger, J Schreiber, N Hannett, E Kanin, T L Volkert, C J Wilson, S P Bell, R A Young, Science. 290Ren B, Robert F, Wyrick JJ, Aparicio O, Jennings EG, Simon I, Zeitlinger J, Schreiber J, Hannett N, Kanin E, Volkert TL, Wilson CJ, Bell SP, Young RA: Genome-wide location and function of DNA binding proteins. Science 2000, 290:2306-2309.

A global map of p53 transcription-factor binding sites in the human genome. C L Wei, Q Wu, V B Vega, K P Chiu, P Ng, T Zhang, A Shahab, H C Yong, Y Fu, Z Weng, J Liu, X D Zhao, J L Chew, Y L Lee, V A Kuznetsov, W K Sung, L D Miller, B Lim, E T Liu, Q Yu, H H Ng, Y Ruan, Cell. 124Wei CL, Wu Q, Vega VB, Chiu KP, Ng P, Zhang T, Shahab A, Yong HC, Fu Y, Weng Z, Liu J, Zhao XD, Chew JL, Lee YL, Kuznetsov VA, Sung WK, Miller LD, Lim B, Liu ET, Yu Q, Ng HH, Ruan Y: A global map of p53 transcription-factor binding sites in the human genome. Cell 2006, 124:207-219.

Genome-wide mapping of in vivo protein-DNA interactions. D S Johnson, A Mortazavi, R M Myers, B Wold, Science. 316Johnson DS, Mortazavi A, Myers RM, Wold B: Genome-wide mapping of in vivo protein-DNA interactions. Science 2007, 316:1497-1502.

Chromosome-wide mapping of estrogen receptor binding reveals long-range regulation requiring the forkhead protein FoxA1. J S Carroll, X S Liu, A S Brodsky, W Li, C A Meyer, A J Szary, J Eeckhoute, W Shao, E V Hestermann, T R Geistlinger, E A Fox, P A Silver, M Brown, Cell. 122Carroll JS, Liu XS, Brodsky AS, Li W, Meyer CA, Szary AJ, Eeckhoute J, Shao W, Hestermann EV, Geistlinger TR, Fox EA, Silver PA, Brown M: Chromosome-wide mapping of estrogen receptor binding reveals long-range regulation requiring the forkhead protein FoxA1. Cell 2005, 122:33-43.

ChIP-based methods for the identification of longrange chromatin interactions. M J Fullwood, Y Ruan, J Cell Biochem. 107Fullwood MJ, Ruan Y: ChIP-based methods for the identification of long- range chromatin interactions. J Cell Biochem 2009, 107:30-39.

Next-generation DNA sequencing of paired-end tags (PET) for transcriptome and genome analyses. M J Fullwood, C L Wei, E T Liu, Y Ruan, Fullwood MJ, Wei CL, Liu ET, Ruan Y: Next-generation DNA sequencing of paired-end tags (PET) for transcriptome and genome analyses.

. Genome Res. 19Genome Res 2009, 19:521-532.

An oestrogenreceptor-alpha-bound human chromatin interactome. M J Fullwood, M H Liu, Y F Pan, J Liu, H Xu, Y B Mohamed, Y L Orlov, S Velkov, A Ho, P H Mei, E G Chew, P Y Huang, W J Welboren, Y Han, H S Ooi, P N Ariyaratne, V B Vega, Y Luo, P Y Tan, P Y Choy, K D Wansa, B Zhao, K S Lim, S C Leow, J S Yow, R Joseph, H Li, K V Desai, J S Thomsen, Y K Lee, Nature. 462Fullwood MJ, Liu MH, Pan YF, Liu J, Xu H, Mohamed YB, Orlov YL, Velkov S, Ho A, Mei PH, Chew EG, Huang PY, Welboren WJ, Han Y, Ooi HS, Ariyaratne PN, Vega VB, Luo Y, Tan PY, Choy PY, Wansa KD, Zhao B, Lim KS, Leow SC, Yow JS, Joseph R, Li H, Desai KV, Thomsen JS, Lee YK, et al.: An oestrogen- receptor-alpha-bound human chromatin interactome. Nature 2009, 462:58-64.

The generic genome browser: a building block for a model organism system database. L D Stein, C Mungall, S Shu, M Caudy, M Mangone, A Day, E Nickerson, J E Stajich, T W Harris, A Arva, S Lewis, Genome Res. 12Stein LD, Mungall C, Shu S, Caudy M, Mangone M, Day A, Nickerson E, Stajich JE, Harris TW, Arva A, Lewis S: The generic genome browser: a building block for a model organism system database. Genome Res 2002, 12:1599-1610.

A Block Sorting Lossless Data Compression Algorithm. M Burrows, D Wheeler, 124Digital Equipment CorporationPalo Alto, CATechnical ReportBurrows M, Wheeler D: A Block Sorting Lossless Data Compression Algorithm. Technical Report 124 Palo Alto, CA: Digital Equipment Corporation; 1994.

Gibbs sampling methods for stick-breaking priors. H Ishwaran, L F James, J Am Stat Assoc. 96Ishwaran H, James LF: Gibbs sampling methods for stick-breaking priors. J Am Stat Assoc 2001, 96:161-173.

Capturing chromosome conformation. J Dekker, K Rippe, M Dekker, N Kleckner, Science. 295Dekker J, Rippe K, Dekker M, Kleckner N: Capturing chromosome conformation. Science 2002, 295:1306-1311.

Paired-end mapping reveals extensive structural variation in the human genome. J O Korbel, A E Urban, J P Affourtit, B Godwin, F Grubert, J F Simons, P M Kim, D Palejev, N J Carriero, L Du, B E Taillon, Z Chen, A Tanzer, A C Saunders, Chi J Yang, F Carter, N P Hurles, M E Weissman, S M Harkins, T T Gerstein, M B Egholm, M Snyder, M , Science. 318Korbel JO, Urban AE, Affourtit JP, Godwin B, Grubert F, Simons JF, Kim PM, Palejev D, Carriero NJ, Du L, Taillon BE, Chen Z, Tanzer A, Saunders AC, Chi J, Yang F, Carter NP, Hurles ME, Weissman SM, Harkins TT, Gerstein MB, Egholm M, Snyder M: Paired-end mapping reveals extensive structural variation in the human genome. Science 2007, 318:420-426.

Integration of external signaling pathways with the core transcriptional network in embryonic stem cells. X Chen, H Xu, P Yuan, F Fang, M Huss, V B Vega, E Wong, Y L Orlov, W Zhang, J Jiang, Y H Loh, H C Yeo, Z X Yeo, V Narang, K R Govindarajan, B Leong, A Shahab, Y Ruan, G Bourque, W K Sung, N D Clarke, C L Wei, H H Ng, Cell. 133Chen X, Xu H, Yuan P, Fang F, Huss M, Vega VB, Wong E, Orlov YL, Zhang W, Jiang J, Loh YH, Yeo HC, Yeo ZX, Narang V, Govindarajan KR, Leong B, Shahab A, Ruan Y, Bourque G, Sung WK, Clarke ND, Wei CL, Ng HH: Integration of external signaling pathways with the core transcriptional network in embryonic stem cells. Cell 2008, 133:1106-1117.

An evaluation of 3C-based methods to capture DNA interactions. M Simonis, J Kooren, W De Laat, Nat Methods. 4Simonis M, Kooren J, de Laat W: An evaluation of 3C-based methods to capture DNA interactions. Nat Methods 2007, 4:895-901.

Nuclear organization of active and inactive chromatin domains uncovered by chromosome conformation capture-on-chip (4C). M Simonis, P Klous, E Splinter, Y Moshkin, R Willemsen, Wit E De, B Van Steensel, W De Laat, Nat Genet. 38Simonis M, Klous P, Splinter E, Moshkin Y, Willemsen R, de Wit E, van Steensel B, de Laat W: Nuclear organization of active and inactive chromatin domains uncovered by chromosome conformation capture-on-chip (4C). Nat Genet 2006, 38:1348-1354.

Chia-Pet , 10.1186/gb-2010-11-2-r22Tool for Comprehensive Chromatin Interaction Analysis with Paired-End Tag Sequencing. ChIA-PET Tool for Comprehensive Chromatin Interaction Analysis with Paired-End Tag Sequencing [http://chiapet.gis.a-star.edu.sg] doi: 10.1186/gb-2010-11-2-r22

ChIA-PET tool for comprehensive chromatin interaction analysis with paired-end tag sequencing. Li, Genome Biology. 1122Cite this article asCite this article as: Li et al., ChIA-PET tool for comprehensive chromatin interaction analysis with paired-end tag sequencing Genome Biology 2010, 11:R22 Received: 3 September 2009 Revised: 30 December 2009 Accepted: 25 February 2010 Published: 25 February 2010


Vitamin A status of healthy children in Manisa, Turkey

Nermin Tansuğ
Muzaffer Polat
Selcan Çeşme
Fatma Taneli
Salih Gözmen
Özlem Tokuşoğlu
Dilek Yılmaz
Gönül Dinç
Vitamin A status of healthy children in Manisa, Turkey
R E S E A R C H Open Access
Background: Vitamin A deficiency is a major public health nutrition problem in the developing world. Even subclinical Vitamin A deficiency is associated with increased childhood mortality. Severe maternal vitamin A deficiency may cause increased mortality in the first months of life. There have been a limited number of studies regarding vitamin A status in Turkey. The aim of this study was to assess vitamin A status of healthy children in Manisa, Turkey. Methods: Vitamin A status of 100 healthy children aged 36-48 months is evaluated. The children were seen during routine examination. Serum retinol concentrations were measured by high-performance liquid chromatography. Duration of breast feeding, age solid foods introduced, use of supplementary vitamins, weight and height, and intake of specific groups of nutrients on a daily, weekly and monthly basis were collected from a questionnaire completed by the mothers. Height and weight z-scores were calculated according to national standards. Mothers of 20 of the 100 children were known to have normal serum and breast milk retinol concentrations. Children with normal serum retinol concentration were compared with the children with VAD. Student's t-test and Mann-Whitney test were used to compare independent variables. The Pearson correlation analysis test was used to test relation between numeric variables. Results: Mean retinol concentration was 0.98 ± 0.32 μmol/L in the whole study group. Serum retinol concentrations were normal (>0.70 μmol/L) in 89% of the children. When children with normal serum retinol concentrations were compared with those with retinol concentrations lower than 0.70 μmol/L, there was no difference in terms of age, gender, weight and height at the time of study, z-scores, birth weight, birth length, duration of breast feeding, time to begin solid food, rate of supplementary vitamin use, and rate of infections (P > 0.05). There was not any relation between vitamin A concentrations and weight and height at the time of study, z-scores, birth weight, birth length, duration of breast feeding, time to begin solid food, vitamin use, and frequency of intake of specific groups of nutrients (P > 0.05).Conclusions: This study showed that VAD is a moderate health problem in Manisa.

Background

Vitamin A is a component of retinal pigments which has an important role in vision in dim light. It especially affects young children, among whom deficiency can cause xerophthalmia and lead to blindness, limit growth, weaken host defenses, exacerbate infection and increase the risk of death [1]. Vitamin A deficiency (VAD) can extend through school age and adolescent years into adulthood. VAD in women of reproductive age may increase morbidity and mortality during pregnancy and the early postpartum period [2,3]. Severe maternal VAD may also cause increased mortality in the first months of life [4,5].

VAD is a major public health nutrition problem in the developing world. According to the World Health Organization (WHO), serum retinol concentrations are classified as normal, marginal, and deficient, ≥0.70 μmol/L, 0.35-0.70 μmol/L, and <0.35 μmol/L, respectively [6]. Public health significance of VAD is categorized as mild, moderate, and severe. Degree of severity of VAD is mild if prevalence of preschool-age children or pregnant women with marginal or deficient serum plasma concentrations is 2-10%, moderate if prevalence is 10-20%, and severe if prevalence is ≥20% [7]. WHO defines two main groups from the viewpoint of VAD: Xerophthalmia and night blindness constitute a serious public health problem in the first group of countries, such as some of those in Africa and South-east Asia. In the second group of countries, clinical signs of VAD are rarely detected but marginal VAD has been determined in 10-30% of these populations and a continuous monitoring of vitamin A status is recommended. The Pan-American Health Organization (PAHO) considers VAD as a public health problem when 15% or more of the population display serum retinol concentrations of 0.70 μmol/L [8]. Among women, two retinol concentration cut-offs are used to estimate VAD and low to deficient vitamin A status, respectively, 0.70 μmol/L and 1.05 μmol/L [6,9]. A retinol concentration in breast milk less than 1.05 μmol/L is considered to be consistent with VAD in lactating women. Prevalence <10%, ≤10 to <25%, ≥25% of breast milk retinol 1.05 μmol/L are considered to indicate mild, moderate and severe VAD, respectively as a public health problem [9,10].

Normal concentration of vitamin A in breast milk is sufficient for normal health and development for infants. Since permeability of the placenta for liposoluble vitamins is limited and plasma retinol concentration of the newborn is low, amount of hepatic vitamin A store depends on breast milk vitamin A concentration. Breast milk of a well-nourished mother is sufficient for storage of vitamin A in the liver of the infant [1,9]. Other foods such as vegetables, fruit, egg, butter, and liver are sources of vitamin A. The risk of VAD is low for healthy children that consume a balanced diet. VAD may also be due to insufficient intestinal absorption secondary to chronic intestinal diseases or low dietary fat intake [1,7].

Diarrhea and severity of measles as well as deaths from these diseases had markedly decreased by the use of supportive vitamin A in developing countries where VAD is endemic. It has been reported that VAD is associated with increased incidence of respiratory and gastrointestinal infections, and that mortality for the age group of 1-5 years can be decreased by 35% by eradication of VAD [11][12][13].

There are a limited number of studies regarding vitamin A status in Turkey. Studies on sub clinical VAD among healthy children is even less. Majority of VAD studies are on VAD in children with malnutrition, diarrhea, measles and acute respiratory infections. On the study by WHO regression based estimate of VAD among preschool aged children inTurkey is moderate (12.4%) [7]. VAD is especially important for preschool children. In a previous study in Manisa province of Turkey, retinol concentrations in breast milk and mothers' serum were found within normal range [14]. We aimed to evaluate vitamin A status of healthy children in Manisa, Turkey, in this study.

Methods

The study was conducted in Manisa, a city in the Aegean region of Turkey. A total of 100 healthy children were enrolled in the study. The children were seen at pediatrics outpatient clinics and health centers where they were followed for growth, development, and routine immunization. They were healthy children visiting the health centers routinely without any health problems. Ages of the children were ranging between 36 and 48 months. Demographic features, duration of breast feeding, age solid foods introduced, use of supplementary vitamins, weight and height, and intake of specific groups of nutrients on a daily, weekly and monthly basis and frequency of infections were collected from a questionnaire completed by the mothers. Height and weight standard deviation scores (z-scores) were calculated according to national standards [15]. Mothers of 20 of the 100 children were known to have normal serum and breast milk retinol concentrations. Blood specimens of each child were obtained and transported to the laboratory for detection of serum retinol concentrations. Samples were protected from light and heat during transportation. Serum retinol concentrations were measured by high-performance liquid chromatography (HPLC) (Chromsystems Diagnostics GmbH, München, Germany). Serum retinol concentrations were classified as normal, marginal and deficient according to WHO criteria [6].

Features, food habits, and measurements of the children with normal serum retinol concentration (non-VAD group) were compared with those of the children with VAD (VAD group).

Data were analyzed statistically by the statistical Package for Social Sciences (SPSS 11.0; SPSS Institute, Chicago, IL, USA) for Windows (Microsoft). Numerical values are given as mean ± standard deviation. Student's t-test and Mann-Whitney test were used to compare independent variables. The Pearson correlation analysis test was used to test relation between numeric variables. Significant relationships between variables were defined as P < 0.05.

The protocol for the study was approved by the ethical committee of Celal Bayar University Medical Faculty.

Results

The age of the children ranged from 36 to 48 months, with a mean of 42.7 ± 5.3 months. Fifty-nine (59%) boys, forty-one (41%) girls were enrolled in the study.

All of the children were within normal range in terms of weight and height. Mean duration of breast feeding was 13.6 months, and mean onset of supplementary food was 7.0 months. Supplementary vitamins were given to 85.2% of the children. All of the children were followed by a physician.

Mean serum retinol concentration was 0.98 ± 0.32 μmol/L. Serum retinol concentrations were normal in 89% of the children (1.06 ± 0.27 μmol/L), whereas low in 11% of them (0.47 ± 0.19 μmol/L). Serum retinol concentrations of two children were lower than 0.35 μmol/L.

The differences between the two groups in terms of age, gender, weight and height at the time of study, z-scores, birth weight, birth length, duration of breast feeding, time to begin solid food, percentage of supplementary vitamin use, and percentage of infections were not statistically significant (P > 0.05) ( Table 1). There was not any relation between vitamin A concentrations and weight and height at the time of study, z-scores, birth weight, birth length, duration of breast feeding, time to begin solid food, vitamin use, and frequency of intake of specific groups of nutrients (P > 0.05).

Discussion

VAD has received little attention since clinical signs such as xerophthalmia and night blindness are rarely seen. However, marginal deficiency may show negative effects on health and increase morbidity and mortality among preschool children even at subclinical concentrations [9]. VAD may worsen infection but vitamin A supplementation reduces the risk of death. VAD in women of reproductive age may increase morbidity and mortality during pregnancy and early postpartum period [2,3]. Severe maternal VAD may also lead to increased mortality in the infant in the first months of life [4]. In a recent study on breast milk and serum retinol concentrations in Manisa mean concentrations of retinol were found as 2.94 ± 0.31 and 1.29 ± 0.23 μmol/L in breast milk and serum, respectively, indicating that there is no VAD among lactating women in this region [14]. In another study on lactating women in İstanbul, retinol concentrations were marginal [16].

Mean serum retinol concentration was 0.98 ± 0.32 μmol/L, which is within normal limits, in this study. However, the rate of children with serum retinol concentration <0.70 μmol/L was 11%. According to data published by WHO, estimate of the prevalence of serum retinol concentration <0.70 μmol/L in preschool-age children 1995-2005 for Turkey is 12.4% which shows significance of VAD as a moderate public health problem [7].

There are a few studies regarding vitamin A status of children in Turkey. Hatun and Teziç, in their study with 80 children at the ages of 9-17 months in Ankara, reported that 30% had serum retinol concentrations <0.70 μmol/L whereas 1.3% had serum retinol concentrations <0.35 μmol/L. They concluded that VAD is an important public health problem in that population [17]. In the study by Açkurt et al, rate of marginal and deficient retinol concentrations among healthy children at the ages 7-17 years were 9.3% and 2.3%, respectively [18]. VAD is not an important concern according to the latter study but the age group of the study population was higher than the others. Kurugöl et al reported serum retinol concentrations <0.70 μmol/L in 15.6% of 6-59 month-old children in İzmir, which is a neighboring city of Manisa [19]. In another study in the same city serum retinol concentrations of 62 children who were admitted in the hospital with pneumonia but without malnutrition or chronic diseases were evaluated. Serum retinol concentrations of 6.5% of the children were 0.35-0.70 μmol/L, but none of the children had serum retinol concentration <0.35 μmol/L [20]. Tıraş et al, in their study in Ankara, reported that 35% of a group of 20 children with a mean age of 11.1 months had serum retinol concentrations <0.35 μmol/L [21].

It has been reported that there was not any relation between feeding habits and retinol concentrations in the previous study on breast milk and serum retinol concentrations in lactating women in Manisa. Similarly we did not find any relation between feeding habits of the children and their serum retinol concentrations. This may be due to insufficient consumption of vitamin A rich foods in our region. Kurugöl et al have reported a positive relation between feeding habits and serum retinol concentrations [19]. In most of the studies serum retinol concentrations were measured to determine vitamin A status [7]. However measuring serum retinol concentration has some disadvantages. Serum retinol concentration reflects the body vitamin A status only when the liver retinol stores are seriously depleted and inflammation affects serum retinol concentration. Indirect methods such as the relative dose response and modified relative dose response tests are more sensitive. The tracer dilution technique is the only method that provides a quantitative estimate of total body vitamin A pool size. However these tests can not be used widely used since they are expensive. Direct measurement of liver reserves of vitamin A status through biopsy is rarely an option and therefore has no utility globally [22]. Thus, serum retinol concentration continues to be widely used to assess vitamin A status. Serum retinol concentration is used in the present study and since the children in the study group were healthy and had no complaints or no sign of illness, inflammation parameters were not measured.

Although the number of children enrolled was small, the percentage of preschool-age children with sub clinical VAD in this study suggests that monitoring of vitamin A status is needed in Manisa province. Considering that serum retinol concentrations are measured low as a consequence of serious depletion of body stores, these children may present with VAD disorders in case of severe infections that cause a decrease in vitamin A intake.

Conclusions

VAD is a moderate public health problem in Manisa. In order to determine VAD category in Turkey, multicenter studies with larger groups should be carried out. Supplementary vitamin A may be beneficial in high risk groups such as malnourished infants and children with chronic infections. Families should be informed about improving feeding habits to increase use of vitamin A rich foods. Studies with larger group of children and with more parameters may be useful.

Table 1
1Differences between VAD and non-VAD groups.Non-VAD group
(n = 89)

VAD group
(n = 11)

P  †

Age (mo.)*
38.8 ± 9.7
40.4 ± 9.6
0.58  †

Gender
Boys 60%, Girls 40% Boys 45%, Girls
55%

0.27  † †

Weight (kg)*
14.1 ± 2.7
13.7 ± 1.8
0.65  †

Weight-for-age z-score
-0.4 ± 1.4
-0.6 ± 1.3
0.59  † †

Height (cm)*
90.0 ± 9.2
94.1 ± 4.1
0.19  †

Height-for age z-score
-1.0 ± 1.5
-1.0 ± 1.5
0.78  † †

Birth weight (g)*
3267 ± 1307
3127 ± 734
0.58  †
Birth length (cm)*
50.3 ± 2.7
49.5 ± 2.1
0.56  †

Duration of breast
feeding (mo.)*

14.2 ± 9.5
13.0 ± 7.2
0.80  † †

Age of onset of solid
food (mo.)*

6.5 ± 3.1
7.5 ± 6.1
0.76  † †

Percentage of vitamin
use

81.3%
85.7%
0.30  † †

Percentage of infections
51.4%
64.3%
0.30  † †

Abbreviations VAD: Vitamin A deficiency; WHO: World Health Organization; PAHO: Pan-American Health Organization; HPLC: High-performance liquid chromatography. Celal Bayar University, Faculty of Medicine, Department of Pediatrics, Manisa, Turkey. 2 Celal Bayar University, Faculty of Medicine, Department of Biochemistry, Manisa, Turkey. 3 Celal Bayar University, Akhisar MYO, Manisa, Turkey. 4 Celal Bayar University, Faculty of Medicine, Department of Public Health, Manisa, Turkey.Author details

1

Tansuğ et al. Nutrition Journal 2010, 9:34 http://www.nutritionj.com/content/9/1/34 Page 2 of 5
Authors' contributions NT: Participated in the conception, design, analysis and interpretation of data, manuscript preparation and revision. MP participated in the design of the study. SÇ carried out acquisition of the data and helped to draft the manuscript. FT carried out the HPLC studies. SG, ÖT, DY participated in the design of the study. GD performed the statistical analysis. All authors read and approved the final manuscript.Competing interestsThe authors declare that they have no competing interests.
Vitamin A deficiencies and excess. M Zile, R E Behrmann, R M Kliegman, H D Jenson, B F Stanton, Nelson Textbook of Pediatrics. Philadelphia, WB Saunders Company18Zile M: Vitamin A deficiencies and excess. In Nelson Textbook of Pediatrics. Edited by: Behrmann RE, Kliegman RM, Jenson HD, Stanton BF. Philadelphia, WB Saunders Company; , 18 2008:242-246.

Night blindness during pregnancy and subsequent mortality among women in Nepal: effects of vitamin A and β-carotene supplementation. P Christian, K P West, S K Khatry, E Kimbrough-Pradhan, S C Leclerq, J Katz, S R Shrestha, S M Dali, A Sommer, Am J Epidemiol. 152Christian P, West KP, Khatry SK, Kimbrough-Pradhan E, LeClerq SC, Katz J, Shrestha SR, Dali SM, Sommer A: Night blindness during pregnancy and subsequent mortality among women in Nepal: effects of vitamin A and β-carotene supplementation. Am J Epidemiol 2000, 152:542-547.

Vitamin A or β-carotene supplementation reduces symptoms of illness in pregnant and lactating Nepali women. P Christian, K P West, S K Khatry, J Katz, S C Leclerq, E Kimbrough-Pradhan, S M Dali, S R Shrestha, J Nutr. 130Christian P, West KP, Khatry SK, Katz J, LeClerq SC, Kimbrough-Pradhan E, Dali SM, Shrestha SR: Vitamin A or β-carotene supplementation reduces symptoms of illness in pregnant and lactating Nepali women. J Nutr 2001, 130:2675-2682.

Maternal night blindness increases risk of infant mortality in the first 6 months of life in Nepal. P Christian, K P West, S K Khatry, S C Leclerq, E Kimbrough-Pradhan, S R Shrestha, J Nutr. 131Christian P, West KP, Khatry SK, LeClerq SC, Kimbrough-Pradhan E, Shrestha SR: Maternal night blindness increases risk of infant mortality in the first 6 months of life in Nepal. J Nutr 2001, 131:1510-1512.

Impact of neonatal vitamin A supplementation on infant morbidity and mortality. J H Humphrey, T Agoestina, L Wu, A Umsan, M Nurachim, D Subardja, S Hidayat, J Tielsch, K P West, A Sommer, J Pediatr. 128Humphrey JH, Agoestina T, Wu L, Umsan A, Nurachim M, Subardja D, Hidayat S, Tielsch J, West KP, Sommer A: Impact of neonatal vitamin A supplementation on infant morbidity and mortality. J Pediatr 1996, 128:489-496.

World Health Organization: Control of vitamin A deficiency xerophthalmia. Keller International IVAGC Meeting. 672ReportWorld Health Organization: Control of vitamin A deficiency xerophthalmia. Report of a Joint WHO/UNİCEF/USAID/Helen Keller International IVAGC Meeting 1982, WHO Technical Report Series, Report No: 672.

World Health Organization: Global prevalence of vitamin A deficiency in populations at risk 1995-2005. WHO Global Database on Vitamin A Deficiency Geneva. World Health Organization: Global prevalence of vitamin A deficiency in populations at risk 1995-2005. WHO Global Database on Vitamin A Deficiency Geneva 2009.

S J Eastman, Vitamin A: Deficiency and xerophthalmia UNICEF Press. Eastman SJ: Vitamin A: Deficiency and xerophthalmia UNICEF Press 1988.

Breast-milk vitamin A as an indicator of the vitamin A status of women and infants Bull. R J Stoltzfus, B A Underwood, 73Stoltzfus RJ, Underwood BA: Breast-milk vitamin A as an indicator of the vitamin A status of women and infants Bull.WHO 1995, 73:703-711.

World Health Organization: Indicators for assessing vitamin A deficiency and their application in monitoring and evaluating intervention programmes Geneva. World Health Organization: Indicators for assessing vitamin A deficiency and their application in monitoring and evaluating intervention programmes Geneva 1996.

Increased risk of respiratory disease and diarrhoea in children with pre-existing mild vitamin A deficiency. A Sommer, J Katz, I Tarwotjo, Am J Clin Nutr. 40Sommer A, Katz J, Tarwotjo I: Increased risk of respiratory disease and diarrhoea in children with pre-existing mild vitamin A deficiency. Am J Clin Nutr 1984, 40:1090-1095.

Sommer A: Vitamin A, infectious disease, and childhood mortality: a 2 solution?. J Infect Dis. 167Sommer A: Vitamin A, infectious disease, and childhood mortality: a 2 solution? J Infect Dis 1993, 167:1003-1007.

Extent of Vitamin A Deficiency among preschool children and women of reproductive age. K P West, J Nutr. 132SupplWest KP: Extent of Vitamin A Deficiency among preschool children and women of reproductive age. J Nutr 2002, 132(Suppl):2857-2866.

Retinol and tocopherol concentration in breast milk of Turkish lactating mothers under different socio-economic status. Ö Tokuşoğlu, N Tansuğ, S Akşit, G Dinç, E Kasırga, Ö Özcan, Int J Food Sci Nutr. 59Tokuşoğlu Ö, Tansuğ N, Akşit S, Dinç G, Kasırga E, Özcan Ö: Retinol and tocopherol concentration in breast milk of Turkish lactating mothers under different socio-economic status. Int J Food Sci Nutr 2008, 59:166-174.

Turkey Demographic and Health Survey. Ankara, TurkeyHacettepe University Institute of Population StudiesTurkey Demographic and Health Survey 2003: Hacettepe University Institute of Population Studies. Ankara, Turkey 2004.

F Açkurt, M Löker, H Wetherilt, Pre ve postnatal dönemdeki annelerin besin tüketimi kan ve anne sütü vitamin ve mineral düzeylerinin degerlendirilmesi. Beslenme ve Diyet Dergisi. 25Açkurt F, Löker M, Wetherilt H: Pre ve postnatal dönemdeki annelerin besin tüketimi kan ve anne sütü vitamin ve mineral düzeylerinin degerlendirilmesi. Beslenme ve Diyet Dergisi 1996, 25:5-15.

Vitamin A status of healthy infants in Ankara, Turkey. S Hatun, T Teziç, Turk J Pediatr. 37Hatun S, Teziç T: Vitamin A status of healthy infants in Ankara, Turkey. Turk J Pediatr 1995, 37:187-192.

F Açkurt, H Wetherilt, B Okan, G Brubacher, S Akta, S Türdü, Türkiyenin Üc Bölgesinde 7-17 Yas grubu Okul Cocuklarinin Büyüme Gelisme Vitamin ve Mineraller Yönünden Beslenme Durumlarının Saptanmasi TÜBİTAK yayinlari. ITAK PressAçkurt F, Wetherilt H, Okan B, Brubacher G, Akta S, Türdü S: Türkiyenin Üc Bölgesinde 7-17 Yas grubu Okul Cocuklarinin Büyüme Gelisme Vitamin ve Mineraller Yönünden Beslenme Durumlarının Saptanmasi TÜBİTAK yayinlari: ITAK Press 1989.

Vitamin A deficiency in healthy children aged 6-59 months in Izmir province of Turkey. Z Kurugöl, A Egemen, P Keskinoğlu, S Darcan, S Akşit, Paediatr Perinat Epidemiol. 14Kurugöl Z, Egemen A, Keskinoğlu P, Darcan S, Akşit S: Vitamin A deficiency in healthy children aged 6-59 months in Izmir province of Turkey. Paediatr Perinat Epidemiol 2000, 14:64-69.

Cocukluk cagi pnömonilerinde vitamin A tedavisinin yeri. E Çeçen, F Atlıhan, Ö Çorumlu, H Demirli, SSK Tepecik Hast Derg. 14Çeçen E, Atlıhan F, Çorumlu Ö, Demirli H: Cocukluk cagi pnömonilerinde vitamin A tedavisinin yeri. SSK Tepecik Hast Derg 2004, 14:49-56.

. Tansuğ, Nutrition Journal. 934Tansuğ et al. Nutrition Journal 2010, 9:34

Respiratuvar sinsisyal virüs enfeksiyonu nedeniyle bronsiolit geciren cocuklarda serum A vitamini düzeyleri. Ü Tıraş, Y Şimşek, Y Dallar, Türk Ped Arş. 42Tıraş Ü, Şimşek Y, Dallar Y: Respiratuvar sinsisyal virüs enfeksiyonu nedeniyle bronsiolit geciren cocuklarda serum A vitamini düzeyleri. Türk Ped Arş 2007, 42:74-76.

Appropriate uses of vitamin A tracer (stable isotope) methology. Washington DC: ILSI Human Nutrition Institute. H C Vitamin A Tracer Task Force ; Furr, M H Green, M Haskell, 10.1186/1475-2891-9-34Nestel P. Lead drafters: Tanumihardjo SAVitamin A Tracer Task Force: Appropriate uses of vitamin A tracer (stable isotope) methology. Washington DC: ILSI Human Nutrition Institute 2004, (Members: Furr HC, Green MH, Haskell M, et al) Lead drafters: Tanumihardjo SA, Nestel P.. doi:10.1186/1475-2891-9-34

Vitamin A status of healthy children in Manisa. Tansuğ, Turkey. Nutrition Journal. 934Cite this article asCite this article as: Tansuğ et al.: Vitamin A status of healthy children in Manisa, Turkey. Nutrition Journal 2010 9:34.