Obesity related methylation changes in DNA of peripheral blood leukocytes

Xiaoling Wang
Haidong Zhu
Harold Snieder
Shaoyong Su
David Munn
Gregory Harshfield
Bernard L Maria
Yanbin Dong
Frank Treiber
Bernard Gutin
Huidong Shi
Obesity related methylation changes in DNA of peripheral blood leukocytes
R E S E A R C H A R T I C L E Open Access
Background: Despite evidence linking obesity to impaired immune function, little is known about the specific mechanisms. Because of emerging evidence that immune responses are epigenetically regulated, we hypothesized that DNA methylation changes are involved in obesity induced immune dysfunction and aimed to identify these changes.Method:We conducted a genome wide methylation analysis on seven obese cases and seven lean controls aged 14 to 18 years from extreme ends of the obesity distribution and performed further validation of six CpG sites from six genes in 46 obese cases and 46 lean controls aged 14 to 30 years.Results: In comparison with the lean controls, we observed one CpG site in the UBASH3A gene showing higher methylation levels and one CpG site in the TRIM3 gene showing lower methylation levels in the obese cases in both the genome wide step (P = 5 × 10 -6 and P = 2 × 10 -5 for the UBASH3A and the TRIM3 gene respectively) and the validation step (P = 0.008 and P = 0.001 for the UBASH3A and the TRIM3 gene respectively). Conclusions: Our results provide evidence that obesity is associated with methylation changes in blood leukocyte DNA. Further studies are warranted to determine the causal direction of this relationship as well as whether such methylation changes can lead to immune dysfunction.

Background

Obesity is the epidemic of our time, with sharply and steadily rising rates [1,2]. The major adverse consequences of obesity including type 2 diabetes, atherosclerosis and essential hypertension, when added together, account for a large number of disease related deaths [3,4]. If the obesity-related cancer cases are added to this number, obesity-related mortality by far exceeds that of other common diseases [5]. The latter indicates the urgent need to develop novel efficient therapeutic modalities for this condition.

The common denominator in the pathogenesis of the co-morbidities of obesity is the presence of an active, low-grade inflammatory process [6]. Despite evidence linking obesity to alterations in inflammatory response, little is known about the specific effects of obesity on the immune system. Recently, there has been a greater appreciation of the role of epigenetics, meiotically and mitotically heritable changes in gene expression that are not coded in the DNA sequence itself, in the immune and inflammatory responses [7][8][9]. Therefore, we hypothesize that DNA methylation changes play a role in obesity induced immune dysfunction. The goal of this study was to characterize DNA methylation profile in peripheral blood leukocytes in obese versus lean subjects using a genome wide approach. Identification of methylation changes in specific genes will provide important targets for further study into the mechanisms of obesity's effect on the immune system and the potential to develop new therapies to treat multiple obesity comorbidities.

Methods

Subjects

The genome wide methylation analysis was conducted in seven obese and seven age-matched lean controls. These 14 subjects were identified from the participants (n = 534) in the Lifestyle, Adiposity, and Cardiovascular Health in Youth (LACHY) study using the following inclusion criteria: (1) African American (AA) ancestry; (2) male; (3) having leukocyte DNA available; (4) obese cases having a body mass index (BMI) ≥ 99 th percentile for age and sex and lean controls having BMI ≤ 10 th percentile for age and sex. The LACHY study consisted of roughly equal numbers of AA and European American (EA) adolescents aged 14 to 18 years of both sexes recruited from high schools in the Augusta, Georgia area [10].

The replication cohort included 46 obese (BMI ≥ 30 kg/ m 2 or BMI ≥ 95 th percentile for age and sex if age ≤ 18) and 46 lean (BMI ≤ 22 kg/m 2 or BMI ≤ 40 th percentile for age and sex if age ≤ 18) AA males selected from three cohorts, the Blood Pressure (BP) stress study (n = 603) [11], the Georgia Cardiovascular twin study (n = 1,183) [12] and the Prevention of Hypertension in African American Teens (PHAT) study (n = 262) [13]. Both the BP Stress study and the twin study are on-going longitudinal studies which have followed the subjects more than 10 years. Both studies included roughly equal numbers of AAs and EAs or males and females. The BP stress study was established in 1989 with subjects aged to 7 to 16 years at baseline and the twin study was established in 1996 with subjects aged 7 to 25 years at baseline [11,12]. The PHAT study was a cross-sectional study and consisted of AA males and females aged 14 to 20 years [13]. Subjects in all the three studies were also recruited from Augusta, GA area. The obese and lean subjects were identified based on the following criteria: (1) having leukocyte DNA available; (2) AA males; (3) only one twin subject from a twin pair was selected if both twins met the criteria; (4) if multiple visits (with multiple leukocyte DNA) were available for a subject, this subject had to be obese or lean on all the visits to be included in the replication sample and the leukocyte DNA collected at the visit when the subject were the most obese or most lean was used. For all four cohorts self identification by self-reports of each subject or by a parent if the subject was under 18 years of age was used to classify ethnicity according to previously described criteria [14]. Subjects in all the four studies were overtly healthy, free of any acute or chronic illness on the basis of parental reports and were not on anti-hypertensive, lipid lowering, anti-diabetic and antiinflammatory medications [10][11][12][13]. The Institutional Review Board at the Medical College of Georgia approved the studies. Informed consent was obtained from all subjects and by parents if subjects were less than 18 years of age.

Measurements

For all the four cohorts, height and weight were measured by standard methods using a wall-mounted stadiometer and a scale, respectively. BMI was calculated as weight/height 2 . Systolic BP (SBP) and diastolic BP (DBP) were measured with Dinamap monitors, using an appropriately sized BP cuff placed on the subject's right arm. BP measurements were taken at 11, 13, and 15 minutes, during a 15-minute supine relaxation period. The average of the last two readings was used to represent SBP and DBP values [10][11][12][13].

Fasting peripheral blood samples in the LACHY cohort and non-fasting peripheral blood samples in the other three cohorts were collected. The buffy coat and plasma samples were separated and stored at -80°C. DNA was extracted from the buffy coat. In the LACHY cohort, fasting glucose levels were measured using Ektachem DT II system (Johnson and Johnson Clinical Diagnostics, Rochester, NY, USA) and fasting insulin was assayed in duplicate by specific radioimmunoassay (Linco Research, Inc., St Charles, MO, USA) [10]. QUICKI (quantitative insulin-sensitivity check index) was calculated to index insulin sensitivity using the following formula: 1/[log(fasting insulin, μU/ml) + log(fasting glucose, mg/dl)]. Out of the screening sample which included the seven obese cases and seven lean controls selected from the LACHY cohort, fasting glucose and insulin levels were not available for one case and one control.

Genome wide methylation chip

The HumanMethylation27 BeadChip from Illumina (Illumina, San Diego, CA, USA) was used. This chip can quantitatively measure 27,000 CpG sites, covering more than 14,000 well-annotated genes at single-CpG resolution. Each chip can accommodate 12 samples. After bisulfite treatment, 200 ng of the converted DNA was whole genome amplified (WGA) and enzymatically fragmented. The bisulfite-converted WGA-DNA samples were purified and applied to the Bead-Chips. Image processing and intensity data extraction were performed according to Illumina's instruction http://www.illumina.com/products/infinium_human-methylation27_beadchip_kits.ilmn. Each methylation data point is represented by fluorescent signals from the methylated and unmethylated alleles. DNA methylation beta values are continuous variables between 0 (completely unmethylated) and 1 (completely methylated), representing the ratio of the intensity of the methylated bead type to the combined locus intensity. Initial array processing and quality control were performed with BeadStudio software. The microarray data discussed in this paper have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE25301 http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc=GSE25301.

Pyrosequencing

The methylation levels of the selected six CpG sites from the six genes in the replication cohort were determined by pyrosequencing technology, a rapid and robust method for quantitative methylation analysis. After bisulfite treatment, 10 ng of the converted DNA was used in a PCR reaction to amplify the target region. One of the PCR primers was biotin labeled. Singlestranded biotinylated PCR products were prepared for sequencing by use of the Pyrosequencing Vacuum Prep Tool according to the manufacturer's instructions. The PCR products (each 10 μl) were sequenced by Pyrosequencing PSQ96 HS System (Pyrosequencing-Qiagen) following the manufacturer's instructions. The methylation status of each locus was analyzed individually as a T/C SNP using QCpG software (Biotage, Kungsgatan, Sweden). PCR primers and sequencing primers for these six genes are listed in Additional file 1. All the samples were assayed in the same run and in a random sequence.

Statistical analysis

For the genome wide methylation analysis, the Limma package [15] was used to analyze each CpG site for differential methylation between obese and lean subjects. CpG sites on the × and Y chromosomes were excluded from the analysis. Each CpG site was assigned a raw Pvalue based on a moderated t statistic. To correct for multiple testing, the set of raw P-values were converted to false discovery rates (FDR) according to Benjamini and Hochberg [16]. For the six CpG sites in the replication cohort, a Student's t-test was used to investigate whether their methylation levels differ between the obese and the lean group. Linear regression was further used to adjust the potential effect of age on methylation levels. We combined the genome wide and the replication steps for these six CpG sites using the weighted z score-based meta-analysis approach implemented in the package METAL [17]. Prior to analysis, methylation levels of the CpG sites in the replication cohort were log-transformed or square root-transformed to obtain a better approximation of the normal distribution. Preliminary analyses, t-tests and regression analyses were done using STATA 8 (StataCorp, College Station, TX, USA).

Gene Ontology analysis was conducted with the FatiGO tool [18]. FatiGO takes two lists of genes and converts them into two lists of GO terms. Then a Fisher's exact test for 2 × 2 contingency tables is used to check for significant over-representation of GO terms in one of the sets with respect to the other one. Multiple testing correction (indexed by adjusted P-values) to account for the multiple hypothesis tested (one for each GO term) is applied to reduce false positives. Since at least two CpG sites were included for the majority of genes in this genome wide chip, we selected the CpG sites with the lowest p value to represent this gene. Table 1 displays the general characteristics of the screening sample. At this age range, obese subjects already showed significantly higher insulin resistance. Figure 1 is a volcano plot showing the raw P-values for all CpG sites versus mean methylation difference between the obese group and the lean group. We did not observe any CpG sites survive multiple testing correction with the most significant CpG site with a FDR of 0.14 and a raw P-value of 5 × 10 -6 . Table 2 lists the top 10 most significant CpG sites and one additional CpG site which showed the largest methylation difference between cases and controls (-27.1% for CREB3L3). Since at least two CpG sites were included for the majority of the genes in this genome wide methylation chip, the P-value for the second CpG site in this gene was also listed in Table 2. Out of the 11 CpG sites listed in Table 2 (under the column "more significant CpG site"), we selected six CpG sites for validation in the replication cohort using the following inclusion criteria: (1) the most significant differentially methylated CpG site, which is the CpG site locating at -156 of the UBA-SH3A (ubiquitin-associated and SH3 domain-containing protein A) gene; or (2) the CpG sites in those genes of which the second CpG site also showed a raw P < 0.05. These include the CpG sites from the TRIM3 (Tripartite motif-containing 3), CTSZ (Cathepsin Z preproprotein), HIPK3 (Homeodomain interacting protein kinase 3), CDH5 (Cadherin 5 type 2 preproprotein) and CREB3L3 (cAMP responsive element binding protein 3-like 3) genes. The validation was conducted on the more significant CpG site (with Illumina ID 13517, 17029, 23241, 25599, 8829 and 23739) in each gene by pyrosequencing. The general characteristics of the replication cohort are displayed in Table 3. Since the DNA from one obese subject failed on four pyrosequencing assays, the data from this subject was excluded from the analysis. Table 4 lists the results in the replication cohort. The findings from the genome wide methylation chip were validated for the CpG sites in the UBASH3A, TRIM3, HIPK3 and CDH5 genes. The methylation status of the CpG site in the UBASH3A gene was 3.3% higher (P = 0.0019) and the methylation status of the CpG site in the TRIM3, HIPK3 and CDH5 genes was 1.21% (P = 0.0004), 7.31% (P = 0.0135) and 3.09% (P = 0.045) lower in the obese versus the lean group. These significant findings persisted after adjustment of age for the association between obesity and methylation status of the CpG site in the UBASH3A gene (P = 0.008) and TRIM3 gene (P = 0.001), but changed to borderline significance for HIPK3 gene (P = 0.05), and disappeared for the CpG site in the CDH5 gene (P = 0.095). The results of the meta-analysis for these six CpG sites on the data from the genome wide step and the replication step are shown in Additional file 2

Results

Although the pyrosequencing assay was designed to target one specific CpG site for each gene, some of the assays covered several surrounding CpG sites. The results of these CpG sites are also listed in Table 4. The correlation within samples among the multiple CpG sites measured within a gene is listed in Additional file 3.

Gene Ontology analysis was performed to test whether some common functional trends in molecular functions and biological processes were associated with the genes exhibiting differences between obese cases and lean controls in the genome wide chip. We included those genes with a raw P ≤ 0.01 to the first list (n = 298) and included all the other genes in the second list. As expected from a pilot study in 14 subjects, we did not observe any GO categories survive multiple testing. Table 5 lists the GO categories with raw P-value less than 0.05. Interestingly, we observed enriched functional processes that are potentially relevant for inflammatory response with immune response (GO:0006955), cell activation (GO:0001775), cytokine production (GO:0001816), response to biotic stimulus (GO:0009607) and antigen binding (GO:0003823) among the top GO categories. The results support that obesity might lead to methylation changes in genes involved in inflammatory pathways.

Discussion

In this study we aimed to identify obesity related methylation changes in peripheral blood leukocytes using a genome wide approach in youth and young adults who are free of obesity comorbidities. The primary findings of this study are increased methylation levels at one CpG site in the UBASH3A gene and decreased methylation level at one CpG site in TRIM3 gene in obese subjects compared with lean controls.

The protein encoded by the UBASH3A gene is the "ubiquitin-associated and SH3 domain-containing protein A", which was previously also known as T-cell ubiquitin ligand (TULA) and suppressor of T-cell signaling 2 (Sts-2). It is expressed predominantly in T-cells, where it has a suppressing effect on T-cell signaling and activation. It acts in part by inhibiting c-CBL-mediated downregulation of protein tyrosine kinases (PTKs) that are activated upon T-cell receptor stimulation [19]. UBA-SH3A is also capable of promoting T-cell apoptosis [20]. UBASH3A and UBASH3B (another protein in this family) gene double knock-out mice were hyperresponsive to T-cell receptor stimulation with increased cytokine secretion, although mice lacking UBASH3A were normal in all respects including T-cell function [21]. However, whether the expression of the UBASH3A gene is regulated by DNA methylation is unknown. No CpG island exists in this gene and the CpG site showing higher methylation levels in obese subjects from this  study locates at the promoter region with a distance of 156 bp to the transcription start site. There is a possibility that methylation of this CpG site or other CpG sites with methylation levels correlated with this CpG site inhibits the interactions between DNA sequence and nuclear proteins, resulting in decreased gene expression.

In-silico analysis of the region of this CpG site using TFSEARCH software [22] did not find this CpG site located at any known transcription factor binding sites. However, methylation of this CpG site may suppress gene transcription by recruiting methylcytosine-binding proteins that in turn associate with large protein complexes containing corepressors and histone deacetylases. The binding of these complexes to DNA may lead to a change in the chromatin structure from an active to an inactive form [23]. This speculation needs to be confirmed.

TRIM3 is one member of the TRIM protein family. These proteins share a conserved tripartite architecture and have a variety of cellular functions including cell proliferation, differentiation, oncogenesis, apoptosis, immune signaling and have been implicated in autoimmune diseases [24]. In peripheral blood leukocytes, TRIM3 is mainly expressed in macrophages and can be up-regulated by viral infection [25,26]. Similar to UBA-SH3A, the role of methylation in TRIM3 gene expression has not been reported yet. The TRIM3 gene has 1 CpG island located in the promoter and exon 1 (nucleotides -446 to 576), one CpG island spanning intron 2 and exon 3 (nucleotides 7759 to 8334), and one CpG island in exon 13 and the downstream region (nucleotides 24883 to 25610). The CpG site showing lower methylation levels in obese subjects in both the scanning cohort and the replication cohort locates in the promoter CpG island (348 bp upstream to the transcription start site). The methylation status of the other CpG site which showed significant differential methylation levels between obese cases and lean controls in the genome wide methylation analysis also locates in this CpG island (331 bp upstream to the transcription start site). Because the pyrosequencing assay also measured the methylation levels of the -331 CpG site (Illumina ID: 19207), we were able to show that also for this site obese subjects had lower methylation levels in  Means ± SD (Range) *: P < 0.01; †: P < 0.001 comparisons with the lean subjects (age adjusted P = 0.022 as shown in Table 4). Similar to the CpG site in the UBASH3A gene, these two CpG sites are not located at any known transcription factor binding sites. Future studies will be needed to test whether the density of the methylation or the methylation of specific CpG sites in this CpG island has effect on TRIM3 gene expression. The observed DNA methylation differences between obese cases and controls were relatively small. They were 10.1% and 2.54% in the genome wide step and 3.3% and 1.2% in the replication step for the UBASH3A gene and the TRIM3 gene, respectively. This modest level of differences raises an important question: what is the biological significance of changes in methylation on this degree? In this study, we used the DNA from leukocytes, which represent different cell populations with distinct epigenetic profiles. It is plausible that only the change in the epigenetic profile of one specific cell type is related to obesity. In this regard, the actual epigenetic differences might be more substantial than reported here but only present in this specific blood leukocyte cell type. Future studies on epigenetic profiling of various types of cell populations in the leukocytes are warranted to gain a greater understanding of the epigenetic dysregulation in obesity. Furthermore, this kind of study will be able to identify the cells specifically reflecting obesity-associated methylation changes, which is of great interest in itself. Transcriptional profiling studies will be very valuable in understanding whether the DNA methylation status differences are associated with differences in gene expression. Unfortunately, cellular RNA is not available for the samples used in the current study, which were selected from several existing cohorts.

There are several strengths of this study. First, we selected obese cases and controls with extreme phenotypes, which maximizes the power to make discoveries. Second, we focused on youth and young adults with the distinct advantage that the results are not confounded by obesity comorbidities or use of medication, both of which are very common in adult subjects with obesity. Third, a hypothesis-free genome wide approach was used. This approach supersedes the limitations imposed by candidate gene methylation studies and allows searching the whole genome in an unbiased manner.

Interpretation of these data is also limited by several constraints. First, in this study we hypothesize that obesity will lead to methylation changes in the DNA of leukocytes, which further lead to obesity related comorbidities. However, our study design cannot determine whether the identified methylation changes are the  cause or the consequence of obesity. Future studies on subjects who changed their body size status will be needed to clarify the causal directions. On the other hand, epigenetic regulation is tissue specific. In this regard, the target tissue to identify epigenetic variations responsible for obesity should be the hypothalamus of the brain rather than peripheral leukocytes. Second, in the current study, the Infinium HumanMethylation27 Beadchip was selected because of its quantitative measure at each CpG site. However, the limited coverage of this genome wide chip will restrict the findings to certain CpG sites within certain genes. Future studies should use chips with more complete coverage of the genome such as the recently released 450K Infinium Methylation BeadChip from Illumina. Third, the current study is a small pilot study with the genome wide step conducted only in seven obese and seven lean subjects. Future studies with much larger sample size are warranted to discover a more complete profile of obesity related methylation changes. Fourth, although all the obese cases in the genome-wide step were free of clinical diseases such as CVD and diabetes, some of them already showed evidence of insulin resistance. It is possible that insulin resistance may be the factor triggering methylation changes and not the obesity per se. Recent literature supports the postulate that glucose restriction alters gene expression through epigenetic mechanisms [27]. We conducted further analysis within the obese cases and did not observe that any of the CpGs showed methylation difference between cases with impaired fasting glucose (n = 3) versus those with normal glucose levels (n = 3), with the most significant CpG site having an adjusted P-value of 0.85. However, the limited sample size prevents us from drawing any conclusion based on this analysis. Unfortunately, fasting glucose was not measured in the replication cohort. Future studies with large sample sizes and more detailed phenotypes will be needed to clarify this question.

Conclusion

In this study, we identified several reproducible changes in DNA methylation of peripheral blood leukocytes between obese cases and lean controls. This study provides evidence that obesity is associated with methylation changes in blood leukocyte DNA. Further studies are warranted to determine the causal direction of this relationship as well as whether such methylation changes can lead to immune dysfunction. Such studies will have the ability to identify new insight into disease etiology and provide new targets for prevention of obesity related diseases such as cardiovascular diseases and type 2 diabetes.

Figure 1
1Volcano plot showing raw P-values versus mean methylation difference between obese cases and lean controls.

Table 1
1General characteristics of the subjects for genome wide methylation analysisCases
Controls

Table 2
2Top 10 differentially methylated CpG sites and the CpG site showing the largest difference. Genes taken forward to the replication stage are shown in bold. ID: ID from Illumina HumanMethylation27 BeadChip; Dis. to TSS: Distance to the Transcription Start Site; FDR: False Discover Rate * For CTSZ, 7 CpG sites were included in the genome-wide methylation platform and the two most significant sites are listed in the table.Gene
More significant CpG site
Second CpG site

ID
Dis. to
TSS

Average Case/Con.
(%)

Difference
(%)

P
FDR ID
Dis. to
TSS

Average Case/Con.
(%)

Difference
(%)

P

UBASH3A 13517
-156
45.6/35.5
10.1
5.00 ×
10 -6

0.14 146
52
45.6/37.7
7.9
0.145

CTNND1
6160
-431
57.4/70.2
-12.8
2.00 ×
10 -5

0.20 18016
415
19.7/21.5
-1.8
0.353

TRIM3
17029
-348
6.74/9.28
-2.54
2.40 ×
10 -5

0.20 19207
-331
12.5/16.2
-3.7
0.035

CTSZ*
23241
-292
13.6/21.0
-7.40
4.40 ×
10 -5

0.24 1644
-592
17.4/25.6
-8.2
0.001

TRAF5
10097
-319
69.7/58.0
11.7
4.50 ×
10 -5

0.24 25786
77
1.90/1.79
0.1
0.738

HIPK3
25599
390
51.5/64.9
-13.4
6.50 ×
10 -5

0.26 5514
314
26.6/35.0
-8.4
0.006

DPCR1
20918
-46
62.5/70.2
-7.70
6.90 ×
10 -5

0.26 4703
-906
72.4/73.0
-0.6
0.709

HIF3A
2879
153
5.35/2.97
2.38
1.08 ×
10 -4

0.34 7009
-1445
57.1/63.0
-5.9
0.091

NOTCH4
5969
-51
59.1/68.5
-9.40
1.15 ×
10 -4

0.34 14700
4
19.3/23.6
-4.3
0.078

CDH5
8829
-243
27.2/36.5
-9.20
1.75 ×
10 -4

0.44 22286
36
72.5/81.3
-8.8
0.012

CREB3L3 23739
42
9.63/36.7
-27.1
1.78 ×
10 -3

0.59 13445
-26
5.74/25.8
-20.1
0.004

Table 3
3General characteristics of the subjects of the
replication cohort

Cases
Controls

N
4 5
4 6

*Age, years
20.3 ± 5.0 (14.1 to 29.5)
17.6 ± 3.1 (12.7 to 27.9)

 †

BMI, kg/m 2
37.4 ± 6.1 (30.5 to 59.9)
18.9 ± 1.2 (16.4 to 22.0)

 †

SBP, mmHg
125.8 ± 14.2 (97 to 175)
113.5 ± 8.5 (100 to 134)

Table 4
4Results from the replication cohort ID, ID from Illumina HumanMethylation27 BeadChip Dis. to TSS, Distance to the Transcription Start Site. The targeted CpG sites are highlighted in bold.Gene
ID
Dis. to TSS
Average
Case/Con. (%)

Difference (%)
P
Age adjusted
P

UBASH3A
13517
-156
45.1/41.8
3.30
0.0019
0.008

TRIM3
-
-357
8.58/8.98
-0.40
0.3128
0.059
17029
-348
8.76/9.97
-1.21
0.0004
0.001

19207
-331
6.91/7.93
-1.02
0.0050
0.022

CTSZ
23241
-292
16.5/17.7
-1.20
0.0953
0.271
-
-279
27.8/29.1
-1.30
0.8425
0.798

HIPK3
-
375
48.0/55.3
-7.30
0.0184
0.061

25599
390
49.8/57.1
-7.30
0.0135
0.050
-
422
39.8/45.8
-6.00
0.0164
0.070
-
438
42.5/49.0
-6.50
0.0173
0.061
CDH5
8829
-243
30.3/33.4
-3.10
0.0450
0.092

CREB3L3
-
30
3.23/2.85
0.38
0.7188
0.828

23739
42
18.7/21.6
-2.90
0.3251
0.288
-
48
18.5/22.3
-3.80
0.1549
0.174

Table 5
5Gene-ontology AnalysisBiological
process

GO. ID
Term
P-
value

Adjust
P

Level 3
GO:0006955 Immune response
0.017
0.690
Level 3
GO:0007588 Excretion
0.028
0.690

Level 3
GO:0001775 Cell activation
0.042
0.690

Level 3
GO:0001816 Cytokine production
0.044
0.690

Level 3
GO:0009607 Response to biotic
stimulus

0.047
0.690

Molecular
function

GO. ID
Term
P
value

Adjust
P

Level 3
GO:0019842 Vitamin binding
0.009
0.522

Level 3
GO:0003823 Antigen binding
0.010
0.522

Level 3
GO:0004872 Receptor activity
0.032
0.756

Level 3
GO:0008289 Lipid binding
0.035
0.756

AcknowledgementsThe participants in this study were recruited by several NIH funded projects including HL69999, HL56622, HL077230 and HL64157. XW is also funded by the American Heart Association (0730156N) and the NIH (HL086530).Additional materialAdditional file 1: Primer sequences and PCR conditions for bisulfitpyrosequencing analysis.Additional file 2: Meta-analysis for the six CpG sites. Authors' contributions XW had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. XW and HS contributed to the study concept and design. XW, SS, HS, HS and DM analyzed and interpreted the data. HZ, YD, BG, FT and GH acquired the data. XW drafted the manuscript. XW, HS, GH and BLM critically revised the manuscript for important intellectual content. XW and SS did the statistical analysis.Competing interestsThe authors declare that they have no competing interests.
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Pre-publication history The pre-publication history for this paper can be accessed here. 10.1186/1741-7015-8-87Pre-publication history The pre-publication history for this paper can be accessed here: http://www.biomedcentral.com/1741-7015/8/87/prepub doi:10.1186/1741-7015-8-87

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Why I Cannot Find the Prostate? Behind the Subjectivity of Rectal Exam

Dmitry Koulikov
Department of Urology
School of Medicine
Shaare Zedek Medical Center
The Hebrew University of Jerusalem
P.O. Box 323591031JerusalemIsrael

Ariel Mamber
Department of Urology
School of Medicine
Shaare Zedek Medical Center
The Hebrew University of Jerusalem
P.O. Box 323591031JerusalemIsrael

Alon Fridmans
Department of Urology
School of Medicine
Shaare Zedek Medical Center
The Hebrew University of Jerusalem
P.O. Box 323591031JerusalemIsrael

Wael Abu Arafeh
Department of Urology
School of Medicine
Shaare Zedek Medical Center
The Hebrew University of Jerusalem
P.O. Box 323591031JerusalemIsrael

Ofer Z Shenfeld
Department of Urology
School of Medicine
Shaare Zedek Medical Center
The Hebrew University of Jerusalem
P.O. Box 323591031JerusalemIsrael

T J Maatman
Department of Urology
School of Medicine
Shaare Zedek Medical Center
The Hebrew University of Jerusalem
P.O. Box 323591031JerusalemIsrael

F Staerman
Department of Urology
School of Medicine
Shaare Zedek Medical Center
The Hebrew University of Jerusalem
P.O. Box 323591031JerusalemIsrael

A C Thorpe
Department of Urology
School of Medicine
Shaare Zedek Medical Center
The Hebrew University of Jerusalem
P.O. Box 323591031JerusalemIsrael

Why I Cannot Find the Prostate? Behind the Subjectivity of Rectal Exam

International Scholarly Research Network ISRN Urology
201210.5402/2012/456821Received 5 November 2011; Accepted 4 December 2011Research Article Correspondence should be addressed to Dmitry Koulikov,
Background. Most physicians use digital rectal examination (DRE) to help detect prostate cancer and to estimate the prostates' size. The accuracy of DRE is known to be limited. We evaluate the ability of doctors to palpate the whole prostate with DRE. Methods. At time of transrectal ultrasound (TRUS) the distances from the anus to the apex and base of prostates were measured. The TRUS's distances were compared to the mean index finger length of our clinic doctors. Results. The ability of the urologist to reach and examine the apex, half, three quarters and the whole prostate was in 93.7%, 66.3%, 23.2% and 3.2% of cases respectively. Conclusions. In most cases it was impossible to palpate the whole prostate. Anatomical location and volume of the examined prostate, as well as the length of his own index finger limit DRE and allow the examination of only a small portion of the prostate.

Introduction

Digital rectal examination (DRE) is widely used in medicine. A large number of physicians perform it to estimate the prostates' size and/or for early detection of prostate cancer.

It is well accepted that DRE is a subjective measure and has a high interobserver variability when estimating prostate size. It has been reported that DRE poorly predicted actual prostate size compared to transrectal ultrasound (TRUS) [1]. Others compared radical retropubic prostatectomy specimen weights with prostate weight estimates using TRUS and DRE and found that DRE correlated poorly with prostate weight and that TRUS was superior to DRE [2].

In the prostate specific antigen (PSA) era the role of DRE in early detection of prostate cancer is not clear. This role was evaluated in a number of large trials [3,4]. The cancer detection rate for DRE was 3.2% and positive predictive value was 21%, PSA or a combination of DRE and PSA were superior to DRE alone for the diagnosis prostate cancer [3]. The positive predictive value and sensitivity of DRE were strongly dependent on PSA level and DRE predicted cancer poorly in patients with low PSA values [4]. Some have suggested that DRE may be unnecessary in patients with PSA values of 3.0 or less [5,6], since in these patients one would need to perform 289 DREs to find one case of clinically significant prostate cancer.

We believe that in addition to DRE's subjectivity, there are other inherent limitations to DRE. When performing DRE, prior to reaching any conclusions, the examiner should be able to feel the whole posterior surface of the prostate. However, in clinical practice this may not always be possible. Patient morphometric variables as well as the length of the examiners index finger may impact on the accuracy of DRE. There is no data concerning these factors. One study examined the adequacy of prostate palpation when performing DRE during colonoscopies and concluded that patient positioning and obesity were affecting factors [7]. These observations prompted us to carry out our study.

In this study we tried to explore the limitations of DRE from an anatomical point of view. For this, the distances from anus to the prostate were measured in patients undergoing TRUS. These were compared to the length of index finger of urologists in our clinic. In other words, we examine the ability of urologist to palpate the whole of the prostates posterior surfaces at the time of DRE.

Materials and Methods

In a prospective fashion, we examined patients who were referred for prostatic biopsy at our outpatient clinic.

At time of TRUS the distances from the anus to the apex and base of prostates were measured using an 8-MHz biplane probe (B-K medical, 8808 probe). For this purpose the probe was marked with 0.5 cm gradients from the US transverse view crystal out to handle. All TRUS were performed with the patients in the left decubitus position with the knees pulled up to the chest. When the apex of the prostate was viewed in the transverse plain, its depth from the anal verge was noted (anal-apex distance). Then the probe was moved in the cephalic direction until the base of the prostate was visualized and a second measurement was noted (anal-base distance). Index fingers (from the top of the finger to the beginning third interphalangeal joint) of our clinic urologists were measured with a centimeter ruler.

All biopsies were done under local anesthesia using periprostatic block with 20 cc of lidocaine 1%. Systematic transrectal biopsies were obtained using a spring-loaded biopsy gun and 18 G biopsy needle. Patients with BMI 30 or more were excluded from the study.

To analyze the data we divided the prostatic surface length into 4 zones. First is the distal or apical zone that included the distal 25% of the prostate. The second is half prostate, the third zone included 75% of surface, and fourth zone was the whole prostate. The ability of the urologist to palpate all prostatic zones, with DRE was examined comparing the distances measured at the time of TRUS to the mean urologists' index finger length.

Commercial software (GraphPad Prism) was used for statistical analysis. The results were expressed as mean ± SEM or as median with range. All relationships were assessed by Pearson correlation analysis. Contingency table with Fisher's exact test was used to assess the accuracy of DRE. A level of significance (P value) < 0.05 was considered statistically significant.

Results and Discussion

Between March and June 2010, ninety-five men were included in the study. The median age was 64 (range 49-82). Median PSA was 6.94 ng/mL (range 1.56-347). In Thirty-six (38%) men this was not the first biopsy session. The median number of biopsies was 12 (range 8-16). In 15 men (16%) the DRE was suspicious for prostate cancer. Median TRUS prostate volume was 53 mL (range 13.7-301) and the median DRE estimated prostate volume was 40 mL (range 10-80). The correlation between TRUS measured volumes and DRE estimated volumes was not high (Pearson r = 0.42) but statistically significant (P < 0.0006) (Figure 1).

The median anal-apex distance was 5 cm (range 3-7.5), and anal-base distance was 10.3 cm (range 7.3-15.7). The median length of our urologists index fingers was 8.25 cm (range 7-9, N = 7). Thus in most cases it was impossible to palpate the whole posterior surface of the prostates. In fact, the ability of the urologist to reach and examine the apex, half prostate, three quarters, and the whole prostate  was in 93.7%, 66.3%, 23.2%, and 3.2% cases, respectively. There was a good correlation between anal-base distance and the TRUS volume of prostate (Pearson r = 0.72, P < 0.001) ( Figure 2). Twenty-nine (30.5%) cases of prostate cancer were diagnosed. The sensitivity and specificity of DRE for the diagnosis of prostate cancer were 21% and 86%, respectively. The positive predictive value of DRE was 40%. According to the Fisher's exact test the DRE was an inaccurate exam (P = 0.38).

This study demonstrated that in most cases it was impossible to palpate the whole posterior surface of the prostate by DRE. This is first time that distance from anal verge to prostate was recorded. Our findings may explain why DRE is a poor predictor of prostate volume and has low sensitivity for the detection prostate cancer.

DRE and Prostate

Volume. The ability to estimate prostate volume is very important before surgical intervention, brachytherapy, benign prostatic hyperplasia management, and calculating PSA density [8][9][10]. According to the available literature, DRE is an inaccurate test. DRE underestimates prostate size, particularly if prostate volume is greater than 30 mL [1]. In our study a strong correlation between the anal-base distance and prostate volume was demonstrated. It follows, then, that in larger prostates even more of the prostatic surface would be beyond the reach of the palpating finger making it even harder to estimate its volume. Therefore DRE volume estimations may correlate well with TRUS measured volumes in small glands. In our study we found that DRE estimated volume did not correlate well with TRUS measured volume (Pearson r = 0.42). Even worse correlation was reported by Loeb et al. in their large study [2]. Although, in their study they compared DRE estimated prostatic volume with the actual weight of radical prostatectomy specimens. Based on our, and others' findings, DRE is not a good predictor of actual prostatic volume though it may help distinguish small prostates from large ones and may estimate precisely prostatic volumes in patients with small glands.

DRE and Prostate Cancer

Screening. The debate concerning prostate cancer screening is still underway. Even after the publication of the results of large trials from USA [11] and Europe [12] the question "to screen or not to screen" remains unanswered.

In the US, the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial, Andriole et al. [11] reported no mortality benefit from combined screening with PSA testing and DRE over a median followup of 11 years. In the European Randomized Study of Screening for Prostate Cancer (ERSPC) trial, Schröder et al. [12] reported that PSA screening without DRE was associated with a 20% relative reduction in the death rate from prostate cancer at a median followup of 9 years, with an absolute reduction of about 7 prostate cancer deaths per 10,000 men screened. In the PLCO study DRE was used as a screening tool whereas in the ERSPC trial DRE was used only in a small portion of the patients. One may conclude then that DRE is not helpful in detection prostate cancer. On the other hand, DRE is an integral part of nomograms and predictive tools for the management of patients with prostate cancer [13][14][15]. And positive DRE may predict worse prognosis comparing to normal DRE. Gosselaar et al. [16], from the Rotterdam section of ERSPC, point out that potentially aggressive cancers (Gleason score 7) are more prevalent among men who have an abnormal DRE compared to normal DRE. However, Gosselaar et al, in another study [17], state that an initially suspicious DRE, after initial cancer-negative biopsy, did not influence the chance for detection of cancer or significant cancer at later repeated biopsies. Moreover, this group of investigators previously reported that an abnormal DRE was not an indication for prostate biopsy in men with PSA < 3 ng/mL [4]. Interestingly Vis et al. [6] reported in their study that up to 63% of prostate cancers were detected coincidentally and not as a result of true positive DRE. In our study DRE was an inaccurate predictor of prostate cancer (P = 0.38) with low sensitivity though its specificity was 86%. According to our morphometric findings, an examiner needs to take into consideration, when performing DRE, that in most cases at least part of the surface of the prostate remains out of his reach and unexamined.

Before discarding DRE one should consider that DRE is also an important part of the physical examination of patients with lower urinary tract symptoms. Perineal sensation, anal tone, and the bulbocavernous reflex are simple and straight forward methods to assess the neurological integrity of the lower urinary tract [18].

There are some limitations to our study. Firstly, we did not take into account the elastic property of human tissue. At the time of DRE one may reach deeper by applying pressure on the perineum. Also, the shape of prostate was not taken into consideration. In large prostates, that protrude more into the rectum the actual length of the prostate grows as the curve is longer than a straight line. In our study we used a straight TRUS probe to measure prostatic length therefore the actual anal-base distance may be underestimated in some cases. This, though, does not weaken our conclusions.

Conclusions

DRE is still an important and inexpensive tool for the physician. One may gain information about the prostates size and consistency as well as the neurological function of the lower urinary tract. However, the examiner has to consider factors that limit the accuracy of DRE such as the anatomical location and volume of the examined prostate, as well as the length of his own index finger. One must remember that in many patients these factors limit DRE and allow the examination of only a small portion of the prostate.

Figure 1 :
1The correlation between TRUS measured volumes and DRE estimated volumes.

Figure 2 :
2The correlation between anal-base distance and the TRUS volume of prostate.

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Language workbench user interfaces for data analysis

Victoria M Benson
Fabien Campagne

Department of Physiology and Biophysics
The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine
The Weill Cornell Medical College
New YorkNYUnited States of America

The Weill Cornell Medical College
New YorkNYUnited States of America

Language workbench user interfaces for data analysis
10.7717/peerj.800Submitted 7 October 2014 Accepted 5 February 2015 Published 24 February 2015 Corresponding author Fabien Campagne,Academic editor Harry Hochheiser Additional Information and Declarations can be found on page 15 Distributed under Creative Commons CC-BY 4.0 OPEN ACCESSSubjects BioinformaticsHuman-Computer InteractionComputational Science Keywords Biological data analysisBioinformatics trainingBiomarker developmentLanguage workenchData analysis abstractions
Biological data analysis is frequently performed with command line software. While this practice provides considerable flexibility for computationally savy individuals, such as investigators trained in bioinformatics, this also creates a barrier to the widespread use of data analysis software by investigators trained as biologists and/or clinicians. Workflow systems such as Galaxy and Taverna have been developed to try and provide generic user interfaces that can wrap command line analysis software. These solutions are useful for problems that can be solved with workflows, and that do not require specialized user interfaces. However, some types of analyses can benefit from custom user interfaces. For instance, developing biomarker models from high-throughput data is a type of analysis that can be expressed more succinctly with specialized user interfaces. Here, we show how Language Workbench (LW) technology can be used to model the biomarker development and validation process. We developed a language that models the concepts of Dataset, Endpoint, Feature Selection Method and Classifier. These high-level language concepts map directly to abstractions that analysts who develop biomarker models are familiar with. We found that user interfaces developed in the Meta-Programming System (MPS) LW provide convenient means to configure a biomarker development project, to train models and view the validation statistics. We discuss several advantages of developing user interfaces for data analysis with a LW, including increased interface consistency, portability and extension by language composition. The language developed during this experiment is distributed as an MPS plugin (available at http://campagnelab.org/ software/bdval-for-mps/).

INTRODUCTION

Popular software available to analyze biological data is often developed with a variety of technology, including programming languages as well as standard and non-standard data formats used to configure the tools for specific analyses. While scientists with a broad computational background have no major difficulty using most of the software tools, this is not the case for scientists whose training did not include computational experience, such as many biologists or physician scientists.

Helping biologists take advantage of analysis tools

Several approaches have been used in the past to make it easier to use tools for biological data analysis. These approaches fall under the following broad categories:

1. Custom graphical user interfaces. User interfaces (UIs) can be programmed with a variety of technology to present the end-user with graphical user interfaces (GUIs) that simplify a specific analysis task. While earlier custom user interfaces were developed as desktop applications (e.g., Clustal-X (Thompson et al., 1997), Viseur (Campagne et al., 1999)), many biological analysis tools are now programmed and offered as web applications (e.g., from the TMHMM server (Krogh et al., 2001) to the vast number of tools described in the Nucleic Acids Research Web Server Special Issue).

2. Generic workflow systems. A minimal workflow system consists of a user interface to create and maintain workflows, and of a runtime engine to execute the workflows. A workflow consists of a set of elements representing units of work (often called workflow components), and connections among the elements representing how data flows from one element to another when the work of an element is completed. Examples of workflow systems include Taverna and Galaxy. These systems were developed to make it easier for biologists to assemble analyses pipelines that require using several tools. Supporting new tools in these systems does not require developing new user interfaces, but in exchange the ability to customize the system is limited to analyses that can be expressed with the workflow abstraction. The workflow abstraction has limited expressiveness. For instance, a workflow system does not support notions of variables or loops, which are often used in scripting or programming languages to automate repetitive tasks. While it is possible to use a scripting or general programming language to implement a work element and therefore perform repetitive tasks inside a workflow component, this requires knowledge beyond that of the workflow system, which the intended audience often does not have.

3. Training workshops. Most universities and medical schools offer training workshops to help biologists and clinical investigators learn the computational skills needed to work with command line analysis tools. Instructors who have taught such workshops know that most of the instructors' time is spent explaining technical aspects of the operating system and command line user interface rather than the concepts needed to perform a given analysis. Training workshops that focus on teaching workflow systems avoid this problem, but teach their trainees only how to solve the subset of problems that workflow systems can represent.

plugin scripts (Simi & Campagne, 2014). In this new study, we investigated whether LW technology-specifically the MPS Language Workbench (see http://www.jetbrains.com/ mps/ and Campagne, 2014)-could be used to model the high-level concepts that an analyst should be aware of when he or she conducts an analysis. The MPS platform is one of several LW platforms that have been developed. Other platforms include Spoofax (Kats & Visser, 2010), Intentional Programming (Simonyi, 1995), or XText (Eysholdt & Behrens, 2010. We have chosen MPS for our study because of FC's increasing familiarity with the platform (Campagne, 2014). The central innovation of the approach we present in this manuscript is to provide the end-user of an analysis tool with user interface elements that directly represent the high-level analysis concepts that the user needs to understand to perform a specific analysis. We will illustrate this idea in this manuscript with an analysis tool that helps end-users develop biomarker models from high-throughput data. We presented an introduction to the biomarker development and validation process in Deng & Campagne (2010). Similar descriptions are also available in the MAQC-II articles (Shi et al., 2010). This manuscript follows the conventions and definitions given in this published work, which are briefly summarized here.

The biomarker development process

Biomarker development studies measure a large number of molecular features across a collection of biological specimens, called samples. Data obtained for k subjects and n samples constitute a dataset in the form of a table of features. In the MAQC-II study, for instance, microarray assays were used to assemble 6 training datasets and 6 validation datasets. Several microarray platforms were used across the study. A total of 13 prediction endpoints were defined across these datasets that encoded a specific condition about each subject. For instance, in one dataset, an endpoint was defined by whether a subject of the breast cancer dataset was ER2 positive. Models were developed against this endpoint to try and predict whether the sample, for which gene expression data was available, was ER2 positive. Simply stated, an endpoint is what a predictive model is trained to predict. In general, an endpoint may have two or more categories, be a continous value, or a survival outcome for each subject of a dataset.

All these concepts are well represented and can be directly manipulated by the end-user in the analysis tool developed in this study.

MATERIALS & METHODS

MPS language workbench

We used version 3.1 of the Jetbrains MPS Language Workbench.

BDVal

We adapted the BDVal Ant script to make it easier to use BDVal with configuration files automatically generated from the MPS project. This modified script is distributed with BDVal version 1.3+.

Unified modeling language diagrams

The UML diagrams presented in this manuscript were generated automatically using the MPS language and the MPS UML Diagrams plugin developed by our laboratory (https:// github.com/CampagneLaboratory/UML Diagrams).

User interface buttons and file selection

Buttons and file selection buttons and dialogs were implemented using the MPS language org.campagnelab.ui (http://campagnelab.org/extending-mps-editors-with-buttons-justgot-easier/). The UI language was used to integrate buttons in the editor of the BDVal language concepts.

Histogram charts

Charts were implemented with the org.campagnelab.XChart language, which integrates the XChart library with MPS (https://github.com/CampagneLaboratory/XChart).

RESULTS

Limitations of existing software

In a previous report, we described the BDVal software, which automates the development and validation of biomarker models from high-throughput data (Dorff et al., 2010). End-users of BDVal must configure a biomarker development project by creating a number of configuration files, as described in detail in the software online manual (Chambwe & Campagne, 2010). Configuring BDVal projects requires editing a number of files in different formats (including Ant build scripts written in XML, Java property files, and tab delimited files). Our experience suggests that this activity can be challenging and error-prone for non-technically savvy users.

Modeling the biomarker development process

We modeled the biomarker development process with the MPS LW by creating concepts to represent each component of a biomarker analysis, as supported by the BDVal software ( Fig. 1):

• Project This concept represents a biomarker discovery project and groups multiple datasets needed for analysis.

• Sample A sample is the biological material that was assayed. Each sample is associated with feature values measured by an assay.

• Platform The platform represents the assay used to measure features in the Sample. The platform concept links the assay probe Ids to the genes that the probe measures. Platforms are particularly important for micro-array based assays where multiple probes may measure the expression of the same gene.

• DataSet A dataset contains feature values for many samples.

• Endpoint An endpoint defines what the models will aim to predict. Endpoints are typically defined by the clinical application at hand (e.g., predict which patient respond to a treatment, or predict which patients have a specific disease).

• Validation protocol BDVal supports Cross Validation to estimate performance on a training set. Cross-validation can be performed with k folds and with r random repeats. The parameters k and r can be configured.

• Feature selection approach A feature selection approach implements a strategy to prioritize features according to their level of association with an endpoint. Several feature selection approaches have been implemented in BDVal and are available to reduce the subset of features used to construct a model. Feature selection is useful when there is a cost or time advantage in measuring few features values for each subject.

• Classification approach A classification approach is able to train a prediction model from feature values in each subject and information about the endpoint for these subjects. Several classification algorithms are implemented in BDVal, including Support Vector Machines with libSVM, Random Forests, Naive Bayes, LogitBoost and the K-Star classification approach.

• Modeling approach The combination of one or more feature selection approaches and a classification approach is a modeling approach and can train a predictive model from a training dataset.

• Model A model contains all the information needed to predict an endpoint when presented with feature values for a subject. BDVal models are written to a self-contained ZIP archive which contains the parameters of the model and threshold value for prediction of an endpoint (Dorff et al., 2010).

We were able to develop MPS concepts that almost directly represent each of these components of the biomarker discovery process. For clarity, we present these concepts as UML diagrams. Figure 1 presents the Project, Dataset, Endpoint and Platform concepts. Figure 2 presents the concepts used to capture the configuration of the user computer. Figure 3 presents how a modeling approach includes both a choice of Feature Selection, Classification method, and a list of models that can be generated with the approach. Figure 4 illustrates how we leveraged concept interfaces and inheritance to support auto-completion for different feature selection and classification approaches. Concepts for project configuration. The Properties concept represents the configuration of the user computing environment. For instance, end-users can configure the project to use a server-class computer with at least 8 GB of memory and multiple cores, or a desktop computer with at least 1 GB of memory and a single core. This is achieved by selecting the appropriate ComputerType in the Project Properties. Project properties also define the location of two software packages that must be installed on the end-user's computer and the directory where models will be developed (outputDirectory).  A FeatureSelection concept interface is implemented by individual concepts that represent each feature selection method supported by BDVal. The inheritance hierarchy is used by the MPS language workbench to offer auto-completion when an end-user needs to identify a feature selection approach (see bottom-right inset). The same mechanism is used to represent the classification approaches supported by BDVal. Figure 4 presents concepts for the feature selection and classification approaches, which map directly to the methods supported by BDVal. Note the use of concept interfaces to indicate that each approach implementation can be used as element of the approach needed to construct a model.

Generating and running a configured project

Instances of the Project concept can be configured by the end-user using the MPS user interface. When an instance is fully configured, it can be compiled within the LW. Compilation, also called generation in MPS, creates executable files from an instance of the Project concept and its children (instances of the concepts contained a Project). In the context of BDVal for MPS, generation converts the high-level analysis concepts described in the previous section to the configuration files needed to run BDVal on the end-user's machine. The generated BDVal files can be executed and will produce results files in configured output directory (a traditional user interface is generated for the Project to help provide feedback as the computation is progressing). The end-user can press the Refresh button (see Figs. 5 and 6, in the Status section) to trigger inspection of the output directory.

Analysis user interface in a language workbench

Developing a user interface in a LW consists of creating an editor for each concept of a language. We developed editors for each concept to layout information in a logical manner. Figure 5 presents an instance of the Project concept rendered in the MPS projectional editor (Campagne, 2014). Project Properties are shown first because they need to be configured before a project can be executed (see Fig. 2).

Approach section

This section makes it possible to define which approaches should be used to develop and evaluate models. It includes the description of the cross-validation parameters (i.e., number of folds and number of random repeats) and provides an interactive editor to configure feature selection methods. The figure shows for instance an approach section configured to select features with a T-test followed by SVM iterative feature selection (recursive feature elimination), a simpler selection by SVM weights (SVM Global) and no selection where all the features (Whole Chip) are used to train a model. The Approach section ends by showing the number of models. This number is calculated dynamically by considering the different feature selection, classification and parameters entered so far in the editor.

Status section

This section offers a button to refresh the status. Pressing this button scans the output directory to identify results directories generated after BDVal is executed. Each directory found is shown with the folder name and the number of models that were generated by the execution.

Endpoints section

This section makes it possible for the end-user to define one or more prediction endpoints, and the various possible categories for each endpoint. In the example shown, the Fusion endpoint models the TMPRSS2-ERG gene fusion fusion event described in Setlur et al. (2008).

Platform section

This section indicates the location of the platform file in the GEO database soft format (Edgar, Domrachev & Lash, 2002). The Dataset section was intentionally left blank to illustrate that the content of Fig. 5 is not a plain text output, but provides error highlighting (red text is automatically highlighted by the MPS editor because the data attributes are required and missing).

We note that the LW user interface that we implemented is not limited to text. It includes user interface elements such as buttons that an end-user can press to perform some action, and can include images either static or dynamically generated at runtime. This is more evident in Fig. 6 where the histograms of the AUC and MCC statistics (estimated by cross-validation on the training set) are shown for a set of models developed with BDVal.

Advantages of language workbench user interfaces

UI consistency

We found that a user interface for data analysis implemented in a LW (LW UI) provides an important advantage over traditional approaches: the LW UI provides a consistent experience to the end-user. Custom developed UIs need to be designed for consistency and periodically reviewed to make sure that end-users have a predictable experience throughout the UI (Nielsen, 1989).

In this context, we note that the LW UI is defined using the jetbrains.mps.editor MPS platform language (see Campagne, 2014) and created from this definition by the LW platform. This mainly declarative approach to UI development ensures that most editing and navigation mechanisms provide a consistent user interface to the end-user.

For instance, auto-completion to set the value of a reference to other nodes already defined in the Project is an example of such a consistent UI behavior. Auto-completion is implemented by the LW platform and always provide a consistent user experience (which needs to be learned once and is then used used many times across LW UIs). Behaviors that are implemented consistently also include: copy and paste, source control, navigation to a referenced node, find usage, undo/redo.

We explain here the first two behaviors in more details: 1. A Project or a part of a project can be copied and pasted by the end-user. This is useful to make copies of a project before customization to explore a different analysis scenario. For instance, in BDVal, users may copy the Sample Project provided with the tool and start customizing this project to a new dataset and assay platform. With traditional technology for constructing user interfaces, copy and paste would need to be implemented explicitly by the programmer who creates the user interface. In a command line environment, copying a project would require copying a file, but copying a part of a project would require understanding the file format(s) for the file(s) that describe this part of the project. The LW UI provides a uniform mechanism for copy and paste that does not require custom programming nor detailed understanding of the file formats affected by the operation.

2. The LW UI provides a tight integration with a source control system (Git, Subversion and CVS are supported). This makes it possible for the end-user to put a BDVal Project under source control to track changes to the analysis configuration. This is an important feature to support reproducible data analysis that requires no special effort from the LW UI developer. Figure 7 provides an illustration of how the MPS LW visually identifies changes made to a model after a commit, shows differences, and provides the ability to revert specific changes to the model. These features are offered by the platform consistently across all editors for the language, and do not require any custom coding.

In contrast, such a source control integration of the software usage would be difficult to achieve with a traditionally developed user application. Developers of such an application would need to capture the state of the application in a file format, write the state to a file, call a source control tool to commit or checkout the application state, and provide a projection from the differences reported by the source control tool, into user interface elements. Because such developments are nontrivial, few if any custom data applications with user interface offer such a tight integration with source control. On the contrary, command line analysis tools integrate more naturally with source control: they process files that their users can put and maintain under source control. Source control over files reports differences at the level of granularity of a line, whereas LW UIs can pinpoint changes at different levels of granularity: from the level of a model property (e.g., see Fig. 7), all the way down to the insertion or deletion of a node of a model (e.g., the Project node for instance). Indeed, LW UIs track changes at the logical level established when designing the concepts of the analysis system. For instance, in BDVal for MPS, adding a feature selection approach to an analysis will record a single change to the Project, which will appear clearly as a change in feature selection when looking at revisions in the Difference tool. When the project is generated, this single change may affect several generated files, which if they were tracked in source control at their physical level would make it harder to recognize that these line-level changes are related.

Language composition

A key feature of the MPS LW is to support seamless language composition (see Voelter & Solomatov (2010) and Campagne (2014), Chapter 2). This feature is particularly useful in a data analysis context because the concepts that represent methods or datasets can be extended by the end-user very easily. For instance, in the context of BDVal, assume an end-user wanted to test the system with a number of random datasets, constructed with varying numbers of subjects and features with random values. With a custom user interface, it would not be possible to change the user interface of the tool to make it easier to configure such random datasets. Using the command line, it would be necessary to create simulated random datasets with the appropriate number of subjects and features, store these in separate files, and configure the project to use these new dataset files. In a LW UI, we are able to create a new language that extends the BDVal language. This means that it is possible to define concepts and associated editors that provide different behavior, customized by the end-user. Assume we create such a language, and define the concept RandomDataset in this new language. We can declare that this concept extends Dataset (defined in the org.campagnelab.BDVal language). We can then define two attributes to encode the number of subjects and samples that the random dataset should have. In the RandomDataset editor, we provide the means to edit these parameters, and a button to create the dataset. The button would be pressed by the end user after he/she has configured the parameters. We can then define a behavior method in the RandomDataset concept that will construct the dataset file and configure the attributes of the super-concept. This behavior will be executed when the button is clicked. When this new language is imported into a model alongside the BDVal language, the end-user is able to create a RandomDataset instance in a BDVal Project, wherever it was previously possible to create a standard Dataset, and use it to configure random datasets. This extension mechanism does not require any modification to the original BDVal language. This simple scenario illustrates how language composition can be useful for data analysis.

Another way to extend the LW UI is to create new intentions for an existing language. Briefly, intentions are context dependent actions that the end user can activate after positioning the cursor over some specific concept instance (see Simi & Campagne (2014) for illustrations, or Campagne, 2014 Chapter 9). The MPS LW allows to define new intentions for concepts of another language. This feature can be used by a data analyst to automate common configurations. For instance, the BDVal language provides an intention that configures a Project with the location of the Sample data files distributed with BDVal. An analyst could define a language that provides intentions suitable to configure a BDVal project with specific analysis protocols (e.g., SVM only model construction, with predefined number of features).

Portability

A LW UI is portable because it runs on the MPS LW, which is implemented in Java, and is available for Windows, Linux and Mac OS operating systems. A given analysis tool could limit portability to a specific platform. However, BDVal is also implemented in Java and the Java-based Ant build system, and will run on the same set of platforms as the MPS platform.

Distribution as language plugin

We have created an MPS build script that generates an MPS plugin for the BDVal language. This plugin was uploaded to the LW plugin repository and has therefore become available to any user of the MPS platform. The MPS LW provides a convenient user interface to manage the discovery and installation of plugins. Different versions of a plugin can be offered and the user of a previous version is notified when a new version of an installed plugin has become available in the pugin repository. In a way, the MPS plugin repository can serve a similar function to the Galaxy ToolShed (Blankenberg et al., 2014).

While language plugins can depend on other plugins, it is currently not possible to specify a range of compatible versions for the target of a language dependency declaration (for instance, when specifying that the BDVal plugin depends on the XChart plugin, we are not able to indicate which range of versions of XChart is compatible with a given version of the BDVal plugin). This drawback is expected to limit the ability to evolve languages iteratively while maintaining compatibility with existing deployments.

DISCUSSION

To our knowledge, our study is the first to propose and prototype the use of a Language Workbench to facilitate data analysis. Language Workbench technology has been developed in the context of general programming, and has been traditionally applied to developing programming languages that generate executable code. In this report, we have shown that the same technology can be successfully used to model the analysis process for biomarker development. Biomarker model development is an example of a complex biological data analysis that can benefit from high-level abstractions. Indeed, this activity requires using validation protocols such as cross-validation (Stone, 1974) or bootstrap (Efron & Tibshirani, 1997), and fully embedding feature selection in the cross-validation loop (or in the boostrap procedure) in order to avoid selection bias (Varma & Simon, 2006). Testing multiple feature selection approaches, machine learning methods, or parameters of these methods often produces tens of thousands of trained models across splits of cross-validation and parameter choices. The large number of models and intermediate files (such as lists of features used to train the models) produced in these studies can quickly become a daunting data management challenge, even for analysts who know enough programming to automate analyses with a language such as R, Python or Java. The LW UI that we have described in this report facilitates the configuration of complete biomarker model projects, which can be executed to produce a large number of models in a single run. All models and intermediate files are automatically organized and, after completing, the LW UI displays performance evaluation statistics. The LW UI requires no programming experience and helps represent succinct solutions to biomarker development problems.

The idea of using a language workbench for data visualization (a component of data analysis) is not completely new. We identified precursors of this idea as early as 1975. For instance, a previous attempt was described as DDA (Guthery, 1976), a system designed to help with graphical data analysis. The DDA prototype was built with the LANG-PACK language design system (Heindel & Roberto, 1975). While a language design system, LANG-PACK was not a LW in the modern sense because it was based on parsing technology and therefore did not avoid the issue of ambiguity during language composition. While using similar wording to the present manuscript (i.e.,"language workbench," "interactive graphical data analysis techniques"), the DDA manuscript described a system to help draw lines on a screen for data visualization, which is a task orders of magnitude simpler than the biomarker data analysis task that we use as a motivating example in the present study.

We have also been conducting a large-scale test of the idea of using a language workbench for data analysis in related projects. The NYoSh analysis workbench, a data analysis platform constructed with the MPS language workbench, focuses on interactive analysis of high-throughput sequencing data and will be presented in detail in another article (http:/ /workbench.campagnelab.org, Simi and Campagne, manuscript under preparation). The MetaR project (http://metaR.campagnelab.org) is a set of composable languages that help users create heatmaps from tables of data, or prepare other visualizations. Analyses written with MetaR generate to R code that can be executed directly within the MPS language workbench (Simi and Campagne, 2015, unpublished).

CONCLUSIONS

In this manuscript, we propose that Language Workbench technology can be used to develop interactive user interfaces to help scientists with data analysis. We tested this idea by developing a prototype of a Data Analysis Language Workbench User Interface (DALWUI). This prototype was designed to help end-users develop and evaluate biomarker models from high-throughput datasets. This manuscript presented this prototype and briefly discussed the advantages of LW technology compared to custom graphical user interface development with traditional programming technology, or compared to command line driven data analysis.

Based on this experience, we propose that LW technology can be used to create a new generation of computational environment for data analysis and expect that these environments will yield key advantages for many data analysts, from the computational expert to the biomedical researcher with limited computational experience.

Figure 1
1Main concepts of the BDVal MPS language.

Figure 2
2Figure 2 Concepts for project configuration. The Properties concept represents the configuration of the user computing environment. For instance, end-users can configure the project to use a server-class computer with at least 8 GB of memory and multiple cores, or a desktop computer with at least 1 GB of memory and a single core. This is achieved by selecting the appropriate ComputerType in the Project Properties. Project properties also define the location of two software packages that must be installed on the end-user's computer and the directory where models will be developed (outputDirectory).

Figure 3
3Concepts for configuring modeling approaches. BDVal makes it possible to combine choices for feature selection approaches and choices for classification approaches.

Figure 4
4Feature selection and classification approaches.

Figure 5 / 17 Figure 6
5176Project user interface overview. This snapshot presents a view of a BDVal Project instance in the MPS editor. A project contains different sections: Properties, Approach, Status with Result folder, Platform (describing the assay and the datasets produced with this assay). The Dataset section is shown still incomplete to illustrate error highlighting in the MPS language workbench. The editor is fully interactive: end users can add datasets, feature selection methods, additional number of features. Some classification approaches have parameters that are only displayed when the feature selection method is part of the Project (e.g., the SVM Tune-C approach performs a parameter scan for the C cost parameter of a linear support vector machine). These features make the MPS editor a convenient user interface to configure a biomarker development project.Benson and Campagne (2015), PeerJ, DOI 10.7717/peerj.800 8The LW user interface can incorporate interactivity and graphics.

Figure 7
7Seamless integration with source control.
Benson and Campagne (2015), PeerJ, DOI 10.7717/peerj.800 6/17
Benson and Campagne (2015), PeerJ, DOI 10.7717/peerj.800 9/17
Benson and Campagne (2015), PeerJ, DOI 10.7717/peerj.800 17/17
ACKNOWLEDGEMENTSThe authors thank the developers of the Jetbrains MPS Language Workbench. We thank Manuele Simi for discussions and feedback on this manuscript. We thank the reviewers of this manuscript for constructive criticisms that helped clarify and strengthen this report.ADDITIONAL INFORMATION AND DECLARATIONS FundingThis investigation was supported by the National Institutes of Health NIAID award 5R01AI107762-02 to Fabien Campagne and by grant UL1 RR024996 (National Institutes of Health (NIH)/National Center for Research Resources) of the Clinical and Translation Science Center at Weill Cornell Medical College. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Grant DisclosuresThe following grant information was disclosed by the authors: National Institutes of Health NIAID: 5R01AI107762-02.Competing InterestsFabien Campagne serves as an Academic Editor for PeerJ. He is the author of the book "The MPS Language Workbench, Volume I" and receives royalties from the sale of this book.Author Contributions• Victoria M. Benson performed the experiments, reviewed drafts of the paper.• Fabien Campagne conceived and designed the experiments, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.
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