Prescription patterns and appropriateness of NSAID therapy according to gastrointestinal risk and cardiovascular history in patients with diagnoses of osteoarthritis

Angel Lanas
Guillermo Garcia-Tell
Beatriz Armada
Angel Oteo-Alvaro
Prescription patterns and appropriateness of NSAID therapy according to gastrointestinal risk and cardiovascular history in patients with diagnoses of osteoarthritis
R E S E A R C H A R T I C L E Open Access
Background: Prescription of non-steroidal anti-inflammatory drugs (NSAIDs) should be based on the assessment of both gastrointestinal (GI) and cardiovascular (CV) risk for the individual patient. We aimed to assess the GI/CV risk profile and the pharmacological management of patients with osteoarthritis (OA) in clinical practice. Methods: We conducted a cross-sectional, multicentre, observational study of consecutive OA patients that visited 1,760 doctors throughout the Spanish National Health System (NHS) in a single day. The presence of GI risk factors, CV histories, hypertension and current pharmacological treatments was recorded.Results: Of the 60,868 patients, 17,105 had a diagnosis of OA and were evaluable. The majority (93.4%) had more than one GI risk factor and 60.3% were defined to be at high-GI risk. Thirty-two percent had a history of CV events, 57.6% were treated with anti-hypertensive therapy and 22.6% had uncontrolled hypertension. One-fifth of patients were treated with non-NSAID therapies, whereas the remaining patients received NSAIDs. Non-selective NSAIDs (nsNSAID) plus proton pump inhibitor (PPI) or cyclooxigenase-2 (COX-2)-selective NSAIDs alone were more frequently prescribed in patients at increased GI risk. Patients with a positive CV history received nsNSAIDs or COX-2-selective NSAIDs in 41.3% and 31.7% of cases, respectively. When both the GI and CV histories were combined, 51% of the overall population was being prescribed drugs that were either not recommended or contraindicated.Conclusions: Over 90% of patients with OA are at increased GI and/or CV risk. In over half of these patients, the prescription of NSAIDs was not in accordance with current guidelines or recommendations made by regulatory agencies.

Background

Therapeutic intervention in osteoarthritis (OA) is focused on reducing pain and improving functional activity [1,2]. Among pharmacological treatments, NSAIDs are commonly prescribed because of their clinical efficacy, despite the well-known gastrointestinal (GI) side effects associated with their use [3]. Cyclooxigenase-2 (COX-2)-selective NSAIDs were developed to diminish the GI adverse events caused by non-selective NSAIDs (nsNSAIDs) [4]. COX-2-selective and most nsNSAIDs can be associated with increased cardiovascular (CV) risk, which has prompted the necessity for assessments of both GI and CV risk in patients who need these medications [5][6][7][8]. All NSAIDs are also associated with other side effects, including hypertension, water retention, heart failure and renal insufficiency [9].

Based on these findings, the Food and Drug Administration (FDA) [10], the European Medicines Agency (EMA) [11], and different scientific societies [12][13][14] agree that the medical management of patients who require NSAIDs must be based upon the previous assessment of GI and CV risk factors in the individual patient. Guidelines recommend the use of nsNSAIDs plus a gastroprotectant or a COX-2-selective NSAID alone in patients with one or more GI risk factors [15,16]. A COX-2-selective agent plus a proton pump inhibitor (PPI) is recommended in those with the highest GI risk, whereas nsNSAIDs and COX-2-selective inhibitors should be avoided in patients with high GI and CV risks [11][12][13][14][15][16]. The FDA clearly states that CV risk is associated with all NSAIDs, excluding aspirin [10]. The EMA contraindicates the use of the COX-2selective agents in patients with previous CV events and establishes that adverse CV events with the use of nsNSAIDs cannot be excluded. In addition, the EMA contraindicates the use of etoricoxib in the presence of uncontrolled hypertension [11].

It is unknown whether these recommendations made by regulatory agencies and guidelines are being followed. Therefore, our study was aimed at evaluating the GI and CV risk profiles in patients with OA as well as the pharmacological management and the appropriateness of therapies based on current recommendations issued by European guidelines and the European Medicines Agency [11,12,14,16].

Methods

Sample and design

This was a cross-sectional, multicenter, observational study in OA patients who were considered candidates for NSAID medication. All data were collected in one single day (25 March 2009) in all participating centers. A total of 1,760 investigators from the Spanish National Health System representing the two specialties that most frequently treat patients with OA in our country's primary care physicians and orthopedists visited a total of 60,868 consecutive patients seen in this one day. The investigators registered the total number of patients who visited their medical centers on 25 March 2009. Patients included in the study had to meet the following criteria: 1) male or female aged over 18 years, 2) established diagnosis of osteoarthritis in medical records, 3) informed verbal consent. Patients received drug treatment and/or medical care according to the usual clinical habits of the attending doctor.

Variables related to GI and CV risk classification

The following well known GI risk factors were assessed [12][13][14][15][16][17]: age 65 or older; concomitant acetyl salicylic acid (ASA), corticosteroid, or anticoagulant use; previous history of ulcers, ulcer bleeding, or dyspepsia; and the use of two NSAIDs or a high dose of one NSAID (a dose was considered high if the NSAID was prescribed at the maximum dose recommended for the symptomatic treatment of arthritis pain, as described elsewhere [17]. CV low dose ASA was considered any dose 300 mg/day or less. Helicobacter pylori infection was not considered because the proportion of patients from whom this information was available was very low.

For the purposes of this study and based on previous reports [12][13][14][15][16][17], we classified patients into three GI categories. Patients at low GI risk were considered to be those without any of the above-mentioned clinical conditions. Patients at moderate GI risk included those with at least one of the following GI risk factors: 1) age 65 or older, 2) concomitant use of low-dose ASA, 3) concomitant use of corticosteroids, 4) history of symptomatic peptic ulcer, 5) history of dyspepsia, 6) current high-dose NSAID use and, 7) current use of two NSAIDs. As described elsewhere [17], patients at high GI risk were those with either a GI bleeding history, concomitant use of NSAIDs and anticoagulants or the presence of three risk factors of those described for moderate GI risk or any of those factors combined with an uncomplicated peptic ulcer. These combinations were based on the estimated incidence of events obtained from combining different risk factors that would put patients at a similar risk level to those with a history of bleeding peptic ulcers [18,19]. In this way, in low GI risk patients, the expected rate of upper GI complications should not exceed 1.5 events per 100 patients per year, whereas for those at moderate GI risk, the rate should be between 1.5 to 10, and for high GI risk patients, the rate should be greater than 10 events per 100 patients per year. Patients being treated with anticoagulants (warfarin or coumadin) and NSAIDs were considered high risk, because bleeding events in anticoagulated patients can be more severe [20].

In addition to GI risk factors, CV data were also collected which, according to recommendations of different government agencies and the EMA, limit the use of some NSAIDs. Information regarding previous CV history including myocardial infarction, angina, stroke, peripheral arteriopathy or heart failure, was collected. These diagnoses were confirmed by examination of patient medical records. The presence of hypertension history and anti-hypertensive drug therapy was also collected. Blood pressure levels were obtained at the clinical visit and compared with previous values recorded in patient charts, if available. Uncontrolled hypertension was considered if the systolic blood pressure was greater than 140 mmHg or the diastolic blood pressure greater than 90 mmHg at the medical visit. Current OA medications taken by patients and prescriptions made at the clinical visit were also recorded as well as the type of NSAIDs and gastroprotective agents used. COX-2-selective agents included celecoxib or etoricoxib, whereas all other prescribed NSAIDs were considered non-selective.

Statistical analysis

The descriptive analysis of the patients included demographic, clinical characteristics, medical history and pharmacological treatments. Quantitative and qualitative variables were analyzed using measurements of central tendency (mean, median) and measurements of dispersion (95% confidence interval). Furthermore, qualitative variables were defined according to their absolute and relative frequencies. The mean and SD are reported for continuous variables and percentages for qualitative variables. Student's t-test was used to analyze continuous variables and the Chi-square test for quantitative variables. Tests were two-tailed with a significance level of 5%. Data were analyzed with SAS 8.2 statistical software (SAS Institute, Cary, NC, USA). Due to a problem with the hard copy of the CRF, gender data were not collected and, therefore, cannot be provided.

Sample size

At least 5,000 patients who met the selection criteria were estimated for inclusion in the study. This sample size would allow the estimation of the percentage of patients with the presence of cardiovascular history (yes/no) and gastrointestinal risk factors (yes/no), with a significant level of 95% and a precision 1.5% in a twosided test, assuming a population proportion in the worst case (P = Q = 50%). A replacement rate of 15% was estimated. A sample size of 17,000 patients covered this estimate with an accuracy of 0.8%.

Ethical considerations

The study complied with all ethical considerations involving human subjects, as adopted in the 18 th World Medical Assembly, Helsinki, Finland. All information recorded was obtained following standard clinical guidelines, and patients were not subjected to any therapeutic or diagnostic experimentation. The study followed standard security and confidentiality measures, complying fully with Spanish legislation regarding data protection (Ley Orgánica de 15/99). The work was presented to and approved by the Regional Ethics Committee for Clinical Research, Hospital Clinic de Barcelona (registration number 2009/4747). The use of identification numbers, instead of other patient identifiers, ensured patient names remained confidential.

Results

Characteristics of patients

From a total of 60,868 patients seen by the 1,760 participating investigators in one day, 21,448 patients suffered from musculoskeletal diseases, but eventually 4,343 were excluded from the analysis because it was unclear whether the underlying disease was OA, leaving a final study sample of 17,105 patients.

Gastrointestinal risk

Figures 1 and 2 summarize the GI risk factors found in the study population. An age of 65 or older was most common (76.1%), whereas a history of complicated ulcers was the least common risk factor (3.3%). Overall, only 6.6% of patients had no GI risk factors, whereas the rest had at least one, and 65% had two or more. Based on these risk factors, >90% of OA patients were at increased GI risk and 60.3% of them could be considered at high risk for GI events.

Cardiovascular history

A history of previous CV events and uncontrolled hypertension are the two CV factors that limit the use of COX-2-selective agents and nsNSAIDs according to regulatory agencies. Of 16,470 patients with recorded CV data, 31.9% (5,256) had a history of at least one previous CV event; 22.6% (3,647/16,157) had uncontrolled hypertension and 57.6% (9,540/16,226) were taking antihypertensive drugs. Among those with a history of CV events, 2,064/5,256 (39.3%) were taking low-dose ASA.

An association between GI and CV risk was observed. More patients with a positive CV history (Figure 3) or receiving antihypertensive therapy (71.4% of 9,354 patients) pertained to the high GI risk group than to the moderate or no GI risk groups.

Pharmacological treatment

The mean number of OA medications prescribed to patients for underlying OA was 1.9 ± 0.9, and 93.9% of patients were receiving between one and three treatments. Of 17,105 patients with information regarding OA therapy, 20.4% were prescribed non-NSAID therapies including paracetamol (alone or combined with opioids or  Appropriateness of drug use in OA patients based on the GI and CV risk Table 1 summarizes drug therapy according to GI risk. Non-selective NSAIDs were similarly prescribed in the three GI risk levels, although gastroprotective therapy and COX-2-selective inhibitors were more frequently prescribed to those at increased GI risk (P < 0·0001).

Concerning the type of drugs prescribed according to CV risk, nsNSAIDs were more frequently prescribed to those with no history of CV events (5,452/11,214 = 48.6%) when compared to those with a previous history of CV events (2,169/5,256 = 41.3%) (P < 0·0001). Over one-third of patients with and without CV history received a COX-2-selective NSAID (33.5% vs. 31.7%; P = 0·02, respectively). Of 3,647 patients with uncontrolled hypertension, 82% were receiving NSAIDs (nsNSAIDs = 48.4% or COX-2-selective = 33.6%). In 10.7% of these cases, etoricoxib was the prescribed drug. The prescription of naproxen was rather small (3.4% of patients; 586 of 17,105). Of all patients treated with naproxen, 31.1% (175/562) had a previous CV history, 27.5% (155/563) had uncontrolled hypertension, and 54.7% (306/559) were taking antihypertensive treatment.

Within the overall OA population of this study, those patients with high GI risk and CV history represents 25.3% of the total. As shown in Table 2, 74.4% of this subpopulation (3,217/4,323 patients) received nsNSAIDs or COX-2-selective NSAIDs, which is considered inappropriate or contraindicated therapy for them, according to current guidelines [11][12][13][14][15][16].

Also, patients at high GI risk and no increased CV risk are recommended to take COX-2-selective NSAIDs plus PPI [12][13][14][15][16]; within this group, 61.8% of patients (3,521/5,697) were treated with COX-2-selective NSAIDs alone or nsNSAIDs plus PPI.

When all figures are combined, (Table 2), 51.03% (8,406/16,470) of the overall population of OA patients are being prescribed medications that were either not recommended or contraindicated, according to current guidelines and understanding.

Discussion

Current guidelines and recommendations issued by regulatory agencies [11][12][13][14][15][16] for patients with GI and CV risk factors require NSAIDs and have introduced an unavoidable complexity in the clinical decisionmaking process. A recent report has shown that 86% of OA patients seen by rheumatologists are at increased GI risk and that almost half of them are at high CV risk [17]. The current study, carried out at primary care and orthopedist sites, reached a similar conclusion.  CV HX = cardiovascular history; GP = gastroprotectant (>91% proton pump inhibitors). * Sysadoa, paracetamol, opioids; ** P-values denotes differences among groups overall; *** nsNSAIDS + GP data express the number of patients and % of patients with nsNSAIDs prescription that also take a gastroprotective agent. Also, COX-2 + GP data express the number of patients and % of patients with COX-2 selective inhibitors prescriptions that also take a gastroprotective agent.

Whether recommendations issued are being followed or implemented in clinical practice is generally unknown. This study has shown that in over half of the OA population examined, the prescription of NSAIDs did not follow current guidelines or recommendations. The most critical areas where the recommendations were not being followed or were over-looked, were in patients with both high GI and CV history and in those with a high GI risk alone. In the first group, over 74% of cases received prescriptions of either non-selective NSAIDs or COX-2-selective NSAIDs. In the second group, 49% received non-selective NSAIDs and a PPI instead of no NSAID therapy or a COX-2-selective NSAID and a PPI. Naproxen was seldom prescribed overall or for those with CV risk, despite being pointed out as the safest NSAID for those with CV events [21]. However, other recommendations seem to be well adopted and followed in clinical practice. For example, our study showed high rates of PPI co-prescription with nsNSAIDs to patients with increased GI risk. These rates increased in parallel with higher levels of GI risk. Similar trends, although with lower rates of prescription, were observed with COX-2-selective NSAIDs alone. However, half of patients with low GI risk and no CV history were still treated with nsNSAIDs plus a PPI or a COX-2-selective NSAID, which are not recommended by current guidelines.

Overall, the data suggest that although some of the recommendations are being followed, others are not; especially those concerning the assessment of CV risk, which seems far from being implemented in routine clinical practice. These data contrast with some other reports at the specialist level (for example, rheumatologists), who seem to comply better with existing guidelines [17,22].

Increased blood pressure is another frequent side effect of NSAID therapy. Patients with hypertension should be monitored when receiving NSAIDs and these drugs should be avoided when patients suffer from uncontrolled hypertension. As shown in this study, over half of the OA study population received antihypertensive therapy and one-fourth had uncontrolled hypertension. An overwhelming majority of these patients with uncontrolled hypertension were receiving NSAIDs, including etoricoxib [11]. This suggests another area for improvement in clinical practice.

This study had both strengths and limitations. The strengths include: the cross-sectional approach for the collection of data in a single day for all patients; and the size of the study carried out at the primary and orthopedist care sites where OA patients are more commonly treated and prescribed therapy. Other strengths were the wide geographical distribution within the country and the fact that the data agreed with recent reports. This supports the value of the study [17]. Another important aspect of the study is that it highlights the existing problem in patients that require NSAID treatment. It reveals the necessity of guideline implementation strategies to improve clinical practice. Limitations include the fact that we had to rely on data reported by the investigators, based on recorded charts, although the consistency of the data across investigators and the multiple sites of the study reduced the impact of this limitation. Also, data on uncontrolled hypertension were based on blood pressure readings obtained during the clinical visit and confirmed with values previously recorded in patient charts when available, which may have overestimated the incidence of hypertension. However, the CV risk probably was underestimated, because it was based on the history of CV events; whereas had CV HX = cardiovascular history; GP = gastroprotectant (>91% PPI). * Sysadoa, paracetamol, opioids; ** P-values denotes differences among groups overall; *** nsNSAIDS + GP data express the number of patients and % of patients with nsNSAIDs prescription that also take a gastroprotective agent. Also, COX-2 + GP data express the number of patients and % of patients with COX-2 selective inhibitors prescriptions that also take a gastroprotective agent.

CV scores been available, the percentage of patients at high CV risk might have been higher [17]. However, we did not use those scores in order to simplify the study due to the size of the surveyed population. Finally, one may question whether the data obtained in patients from one country may apply to other countries. Whereas this may be true, it is possible that the validity of the conclusions may apply especially to other European countries due to similarities among health systems and the similar strategies and guidelines issued by different European scientific societies under the same regulatory body (the EMA). However, the high rates of PPI prescription found in Spain may be different from those observed in other European countries [23], which may even increase the gap of appropriate prescriptions for these patients.

Conclusions

This study provides valuable information on the high prevalence of both GI and CV risk in OA patients who receive NSAID therapy. It also reports the high proportion of NSAID prescriptions that are not considered appropriate according to recommendations given by professional scientific societies and regulatory agencies. These data must serve not only to increase awareness of the limitations and difficulties on the translation of these recommendations into clinical practice, but also stimulate the creation of strategies or tools to increase the appropriate therapy for OA patients at primary care and orthopedist levels. These strategies may include not only educational programs but electronic tools [16], online flags and so on, that may suggest the appropriate therapy for the individual patient.

Figure 1 Figure 2
12GI risk factors in the study population (N = 17,105). Percentage of patients with each GI risk factor. Number of GI risk factors by number of patients (N = 17,105).

Figure 3
3Gastrointestinal risk and CV history in the OA study population.

Table 1
1Type of prescription according to GI riskType of drug
Low GI risk
N (%)

Moderate GI risk
N (%)

High GI risk
N (%)

P-value**

N = 1283
N = 5511
N = 10311

SYSADOA
55 (4.3)
120 (2.2)
80 (0.8)
<0.0001

Paracetamol
130 (10.1)
535 (9.7)
841 (8.2)
0.001

Opioids
12 (0.9)
55 (1.0)
59 (0.6)
0.008
Combinations of any of the 3*
80 (6.2)
424 (7.7)
1103 (10.3)
<0.0001

NsNSAIDs
615 (47.9)
2,567 (46.6)
4,734 (45.9)
0.337
nsNSAID+ GP***
344/615 (55.9)
2,093/2,567 (81.5)
4,367/4,734 (92.2)
<0.0001

Cox-2 NSAID
294 (22.9)
1,636 (29.7)
3,669 (35.6)
<0.0001
Cox-2 + GP***
96/294 (32.7)
880/1,636 (53.8)
2.800/3,669 (76.3)
<0.0001

Table 2
2Type of prescription based on GI risk and CV historyPrescription
Low GI risk
Mod. GI risk
High GI risk
P** value

No CV Hx*
CV Hx
No CV Hx
CV Hx
No CV Hx
CV Hx

N (%)
N (%)
N (%)
N (%)
N (%)
N (%)

N = 1,144
N = 62
N = 4,373
N = 871
N = 5,697
N = 4,323

SYSADOA
52 (4.6)
0 (0.0)
92 (2.1)
22 (2.5)
44 (0.8)
33 (0.8)
<0.0001

Paracetamol
108 (9.4)
13 (21.0)
391 (8.9)
121 (13.9)
331 (5.8)
480 (11.1)
<0.0001

Opioids
11 (1.0)
1 (1.6)
40 (0.9)
11 (1.3)
28 (0.5)
28 (0.7)
0.03

Combinations of any of the 3*
72 (6.2)
5 (8.0)
306 (7.0)
97 (11.1)
498 (8.7)
586 (13.5)
<0.0001

nsNSAIDs
554 (48.4)
27 (43.5)
2,091 (47.8)
353 (40.5)
2,807 (49.3)
1,789 (41.4)
<0.0001
nsNSAID+ GP***
307/554 (55.4) 18/27 (66.7) 1,687/2,091 (80.7) 302/353 (85.6) 2,552/2,807 (90.9) 1,691/1,789 (94.5) <0.0001

Cox-2 NSAID
267 (23.3)
13 (21.0)
1,335 (30.5)
227 (26.1)
2,154 (37.8)
1,428 (33.0)
<0.0001
Cox-2 + GP***
89/267 (33.3) 4/13 (30.8)
694/1,335 (52.0) 149/227 (65.6) 1,554/2,154 (72.1) 1,177/1,428 (82.4) <0.0001

AcknowledgementsPfizer Inc. sponsored the study. We are grateful to all patients and doctors who voluntarily contributed to the development of this study. We also thank George Sands and Nathalie ter Wengel for critical reviews and comments, Xavier Masramón and Francisco Hernández for statistical advice and Juan Martín for facilitating the authors' work.Authors' contributions AL revised the study design and requested the analysis to be performed. He carried out the interpretation of data and drafted the original and successive versions of the article. GGT and AOA were active investigators recruiting patients, and were involved in the interpretation of the data and in the critical and intellectual revision of the manuscript. BA had the idea to develop this study and was in charge of the coordination of the study. She also contributed to interpretation of data and contributed to the drafting of the manuscript. All authors had full access to the data and have given final approval of the version to be published. The guarantor of the article is AL Competing interests Dr Lanas did not receive any compensation for conducting this study. Dr Lanas has received research support for investigator-initiated projects from Pfizer and AstraZeneca. He is advisor for, or has participated in, studies conducted by AstraZeneca, Pfizer, Bayer and Nicox. Beatriz Armada is currently employed by Pfizer Inc. and owns Pfizer stock. The other authors have not declared any conflict of interest
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Genome-wide comparative analysis reveals human-mouse regulatory landscape and evolution

Olgert Denas
Richard Sandstrom
Yong Cheng
Kathryn Beal
Javier Herrero
Ross C Hardison
James Taylor
Genome-wide comparative analysis reveals human-mouse regulatory landscape and evolution
10.1186/s12864-015-1245-6R E S E A R C H Open Access
Background: Because species-specific gene expression is driven by species-specific regulation, understanding the relationship between sequence and function of the regulatory regions in different species will help elucidate how differences among species arise. Despite active experimental and computational research, relationships among sequence, conservation, and function are still poorly understood.Results:We compared transcription factor occupied segments (TFos) for 116 human and 35 mouse TFs in 546 human and 125 mouse cell types and tissues from the Human and the Mouse ENCODE projects. We based the map between human and mouse TFos on a one-to-one nucleotide cross-species mapper, bnMapper, that utilizes whole genome alignments (WGA). Our analysis shows that TFos are under evolutionary constraint, but a substantial portion (25.1% of mouse and 25.85% of human on average) of the TFos does not have a homologous sequence on the other species; this portion varies among cell types and TFs. Furthermore, 47.67% and 57.01% of the homologous TFos sequence shows binding activity on the other species for human and mouse respectively. However, 79.87% and 69.22% is repurposed such that it binds the same TF in different cells or different TFs in the same cells. Remarkably, within the set of repurposed TFos, the corresponding genome regions in the other species are preferred locations of novel TFos. These events suggest exaptation of some functional regulatory sequences into new function. Despite TFos repurposing, we did not find substantial changes in their predicted target genes, suggesting that CRMs buffer evolutionary events allowing little or no change in the TFostarget gene associations. Thus, the small portion of TFos with strictly conserved occupancy underestimates the degree of conservation of regulatory interactions.Conclusion: We mapped regulatory sequences from an extensive number of TFs and cell types between human and mouse using WGA. A comparative analysis of this correspondence unveiled the extent of the shared regulatory sequence across TFs and cell types under study. Importantly, a large part of the shared regulatory sequence is repurposed on the other species. This sequence, fueled by turnover events, provides a strong case for exaptation in regulatory elements.

Background

Most eukaryotic gene regulation occurs at the level of transcription [1,2]. This form of regulation involves the interaction of transcription factors (TFs) with element and function specific DNA sequences, referred to as cisregulatory modules (CRMs; reviewed by [3]). Their modular organization allows for elaborate regulatory mechanisms and fine control of gene expression [4].

Evolutionary changes in CRMs have a profound effect on species divergence. Many studies suggest that species specific CRMs are the defining factor for species identity [5][6][7]. Differences in human and chimpanzee, for example, are almost completely due to changes in functional noncoding sequence [8]. Efforts to locate the "human gene" have only revealed differences in a small number of genes [9][10][11]. Moreover, comparisons of organisms at the extremes of eukaryotes show that the genes encoding TFs and signaling components (e.g. for temporal/ spatial gene expression patterns) are largely conserved [4]. Taken together, this evidence suggests a hierarchical organization of regulatory networks. Modules at the top, performing essential upstream functions, span large evolutionary distances virtually unchanged, while lower level modules, involved in peripheral sub-networks, show a higher level of adaptation [12]. Under this model, part of the regulatory material must be under purifying selection and thus conserved between any two species of sufficiently small evolutionary divergence.

Evidence of conservation of regulatory sequence among species has inspired a series of computational methods. Some of these methods use machine learning and phylogenetic profiling to discover CRMs (reviewed by [13]), others use comparative analysis to identify genomic material under purifying evolutionary constraint as a representative of the functional genome (reviewed by [14]).

At the same time, a number of experimental studies suggest that while sequence might encode enough information to drive TF binding [15], the way this information is encoded is not trivialsimilar sequence does not necessarily translate in similar function and vice versa. For example, regulatory elements have been found to tolerate sequence rearrangements [16] or even be under positive selection while maintaining the downstream regulatory machinery unchanged [17,18]. Recently, it was found that GATA1 changes its motif preferences during cell differentiation [19], serving as an example of TFs having multiple preferred motif sequences [20] while maintaining a regulatory function.

Both computational and experimental approaches have provided valuable insight into the regulatory portion of the genome, but they have limitations. Computational methods are biased toward well-annotated and evolutionarily conserved genomic regions, indeed comparative analyses based on evolutionary conservation alone ignore species-specific functional elements. Experimental approaches based on direct genome wide measurements of TFos are a powerful resource for the identification and analysis of TF binding sites. However, the number of cell types and TFs assessed so far has often been limited.

With some studies pointing at conservation, others at divergence, and others yet at turnover of motifs and the importance of occupancy, the level of constraint on the CRMs is still an open question. This apparently contradicting evidence can be reconciled by considering conservation as specific to the TF or the cell type.

In this study, we combined several types of functionassociated datasets from a large number of TFs from a wide variety of tissues and cell lines in both mouse and human. Our main data source is ChIP-Seq experiments performed by the mouse and human ENCODE projects. These data give evidence on the locations where TF have come close enough to the DNA to cross-link in cells. The question remains whether these locations, termed TFos [13], could represent direct binding to a specific motif in the DNA or co-association with another TF. However, there is evidence that the TFos are active in assays for regulatory function at a far higher rate than non-occupied DNA segments, or DNA segments predicted as regulatory based on sequence motifs or conservation making them likely to have regulatory function. We also used DNase I Hypersensitive Sites (DHS) generated by the Human [21] and the Mouse ENCODE [22] projects. DHS regions are markers of regulatory DNA and have underpinned the discovery of all classes of cis-regulatory elements including enhancers, promoters, insulators, silencers and locus control regions. We conduced a comparative analysis, by integrating TFos with human-mouse WGA, gene annotations, and TF-gene associations. We have also compiled the data and annotations derived from this study into a database to serve as a resource in exploring the relationship between sequence evolution and function of regulatory elements (Additional file 1: Figure S1).

Results

An alignment based map for human-mouse TFos

Differences between present day genomes are the result of a series of evolutionary events originating on their most recent common ancestor [23]. Many of these events can be explained under probability models and represented in the form of WGA (See [24] for a review). Sequence that has undergone a moderate number of evolutionary events will appear aligned, while highly divergent regions will usually be left un-aligned.

We used WGA to obtain a consistent map for TFos across human and mouse (Figure 1(A)). These alignments provide long, inferred homologous regions in the form of chains of gapless blocks. They can account for inversions, translocations, duplications, large-scale deletions, and overlapping independent deletions. We considered chained blastz [25] alignments available from the UCSC browser [26] and the 12-way mammalian WGA from the EPO pipeline [27] available from Ensembl version 65 [28]. To choose the most appropriate alignment for our mappings we used several criteria: symmetry, coverage, feature enrichment, and other method specific properties.

Symmetry is important for unambiguously mapping features from one genome to the other and back. The EPO based map is inherently symmetric; we only need to extract human-mouse alignments. We remove segmental duplications based on criteria such as the score or the length of the alignment to obtain an unambiguous map. UCSC alignments, on the other hand, are based on blastz pairwise alignments, which are not symmetrica humanmouse alignment is different from a mouse-human alignment in general. However, it is possible to circumvent the problem by using a netting procedure and chaining again only the first layer in the resulting net. This corresponds to heuristically cleaning overlapping chains based on their score. The resulting reciprocal map is identical to the one derived from the mouse-human alignments at the cost of losing around 10% sequence coverage on both species [29].

UCSC alignments align a slightly larger fraction of the mouse genome (31% vs. 28% for UCSC and EPO respectively) to human. However, EPO alignments can assign a substantially higher amount of inserted mouse sequence (28% vs. 1.9%). In total 65.7% of the mouse genome remains unmapped by UCSC alignments and 42.5% by EPO alignments.

Next, we computed the number of features that each alignment could map on the other species for all available cell types in human and mouse. We found that UCSC alignments mapped more features on the other species (Additional file 1: Figure S2 and Tables S4-S6).

Based on the above considerations, we adopted the reciprocal UCSC alignments. The alignments and the comparative pipeline described here were adopted by the Mouse Encode Consortium for the cross-species mapping of TFos.

Function and sequence conservation of TFos

We processed TFos generated by ChIP-Seq for 206 human and 55 mouse cell lines and tissues [22], with a variable number of factors for each cell or tissue (from 1 to 109 for human and from 1 to 38 for mouse). The data were generated by the Human and the Mouse ENCODE projects. The ChIP-Seq experiments on mouse were conducted following the human ENCODE guidelines [30]. Data processing, including Irreproducible Discovery Rate (IDR) analysis, was done using a uniform data processing pipeline for both datasets.

Elements recovered by the ChIP-Seq experiments and the subsequent peak-calling pipeline were subjected to thresholds for False Discovery Rate at 1% and IDR at 2%. We then filtered these sets to retain only those TFos showing DHS enrichment, thereby increasing our confidence in the functional role of the filtered elements. Roughly 41.63% of ChIP-Seq peaks were filtered out from each species by DHS filtering. The resulting data contained 5330864 and 727680 elements covering 121.08 Mb and 31.6 Mb of the genome for human and mouse respectively (Additional file 1: Table S3). The higher human coverage is due to the larger number of assays available in human.

We asked whether the selected putative regulatory material is significantly conserved. At the sequence level, we find that the fraction of TFos intersecting homologous regions is higher than one would expect by chance (Binomial test 0.99% CI: (0.7218, 0.7228) and 0.99% CI: (0.6908, 0.6936) with expected values of 0.3129 and 0.3648 for human and mouse respectively). At the TF-binding activity level (i.e., having TF occupancy by any TF), mapped human and mouse TFos overlapped TFos on the other species more often than expected by chance (Binomial test 0.99% CI: (0.5358, 0.5371) and 0.99% CI: (0.7883, 0.7913) with expected values 0.0231 and 0.0857 for human and mouse respectively). Significant conservation at the TF-binding activity level continues to hold largely for individual TFs-cell pairs (Additional file 1: Figures S3-S4). These results indicate that most of these human and mouse regulatory regions have been under selective pressure both at the sequence and functional level.

Despite the selective pressure on the TFos, the data suggest extensive evolution of the regulatory material between human and mouse. On average 74.2% and 74.9% of TFos can be mapped on the other species resulting in a 36.1% and 31.4% of regulatory sequence coverage that is lineage-specific for human and mouse respectively (Figure 1(B) and Additional file 1: S5-S12). Importantly, we found that the extent to which the remaining regulatory material is conserved varies substantially between species and among cell types and TFs. The variability fits with previous studies on smaller numbers of TFs and cell types, which have emphasized both, conservation of TFos [31] and extensive regulatory sequence evolution [32].

Binding signal differences between functionally and sequence conserved TFos

We focus our TFos comparative analysis on two Tier 1 ENCODE cell lines and their mouse analogs as chosen by the Mouse ENCODE consortium. The data consist of 17 TFs from human chronic myelogenous leukemia cell line (K562, [33]) and mouse erythroleukemia cell line (MEL, [34]); and 15 TFs from lymphoblast cell lines (GM12878 vs. CH12) (Additional file 1: Table S2). In total we have thus selected 442527 and 215251 TFos covering 33.61 Mb and 18.77 Mb on human and mouse respectively. We mapped TFos of a species to homologous locations in the other and asked what function do those sites show.

The homologous site of a TFos can be: (i) not occupied by TFs, in which case we call the TFos SeqCons; (ii) repurposed, thus active in another cell or bound by another factor in the same cell type, in which case we call the TFos FunctActive; or (iii) bound by the same factor on the same cell type, in which case the TFos is called FunctCons. SeqCons and FunctActive elements represent differences in human and mouse TF binding patterns due to TFos loss, gain, or both. Turnover occurs in the case of loss and gain of the same TFos at different, but nearby, positions. However, more complex differences can arise when the loss of one TFos is followed by compensatory gains of TFos of other TFs. For the pairs of analogous cell types in human and mouse, the largest proportion of TFos (considered together) are FunctActive, while the relative proportions of FunctCons and SeqCons vary between cell types and species (Figure 2(A)).

Conservation of occupancy in the other species was associated with peak signal strength for several cell type-TF pairs. For example, binding signal on FunctCons or FunctActive elements was higher than that on SeqCons elements for 59% and 50% of cell type-TF combinations for human and mouse respectively (with a Bonferronicorrected error rate of 1%; Additional file 1: Figures S13, S14). Overall, FunctCons peak signal is on average 1.3 and 1.9 times larger than SeqCons peak signal on human and mouse respectively (Additional file 1: Table S1).

TF binding site turnover and TFos repurposing

If SeqCons and FunctActive elements are the result of turnover of TF binding sites (TFBS) in human and mouse, then we should observe TFBS close to regions orthologous to TFBS on the other species. We discovered TFBS using motif discovery and motif matching on TFos for both species and analyzed their alignment. We found that for several TFs, SeqCons TFBS map within 150 bp of a TFBS on the other species. On average, 51% and 48% of SeqCons TFBS have been subject to turnover in a 150 bp neighborhood for human and mouse respectively (Figure 2(B)).

The large number of FunctActive elements (Figures 2(A) and Additional file 1: S15, S16) suggests recycling of TFos among cells and TFs. We examined this more carefully with a novel approach, based on the intuition that extensive recycling would allow any set of TFos in a comparison species to identify the occurrence of specific TFos in the reference species (after cross-species mapping). To conduct this analysis, we sampled (multiple times) mapped TFos from k assays from the comparison species, took the union of these TFos, and computed the number of TFos a given assay (in the reference species) shares with this union. The results strongly support extensive recycling of TFos. Specifically, we find that about 93% of the (Mel, Max) assay is covered by TFos from just 35% of the query assays. As k increases, coverage saturates (Figures 2(C) and Additional file 1: S17, S18). This ability of different assays in the comparison species to capture TFos in the reference species is substantially different than what is obtained by simulating non-associated assays (Additional file 1: Figure S19).

We tested the hypothesis that new TFos tend to arise over existing TFosactive in other cells or occupied by other factors. Under the null hypothesis, the fraction of FunctActive elements with respect to the non-FunctCons elements should not be significantly higher than the fraction of the genome that can turn into a FunctActive TFos, should a new TFos occur (See Methods for details). We found that fraction to be higher than expected (One sided Binomial test 99% CI: (0.377, 1.000) and (0.477, 1.000) with expected values 0.010 and 0.028 for human and  Table S2). In both cell lines, a small fraction of mappable regulatory elements is FunctCons (33% and 15% cell average for human and mouse respectively). A larger fraction (46% and 50% cell average for human and mouse respectively) is FunctActive (plots for each cell are in Additional file 1: Figures S15, S16). (B) Loss and gain of TF binding sites. We used TFos in our data to discover TF binding sites (TFBS) on both species. We mapped human (respectively mouse) TFBS to mouse (respectively human) and computed their distance to the closest mouse (respectively human) TFBS of the same TF on an analogous cell. Among these TFBS those with a positive distance but less than 150 bp contribute to the corresponding bar in the human (respectively mouse) subplot. In other words, each bar is the count of TFBS that were lost and gained within a 150 bp window of the original site. (C) Reuse of FunctActive TFos between different cells and factors. A point on the scatterplot indicates the number of TFos of the corresponding assay in the reference species that can be classified as FunctCons or FunctActive when considering a fixed size set of randomly chosen assays in the comparison species. We performed multiple computations for each chosen size of such sets. Lines indicate the accumulated number of FunctCons, FunctActive, and Seqcons TFos. In this figure, about 93% of the (Mel, Max) assay is covered by TFos from just 35% of the query assays (plots for other cells/TFs in Additional file 1: Figures S17, S18). mouse respectively), suggesting that existing CRMs are sites where new TFBS are likely to arise.

Turnover events or the appearance of novel TFBS lead to compositional changes in CRMs. However, these changes do not always reflect downstream in, for example, the set of target genes. Following [35] we extracted a set of TSS-enhancer connections based on synchronized DHS activity during the transcription of a gene. Restricting on 15,736 human-mouse orthologous genes [22], we were thus able to define putative target genes for 1928211 and 204758 TFos for human and mouse respectively. We observe that, similarly to FunctCons TFos, about 39% and 43% of FunctActive TFos retained at least one putative target gene for human and mouse respectively. Furthermore, the amount at which the set of target genes is conserved across species is not significantly different between the two classes of TFos (Kolmogorov-Smirnov test. p-values: < 2.2e-16 and < 2.2e-16 for human and mouse respectively). The example of the candidate enhancer linked to the ACAP3 gene in human illustrates this situation ( Figure 3). The DNA segment in mouse that is orthologous to the human enhancer has been repurposed such that it binds different TFs, but the mouse Acap3 gene is still the presumptive target.

Discussion

We reported on the construction and use of a map of functional elements between human and mouse based on WGA. This map is consistent and symmetrical in that it provides a one to one correspondence between genome elements in both directions. Furthermore, it constitutes an improvement over aligning TFos directly on the other species guided by gene orthology; this approach is likely to deteriorate with the distance of an element from the nearest gene with an ortholog on the other species.

Using this mapping approach, we were able to recover, on average, 75% and 73% of TFos in the other species, of which 63% and 78% are bound by any TFs and 13% and 25% by the same TF for human and mouse respectively. The rest of the regulatory material is species-specific either at the sequence or functional level and is likely to account for the phenotypic differences between human and mouse [8,12]. However, some of the species-specific material retains target genes suggesting that CRMs buffer changes toward downstream regulation [36].

One potential difficulty, which affects this analysis, is the variation that can be introduced by technical factors such as signal thresholding or by differences in the environment of the cells being compared. By using data processed for reproducibility under the stringent standards developed by the Human and Mouse ENCODE projects, restricting to regions overlapping DHS for greater confidence, considering the TF activity on analogous cell types, and employing statistical controls we hope to have ameliorated these issues.

Previous studies have mapped the binding patterns of TFs between species, including mouse and human, by focusing on a single cell type and a few TFs (e.g., [32]). While these important studies have revealed substantial divergence in the binding patterns of TFs between species, they do not explore FunctActive elements or related dynamics such as repurposing. One important conclusion in our study is that the evolution of regulatory sequences varies considerably among TFs and cell types, and furthermore we document which TFos fall into the various evolutionary categories.

The traffic between noncoding functional and nonfunctional DNA in a genome is two-way. On the one hand, loss of a TFos can increase fitness, as dramatically illustrated by changes in the regulation of pitx1 in three-spined Figure 3 Conservation of presumptive gene targets for a repurposed TFos. We combined cross-species gene-gene and TFos-TFos associations and gene-TFos associations to determine whether the sets of target genes of FunctCons and FunctActive TFos differ significantly. Gene-gene association data are based on a set of orthologous genes between human and mouse produced by the mouse ENCODE consortium, gene-TFos data are based on synchronized DHS activity during gene transcription [35], and TFos-TFos associations are based on our cross-species map. In the figure, we show a human TFos of Mxi on K562 (empty oval) associated with PUSL1 and ACAP3. This TFos is FunctActive, since its analogous location (broken oval linked by the dashed line with spaces between ovals indicating insertions in mouse) in mouse is bound by other TFs on other cell types (not shown). However, its analogous site in mouse is linked to gene Acap3. Incidentally, ACAP3 and Acap3 are orthologous and in our gene-gene association set. The human TFos and its analogous site in mouse bind different TFs and are active in different cell types, but they share a target gene.

stickleback that lead to advantageous anatomical changes in a new environment. On the other hand, new functional noncoding sequence can arise from nonfunctional sequence through turnover [37]. The differences between the sizes of FunctActive and FunctCons pools suggests another source of functional noncoding material traffic, characterized by the exaptation of TFos into functional noncoding material with novel TF or cell type activity [38]. With a partial catalog of regulatory elements it is hard to distinguish the newly created functional material from the exapted one. However with ever-increasing numbers of experimental assays the picture should become more clear.

Conclusions

Despite previous elegant studies, the level of constraint on CRMs remains controversial. We provide further evidence that TFos are more conserved than random sequence and that conservation is cell and TF specific. Furthermore, we study the type of conservation of TFos and observe that 47.67% and 57.01% of TFos conserved at the sequence level for human and mouse respectively have been repurposed and are active in cell types or bind different TFs. We show how this sequence makes a case for TFos exaptation into new function. Finally, we find that repurposing does not necessarily lead to changes in the TFostarget gene association.

Comparative studies of CRMs involve a mapping strategy of regulatory elements, often by aligning elements or reads on both genomes, possibly guiding mapping with gene homologies. We implemented a multiple WGA based process for TFos mapping. Using multiple WGA we should have a more sensitive and consistent mapping process. Importantly, the mapper can provide one-toone mappings between species, which is very useful during analysis.

Methods

Data processing ChIP-Seq data

We download all of the ChIP-Seq data generated by the Human and the Mouse ENCODE from the ENCODE DCC. The pipeline that filters the original ENCODE peaks is available in the supplementary material.

Distal DHS-to-promoter connections As described in [35] many cell-selective enhancers become DHSs synchronously with the appearance of hypersensitivity at the promoter of their target gene. This has been used to infer a genome-wide DHS/enhancer-promoter connection set. Using a conservative list of orthologous genes [22] we inferred a list of correspondences between TFos regulating human-mouse orthologous genes.

EPO and UCSC based maps

We considered the EPO 12-way mammalian whole genome alignments from Ensembl project version 65 [28] and the UCSC human mouse chain and net alignments from the UCSC [26] genome browser to generate the reciprocal maps. Details and programs for processing of the alignments are available at https://bitbucket.org/bxlab/ mapper_comparisons.

Mapping strategy

We built and used a one-to-one nucleotide mapper (bnMapper) to map elements between human and mouse. The mapping is bijective, so reverse application of the mapping to a mapped nucleotide, returns the original nucleotide. Elements that span matching blocks of different chains and elements that map to multiple chromosomes are filtered out. The mapper and a detailed analysis of performance between EPO and UCSC alignments are available at https://bitbucket.org/bxlab/mapper_comparisons. Details on the mapping strategy are on Section 1.1 of the Additional file 1.

Significance tests

For a given genomic region of coverage C, the number of n randomly positioned features over the genome (length L) would follow a Binomial (n, C/L). For significance of TF conservation at the sequence level we set L, C, and n, to be the length of the genome, total TF coverage, and total number of peaks. A success event is an overlap of 1 bp with the one to one mappable sequence. Similarly, for functional conservation, L, C, and n, are set to total one to one mappable sequence, coverage of mapped peaks, and number of mapped peaks overlapping with peaks on the target genome, respectively.

For the paired Wilcoxon tests we consider all TFs within a cell line that appear on both species. Ranks are computed from FunctCons to SeqCons ratios of corresponding TFs on each species.

To test that a new TFos is more likely to occur in an existing TFos, possibly occupied by another TFs or active in another cell, consider the following quantities with respect to a fixed genome:

The number of non-FunctCons TFos in this genome not less than the number of new TFos in this genome. The inequality accounts for TFos deletions on the other genome. We write nFCo > = nFCt. Coverage of SeqCons of the other genome after being mapped in this genome and FA elements in this genome not less than regions in this genome that would become FA should a new TFos occur. The inequality accounts for those regions that would become FunctCons should a new TFos appear. We write Mo > = Mt.

Figure 1
1We built a one to one map between the human and mouse genomes from their WGA. (A) The track diagram shows TFos (unfilled features on outer tracks) and how they map to the other species (middle track). The thick part of the middle track is the mappable DNA. The features on the middle track can be mapped on the other species and are: SeqCons if not overlapping features from the other species (respective color), or FuncCons or FunctActive otherwise (green). The Venn diagrams use the same color code to show the amount (rounded to the closest Mb) of mappable material from one species to the other for DNA sequence, TFos, and DHS. For example, human TFos cover 121 Mb of the human genome. When mapped to mouse, these TFos cover 83 Mb of the mouse genome and 12 Mb of the sequence covered also by mouse TFos. The diagram for DHS is labeled in a similar fashion. (B) The distribution of mappable TFos nucleotides across cell types. The box-plot for each cell type summarizes the distribution of values for the fraction of nucleotides covered by TFos that can be mapped on the other species.

Figure 2
2Conservation and re-use of TFos. (A) The distribution of SeqCons, FunctActive, and FunctCons regulatory elements summarized for all human mouse analogous cell lines (Additional file 1:
AcknowledgmentsThanks to members of the Taylor and Hardison labs and the Mouse ENCODE consortium for their input and discussion. This project was supported by grants from the National Institutes of Health to RCH and JT, specifically American Recovery and Reinvestment Act (ARRA) funds through grant number RC2 HG005573 from the National Human Genome Research Institute, and grants with numbers R01 DK065806 and R56 DK065806 from the National Institute for Diabetes, Digestive, and Kidney Diseases. This work was also supported by the Wellcome Trust (grant number 095908) and the European Molecular Biology Laboratory. The funding body had no role in the design of the experiment, in the collection, analysis, and interpretation of data, in the writing of the manuscript, or in the decision to submit the manuscript for publication.Length of this genome. We write L. The number of FunctActive Tfos in this genome. We write FA.Here we are making the assumption that FunctCons elements have existed before human-mouse split and FunctActive are a result of a deletion or creation (or both) after the human-mouse split. Thus, the fraction FA/nFCt is that of new elements that occurred in existing TFos involving other TFs or cell types, whether the fraction Mt/L indicates the chance of a new TFos to become FunctActive should it occur randomly in the genome. Under the alternative hypothesis, FA/nFCt > Mt/L. However, because Mt/L < = Mo/L and FA/nFCo < = FA/ nFCt, it suffices to show that FA/nFCo > = Mo/L. Notice that we observe all the quantities in the last expression.Data accessUCSC alignments can be downloaded from the UCSC website http://hgdownload.soe.ucsc.edu/goldenPath/hg19/ vsMm9/.UCSC bijective (netted) alignments, the list of one-toone orthologous genes, the list of DHS-gene associations, and the list of Mouse ChIP-Seq TFos and DHSs can be downloaded from the Mouse ENCODE web portal http://mouse.encodedcc.org/data and http://www. mouseencode.org/.The mapping software can be freely downloaded as part of the bx-python software library from https://bitbucket. org/james_taylor/bx-python/wiki/Home.Ensembl EPO 12-way alignments version 65 can be downloaded from the Ensembl (December 2011) at ftp:// ftp.ensembl.org/pub/release-65/emf/ensembl-compara/epo_ 12_eutherian/ and http://www.ebi.ac.uk/~kbeal/species_ mapper/epo_547_hs_mm_12way_mammals_65.out.gz.The human ChIP-Seq TFos and DHSs can be downloaded from the Human ENCODE website at http://genome.ucsc. edu/ENCODE/.Additional fileAdditional file 1: Supplemental Figures and Tables.Competing interestsThe authors declare that they have no competing interests.Authors' contributions OD wrote the main text, performed the comparative and statistical analysis, and wrote the mapper tool. RS helped designing the mapper, carried out statistical analysis for choosing the best whole genome alignments, prepared the DHS data, and the gene -TFos link data. YC helped on the design of the mapper and performed analyses on the whole genome alignments. KB worked on the design of the mapper and the preparation of the whole genome alignments. JH worked on the design of the mapper and the preparation of the whole genome alignments, helped design the study and write the main text. RC wrote the main text and designed the study -JT wrote the main text, carried out statistical analyses, and designed the study. All authors read and approved the final manuscript.Submit your next manuscript to BioMed Central and take full advantage of:
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