Assessment of clusters of transcription factor binding sites in relationship to human promoter, CpG islands and gene expression

Background Gene expression is regulated mainly by transcription factors (TFs) that interact with regulatory cis-elements on DNA sequences. To identify functional regulatory elements, computer searching can predict TF binding sites (TFBS) using position weight matrices (PWMs) that represent positional base frequencies of collected experimentally determined TFBS. A disadvantage of this approach is the large output of results for genomic DNA. One strategy to identify genuine TFBS is to utilize local concentrations of predicted TFBS. It is unclear whether there is a general tendency for TFBS to cluster at promoter regions, although this is the case for certain TFBS. Also unclear is the identification of TFs that have TFBS concentrated in promoters and to what level this occurs. This study hopes to answer some of these questions.
Background
Understanding the regulation of gene expression is a crucial issue in molecular biology. Since gene expression is mainly regulated by transcription factors (TFs), the elucidation of relationships among TFs, their binding sites (TFBS) and their controlling genes, is of great importance.
Although TFBS can be predicted by computer searches on DNA sequences, false positives (FP) are often produced. Several computer programs use position weight matrices (PWMs) [1] to predict TFBS in silico, including MatInspector [2], MATCH [3], and TFBS perl modules [4]. PWMs represent positional base preferences or frequencies constructed by a set of experimentally determined TFBS, and typically correspond to a single TF. The transcription factor database, known as TRANSFAC, is a widely used collection of PWMs [5]. TRANSFAC provides several PWMs for single TFs with different quality levels. Computer programs predict TFBS from DNA sequences, which are the same or similar to known TFBS. The low information contents in the matrices leads to many false positives, due to the weak preference or shortness of the site length (6–30 bp). Various strategies have been proposed to allow correct identification of true positives (TPs) from predicted TFBS. One approach is to employ information from conserved regions in DNA sequences between different species, known as phylogenetic footprinting. Bayes block aligner (BBA) is a tool used to extract conserved regions from an alignment of two DNA sequences [6]. It was demonstrated that it could identify binding sites of muscle-specific transcription factors [6]. Another approach is to identify multiple TFBS that form a structural cluster on a DNA sequence coordinate. This seems a reasonable technique because the density of predicted TFBS in promoter sequences is reported to be higher than non-promoter sequences, especially in the region 300 bp upstream from the transcription start site [7]. Genes are regulated by interactions with multiple functional TFs in metazoans [8]. Therefore, many promoter prediction programs, such as promoterscan [9], TSSG, and TSSW [10], have been developed based on the density of TFBS. The identification of genuine TFBS by searching clusters of predicted TFBS has been successful; however, these studies were evaluated with only specific genes and TF sets, such as those found in Yeast[11], Drosophila (early developmental enhancer) [12-14], liver [15], LSF and muscle specific regulatory regions [16,17]. It is unknown whether this method is applicable to other species, or genes. Although many vertebrate promoter sequences have CpG islands (CGI), the relationship between clusters of predicted TFBS and CGI is often underestimated [18]. Another strategy for the identification of putative TFBS includes a combinatorial approach that uses both phylogenetic footprinting and cluster analysis [12,15,19]. The program rVISTA utilizes information from conserved regions between human and mouse, in addition to clusters of TFBS predicted by the MATCH (BIOBASE) program [19]. This approach was evaluated using several known TFs (AP-1, NFAT, and GATA-3) and genes from the cytokine gene cluster. It remains unclear whether the properties used for clusters of TFBS are general and can be applied to other TFs or regulatory regions. Several reports have described methods for determining the statistical significance of predicted TFBS [11,12,17,20-22]. These studies assume the use of appropriate PWMs to identify clustered TFBS. To determine if a particular cluster is genuinely related to the promoter, it is important to assess clusters of predicted TFBS for each individual PWM. This is done using real non-promoter sequences for the appropriate selection of the PWM and for the interpretation of clusters of predicted TFBS. Most of these studies use specific sets of coregulated genes to identify common predicted TFBS clusters, and therefore cannot be applied directly to the study of general properties of promoters.
In this study, we developed a measure that evaluates the degree of concentration of predicted TFBS to clarify whether predicted TFBS have a tendency to cluster in human promoter sequences rather than in non-promoter sequences for each PWM. We identified some PWMs in which predicted TFBS clusters occur more significantly in promoter than non-promoter sequences and vice versa. Using partial correlations among three properties (promoters, CGI and clusters of predicted TFBS), we identified two PWM groups, (1) those in which TFBS cluster in promoters as a result of the presence of CpG islands, and (2) those in which TFBS cluster in promoters independent of CpG islands. We show that transcription factors corresponding to the latter PWM group tend to be tissue-specific. In summary, this analysis is useful for the interpretation of predicted TFBS in regulatory regions.
Results
Divergent preferences of TFBS for promoter sequences
We determined whether predicted TFBS formed clusters in human promoter sequences or in non-promoter sequences for each PWM using the cluster score described in the Method section. The higher the cluster score (derived from a logarithm of the p-value), the stronger the cluster of predicted TFBS is related to the promoter sequence. The threshold T, used to determine whether a cluster of predicted TFBS is found on a sequence, was calculated simultaneously. Since a prediction for the presence of a TFBS was performed for each PWM, an assessment for TFBS clusters was performed using the cluster score for each PWM. As a result, a number of PWMs do not tend to have clusters of TFBS in the promoter sequence. We observed a divergence of cluster scores. Of the 199 vertebrate PWMs in TRANSFAC, 94 (47%) PWMs had significantly high cluster scores, while 22 (11%) PWMs had significantly low cluster scores. The remaining 83 (42%) PWMs did not show significant cluster scores. A p-value of 1.0% was used to identify the above PWM set with Bonferroni correction for multiple testing ([23] Section 3.8). Figure 1 shows a histogram of cluster scores. Although these results were derived from genes on chromosome 20, the results from other chromosomes were similar as described in the following subsection. PWMs with high cluster scores are shown in Table 1. Some of the PWMs have thresholds T (of accumulated score C) equal to or less than 1.0. This indicates that the occurrence of single predicted TFBS is more discriminative than clusters. Sequence logo [24] of the top three PWMs are depicted in Fig. 2-(a). See additional file 1 'PWMs sorted by cluster score' for the entire PWM list.
Figure 1
A histogram of cluster scores for PWMs.Each number of X-axis indicates the maximum score of PWMs in the bin.
Table 1
Top 50 PWMs for chromosome 20 sorted by cluster score S in descending order. Each column represents rank number, accession number in TRANSFAC, identifier in TRANSFAC, cluster score, and threshold.
Rank
ACCESSION
ID
S
T
1
M00736
E2F1DP1_01
189.3
2.75
2
M00332
WHN_B
176.0
1.90
3
M00652
NRF1_Q6
122.0
0.93
4
M00649
MAZ_Q6
117.2
4.35
5
M00491
MAZR_01
111.4
1.78
6
M00739
E2F4DP2_01
103.8
0.93
7
M00737
E2F1DP2_01
103.6
0.94
8
M00108
NRF2_01
81.4
0.92
9
M00665
SP3_Q3
72.1
2.39
10
M00706
TFIII_Q6
61.4
4.23
11
M00740
E2F1DP1RB_01
58.4
0.90
12
M00324
MINI20_B
58.2
1.61
13
M00032
CETS1P54_01
57.3
3.70
14
M00743
CETS168_Q6
51.1
1.75
15
M00341
GABP_B
48.6
0.88
16
M00055
NMYC_01
41.1
0.90
17
M00329
PAX9_B
39.2
0.73
18
M00243
EGR1_01
37.3
0.87
19
M00072
CP2_01
36.5
1.66
20
M00054
NFKAPPAB_01
35.5
0.85
21
M00056
MYOGNF1_01
35.1
1.34
22
M00694
E4F1_Q6
35.0
0.86
23
M00738
E2F4DP1_01
34.9
0.91
24
M00143
PAX5_01
34.7
0.84
25
M00235
AHRARNT_01
34.6
0.92
26
M00698
HEB_Q6
33.6
0.91
27
M00039
CREB_01
33.6
1.00
28
M00514
ATF4_Q2
33.1
1.71
29
M00650
MTF1_Q4
31.4
0.88
30
M00194
NFKB_Q6
30.8
0.82
31
M00007
ELK1_01
30.0
0.85
32
M00733
SMAD4_Q6
29.7
0.81
33
M00261
OLF1_01
28.8
0.84
34
M00017
ATF_01
26.7
0.98
35
M00053
CREL_01
25.6
0.81
36
M00691
ATF1_Q6
25.5
0.89
37
M00244
NGFIC_01
25.2
0.88
38
M00041
CREBP1CJUN_01
24.9
1.00
39
M00086
IK1_01
24.2
0.90
40
M00287
NFY_01
24.0
1.95
41
M00466
HIF1_Q5
22.7
0.90
42
M00634
GCM_Q2
22.6
0.84
43
M00273
R_01
21.8
0.85
44
M00373
PAX4_01
21.7
2.57
45
M00097
PAX6_01
21.5
1.15
46
M00134
HNF4_01
21.1
0.64
47
M00670
TCF1P_Q6
21.1
0.80
48
M00057
COMP1_01
21.1
0.59
49
M00035
VMAF_01
21.0
1.32
50
M00222
HAND1E47_01
20.3
0.81
Figure 2
Sequence logos. (a) Top three PWMs from Table 1, (b) representative PWMs from Table 2, (c) representative PWMs from Table 3.
Cluster scores for different datasets
To assess the robustness of the cluster score we compared cluster scores for different datasets from chromosomes 20, 21 and 22, respectively. Fig. 3 shows the correlation of cluster scores between chromosomes 20 and 21 (a), and between chromosomes 20 and 22 (b). Some PWMs, the matches of which were detected on less than 50 subsequences, are not shown. The correlation coefficient points were 0.91 (a) and 0.93 (b).
Figure 3
Title: Correlation of cluster scores (a) between chromosomes 20 and 21, (b) chromosomes 20 and 22. Each dot represents a distinct PWM (defined by the TRANSFAC matrix). The correlation coefficients were (a) 0.91 and (b) 0.93.
Correlations among promoter sequences, CpG islands, and clusters
About half of the human coding genes have a compositional bias for CGI over transcription start sites [25]. It is possible that some of the predicted TFBS clusters might be the consequence of the existence of a CGI. To investigate this possibility, we computed partial correlation coefficients of three categories of promoters, CGI and predicted TFBS clusters. In general, a partial correlation coefficient measures the correlation between any pair of variables when other, specified variables, have been held constant. For example, a partial correlation coefficient rIC.P is the correlation between I and C while controlling for P, where I denotes CGI, C denotes accumulated score (strength of predicted TFBS clusters, see Methods) and P denotes promoters. If we calculate simple correlation coefficients, rPI = 0.69, rIC ranged from -0.25 to 0.57 for various PWMs, and rPC ranged from -0.24 to 0.53 for various PWMs. These correlation coefficients are apparent ones. The partial correlation coefficients provide essential information and pure correlations, without the effect of the third variable. Fig. 4 shows a plot of rIC.P against rPC.I for various PWMs. For most of the PWMs, rPC.I is positive, although not particularly high (<0.3). This implies a correlation between clusters of these PWM matches and promoter sequences, separate to the effect of CGI. For the PWMs in the right circle in Fig. 4, rPC.I is high and rIC.P is approximately zero, where the cluster is more correlated with the promoter than the CGI. Some PWMs have a negative rPC.I, implying the absence of promoter sequences for these PWM matches. For the PWMs in the top circle in Fig. 4, rIC.P is high and rPC.I is approximately zero, suggesting that the correlation between promoters and clusters for these PWM matches is attributable to the presence of the CGI. While these promoters and clusters do not correlate directly, they appear to correlate because both are associated with CGI.
Figure 4
Title: Plot of rIC.P against rPC.I for various PWMs. The top circle is the area where rPC.I is around zero and rIC.P is high. The right circle is the area where rIC.P is around zero and rPC.I is high. The two circles were drawn manually. Ideal CGI-related and CGI-independent PWMs are to be plotted in the top and right circles, respectively.
Using these two values, we identified two PWM sets, (1) a CGI-related set (37 PWMs, Table 2) in which TFBS clusters are correlated with CGI (independent of promoter), and (2) a CGI-independent set (54 PWMs, Table 3) in which clusters of TFBS are correlated with promoters (independent of CGI). These sets were used for the following analysis.
Table 2
CGI-related PWMs in descending order of rIC.P = Y. The columns are: rank number, accession number, Identifier in TRANSFAC, rPC.I (=X), rIC.P (=Y), cluster score and threshold.
Rank
ACCESSION
ID
X
Y
S
T
1
M00332
WHN_B
0.09
0.43
158.4
1.9
2
M00736
E2F1DP1_01
0.06
0.39
151.8
2.6
3
M00739
E2F4DP2_01
0.09
0.29
91.4
0.9
4
M00737
E2F1DP2_01
0.06
0.27
81.9
0.9
5
M00108
NRF2_01
0.09
0.25
72.6
0.9
6
M00055
NMYC_01
0.05
0.25
34.6
0.9
7
M00235
AHRARNT_01
0.02
0.23
26.8
0.9
8
M00740
E2F1DP1RB_01
0.04
0.23
48.1
0.9
9
M00652
NRF1_Q6
0.05
0.22
105.3
0.9
10
M00466
HIF1_Q5
0.01
0.22
19.7
0.9
11
M00341
GABP_B
0.1
0.19
46.6
0.9
12
M00738
E2F4DP1_01
0.02
0.19
28.6
0.9
13
M00538
HTF_01
0
0.16
9.7
0.8
14
M00694
E4F1_Q6
0.03
0.16
23.6
0.9
15
M00743
CETS168_Q6
0.13
0.14
47.1
1
16
M00650
MTF1_Q4
0.04
0.14
22.6
0.9
17
M00243
EGR1_01
0.07
0.12
32.4
0.9
18
M00251
XBP1_01
0.01
0.12
7.8
0.9
19
M00691
ATF1_Q6
0.07
0.12
17.3
0.9
20
M00236
ARNT_01
0.02
0.11
6.5
1
21
M00143
PAX5_01
0.09
0.11
25.7
0.8
22
M00273
R_01
0.06
0.11
23.8
0.8
23
M00244
NGFIC_01
0.06
0.1
23
0.9
24
M00280
RFX1_01
0.06
0.1
11.1
0.9
25
M00121
USF_01
0.03
0.1
7.6
1
26
M00287
NFY_01
0.04
0.1
21.3
1.9
27
M00039
CREB_01
0.04
0.09
23.2
1
28
M00309
ACAAT_B
0.04
0.09
6.8
0.9
29
M00651
NFMUE1_Q6
0.03
0.09
13
1.8
30
M00017
ATF_01
0.06
0.08
19.2
1
31
M00481
AR_01
0.05
0.08
7.5
0.8
32
M00041
CREBP1CJUN_01
0.04
0.08
20.4
1
33
M00040
CREBP1_01
0.03
0.08
4.7
0.9
34
M00114
TAXCREB_01
0.02
0.06
7.3
0.9
35
M00279
MIF1_01
0.02
0.06
10.9
1.8
36
M00246
EGR2_01
0.04
0.06
9.7
0.9
37
M00085
ZID_01
0.05
0.06
8
0.8
Table 3
CGI-independent PWMs in descending order of rPC.I (=X). The columns are: rank number, accession number, Identifier in TRANSFAC, rPC.I (=X), rIC.P (=Y), cluster score and threshold.
Rank
ACCESSION
ID
X
Y
S
T
1
M00491
MAZR_01
0.27
0.15
117.4
1.8
2
M00706
TFIII_Q6
0.24
0.06
52.7
3.5
3
M00324
MINI20_B
0.22
0.1
53.2
0.8
4
M00056
MYOGNF1_01
0.22
0
31.6
1.3
5
M00649
MAZ_Q6
0.21
0.19
114.4
3.7
6
M00665
SP3_Q3
0.2
0.14
67.7
1.7
7
M00032
CETS1P54_01
0.19
0.1
47.7
1.8
8
M00053
CREL_01
0.19
0.04
26.9
0.8
9
M00054
NFKAPPAB_01
0.19
0.06
33.5
0.9
10
M00632
GATA4_Q3
0.19
0.04
25.1
0.6
11
M00373
PAX4_01
0.19
0.05
26.1
0.6
12
M00072
CP2_01
0.19
0.08
32
0.9
13
M00733
SMAD4_Q6
0.18
0.05
26.3
0.8
14
M00134
HNF4_01
0.18
0.06
25.7
0.6
15
M00194
NFKB_Q6
0.18
0.02
28.5
0.8
16
M00445
XVENT1_01
0.17
0.01
19.9
0.7
17
M00057
COMP1_01
0.17
0.05
24.1
0.5
18
M00097
PAX6_01
0.17
0.06
24.1
0.5
19
M00104
CDPCR1_01
0.17
0.03
21.3
0.6
20
M00222
HAND1E47_01
0.17
0.02
20.4
0.8
21
M00626
EFC_Q6
0.17
0.05
22.6
0.6
22
M00745
LEF1_Q6
0.16
-0.02
15.9
0.8
23
M00707
TFIIA_Q6
0.16
0.03
20.2
0.7
24
M00086
IK1_01
0.16
0.06
24.1
0.9
25
M00329
PAX9_B
0.16
0.1
33.7
0.7
26
M00478
CDC5_01
0.15
0.03
19
0.6
27
M00670
TCF1P_Q6
0.15
0.06
22.7
0.8
28
M00257
RREB1_01
0.15
-0.02
15.8
0.8
29
M00007
ELK1_01
0.15
0.08
31
0.8
30
M00698
HEB_Q6
0.15
0.08
28.7
0.9
31
M00052
NFKAPPAB65_01
0.14
-0.05
9.4
0.9
32
M00514
ATF4_Q2
0.14
0.05
21.8
1.7
33
M00191
ER_Q6
0.14
-0.03
11
0.8
34
M00003
VMYB_01
0.14
0.05
18
0.8
35
M00261
OLF1_01
0.14
0.07
24.6
0.8
36
M00490
BACH2_01
0.13
-0.03
9.3
0.7
37
M00001
MYOD_01
0.13
-0.03
10.4
0.9
38
M00634
GCM_Q2
0.12
0.05
19.8
0.8
39
M00035
VMAF_01
0.12
0.06
17.5
0.7
40
M00340
ETS2_B
0.12
-0.08
5
0.8
41
M00005
AP4_01
0.12
0.01
14.1
0.8
42
M00701
SMAD3_Q6
0.11
0.03
11.4
0.8
43
M00531
NERF_Q2
0.1
-0.08
4.8
0.9
44
M00339
ETS1_B
0.1
-0.07
5.7
0.9
45
M00657
PTF1BETA_Q6
0.1
0
7.5
0.9
46
M00254
CAAT_01
0.1
-0.01
6.6
0.9
47
M00118
MYCMAX_01
0.09
-0.02
6.2
0.9
48
M00693
E12_Q6
0.09
-0.01
6.5
0.9
49
M00004
CMYB_01
0.08
0
7.1
0.9
50
M00238
BARBIE_01
0.08
0.02
9.4
0.9
51
M00648
MAF_Q6
0.07
0.01
5.8
0.8
52
M00002
E47_01
0.06
0.02
5.3
0.9
53
M00262
STAF_01
0.05
0
9.2
0.9
54
M00119
MAX_01
0.05
0.03
4.9
1
Correlation between clusters of predicted TFBS and gene expression
Since all widely expressed, or housekeeping, genes have CGI [25], it is possible that clusters of PWM matches for CGI-independent sets are associated with tissue specific promoters. For this reason we examined the relationship between clusters of PWM matches and the tissue specificity of the associated genes using published gene expression data ([26,27]). The two resources used for this analysis are not consistent. Some genes annotated as housekeeping genes in one resource are referred to as tissue specific in another resource. We refer to these genes as mixed annotated genes. Genes with associated expression data were analysed and of these 72 were identified among the gene set covering the three chromosomes used in this study. They included 12 housekeeping genes, 9 mixed annotated genes, and 51 tissue specific genes. With the CGI-independent PWM sets we detected promoters with clusters of PWM matches. These clusters have significantly high Z-scores (see Methods) based on the accumulated score C in randomly generated DNA sequences (as control) with the same dinucleotide frequency of each promoter sequence. Table 4 shows the 40 genes detected, the DCC score (described below), their tissue specificity and start_p score. These genes are sorted according to the DCC score indicating the extent of association with CGI-independent PWMs over CGI-related PWMs. Results show that tissue specific genes tend to have high DCC scores and that transcription factors corresponding to CGI-independent PWMs are related to tissue specific genes. If we extract 20 genes which have DCC scores equal to or higher than -0.03, 18 (90%) of these are tissue specific genes. The 72 genes with known gene expressions data included 51 tissue specific genes (71%). The p-value of the event of extraction was 0.04 under cumulative hypergeometric distribution. The p-value of the ranking of the two groups (11 housekeeping and 29 tissue-specific) in Table 4 was 0.01 by Wilcoxon rank test. Note that DCC is not correlated with the CGI score (start_p).
Table 4
The gene list sorted by DCC score. The genes, in which clusters of TFBS are found on promoters using CpG-related/independent PWMs, and tissue specificity, have been previously identified. HK denotes housekeeping. Tissue specific genes can be selected independent of CpG islands (start_p) using DCC score.
1
NM006272
0.43
brain
0
2
NM007341
0.4
muscle
0
3
NM002592
0.37
brain
0.86
4
NM001819
0.27
brain
0.68
5
NM004414
0.23
kidney
0.89
6
NM002999
0.19
kidney
0.73
7
NM003195
0.16
brain
0.73
8
NM002591
0.14
liver
0
9
NM000454
0.11
HK.liver
0.87
10
NM003312
0.1
liver
0.72
11
NM004339
0.09
brain
0.9
12
NM020708
0.08
brain
0.64
13
NM006870
0.05
HK
0.7
14
NM003277
0.04
lung
0.74
15
NM005194
0.04
brain
0.86
16
NM003610
0.01
brain
0.76
17
NM000355
-0.03
kidney
0
18
NM002430
-0.03
muscle
0.75
19
NM006767
-0.03
brain
0.74
20
NM005137
-0.03
muscle
0.76
21
NM003279
-0.05
muscle
0
22
NM004535
-0.05
brain
0
23
NM007019
-0.05
HK
0.72
24
NM013236
-0.07
HK
0.69
25
NM004175
-0.07
brain
0.72
26
NM001958
-0.07
muscle
0
27
NM001338
-0.13
vulva
0.84
28
NM002676
-0.14
HK
0.63
29
NM003098
-0.16
muscle
0.71
30
NM002854
-0.17
brain
0
31
NM002305
-0.23
HK
0
32
NM005080
-0.25
HK
0.84
33
NM001024
-0.25
HK
0.76
34
NM021974
-0.26
HK
0.63
35
NM014876
-0.3
HK
0.95
36
NM001098
-0.34
muscle
0.65
37
NM000071
-0.37
liver
0.8
38
NM006198
-0.37
brain
0
39
NM001675
-0.39
HK.muscle
0.8
40
NM005423
-0.68
brain
0
Discussion
Clusters of TFBS are an important property of regulatory regions [7,8,19,28]. To determine if this is a general tendency for PWM matches and all protein coding genes, we have developed a measure that evaluates the correlation between predicted TFBS concentrations and promoter sequences. We then examined the correlation for individual PWMs using an unbiased sequence set. Our results show that not all TFBS are clustered in promoter sequences. We found that TFBS clusters corresponding to 47% of PWMs are positively correlated with promoter sequences, and that TFBS clusters corresponding to around 11% of PWMs are negatively correlated with promoter sequences.
It is important to ascertain the relationship between cluster scores of PWMs and CGI, because CGI are a prominent feature of promoter sequences. The consensus sequences of the top-ranked PWMs (Table 1) are, 'ANNGACGCTNN' (WHN_B), 'TTTCSCGC' (E2F1DP1_Q6), 'NSGGGGGGGGMCN' (MAZR_01), and 'GGGGAGGG' (MAZ_Q6), where S represents C or G, M represents A or C, and N represents any bases. The sequence logos of the PWMs are depicted in Fig. 2-(a). The G+C % of base composition of each matrix is 70%, 56%, 91%, and 86%, respectively. The sequences with high cluster scores appear to be GC-rich. Larsen et al found that 57% of human genes are associated with CGI, that all housekeeping genes have CGI covering transcription start sites (TSS), and that 40% of tissue specific genes have CGI [25]. Therefore, the association of PWM-PCP with CGI may be significant, and CGI-related PWMs may play important roles in housekeeping regulation.
To evaluate the relationship between PWM-PCP and CGI, we calculated the partial correlation coefficient for each PWM. In general, if a correlation coefficient rXY is not small and rXY.Z (defined in Methods) ≈ 0, the probable hypotheses concerning cause and effect will be either 1) the correlation of X and Y is a consequence of Z, or 2) Z intervenes between X and Y. For the PWMs in the top circle in Fig. 4, rIC.P is high and rPC.I is approximately zero. This suggests that the correlation between promoters and TFBS clusters is attributable to the presence of the CGI and that while they do not directly correlate they appear to because both independently correlate with CGI. The characteristic PWMs in Table 2 are NMYC_01 (M00055:0.25), AHRARNT_01 (M00235:0.23), and HIF1_Q5 (M00466:0.22), where parentheses include the accession number referred to in TRANSFAC and the recorded rIC.P (Y-value in Fig. 4). Sequence logos are depicted in Fig. 2-(b). The PWMs in the middle right circle in Fig. 4 have an rIC.P of approximately zero and a high rPC.I  rPC.I value showing that the cluster is correlated with promoters independent of CGI. The predicted TFBS clusters corresponding to these PWMs could not be explained by the presence of CGI. Some of these PWMs have thresholds T less than 1.0 indicating that even the single occurrence of a predicted TFBS is more discriminative than clusters. Particular examples with high recorded rPC.I values and values for rIC.P < 0.1 are TFIII_Q6 (M000706), MYOGNF1_01 (M00056) and CREL_01 (M00053). Sequence logos are depicted in Fig. 2-(c). TFIII_Q6 is a matrix associated with a general transcription factor II-I with the consensus sequence RGAGGKAGG, where the K represents G or T. The matrix TFIII_Q6 contains many 'G', and 'C' is allowed only the fourth position with low frequency. MYOGNF1_01 is a matrix associated with myogenin, nuclear factor 1 or related factors, and is therefore involved in the regulation of differentiation. CREL_01 is a matrix associated with the C-Rel proto-oncogene protein (C-Rel protein). An understanding of the function of these factors is important to this study. The PWM groups described above may be involved in tissue-specific gene regulation. If all housekeeping genes have CGI [25] then genes without CGI can be assumed to be tissue-specific or rarely expressed. Thus, genes with a cluster of predicted TFBS not associated with CGI might be associated with tissue-specific regulation. Further analysis of extractions of tissue specific genes, shown in Results, supports the hypothesis.
Results from this analysis provide a solution to the promoter prediction problem. Hannenhalli et al. used additional information, including profiles of TF binding sites, for promoter prediction based on CGI [29], with no significant improvement to prediction performance. The report using 7 manually selected PWMs confirmed that CGI are the most dominant feature. Our results show that Sp1 and ATF have a strong correlation with CGI; a result consistent with their result that information including both PWM did not improve prediction accuracy. This observation is consistent with other PWMs. More stringent selection of PWMs is required for an improved accuracy of promoter prediction. One strategy is to utilise the CGI-independent PWMs identified in this study. Another problem is exemplified by the under-representation of Oct-1 (M00138) in the (-600:600) region of the human promoter and the absence of positional preferences [29]. This under-representation was not expected but is observed in 10% of known PWMs. OCT1_04 (M00138) is not in the high quality list of TRANSFAC, OCT1_01 (M00135) and OCT1_C (M00210) was found to have minus cluster scores (-0.63 and -2.89) in our table (additional file 1).
It is noteworthy that Fig. 5 shows TFBS (AP2_Q6) in non-promoters to occur randomly under a certain distribution. This distribution can be modelled by a binomial probability distribution. A model of Poisson distribution, which is an approximation of binomial probability distribution for a certain condition, was proposed in [11] as the probability distribution of TFBS density. Although we have not tested the goodness-of-fit, our observation does not contradict the Poisson distribution model.
Figure 5
Title: Distribution of accumulated score C for promoters and non-promoters for AP2_Q6
To assess the robustness of the cluster score, we compared cluster scores for different datasets from chromosomes 20, 21 and 22 (Fig. 3). The correlation coefficients were 0.91 (a) and 0.93 (b), proving that the significance would be similar if we utilized the whole human genome dataset in the analysis. The scale of the figures between the Y-axis and X-axis are different because of the different number of sequences taken from each chromosome.
Conclusions
We have developed a measure that statistically evaluates the degree of concentration of predicted TFBS in promoter sequences. Using this strategy to analyse various PWMs we have determined that predicted TFBS tend to cluster in human promoter sequences rather than in non-promoter sequences. Our results show that local concentrations of predicted TFBS in human promoter sequences are not a general characteristic of PWMs. Only a portion of identified PWM matches corresponded to TFBS occurring in clusters in promoter sequences. By computing partial correlation coefficients, we identified PWM sets associated with CGI and others that are independent of CGI. Transcription factors and binding sites associated with CGI-independent PWMs are likely to be involved in tissue-specific gene regulation. Indeed, using the CGI-related/dependent PWM sets, we extracted tissue-specific genes with high accuracy by detecting clusters of predicted TFBS. These results will be useful to interpret predicted transcription factor binding sites and to further understand the role of their formation into clusters. Ultimately, these findings will further elucidate the various functions of promoters, genes and transcription factors.
Methods
Data
DNA sequences from the fully sequenced chromosomes (chromosomes 20, 21 and 22) were taken from the November, 2002 GenBank freeze (build 31) and assembled by NCBI, in accordance with the annotation of the UCSC genome browser [30]. RefSeq [31] genes were used as they have been reviewed by NCBI staff, are well studied, and are unlikely to be spurious. Some genes in the human genome have alternative promoters [32], complicating our analysis. For this reason, overlapping genes identified using the UCSC annotation were discarded. This check of RefSeq genes reduced the number of genes in the analysis from 527 to 373 for chromosome 20, 224 to 142 for chromosome 21, and 449 to 294 for chromosome 22. The resultant gene set U consists of 809 genes.
To increase the accuracy of the annotation of transcriptional start sites, we modified the annotation of RefSeq according to DBTSS (version 2, Mar 2002) [33], a database of transcriptional start sites for 5' end mRNA sequences. Suzuki et al. reported that a certain portion of sequences in DBTSS were longer (extended) toward 5' end of mRNA sequences than those in RefSeq [33]. We describe how the modification improved the first gene set U. Fig. 6 shows the composition of different gene collections. The RefSeq database is updated daily by increasing the number of entries and correcting others. For illustration purpose, two versions of RefSeq are shown in Fig. 6. The old RefSeq is the version analysed by Suzuki et al. The new RefSeq is the current version used in this study. Of the 217,402 sequences contained in DBTSS 7,889 correspond to sequences in RefSeq and are referred to as cloned RefSeq sequences. The extension rate, defined as the rate of extension of mRNAs sequences from cloned RefSeq by DBTSS, was 0.34. Therefore, |{D,G}| (the number of gene set {D,G}) = 7,889 genes, |{Dex,Gex}| = 2,683 genes and |{Dex,Gex}|/|{D,G}| = 0.34. The ftp site of DBTSS provides the set of extended mRNA sequences (Iftp = {Dex,Gex}). The gene set from chromosomes 20, 21 and 22 is a partial set U = {Cu, Eu, Fu, , } from New RefSeq. We can identify the genes in set  as the conjunction of Iftp and U. The number of  was counted (273 genes). If the extension rate of mRNAs sequences for {, } is also 0.34, then |{}|/|{, }| = 0.34. Therefore, {|, }| is estimated to be 802.9 genes. As |U| = 809, the number of genes in V = {Cu,Eu,Fu} is estimated to be 6.1 genes. If all human genes are cloned by the cap-targeted selection method called oligo-capping, a greater number will have extended 5' ends. If this were the case then 34% (or 2.1 genes) of V would have extended 5' ends. Thus, any further correction of TSS for our gene set is expected to be quite small.
Figure 6
A Venn diagram of three gene sets (DBTSS, old RefSeq, and new RefSeq). Gene sets from A to G (Bold alphabet) consist of genes in the regions bounded by the thick lines. D consists of Dn (genes whose 5' end sequences were not extended from the old RefSeq sequences with DBTSS data) and Dex (genes whose 5' end sequences were extended). G consists of Gn (genes whose 5' end sequences were not extended from the old RefSeq sequences with DBTSS data) and Gex (genes whose 5' end sequences were extended). Namely D  = {Dn,Dex} and G = {Gn,Gex}. Genes in chromosomes 20, 21, 22 were denoted by U = {Cu, Eu, Fu, , }. Gene sets Cu, Eu,Fu,  and  are parts of C, E, F, Gn and Gex, respectively. Some of the numbers of the sets are given in [33], that is, |{D,G}| = 7889, |{Dex,Gex}| = 2683 and |{Dex,Gex}| / |{D,G}| = 0.34, where |{D,G}|  denotes the number of genes in set {D,G}.
We identified the conjunction set  (273 genes) of the above collected 809 RefSeq genes and 2,683 genes in DBTSS. The set  was examined to determine if extended sequences existed on human genome sequences and if they are registered in the new RefSeq. Of this set, the BLAT program[34] identified 30 genes in which the 5' end sequences could not be detected. Due to the uncertainty of TSS these genes were not used in this study. Forty-one DBTSS mRNA sequences from  were shorter than corresponding sequences in the new RefSeq with regard to 5' end sequences. It is assumed that these RefSeq sequences were corrected following the old RefSeq release independent of DBTSS and were used as they were. Finally, we modified the exon annotation of 202 genes according to DBTSS.
We extracted promoter sequences at relative positions (-600:-1) from the TSS, and intron subsequences 600 bp in length from genome sequences. Only intron sequences were used for the non-promoter sequence data sets as exon sequences are known to have preferences in their oligomer statistics, such as G+C % and codon bias [35]. The first intron was not included in the data set as although regulatory elements are rare in introns, intron 1 occasionally contains regulatory elements such as enhancers. We investigated the frequency of enhancers in human introns by searching NCBI PubMed [36] with the keywords 'human', 'first intron', and 'enhancer'. This search yielded 194 papers. Replacing the keyword 'first intron' with 'second', 'third', 'fourth intron', 'fifth' or 'last intron' yielded 40, 15, 1, 1 and 6 papers, respectively. Replacing 'enhancer' with 'silencer' resulted in 281, 6, 3, 0, 0 and 0 papers, respectively. Removal of intron 1 from the data set greatly reduces the overall occurrence of regulatory elements in human intron sequences and allows our statistical analysis to be performed without significant interference from intronic regulatory sequences. Intergenic sequences are left out of the non-promoter dataset due the unknown occurrence of regulatory sequences.
Prediction of TFBS
Each promoter or non-promoter sequence was scanned by the MATCH program using 423 matrices in TRNASFAC version 6.3 (a transcription factor database) with options including 'vertebrate', 'minimize false negatives' (in cut-off selection) and 'use high quality matrices only'. As Kel et al. described, the cut-off was determined so that the false negative rate is 10% [3]. The option 'use high quality matrices only' uses approximately 70% of matrice [3]. Any PWM in the 'high quality' PWMs meet the criteria; When the PWM is used with a cut-off value which allows a false negative rate of 50%, then the match rate dropped below 1 match/kb in exon2 sequences [3]. If more than one matrix was matched to same transcription factor (prefix of "Identifier"), we selected a representative matrix with the highest quality and smallest suffix number according to the TRANSFAC definition. After scanning the sequences by MATCH, we set consecutive sampling windows (600 bp) in introns and promoter sequences, and then recorded corresponding TFBS predictions. To prevent double counting of palindromic binding sites, two matches for the same matrix at the same position was regarded as a single match and the match with the higher score was taken. Before MATCH ran, repeat sequences were masked to 'N' according to the annotation by RepeatMasker in the UCSC genome browser. From the above analysis we extracted 361, 129, and 278 promoter sequences from chromosomes 20, 21 and 22, respectively. The promoter sequences identified contained repeat sequences (e.g. ALU, L1) and simple repeats with low complexity, as observed in intron sequences. These sequences account for about 20% of all bases. To balance the rate of repeats between promoters and introns, we discarded intron sequences with high rates of repeats, so that the average rate of repeats in the intron samples was at the same level as in promoter sequences. The number of 600 bp intron sequences included in the analysis was 6,589 (chromosome 20), 4,324 (chromosome 21) and 4,531 (chromosome 22).
Accumulated scores of TFBS
When predicted TFBS occur many times in a sequence there is a high probability that it contains functional regulatory regions or promoters [7,8,19,28]. We tested this hypothesis for individual PWMs. The degree of concentration of predicted TFBS in a sequence was defined as the accumulated score C, which is a summation of the MATCH score for PWMs in the subsequence and is calculated for each PMW and corresponding sequence. C is assumed to be almost proportional to the frequency of predicted TFBS for the corresponding PWM. Many sequences generate different C values although some are identical. We then generated a series of Cj (j = 1 ... n) values for a PWM, where n is the number of different C values. Fig. 5 shows the histogram of C for promoters and non-promoters respectively, using the TRANSFAC matrix of identifier 'AP2_Q6' as an example. Since C reflects the number of predicted TFBS found in a sequence, the figure shows the density of predicted TFBS in a sequence. This result is similar to the density plot described by Pestridge and Burks [7], although our figure (Fig. 5) is not a plot of predicted TFBS density for mixed PWMs, but instead is a plot of predicted TFBS density for individual PWMs. Also, the X-axis in our plot does not indicate the number of predicted TFBS but instead indicates the accumulated score C. The Y-axis is smoothed by averaging for the width of 5 in C value.
Cluster score and statistical significance for a PWM
Significance values for an individual PWM from a series of Cj can be determined from a contingency table. Table 5 shows a contingency table for the number of promoters and non-promoters above and below the threshold Cj for a given PWM. From this table, χ2 value for a given Cj is defined as
Table 5
A contingency table when predicted TFBS and a threshold T are given.
Sequences where TFBS clusters found
Sequences where TFBS clusters not found
Sum
# of promoter
A
1
A
2
A
# of non-promoter
B
1
B
2
B
where
described in [37]. From the χ2 value, we computed the probability P that the χ2 value or greater is obtained by chance. The probability P was calculated from the χ2. Since P is calculated for many PWMs, we must deal with the problem of multiple testing. Using the Bonferroni correction [23], Pn was calculated using the formula 1 - Pn = (1 - P)n, approximately Pn = P × n for small P × n. The n is the number of PWMs. When we determine the set of significant PWMs, Pn were compared with the significance level (i.e. 0.01). We also defined the statistical significance Qj as Qj = -log10(Pn) if Rprom >Rnonprom and Qj = + log10(Pn) otherwise, where Rprom = A1/A (a rate of sequences in promoters where clusters found), Rnonprom = B1/B (a rate of sequences in non-promoters where clusters found). Although the P is an indicator of the difference between the occurrence of promoters and non-promoters, the probability P itself does not represent the preferences of PWMs for promoters. To represent the preference of predicted TFBS for or against promoters, we add signs for statistical significance Qj. Positive Qj indicates that predicted TFBS tend to appear frequently in promoters, while negative Qj indicates that predicted TFBS tend to avoid promoters.
We studied how statistical significance Qj varies with the threshold of C j. Fig. 7 shows the presence of a peak of Qj when we change the threshold. We define the cluster score S of a PWM in such a way that the significance is the maximum, namely
Figure 7
Title: significant score Qj of matrix AP2_Q6 for different thresholds.
We simultaneously define a unique threshold T of the PWM by
For the all-vertebrate TRANSFAC PWMs, we determined thresholds T and calculated significance scores (or cluster scores) S. The highest scoring PWMs are listed in Table 1.
Correlations among promoter sequences, CpG islands, and clusters
For every 600 bp sequence, three numerical features (promoter, CGI, and clusters) were annotated. CGI were identified using the CpGProD program [38] for original long sequences (not for short sequences of 600 bp). Regions larger than 500 bp with a G+C % equal to or greater than 50% and 'observed CpG / expected CpG' equal to or greater than 0.60 were classified as CGI [38,39]. The CpGProD program outputs 'start_p' scores for the predicted CGI. This score indicates the probability that the region is a CGI located over a transcription start site (start CGI). Short 600 bp sequences sampled from long sequences containing CGI were annotated as CGI if the overlapping CGI region was longer than 300 bp. The accumulated score C was used for cluster annotation. From sequences with feature annotation, the correlation coefficients between every two of the three features were computed for each PWM by the statistical language R [40]. We use P to denote whether the sequences is promoter or not, namely P = {1,0}, and I to the denote 'start_p' score for CGI calculated using the CpGProD program [38]. A partial correlation coefficient for each PWM was calculated using the subsequences. For example, a partial correlation coefficient rPC.I is the correlation between P and C while controlling for I, defined by
where rPI is a correlation coefficient between variable P and I, rPC is a correlation coefficient between variable P and C and rCI is a correlation coefficient between variable C and I. A partial correlation coefficient differs from a correlation coefficient. If the correlation between P and C depends entirely on the common cause I, then when I is constant, the correlation between P and C should be zero. The partial correlation rPC.I expresses such a relationship. Even when I varies, rPC.I is expected to be zero in such a situation, while the correlation coefficient rPC may not be zero. See chapter 16.4 in [41] for details.
Fig. 4 shows a plot of rIC.P against rPC.I for various PWMs. Using these two values, we identified two PWM sets including, (1) a CGI-related set consisting of 37 PWMs in which the clusters are correlated with CGI independent of promoters, and (2) a CGI-independent set consisting of 54 PWMs, in which the clusters are correlated with promoters independent of CGI. The CGI-related set requires that rIC.P >rPC.I and that the partial correlation coefficient (PCC) rIC.P is significantly high (p < 0.01) under the hypothesis that rIC.P is zero (see below). The CGI-independent set requires that rIC.P <rPC.I and that rPC.I is likewise significantly high. To calculate the statistical significance of PCC, a PCC r was subjected to z-transformation defined as
The values of z are supposed to be normally distributed and the expected variance is
where n is the sample size [41]. The CGI-related PWMs and CGI-independent PWMs are listed in Tables 2 and 3.
Gene expression data
To examine the relationship between clusters of predicted TFBS and the tissue specificity of the genes where clusters were found, we generated a list of genes with expression data. This list includes 535 housekeeping or maintenance genes expressed in 11 human adult and foetal tissues from [26], and 451 housekeeping or maintenance genes available at HugeIndex database [27]. We then identified 581 non-redundant housekeeping genes and 'tissue-selective' genes, which are predominantly, but not exclusively, expressed in one tissue type. Tissue-selective genes were expressed in brain (589 genes), kidney (129 genes), liver (271 genes), lung (68 genes), muscle (302 genes), prostate (45 genes) and vulva (95 genes) [27]. These genes corresponded to 2,069 RefSeq entries. Seventy-two of these genes were identified in our gene set covering chromosome 20, 21 and 22 and were used for further analysis.
Tissue specific gene detection based on clusters of predicted TFBS
Using the two PWM sets described above, we searched clusters of predicted TFBS in promoter sequences. We calculated C and statistical significance of C as follows. For each promoter sequence, 30 random 600 bp sequences were generated under the first order Markov model, which is based on dinucleotide frequency and can identify promoter CpG bias and G+C%. The MATCH program was run with given matrix thresholds. The accumulated score C of PWMs was computed in every random sequence. A mean  and a variance  of C for each PWM were estimated from the random sequences. Then, for the given promoter sequence Sp and the PWM (M), we can run MATCH and calculate C and its significance score (Z-score), namely
If the Z-score value was above three, the promoter sequence was taken and considered together with the PWM. Table 4 lists genes identified with clusters of predicted TFBS with significant C values for the CGI-related/dependent PWM set. The computation of p-value of cumulative hypergeometric distribution was performed using the AS R77 algorithm [42].
Authors' contributions
KM designed the study and carried out statistical analysis. TK participated in the design and carried out functional analysis. YS directed the study. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
The list of PWM-PCP/NCP sorted by cluster score. Each column represents rank number, accession number in TRANSFAC, identifier in TRANSFAC, cluster score, threshold.
Click here for file

promoter
tissue-specific gene expression
position weight matrix
regulatory motif


Bench-to-bedside review: Inotropic drug therapy after adult cardiac surgery – a systematic literature review

Many adult patients require temporary inotropic support after cardiac surgery. We reviewed the literature systematically to establish, present and classify the evidence regarding choice of inotropic drugs. The available evidence, while limited in quality and scope, supports the following observations; although all β-agonists can increase cardiac output, the best studied β-agonist and the one with the most favourable side-effect profile appears to be dobutamine. Dobutamine and phosphodiesterase inhibitors (PDIs) are efficacious inotropic drugs for management of the low cardiac output syndrome. Dobutamine is associated with a greater incidence of tachycardia and tachyarrhythmias, whereas PDIs often require the administration of vasoconstrictors. Other catecholamines have no clear advantages over dobutamine. PDIs increase the likelihood of successful weaning from cardiopulmonary bypass as compared with placebo. There is insufficient evidence that inotropic drugs should be selected for their effects on regional perfusion. PDIs also increase flow through arterial grafts, reduce mean pulmonary artery pressure and improve right heart performance in pulmonary hypertension. Insufficient data exist to allow selection of a specific inotropic agent in preference over another in adult cardiac surgery patients. Multicentre randomized controlled trials focusing on clinical rather than physiological outcomes are needed.
Introduction
Despite improvements in surgical technique and myocardial protection, pharmacological support for low cardiac output is often required during and after weaning from cardiopulmonary bypass (CPB) [1]. This acute deterioration in ventricular function may continue into the postanaesthesia care unit or intensive care unit (ICU). Because cardiac surgery is conducted in an increasingly aged population, with coexisting pathology, these patients are at increased risk for developing a low cardiac output syndrome (LCOS) during the postoperative period.
There is no consensus definition of what constitutes LCOS, but it would be reasonable to define it as a low cardiac output (cardiac index [CI] < 2.4 l/min per m2 is used as a criterion in some studies) with evidence of organ dysfunction, for example elevated lactate or urine output under 0.5 ml/hour for more than 1 hour. Such LCOS can persist for several hours to days, despite optimization of volume status, temporary pacing, or exclusion of mechanical factors (e.g. cardiac tamponade and mechanical assistance with intra-aortic balloon counter-pulsation). Causes are multifactorial but include myocardial ischaemia during cross-clamping, reperfusion injury, cardioplegia-induced myocardial dysfunction, activation of inflammatory and coagulation cascades, and unreversed pre-existing cardiac disease. LCOS can result in reduced oxygen delivery to vital organs [2]. Organ dysfunction and multiple organ failure are among the main causes of prolonged hospital stay after cardiac surgery, and this increases resource use and health care costs as well as increasing morbidity and mortality. Optimization of cardiac output and oxygen delivery may decrease morbidity and reduce length of stay [3].
Despite a wide range of available inotropic agents, no consensus exists regarding the treatment of LCOS post-CPB. This review examines the pharmacological options for providing inotropic support in the period after CPB and evaluates the literature systematically in order to establish, present and classify the available evidence regarding the use of inotropic drugs after cardiac surgery in adults. We do not discuss exclusive or mostly vasopressor drugs such as vasopressin and norepinephrine (noradrenaline).
Methods
We conducted a systematic Medline and PubMed search, over the period 1982–2003, using the following keywords: cardiac surgery, cardiopulmonary bypass, coronary artery bypass grafting, inotropic support, epinephrine, dopamine, dopexamine, dobutamine, amrinone, enoximone, milrinone and levosimendan. Agents considered to be primarily vasopressors (e.g. norepinephrine, arginine vasopressin and phenylephrine) and mechanical support (e.g. intra-aortic balloon counter-pulsation and assist devices) are not considered. For reasons of space and the likelihood that they would behave like other agents in their class, the more obscure phosphodiesterase inhibitors (PDIs; e.g. olprinone) not in common usage in the UK and Australia are not considered.
The bibliographies of articles identified through this methodology were also studied for reports that might have been missed in our initial searching of electronic reference libraries. Non-English language papers, animal studies, paediatric studies and in vitro studies are not included. Using this search strategy we identified 210 papers. This selection was further refined to 142 reports in which the agent in question was used for support of cardiac function or vital organ perfusion in patients who had undergone cardiac surgery. All articles in question were obtained.
Papers were selected and graded for quality of evidence according to the methodology of Cook and coworkers [4] (Table 1). Particular attention was given to the following issues regarding each agent in patients who have undergone cardiac surgery: what are the effects of each inotropic drug on systemic haemodynamics?; does the inotropic drug alter vital organ perfusion?; does the inotropic drug affect major clinical outcomes (e.g. time spent in hospital or ICU or requiring ventilation or artificial renal support) or survival?; and does the inotropic drug have any important side effects?
Data covering the application of each therapy were examined. Where possible, 'evidence-based' recommendations were developed.
Results
The results of our literature search are considered by pharmacological groups and agent. A full pharmacological profile of each agent is beyond the scope of the present review, but the proposed cellular mechanisms of action and receptor activation for each agent are schematically summarized in Fig. 1.
Catecholamines
Natural and synthetic catecholamines have different haemodynamic effects because of their differential abilities to stimulate adrenergic receptors. Accordingly, each must be considered separately.
Epinephrine
Epinephrine (adrenaline) is a naturally occurring catecholamine that binds to both α- and β-receptor subgroups, with β effects predominating at low doses and α effects predominating at high dose. Fifteen reports relating to the use of epinephrine in cardiac surgical patients were retrieved using our search strategy. No study yielding 'level I' evidence (Table 1) was identified. Only one uncontrolled study, that by Gunnicker and coworkers [5], specifically investigated its effectiveness in LCOS.
In that report [5], epinephrine at a dose of 0.03 μg/kg per min produced significant increases in CI and heart rate (HR) of 24.1% and 14.1%, respectively, compared with placebo. HR was minimally affected in all studies except that of Gunnicker and coworkers, in which it increased by 14%. All studies recorded significant increases in mean arterial pressure (MAP) [6]. A recent observational study on the effects of 5 μg boluses in patients undergoing cardiac surgery [7] revealed a biphasic effect on systemic vscular resistance (SVR), with an initial increase followed by reduction.
Epinephrine has also been directly compared with amrinone, milrinone and dobutamine. Two small, randomized controlled trials [5,8] compared epinephrine with the PDI amrinone. In both studies, both drugs significantly increased CI from baseline. In the randomized, open-label trial conducted by Gunnicker and coworkers [5] in 20 patients with LCOS, epinephrine produced a significantly greater increase in CI, HR and MAP than did amrinone. However, this was accompanied by significantly greater increases in myocardial workload and oxygen consumption. Lobato and coworkers [9] conducted a prospective, randomized, blinded trial comparing the myocardial relaxation effects of epinephrine with those of milrinone in patients undergoing elective coronary artery bypass grafting (CABG). Epinephrine (0.03 μg/kg per min) had no effect on left ventricular end-diastolic area, as measured using trans-oesophageal echocardiography, whereas milrinone significantly increased it by 15%. Two studies [10,11] compared epinephrine with dobutamine and found epinephrine to be less effective at increasing CI than dobutamine over what the authors considered a reasonable clinical range of doses (0.01–0.03 μg/kg per min for epinephrine and 2.5–5 μg/kg per min for dobutamine). However, dobutamine was associated with significantly more tachycardia for the same stroke volume index (SVI).
No studies were found regarding the effect of epinephrine on blood flow to vital organs. A prospective, randomized trial [12] showed that epinephrine produces lactic acidosis in some post-CPB patients.
Two randomized controlled trials, one of which used laser Doppler flowmetry, investigated the effect of epinephrine on internal mammary artery (IMA) graft flow [13,14]. Both of these studies found epinephrine to have no effect on IMA blood flow. An earlier crossover study of 28 patients [15], using an electromagnetic flowmeter, had shown that epinephrine significantly increased flow through IMA and saphenous vein grafts.
No studies were found regarding the effect of epinephrine on major clinical outcomes or survival.
Dopamine
Dopamine is a naturally occurring catecholamine that binds to both α- and β-receptor subgroups, with β effects predominating at low dose and α effects predominating at high dose. Doses of 2–10 μg/kg per min are commonly used for inotropy, with doses of 1.5–3.0 μg/kg per min still used by some for renal protection ('renal dose') because of the binding of the drug to specific dopaminergic receptors in the kidney. Our literature search identified a total of 21 papers relating to the use of dopamine in cardiac surgical patients, all of which were retrieved.
None of these papers specifically compared the haemodynamic effects of dopamine with those of placebo. When the effects of dopamine on CI were compared with baseline data, dopamine at a dose of between 2.5 and 5.0 μg/kg per min produced significant increases in CI (range 16.3–57.9%). In all studies except one there was a significant rise in HR (range 4.5–45.7%).
At doses of up to 5 μg/kg per min, significant decreases in SVR (range 13.1–46.1%) were recorded. However, in 1982 Saloman and coworkers [16] conducted a prospective, randomized, blinded trial of 20 patients and found that increasing dopamine from 5.0 to 7.5 μg/kg per min caused significant increases in MAP and pulmonary vascular resistance (PVR) without increasing cardiac output. In a multicentre, prospective, blinded, randomized trial of 70 patients, Rosseel and coworkers [17] examined the use of dopamine in LCOS after cardiac surgery. The study compared dopamine with dopexamine in patients with CI below 2.2 l/min per m2. Dopamine produced a 57.9% increase in CI compared with baseline. However, this was accompanied by a 25.5% increase in HR. Clinical efficacy (defined as CI > 2.5 l/min per m2 and urine output > 0.5 ml/kg per hour) was significantly greater in the dopexamine group at 1–2 hours after commencement of the infusion and approached significance at other time points. Moreover, 63% of patients in the dopamine group had an adverse cardiac event (defined as arrhythmias, ischaemia and hypertension), which was significantly greater than with dopexamine.
Tarr and coworkers [18] compared the efficacies of dopamine, dobutamine and enoximone for weaning from CPB in a randomized trial of 75 patients. Nine of the 25 patients randomly assigned to dopamine failed to respond adequately, and the remaining 16 recorded an increase in CI of 25.7% but this was accompanied by an increase in HR of 44.3%, with little change in SVI. The CI in the dopamine treated group was significantly lower than in patients treated with either dobutamine or enoximone.
Dopamine has been studied extensively with regard to regional perfusion of the gut and kidney. Other than a case series of 15 patients reported by Davis and coworkers in 1982 [19], which suggested that low-dose dopamine might increase postoperative urine output and serum creatinine in CPB patients, several level II studies [20-23] have failed to provide any evidence to support its use. Jakob and coworkers [24,25] and Thoren and colleagues [26] conducted observational studies on the effect of dopamine on splanchnic perfusion using indocyanine green (ICG) dye clearance and laser Doppler flowmetry, respectively. They observed significant increases in splanchnic blood flow in the order of 27–36%. Two level II studies [27,28] failed to demonstrate any effect of dopamine on gastric intramucosal pH (pHi). A significant worsening in pHi associated with low CPB flow rate and dopamine was observed by Schneider and coworkers [29] in a randomized, double-blind, placebo-controlled trial (n = 100) conducted in 1998.
No data were found regarding the effect of dopamine on major clinical outcomes or survival.
Dobutamine
Dobutamine is a synthetic catecholamine and is a derivative of isoprenaline. It has strong affinity for β-receptors with little affinity for α-receptors because of the configuration of the terminal amine. Twenty-six studies investigating the effects of dobutamine in cardiac surgical patients were identified and retrieved. These studies are summarized in Table 2.
Administration of dobutamine in cardiac surgery patients produces a dose-dependent rise in CI. In the study conducted by Ensinger and coworkers [31], in which they compared dobutamine at 6.0 μg/kg per min with placebo, a significant increase in CI of 46% was recorded. Studies by Feneck and coworkers [2] and Tarr and colleagues [18], investigating the haemodynamic effects of dobutamine in LCOS, identified increases in HR in excess of 25%. Significant reductions in SVR (> 40% in the study by Tarr and coworkers) were also recorded.
Romson and coworkers [32] conducted an observational study of 100 patients who had undergone cardiac surgery and were administered dobutamine at doses of 0–40 μg/kg per min, where tolerated, and compared these with 10 control patients who received no dobutamine. Those investigators found that HR increased by an average of 1.45 beats/min per μg/kg per min in patients who were able to receive the full dose (66 out of 100 patients). Of the patients who were unable to receive the full dose, more than half (52%) developed tachycardia greater than 85% of predicted maximum HR by age. Romson and coworkers concluded that, in post-CPB patients, the dominant method of increasing CI was by increasing HR.
The Milrinone Multicentre Trial Group provided the most recent randomized controlled trial data concerning dobutamine [2]. That multicentre, randomized but not blinded study compared the haemodynamic effects of dobutamine with those of milrinone. A total of 120 patients with CI below 2.0 l/min per m2 were studied and dobutamine was used at doses of 10–20 μg/kg per min. Dobutamine increased CI by 55% versus 36% with milrinone at 1 hour, and this effect was accompanied by a 35% increase in HR (versus 10% with milrinone) and a 31% increase in MAP (versus 7% with milrinone). Dobutamine was also associated with significantly higher incidences of hypertension and new atrial fibrillation (18% versus 5%; P < 0.04).
The randomized trial of 75 patients conducted by Tarr and coworkers in 1993 [18] identified no statistically significant difference in CI between enoximone and dobutamine (both drugs effectively increased CI). However, dobutamine produced significantly more tachycardia, and enoximone produced significantly greater increases in SVI. A further five small randomized trials compared dobutamine with enoximone [33,41-44], but only one of these studies [44] demonstrated any difference between drugs, specifically a significantly greater increase in CI in the enoximone-treated group.
Two small randomized trials [34,39] compared dobutamine with amrinone and found no significant differences in haemodynamic effect. Dupuis and coworkers [39], however, did note an increase in incidence of arrhythmias in the dobutamine group, and 40% of patients treated with dobutamine suffered postoperative myocardial infarction versus none in the amrinone group (P = 0.017).
Regarding comparisons with other catecholamines, MacGregor and coworkers [37] conducted a randomized, blinded comparison of dopexamine and dobutamine in 10 patients undergoing CABG. No significant differences in haemodynamic variables were found, but there was a significantly greater incidence of supraventricular tachycardias in the dopexamine group. As mentioned above, Butterworth and coworkers [10] found no significant differences between epinephrine and dobutamine other than a significantly greater HR in the dobutamine-treated group.
Six studies investigated the effects of dobutamine on regional perfusion. The study by MacGregor and coworkers [37], outlined above, showed no difference in net sodium excretion or urinary output compared with dopexamine. The remaining studies investigated the effects of dobutamine on splanchnic blood flow. Four studies demonstrated significant increases in splanchnic blood flow as measured by ICG clearance [31,36,38] or laser Doppler flowmetry [26]. In studies in which pHi was measured, dobutamine had no effect [31,34] or decreased pHi [36].
We were unable to find any data relating to the effect of dobutamine on major clinical outcomes or survival.
Dopexamine
Dopexamine is a synthetic catecholamine with agonist activity at β2-receptors and indirect action at β1-receptorsby inhibiting the uptake of endogenous catecholamines [46]. This agent is not available in some developed countries. Our literature search identified 20 papers investigating the effects of dopexamine in patients who had undergone cardiac surgery, all of which were retrieved.
Two randomized controlled trials [47,48] compared dopexamine with placebo. Hurley and coworkers [47] reported a study of 23 low-risk post-CABG patients in 1995. In that study, dopexamine at a dose of 2.0 μg/kg per min significantly increased CI by 41% and HR by 19%. SVR was also reduced by 45%. In their randomized, double-blind, placebo-controlled trial, Sherry and coworkers [48] similarly found significant increases in HR and CI over placebo (one patient was withdrawn from the study because of tachycardia).
We identified five studies investigating the effects of dopexamine in LCOS, the largest of which is the multicentre, randomized, blinded comparison of dopexamine with dopamine reported by Rosseel and coworkers [17]. In that study, the increased CI in the dopexamine group was accompanied by an increase in HR of 37%. As previously discussed, there was significantly greater efficacy and fewer adverse events in the dopexamine group (although 54% of the dopexamine group still suffered an adverse cardiac event in the form of arrhythmia or ischaemia).
McGregor and coworkers [37] conducted a prospective, randomized, blinded comparison of dopexamine with dobutamine (n = 10) in patients with LCOS after CABG. They found no difference between the agents other than the fact that tachycardia of greater than 120 beats/min was more common in the dopexamine group.
We were unable to find any studies comparing dopexamine with PDIs. One study, reported by Honkonen and coworkers [49], compared dopexamine with iloprost (a prostacyclin analogue) in a randomized, double-blind, crossover trial of 20 patients with total proximal occlusion of the right coronary artery. Dopexamine increased right ventricular ejection fraction significantly more than did iloprost at a dose of 0.68 μg/kg per min.
Eight studies investigated the effects of dopexamine on regional perfusion. A randomized, placebo-controlled trial of 44 patients undergoing CABG conducted by Berendes and coworkers [50] in 1997 found improvement in creatinine clearance in the dopexamine-treated groups. However, four subsequent small randomized trials [37,48,51,52] failed to provide any evidence that the use of dopexamine improves renal function or perfusion.
Berendes and coworkers [50] also assessed the effects of dopexamine on splanchnic oxygenation in a randomized, placebo-controlled trial of 44 patients with normal left ventricular ejection fraction (LVEF; > 0.5) who received dopexamine at doses of 0.5, 1.0 and 2.0 μg/kg per min. There was no difference in hepatic venous oxygenation, and pHi decreased during and after CPB in all patients. A further three randomized controlled trials [28,53,54] concluded that dopexamine had no influence on pHi compared with dopamine or placebo. Dopexamine has also been shown to increase jejunal perfusion (as measured by laser Doppler flowmetry) and ICG dye clearance [55].
No studies were found relating to the effect of dopexamine on major clinical outcomes or survival.
Phosphodiesterase inhibitors
The cardiac effects of PDIs are characterized by positive inotropy and improved diastolic relaxation (lusitropy; Fig. 1). These agents also cause potent vasodilation, with reductions in preload, afterload and PVR. Acute tolerance is not a feature.
Amrinone
Amrinone (known as inamrinone in North America) is a bipyridine phosphodiesterase-III inhibitor. It is typically given as a loading dose of 0.75–1.5 mg/kg, followed by an infusion of 10 μg/kg per min. It has an elimination half-life of 3.5 hours in post-CPB patients [56]. Our literature search identified 27 papers, all of which were retrieved. One of these studies provided level I data regarding the use of amrinone in patients who have undergone cardiac surgery [57].
Lewis and coworkers [57] reported a prospective, randomized, placebo-controlled trial of 234 patients. In that study the amrinone group received a bolus of 1.5 mg/kg followed by an infusion of 10 μg/kg per min to wean from CPB. Phenylephrine or glyceryl trinitrate were also used to optimize perfusion pressure. Significantly fewer patients failed to wean in the amrinone group than in the control group (7% versus 21%; P = 0.002). Amrinone improved weaning success regardless of LVEF, although this benefit was only statistically significant in the group with a preoperative LVEF greater than 55%.
Another randomized controlled trial was undertaken by Ramsay and coworkers [58]. A total of 100 patients undergoing CABG were randomly assigned to receive a single bolus of 0.75 mg/kg amrinone (with no subsequent infusion) or saline before separation from CPB. Haemodynamic measurements were similar between the two groups at all times, but the amrinone group received a higher dose of phenylephrine. The authors of that study conceded that an insufficient amrinone dose might explain the lack of haemodynamic effect.
Of the remaining level II evidence available, Badner and coworkers [59] also conducted a randomized, blinded, placebo-controlled trial of 30 patients undergoing mitral valve replacement in which amrinone at 2.0 mg/kg or placebo was given before weaning from CPB. The amrinone group had a significant increase in CI (52% versus 10%) and decreases in SVR index (47% versus 10%), but there was no statistically significant difference in requirement for other inotropes or vasopressors between groups. Kikura and Sato [60] conducted a randomized, blinded comparison of amrinone, milrinone and placebo in 45 patients for weaning from CPB. Compared with placebo, amrinone significantly improved CI and SVI, and reduced dopamine requirements.
The remaining studies largely echo these findings. In two of these studies [61,62], however, more than 50% of patients in the amrinone group required concomitant infusions of phenylephrine to maintain MAP.
Two studies compared amrinone with milrinone [60,62] and one compared amrinone with enoximone [63]. None of these studies found significant differences in haemodynamic profiles. A further two randomized trials [5,8] compared amrinone and epinephrine; in both these studies amrinone produced a similar increase in CI and SVI, with significantly greater reductions in SVR.
Jenkins and coworkers [34] conducted a randomized, double-blind comparison of amrinone with dobutamine in 20 patients with severe pulmonary hypertension undergoing mitral valve replacement. Amrinone was associated with a reduction in pulmonary artery pressures and an increase in CI and right ventricular ejection fraction compared with dobutamine. Six patients in the dobutamine group suffered postoperative myocardial infarctions, as opposed to none in the amrinone group – a similar finding to that reported by Dupuis and coworkers [39].
Our literature search returned only one study relating to the effect of amrinone on vital organ perfusion. This was a prospective, randomized study of 29 patients, reported by Iribe and coworkers in 2000 [64]. That study compared the effects of amrinone, milrinone and olprinone on hepatic venous oxygen saturation. No significant change in hepatic venous oxygen saturation was demonstrated in the amrinone group (n = 8).
Although no studies used major clinical outcomes as primary end-points, the study by Lewis and coworkers [57], the largest randomized controlled trial, did not detect any statistically significant difference in length of ICU or hospital stay and mortality. The study by Butterworth and colleagues [61] similarly found no difference in mortality between amrinone and placebo groups.
Amrinone has been reported to impair coagulation because of a reduction in platelet count and function [65,66], and concerns over this have limited its use in some countries.
Enoximone
Enoximone is an imidazolone derivative phosphodiesterase-III inhibitor. It is typically used in doses of 0.5–1.5 mg/kg followed by an infusion of 5–10 μg/kg per min. It has a half-life of 2 hours in normal patients but this may be prolonged in patients with cardiac failure.
Of the 24 papers identified in our literature search, 19 investigated the effects of enoximone on systemic haemodynamics in post-CPB patients. Of these 19 papers, two were prospective, randomized, placebo-controlled trials. Boldt and coworkers [67,68] conducted both of these studies. The most recent of these studies [67] was published in 2002 and is a prospective, randomized, blinded, placebo-controlled trial of 40 patients aged 80 years or older. The patients in that study received either enoximone (bolus dose of 0.5 mg/kg followed by an infusion of 2.5 μg/kg per min) or normal saline. Compared with placebo, enoximone-treated patients recorded a significant increase in CI (25.9%) and reduction in SVR (27.5%). No significant differences in HR and MAP were recorded. In 1992, Boldt and coworkers [68] conducted a further prospective, randomized, blinded, controlled study, again of 40 patients. Patients received either a single dose of enoximone 1.0 mg/kg or served as controls. Enoximone produced significant increases in CI (50%) and SVI (28.8%), and decreases in SVR (45.3%) and PVR (30.4%). No significant changes in HR and MAP were recorded. Oxygen delivery and consumption were also significantly higher in the enoximone group. Another study conducted by Boldt and coworkers [69] compared 40 patients who had received a single dose of enoximone 1.0 mg/kg with 40 historical controls. The enoximone-treated group required significantly less epinephrine, calcium and nitroglycerin than did the control group.
Several level II studies compared enoximone with other inotropic agents, both catecholamines and other phosphodiesterase-III inhibitors. One small, prospective, randomized trial failed to show any significant haemodynamic differences between amrinone and enoximone [62]. In the prospective, randomized, blinded trial conducted by Tarr and coworkers in 75 patients [18], only in the enoximone group were all patients successfully weaned from CPB, whereas three patients from the dobutamine group and nine from the dopamine group were withdrawn from the study because of inadequate response. The enoximone group exhibited a significantly lesser increase in HR and a greater increase in stroke index than did either the dopamine or dobutamine group, and also exhibited significantly a greater increase in CI and decrease in SVR in comparison with dopamine. Birnbaum and coworkers [70] conducted an earlier, prospective, randomized, blinded comparison of enoximone (two boluses of 0.5 mg/kg followed by an infusion of 5 μg/kg per min) with dopamine (3.0–4.0 μg/kg per min) in 20 patients and obtained similar results.
As previously mentioned, we were able to find a further five studies comparing enoximone with dobutamine. The largest of these studies is the previously cited study conducted by Zeplin and coworkers [44]. That study (n = 50) found that enoximone significantly increased CI in comparison with dobutamine.
Two small, randomized, controlled trials investigated the effect of enoximone on vital organ perfusion. These showed no effect on pHi and significant reductions in endotoxin release [71], interleukins and α1-microglobulin in the enoximone-treated group [67]. Two randomized controlled trials (n = 80 and n = 36) [72,73] investigated the effects of enoximone on coagulation parameters and platelet count and function, and found no difference from control groups.
Finally, in a prospective trial [74] 88 elective CABG patients were randomly pretreated with enoximone, clonidine, enalalapril, or placebo. The enoximone-treated group exhibited lower troponin T and creatine kinase-MB levels compared with clonidine or placebo [74].
There are no data regarding the effect of enoximone on major clinical outcomes or survival other than those from the study conducted by Boldt and coworkers in 2002 [67], which found that tracheal extubation was performed significantly earlier in the enoximone-treated group.
Milrinone
Milrinone is a bipyridine methyl carbo-nitryl phosphodiesterase-III inhibitor. Loading doses of 20–50 μg/kg are typically given, followed by an infusion of 0.2–0.75 μg/kg per min. It has a half-life of 30–60 min.
Our literature search identified 29 papers relating to the use of milrinone in adults after cardiac surgical procedures. These papers are summarized in Table 3. Nineteen of the papers provided data on the haemodynamic effects of milrinone following cardiac surgery and 14 of the papers were prospective randomized trials.
Four prospective randomized trials [60,75,83,85] demonstrated the effectiveness of milrinone compared with placebo for weaning from CPB. In the study by Doolan and coworkers [85], all patients in the milrinone group (n = 15) were successfully weaned from CPB, as compared with only five out of the 15 in the group randomly assigned to placebo. In their prospective, blinded, randomized controlled trial, Yamada and coworkers [80] compared two groups of 24 patients with low and normal pre-CPB CI. In both these groups, the patients randomly assigned to milrinone exhibited significantly higher CI (46% in the low pre-CPB CI group) and significantly lower SVR (52% in the low pre-CPB CI group) than controls. HR was not significantly affected, but six out of 12 patients with a low CI required norepinephrine to maintain adequate systemic blood pressure. Similarly, Lobato and coworkers [83] found that a single dose of milrinone 50 mg/kg administered before separation from CPB significantly increased CI (43%) and decreased SVR and catecholamine requirement compared with placebo in 21 patients with pre-existing left ventricular dysfunction. Again, more patients in the milrinone group required vasopressor support. Kikura and Sato [60] obtained similar results with milrinone, and these effects were sustained into the first 24 hours after surgery.
Milrinone has been compared with catecholamines for postoperative support in LCOS, notably by the European Milrinone Multicentre Trial Group, which published the results of a randomized, open label, multicentre study of 120 patients treated with milrinone or dobutamine for LCOS after cardiac surgery [2]. The significant findings of this study are described above in the section on dobutamine.
As previously mentioned, two studies [60,62] compared amrinone with milrinone. Neither of these studies found significant differences in haemodynamic profiles.
Solina and coworkers [79] compared the use of milrinone with nitric oxide (NO) in cardiac surgery patients with pulmonary hypertension. Those investigators found that the effects of milrinone on right ventricular ejection fraction were comparable with NO at 20 ppm but significantly less effective than NO at 40 ppm.
Small randomized controlled trials [27,64,78,81] have examined the effects of milrinone on pHi, splanchnic blood flow and inflammatory markers. Two of these [78,81] suggested that the administration of milrinone may attenuate the fall in pHi associated with CPB and the increase in some markers of inflammation. The remaining studies showed no difference.
In a prospective, randomized study of 20 patients conducted by Lobato and coworkers [13], milrinone produced a 24% increase in grafted IMA flow, as measured by laser Doppler flowmetry. In an observational study of 25 patients [76] milrinone also increased cerebral blood flow, as measured by transcranial Doppler, after separation from CPB.
We were unable to find any data relating to the effect of milrinone on major clinical outcomes or survival in cardiac surgery patients.
Levosimendan
There is little published work on the use of the novel calcium sensitizer levosimendan in cardiac surgical patients with LCOS. Levosimendan is a new inodilator that exerts its inotropic effect by interacting with troponin C (the binding protein for calcium) to enhance the calcium sensitivity of cardiac myocytes.
In a multicentre, randomized, double-blind trial of 203 patients [95], the efficacy and safety of levosimendan were compared with those of dobutamine in severe low-output heart failure (the LIDO study). Levosimendan improved haemodynamic performance more effectively than did dobutamine in patients with severe, low-output heart failure, and there was significantly lower mortality in the levosimendan group. However, only 2–4% of the study population had postoperative cardiac failure. A recent uncontrolled pilot study in cardiac surgery patients with LCOS found that levosimendan increased CI and stroke volume while lowering pulmonary artery occlusion pressure [96].
Conclusion
It is well recognized that myocardial dysfunction occurs after cardiac surgery. Because LCOS is common, contributes to morbidity and mortality, and increases length of ICU and hospital stay and costs, it is desirable to minimize its occurrence or attenuate its severity. A summary of the significant findings of our literature review are presented in Table 4. It is evident that there are two main classes of inotropic agents that should be used for support of cardiac output after cardiac surgery: catecholamines and PDIs (data for use of calcium sensitizers in this setting is scant). Moreover, all of these agents have been demonstrated to be effective at improving myocardial contractility or HR, or both. Although some reports in the literature suggest that catecholamines are more potent inotropic and chronotropic agents, serious drawbacks associated with their use include increased myocardial oxygen consumption, tachycardia, increased afterload and arrhythmias. β-Adrenergic receptors may also be downregulated in patients with pre-existing cardiac failure. This has led to interest in the use of phosphodiesterase-III inhibitors and, more recently, the calcium sensitizer levosimendan.
Studies investigating the use of PDIs in cardiac surgery have shown them to be potent inotropes, but vasodilation is a prominent feature of their use, and so concomitant administration of a vasoconstrictor such as norepinephrine or phenylephrine is often required. Such vasoconstrictor agents may or may not have adverse effects of their own. The effects of PDIs on HR appear to be minimal, and there is evidence to suggest that diastolic relaxation and flow through arterial grafts is improved. However, because of their pharmacokinetic profile, the time of onset and offset are longer (a loading dose is required) and they have the potential to accumulate in renal failure. These features can render the PDIs clinically less practical.
The effect of using either catecholamines or PDIs on major clinical outcomes or survival is unknown. We conducted an extensive literature search for data reported during the past 20 years relating to the use of inotropic agents in adult patients who have undergone cardiac surgery. Perhaps the most important finding of this review is the lack of large, double-blind, randomized controlled trials focusing on important clinical outcomes for drugs that are probably given to 250,000–500,000 people each year in western countries alone. Of course, there is overwhelming evidence that the agents considered in this review increase cardiac output, but the question of their comparative effects in the post-CPB heart and their effects on important clinical outcomes remains unclear. The available evidence is often not homogenous and is completely unsuitable for meta-analysis. Also, many of the data rely on physiological end-points, and there are clearly inherent pitfalls in this. Of 125 retrieved papers, only one 'level I' study was identified. The study by Feneck and coworkers [2] provides the only direct comparison between catecholamines and PDIs in patients with LCOS. A summary of haemodynamic changes between the milrinone group and the dobutamine group from the study is outlined in Fig. 2. Although that study included a reasonably large number of patients (n = 120), no convincing advantage was shown for either drug. Moreover, the observation period was only 4 hours and the outcomes were only physiological. This is disappointing because there are several theoretical advantages of PDIs over catecholamines: less tachycardia and myocardial oxygen consumption, improved diastolic relaxation, peripheral and pulmonary vasodilation, and increased IMA graft flow. On the other hand, such advantages are theoretically diminished by the need for vasopressor support.
Hoffman and coworkers recently reported the findings of the PRIMACORP study [97], a randomized, blinded, placebo-controlled trial that investigated the efficacy and safety of prophylactic milrinone in paediatric patients at risk for developing LCOS. Of the 239 patients investigated, high-dose milrinone (75 μg/kg per min followed by an infusion of 0.75 μg/kg per min) reduced the risk for LCOS by 48%. A randomized controlled study of suitable statistical power must be conducted to compare fully the benefits of PDIs with those of dobutamine in adult cardiac surgical patients, with the focus on clinical rather than just physiological outcomes.
Following our systematic analysis of the literature, we believe that – despite the limitations of the data – some recommendations can be made, each with a particular level of evidence.
• Recommendation 1 (level C). β-Agonists or PDIs are more efficacious at increasing cardiac output than placebo for the treatment of LCOS after cardiac surgery. Beta-agonists are associated with a greater incidence of tachycardia and tachyarrhythmia. Administration of a vasoconstrictor is often required with PDIs.
• Recommendation 2 (level C). Catecholamines such as dopamine, epinephrine and dopexamine have no clear advantages over dobutamine and may be associated with a greater incidence of adverse effects. Epinephrine has been successfully used as salvage therapy.
• Recommendation 3 (level C). Administration of PDIs before separation from CPB increases the likelihood of successful weaning compared with placebo, and decreases the use of catecholamines during the postoperative period. Concerns regarding amrinone and thrombocytopenia have limited its use.
• Recommendation 4 (level C). There is no evidence that inotropes should be selected for their effects on regional perfusion.
• Recommendation 5 (level C). Administration of milrinone increases flow through arterial grafts.
• Recommendation 6 (level C). Milrinone and probably other PDIs reduce mean pulmonary artery pressure and improve right heart performance in pulmonary hypertension.
We believe that the field of clinical research into inotropic support for adult cardiac surgery has reasonably established the superiority of catecholamines and PDIs over placebo. However, insufficient evidence exists to guide the choice of one group of drugs versus the other. The role of the new calcium sensitizers remains unknown. It is biologically plausible that the use of catecholamines or PDIs may lead to different clinical outcomes and the clinical scenario of LCOS is relatively common, and so suitably powered, multicentre, randomized controlled trials should be a clinical research priority in adult cardiac surgery patients.
Abbreviations
CABG = coronary artery bypass grafting; CI = cardiac index; CPB = cardiopulmonary bypass; HR = heart rate; ICG = indocyanine green; ICU = intensive care unit; IMA = internal mammary artery; LCOS = low cardiac output syndrome; LVEF = left ventricular ejection fraction; MAP = mean arterial pressure; NO = nitric oxide; PDI = phosphodiesterase inhibitor; pHi = intramucosal pH; PVR = pulmonary vascular resistance; SVR = systemic vascular resistance; SVI = stroke volume index.
Competing interests
The author(s) declare that they have no competing interests.

cardiovascular drugs
coronary artery bypass surgery
postoperative care