Local coexpression domains in the genome of rice show no microsynteny with Arabidopsis domains

Chromosomal coexpression domains are found in a number of different genomes under various developmental conditions. The size of these domains and the number of genes they contain vary. Here, we define local coexpression domains as adjacent genes where all possible pair-wise correlations of expression data are higher than 0.7. In rice, such local coexpression domains range from predominantly two genes, up to 4, and make up ∼5% of the genomic neighboring genes, when examining different expression platforms from the public domain. The genes in local coexpression domains do not fall in the same ontology category significantly more than neighboring genes that are not coexpressed. Duplication, orientation or the distance between the genes does not solely explain coexpression. The regulation of coexpression is therefore thought to be regulated at the level of chromatin structure. The characteristics of the local coexpression domains in rice are strikingly similar to such domains in the Arabidopsis genome. Yet, no microsynteny between local coexpression domains in Arabidopsis and rice could be identified. Although the rice genome is not yet as extensively annotated as the Arabidopsis genome, the lack of conservation of local coexpression domains may indicate that such domains have not played a major role in the evolution of genome structure or in genome conservation.
Introduction
The fast-growing data sets on genome annotation and genome-wide gene expression facilitate the study and comparison of gene activity between and among genomes. The genomic context of genes is supposed to play an important role in the regulation of gene expression (van Drunen et al. 1997). Non-random clusters of similarly expressed (co-regulated, coexpressed, highly expressed and/or broadly expressed) genes have been described in almost all organisms, ranging from prokaryotes to eukaryotes. In eukaryotes, from yeast to Arabidopsis to human, both short-range co-regulated/coexpressed clusters of two to five genes (Cohen et al. 2000; Ren et al. 2005; Zhan et al. 2006) and longer-range coexpression domains of up to 30 genes spanning up to 100 kb and more (Lercher et al. 2003; Ma et al. 2005; Spellman and Rubin 2002; Zhan et al. 2006) have been described. Duplicated genes (Lercher et al. 2003), shared promoter regions (Kruglyak and Tang 2000), shorter gene distance (Cohen et al. 2000; Roy et al. 2002; Semon and Duret 2006; Williams and Bowles 2004) and/or functional relatedness (Cohen et al. 2000; Lee et al. 2004; Spellman and Rubin 2002; Williams and Bowles 2004) were found to account for only part of the coexpression between genes. Therefore, most studies postulate that the occurrence of coexpression domains, small or large, is regulated on the level of higher-order chromatin structure (Cohen et al. 2000; Hershberg et al. 2005; Ren et al. 2005; Spellman and Rubin 2002; Williams and Bowles 2004), although alternative views exist (Semon and Duret 2006).
Previously we have defined and demonstrated the existence of local coexpression domains in the genome of Arabidopsis (Ren et al. 2005). A local coexpression domain was defined as any set of physically adjacent genes that are highly coexpressed with a pair-wise Pearson’s correlation coefficient larger than 0.7. It was shown that a small (5–10%) yet significant fraction of genes in the Arabidopsis genome is organized in such local coexpression domains. Genes in such local domains were for the major part not categorized in the same functional category (GOslim). Neither tandemly duplicated genes nor shared promoter sequence, nor gene distance, explained the occurrence of coexpression of genes in such chromosomal domains. This indicates that other parameters in genes or gene positions are important to establish coexpression in local domains of Arabidopsis chromosomes. Here it is analyzed whether a similar situation exists in the genome of the monocotyledonous model plant rice (Oryza sativa), for which an earlier study had shown the existence of longer-range domains (Ma et al. 2005). We combined the whole genome rice annotation data (TIGR version 3; www.tigr.com) with Massively Parallel Signature Sequencing (MPSS; mpss.udel.edu/rice) expression data as well as Affymetrix array expression data (GEO, GSE4438; www.ncbi.nlm.nih.gov/geo) for the rice cultivar japonica in a way similar to the analysis performed for the Arabidopsis genome (Ren et al. 2005). The results show that the characteristics of the two genomes with respect to the occurrence and configuration of local expression domains are remarkably similar.
Also in the rice genome, a small yet significant fraction of genes is organized in local coexpression domains that predominantly consist of two, up to 4, genes that are not categorized in the same functional category, irrespective of the expression platform used for analyses. The presence of tandemly duplicated genes, shared promoter sequence or gene distance is not fully explaining the occurrence of coexpression of genes in such chromosomal domains. Therefore, the regulation of local coexpression domains is postulated to be at the level of higher-order of chromatin structure. Given the similarities in the characteristics and occurrence of coexpression domains between Arabidopsis and rice, we investigated whether the genes involved showed microsynteny between the two genomes. These analyses did not identify the presence of syntenic local coexpression domains between Arabidopsis and rice.
Material and methods
Genome data
The rice (Oryza sativa) genome was obtained from the website of The Institute of Genomic Research (TIGR; www.tigr.org). The rice TIGR version 3 [Jan. 2005] annotation has 57,915 gene loci. In case of alternative splicing, the longest variant of the gene was used. The genes along each chromosome were sorted based on ascending start coordinates and were numbered consecutively. These rank numbers (rank ID) helped to eliminate any discontinuity in the unique Os gene identifiers of the annotated genes and facilitated analyzing physically adjacent genes. In case of overlapping gene loci, the smaller one of the overlapping genes was removed from the data set. This way the order of both gene and rank ID numbers was maintained.
Expression data
The MPSS expression data for rice (cv. Japonica) were obtained from the rice MPSS database (http://mpss.udel.edu/rice). Only the unique MPSS tags (mapping to the genome only once) and those mapping to unique gene identifiers in TIGR v3 were used in the analyses. The MA expression data were obtained from the Gene Expression Omnibus repository (GEO; www.ncbi.nlm.nih.gov/geo). The data with accession number GSE4438 were chosen for analysis, because they represent the only Affymetrix chip-based data set in the public domain with a reasonable coverage of the Oryza sativa cv. japonica transcriptome under four different experimental conditions (Walia et al. 2007). The earlier analysis in Arabidopsis also concerned Affymetrix chip data (Ren et al. 2005). The MA expression data were mapped to the TIGR v3 annotation based on gene accession numbers.
The expression values in libraries representing biological replicates, i.e., the same tissues under the same experimental conditions, were averaged. This way, for the MPSS data 18 different libraries were generated, that cover expression in 9 tissues (callus, panicle, leaves, root, germinating seed and seedling meristem, ovary and stigma, pollen, stem) under different experimental treatments or in different developmental stages. From the microarray (MA) data, only the expression data from cv. japonica were used to allow comparison with the MPSS data. Here, 4 different libraries were generated: a sensitive japonica genotype under control conditions and under salt stress; and a tolerant japonica genotype under control conditions and under salt stress (Walia et al. 2007). The expression data is from crown and growing point tissue.
Identification of local coexpression domains
Pearson’s correlation coefficient (R) was calculated between all adjacent pairs (duplets) of genes using the expression data from all 18 libraries. If R was higher than 0.7, the gene pair concerned was considered to be coexpressed. The value of R > 0.7 is generally considered a rule-of-thumb threshold (see for example bbc.botany.utoronto.ca/affydb/BAR_instructions) and is used in various analyses (Cohen et al. 2000; Lee et al. 2004; Ren et al. 2005). The number of coexpressed adjacent pairs was counted. To evaluate the statistical significance of these numbers, they were compared with the number of coexpressed pairs from 100 randomizations of the population of expressed genes using the cumulative binomial distribution (Cohen et al. 2000). Previous analyses indicated that more than 100 randomizations did not result in significant changes in the numbers obtained (Ren et al. 2005). In each round of randomization, non-adjacent pairs of genes were randomly selected with replacement from the list of expressed genes that have expressed neighbors till the same total number of pairs was obtained. Similarly, coexpressed adjacent triplets, quadruplets and pentaplets were identified as series of genes with consecutive IDs in which all possible (that is, (n!/(n−2)!)*2; where n is the number of genes involved) pair-wise R’s should be above the cut-off of 0.7. The significance of results was evaluated with randomizations equivalent to the procedure used in case of duplets.
Duplicated genes
Duplicated genes were identified by local pair-wise protein BLAST (BLASTP 2.2.6 [Apr-09-2003]; (Altschul et al. 1997)), on all gene pairs in the rice genome. A gene pair was considered to be duplicated (dup) if BLASTP yielded an E-value < 0.2 (Fukuoka et al. 2004; Lercher et al. 2003; Williams and Bowles 2004). To determine duplicated triplets, quadruplets and pentaplets, it was required that any pair of the genes concerned had a BLASTP E-value <0.2.
Analyses of gene orientation and gene distance
Adjacent gene pairs were separated into tandemly, divergently and convergently transcribed pairs according to their relative direction of transcription. The number of coexpressed pairs in each orientation group was expressed as percentage relative to the total number of adjacent pairs in that group. Random pairs were made by randomly picking two non-adjacent genes from the list of expressed genes represented in pairs, analyzed for their orientation and compared with the real genome using a variant of the two-sample t test for proportions for determining the significance of a difference between two population proportions (Ott and Longnecker 2001). The test statistic is based on the z statistic from the normal distribution and is given by (p1−p2)/ √ (p1*(1−p1)/n1 + p2*(1−p2)/n2), with p1 and p2 the two sample proportions, n1 and n2 the two sample sizes, under the condition that n1*p1, n1*(1−p1), n2*p2 and n2*(1−p2) are all larger than 5. The z value is converted to a p value using standard normal tables.
To determine the gene distance, the intergenic distance is used. This distance is defined as the length in nucleotides from the annotated end of one gene to the annotated start of the next gene, including the UTRs when known, otherwise the translation start and stop sites were taken. The data sets excluding the duplicated gene pairs were analyzed. For each data set, gene pairs were sorted based on gene distance from short to long and bins of 1,000 pairs were taken and analyzed, excluding the last bin with less than 1,000 pairs. The advantage of using equal pair bin is that it avoids unequal number of gene pairs in different distance categories. Per 1,000-pair bin, gene distance was calculated as the average over all 1,000 pairs. For each 1,000-pair bin, the fraction of coexpressed pairs relative to the total number of pairs in each orientation group in each bin was calculated and plotted.
Functional categorization of genes
TAIR’s GOslim, the Gene Ontology (GO) developed for plants (Berardini et al. 2004) was used to classify the genes present in local coexpression domains. The three aspects of GOslim, molecular function, biological process and cellular component, were analyzed in parallel. With Python scripts, the number of pairs of which both members could be classified in GOslim was determined, and the number of pairs of which both members fall into the same well-defined GOslim category was also determined. The GOslim categories of ‘unknown’ and ‘other’ were not included into well-defined categories, because they give less (or no) information about functional categorization. The percentage of coexpressed pairs falling into the same well-defined category was compared with that of non-coexpressed pairs to determine whether coexpressed genes are more enriched in the same functional category than non-coexpressed genes.
Assessing synteny between Arabidopsis and rice
The Inparanoid Eukaryotic Orthologous groups database (inparanoid.cgb.ki.se; O’Brien et al. 2005) was used to download all known orthologous and inparalogous clusters between Arabidopsis and rice. Inparanoid defines inparalogs as paralogs that arose through gene duplication after speciation. Inparalogs can form a group of genes that together are orthologous to a gene in another species. There are 9,044 orthologous clusters between Arabidopsis (from Ensemble) and rice (from the Model Organism database) and all of them were taken into account. These clusters were downloaded on Dec. 12, 2005. In the orthologous clusters, 15,544 sequences (proteins) from Arabidopsis are inparalogs and 14,807 sequences (proteins) from rice are inparalogs. More than half of Arabidopsis and rice inparalogs are many-to-many or many to-one orthology cases. Less than half of the cases are one-to-one orthology cases.
The Ensembl protein IDs (for Arabidopsis) and the Model organism database protein IDs (for rice) in Inparanoid were first translated to their unique gene identifiers in the respective TIGR annotation by BLASTP using an E-value < e−20. This yielded 14,753 unique Arabidopsis genes and 12,428 unique rice genes as inparalogs. The pairs of genes in local coexpression domains were analyzed to determine which genes in a rice local coexpressed pair have orthologs in an Arabidopsis local coexpressed pair, and vice versa. Because of the larger coverage of genes and tissues, only the MPSS expression data were used for this analysis. As coexpressed triplets and quadruplets are always combinations of coexpressed pairs, they were not further analyzed. For comparison, the pairs of genes that are not coexpressed were analyzed to determine how many non-coexpressed pairs, or one of their member genes, have orthologs in the other plant species. The numbers were then compared between coexpressed pairs and non-coexpressed pairs to determine the significance of occurrence of syntenic local coexpression domains.
Results
Local coexpression domains consist of two to four neighboring genes
The TIGR version 3 of the rice genome has 57,915 predicted genes. This is about twice the number of genes predicted for Arabidopsis (28,952 genes; TIGR5 annotation). The coverage of the MPSS expression data for the rice genome is 40%. The expression coverage in the Affymetrix array data set we used is 26% (Table 1). This is about half or less of the expression coverage for the Arabidopsis genome (72% in the TIGR5 update; Ren et al. 2005). This difference can reflect the more advanced annotation of the Arabidopsis genome at this time, and/or the more complex (duplicated) organization of the rice genome. The rice genome annotation has more genes that are physically overlapping than the Arabidopsis genome. Excluding the smaller overlapping genes from the analyses, we were able to identify 12,920 gene pairs with MPSS expression data and 6,032 pairs with MA expression data in rice (Table 1; see also Materials and methods). Of these, 584 (4.5%) in MPSS and 320 (5.3%) in MA were identified to represent a local coexpression domain as defined as being coexpressed with a pair-wise Pearson’s correlation coefficient larger than 0.7 (Table 1). This percentage is similar to what we have found previously for Arabidopsis (Ren et al. 2005) and agrees well with other findings that ∼3–5% of a genome is tightly coexpressed (Semon and Duret 2006).
Table 1Description of rice expression data used for whole-genome local coexpression analysisMPSSMAGenes with expressionExcluding overlapping genes23,14614,789Without expressed neighbor(s)5,0815,438represented in pairs18,0659,351Adjacent pairsTotal12,9206,032Tandemly duplicated pairs (td) 1,663 (12.9%)a573 (9.5%)aCoexpressed584 (4.5%)b320 (5.3%)bTotal excluding td11,2575,459 Coexpressed excluding td 438 (3.9%)c288 (5.3%)cCoexpressed adjacent pairsTotal584320Tandemly duplicated pairs146 (25%)d32 (10%)dTandemly duplicated pairsTotal1,663573Coexpressed146 (8.8%)e32 (5.6%) eaPercentage of tandemly duplicated pairs relative to the total number of adjacent pairsbPercentage of coexpressed adjacent pairs relative to the total number of adjacent pairscPercentage of coexpressed adjacent pairs excluding td relative to the total number of adjacent pairs excluding tandemly duplicated pairsdPercentage of coexpressed tandemly duplicated pairs relative to the total number of coexpressed adjacent pairsePercentage of coexpressed tandemly duplicated pairs relative to the total number of tandem duplicated pairs
Notably duplicated genes are supposed to influence coexpression statistics due to their common origin (Lercher et al. 2003), although a surprising finding for the Arabidopsis coexpression domains was that only a minor fraction of duplicated genes were actually coexpressed (Ren et al. 2005). The occurrence of duplicated pairs in the rice set was determined with pair-wise protein BLAST using a cut-off of E < 0.2 (Fukuoka et al. 2004; Lercher et al. 2003; Williams and Bowles 2004). This identified 1,663 (12.9%) duplicated gene pairs in the MPSS gene pair data set and 573 (9.5%) duplicated gene pairs in the MA gene pair data set.
Of these, only 146 (8.8%) in the MPSS data and 32 (5.6%) in the MA data were coexpressed (Table 1). Although this percentage is somewhat higher than the percentage of coexpression in non-duplicated pairs (3.9%), the majority of all duplicated pairs (91.2% in MPSS data and 94.4% in MA data) are not coexpressed. This shows that also in rice gene duplication does not correlate well with coexpression and suggests that expression divergence is a common phenomenon after duplication (Williams and Bowles 2004). Excluding the duplicated pairs from the coexpressed sets, there are 438 gene pairs in the MPSS data and 288 gene pairs in the MA data coexpressed in rice. This accounts for 75% (=438/584) in MPSS and 90% (=288/320) in MA of all coexpressed pairs. Therefore, also in rice the occurrence of duplicated genes cannot explain the occurrence of local coexpression domains. Extending the size of the local coexpression domain to triplets, quadruplets, pentaplets and on, requiring that all pair-wise combinations of genes have a tightly correlated expression, shows that few larger local coexpression domains exist (Table 2). No quadruplet domains could be identified when tandemly duplicated genes were excluded (Table 2). To assess the significance of the occurrence of the various local coexpression domains, we compared the number of coexpressed pairs, triplets and quadruplets with the average of such domains in 100 randomly generated genomes using the cumulative binomial distribution (Cohen et al. 2000). Such comparisons revealed that local coexpression pairs occur in the rice genome significantly more often than expected by chance alone (Table 2). However, when excluding the duplicated genes, triplets and quadruplets do not occur significantly more often than by chance (at P < 0.05) in both expression datasets. Local coexpression domains therefore consist of at most 2 genes when duplicated genes are not taken into consideration. This number appears smaller than in the Arabidopsis genome (Ren et al. 2005), but this may reflect the lesser coverage of the annotation of the rice genome.
Table 2Local coexpression domains in the rice genomeRice genomeRandom genome (100×)TotalaCoexpressedbAveragecP-valuedPairsMPSS + tde12,920584 (4.52%)408 ± 171.46 × 10−17MPSS-tdf11,257438 (3.89%)356 ± 212.17 × 10−6MA + tdg6,032320 (5.30%)301 ± 170.012MA-tdh5,459288 (5.28%)271 ± 160.014TripletsMPSS + td7,77523 (0.30%)8.78 ± 2.92.95 × 10−5MPSS-td6,83113 (0.19%)7.74 ± 3.00.025MA + td2,4615 (0.20%)6.54 ± 2.7n.s.MA-td2,1493 (0.14%)5.10 ± 2.4n.s.QuadrupletsMPSS + td4,8873 (0.06%)0.24 ± 0.471.81 × 10−3MPSS-td4,3180 (0%)0.18 ± 0.39n.s.MA + td1,0790 (0%)0.14 ± 0.37n.s.MA-tdndindndndaTotal number of pairs, triplets, quadruplets in each data setbCoexpressed pairs, triplets, quadruplets in each data set. Percentages in brackets are coexpressed relative to the totalcAverage plus/minus standard deviation from 100 randomizationsdP-value according to the cumulative binomial distribution (Cohen et al. 2000) for obtaining such a result by chance. P < 0.05 is considered significant; n.s.: not significanteMPSS data set including tandemly duplicated genesfMPSS data set excluding tandemly duplicated genesgMA data set including tandemly duplicated geneshMA data set excluding tandemly duplicated genesiNot determined
Local coexpression domains seem randomly distributed over the genome (Fig. 1; MPSS data only). Only 10 coexpressed pairs are common between the MPSS and MA coexpressed sets, out of a total of 4,643 common pairs (excluding tandemly duplicated pairs). Detailed information about locations, orientations, expressions and gene distances (only for the pairs) for all pairs, triplets and quadruplets in both the MPSS and MA data sets are given in the supplementary data. All subsequent analyses were focused on domains consisting of non-duplicated gene pairs, unless stated differently.
Fig. 1Distribution of local coexpression domains over all 12 rice chromosomes. Rectangles are schematic representation of chromosomes 1–12 from top to bottom. The numbers on the top show the scale in million bases along the chromosomes. Each gene in a local coexpression domain is depicted with a black bar. Only MPSS datasets excluding tandemly duplicated genes are shown. The orders of the drawings in each rectangle are: first lane, coexpressed pairs; second lane, coexpressed triplets; third lane, coexpressed quadruplets, fourth lane, partially syntenic coexpression domains (PSCDs) between Arabidopsis and rice
Orientation and distance do not solely explain the occurrence of local coexpression
In yeast, there are several examples that divergently transcribed promoter regions are the cause of co-regulated neighboring genes (Korbel et al. 2004; Kruglyak and Tang 2000). If promoter sharing is an important mechanism for coexpression in the rice genome, divergently transcribed gene pairs should be over-represented in the sub-population of coexpressed pairs, compared to coexpressed pairs that are tandemly or convergently transcribed. For all three-orientation groups, the number of pairs and the number of coexpressed pairs in the rice genome were determined (Table 3). For each orientation group, the fraction of coexpressed pairs relative to the total number of pairs in that group was calculated (Table 3). None of the fractions are significantly different from each other using a statistical test for comparing population proportions (Ott and Longnecker 2001). The fraction of coexpressed divergent pairs in the MPSS data is the lowest of the three groups (Table 3). Therefore, shared promoter regions cannot solely explain the coexpression of adjacent genes.
Table 3Orientation of coexpressed gene pairsOrientation groupsaTotalbCoexpressedcMPSStan-td5,621239 (4.25%)div-td2,41882 (3.39%)con-td3,218117 (3.64%)MAtan-td2,707143 (5.28%)div-td1,22472 (5.88%)con-td1,52873 (4.78%)atan-td, div-td, con-td, respectively are the sub-groups of tandemly, divergently, convergently transcribed pairs excluding tandem duplicatesbTotal number of pairs in each direction groupcNumber of coexpressed pairs in each direction group. Percentages in the brackets are number of coexpressed pairs relative to the total number of pairs. None of the proportions are significantly different from each other according to the z test for comparing population proportions
The physically closer two genes are, the higher the likelihood is that they are coexpressed due to either cis or trans-activation (Hershberg et al. 2005). Therefore, we determined the intergenic distance, defined as the sequence length in nucleotides from the annotated end of one gene to the annotated start of the neighboring gene, including UTRs when known, otherwise taking the start and stop site for translation. This distance was used to investigate whether it would explain the characteristics of local coexpression domains. In Fig. 2, the fraction of coexpressed pairs is plotted for each orientation and for each 1000-pair bin after sorting based on intergenic distance. The results show that the fraction of coexpressed pairs, irrespective of gene orientation, does not decrease with larger gene distance. When gene distance is defined as the sequence length from the start of one gene till the start of the next gene (Ren et al. 2005), the result is similar (data not shown). As a consequence, increasing intergenic distances do not seem to be a barrier for the occurrence of local coexpression and short intergenic distances do not favor coexpression. Therefore, intergenic distance does not solely explain local coexpression in the rice genome, as it did not in the Arabidopsis genome (Ren et al. 2005).
Fig. 2Gene distance does not solely explain the occurrence of coexpression. Gene distance, defined as the length in nucleotides from the annotated end of one gene to the annotated start of the next gene relative to the strand the genome that is given, with annotated start always smaller than the annotated end. X-axis is the averaged gene distance (in base pair) in each 1,000-pair bin. The Y-axis depicts the number of pairs (A, D), number of coexpressed pairs (B, E) and the fraction of coexpressed pairs (C, F), relative to the total number of pairs in each orientation (tan: tandem pairs; div: divergent pairs; con: convergent pairs) in each 1,000-pair bin
Functional categorization of coexpressed genes
To characterize the kind of genes that are present in the rice coexpression domains, the gene ontology (GO) developed for plants (GOslim; Berardini et al. 2004) was used. The GOslim ontology provides a controlled vocabulary to describe gene and gene product attributes in plants, focusing on three aspects of annotation: molecular function, biological process and cellular component. Each aspect has 15–16 categories with 4–5 categories having terms like “unknown” or “other”. To all pairs of genes, each aspect of the GOslim annotation was assigned. For each aspect, the number of pairs was determined for which both member genes were covered by a GOslim assignment. In addition, the number of pairs for which both member genes fall into the same well-defined categories (excluding the “unknown” and “other” subcategories) was determined. The fraction of the latter was compared between coexpressed pairs and non-coexpressed pairs to determine whether coexpressed pairs were enriched in the same categories (Table 4). In the MPSS data, the GOslim annotation coverage for both member genes in a pair is 22% for molecular function, 12% for biological process and only 3.4% for cellular component. In the MA data, these 3 percentages are: 25%, 13% and 3.7%, respectively. Comparing these figures with the GOslim coverage of the Arabidopsis genes (data from Ren et al. 2005, but updated to the TIGR5 annotation), which is ∼94% for all 3 aspects, shows that currently the rice genome is considerably less well annotated than the Arabidopsis genome. When comparing the coexpressed and non-coexpressed pairs in rice for the fraction of gene pairs falling into the same well-defined GOslim category, there is no significant difference (Table 4). Therefore, also in the rice genome coexpressed gene pairs are not enriched for the same functional category.
Table 4Distribution of gene pairs over GOslim categories (Non-duplicated pairs)AllaCoexpressedbNon-coexpressedcP-valuedMPSS    GO_func        coverede250210024020.42        sameKnCatf365 (14.6%)12 (12.0%)353 (14.7%)    GO_proc        Covered13665013160.47        sameKnCat144 (10.5%)7 (14%)137 (10.4%)    GO_comp        Covered383173660.60        sameKnCat113 (29.5%)6 (35.3%)107 (29.2%)MA    GO_func        coverede13658312820.13        sameKnCatf177 (13.0%)7 (8.43%)170 (13.3%)    GO_proc        Covered707436640.13        sameKnCat67 (9.48%)2 (4.65%)65 (9.79%)    GO_comp        Covered202101920.24        sameKnCat45 (22.3%)4 (40%)41 (21.4%)aNumber of neighboring pairs excluding td. All other pairs are all duplicate-free, unless stated otherwisebNumber of coexpressed pairscNumber of non-coexpressed pairsdP value from the standard normal tables of the z statistic for the difference of the population proportion between coexpressed pairs and non-coexpressed pairs in the rice genome; *, significant (two-tailed; P < 0.05). The P value is the probability under the null hypothesis that the two population proportions are the sameeNumber of pairs of which both members are assigned (covered) with GOslim categoriesfNumber of pairs of which both members fall into the same “known” GOslim category (excluding the categories with the indications ‘unknown’ and ‘other’). Percentage is the number of pairs relative to the number of pairs covered
Microsynteny of local coexpression domains between rice and Arabidopsis
The structural characteristics of local coexpression domains in rice and in Arabidopsis (Ren et al. 2005) are remarkably similar. This prompts the question whether such domains also share functional characteristics and possibly consist of the same or related genes. Microsynteny in local expression domains of these two genomes would reflect conservation of such domains. The Inparanoid Eukaryotic Orthologous database (O’Brien et al. 2005) was used to retrieve the current list of genes that are supposed to be orthologous between Arabidopsis (14,753 genes) and rice (12,428 genes), including all many-to-many relationships. The genes establishing coexpressed pairs based on MPSS expression data in either Arabidopsis (944 pairs including 116 duplicated pairs; data from Ren et al. 2005, but updated to the TIGR 5 annotation) or rice (584 pairs, including 146 duplicated pairs) were searched against these lists. This way, we aimed to identify the pairs of which both genes in the pair have an ortholog in the other plant and these orthologs are also coexpressed The analyses showed that there was not a single coexpressed pair in either Arabidopsis or rice of which both genes are orthologous to a gene of a coexpressed pair in the other species. Therefore, given the current annotation of the two genomes, there are no syntenic local coexpression domains between Arabidopsis and rice.
Partially syntenic local coexpression domains can occur by chance
In 34 cases though, one gene of a coexpressed pair in one plant species was orthologous to at least one gene of a coexpressed pair in the other plant. That is 3.6% of all (944) coexpressed pairs in Arabidopsis and 5.8% of all (584) coexpressed pairs in rice. We will refer to such a case as a partially syntenic coexpression domain (PSCD). To assess the significance of such partially syntenic domains, we evaluated all the genes in non-coexpressed pairs, comparing Arabidopsis (15,629 pairs including 617 duplicated pairs) and rice (12,336 pairs including 1,517 duplicated pairs) to establish whether PSCDs are more enriched in the genome than partially syntenic non-coexpressed domains (PSND). We identified 4,488 PSNDs (72 due to duplicated pairs) between all non-coexpressed pairs of genes in both plant genomes. This is 28.7% of all Arabidopsis non-coexpressed pairs and 36.4% of all rice non-coexpressed pairs. The percentage of PSNDs among non-coexpressed pairs is 6–8 times higher than that of PSCDs from coexpressed pairs. Therefore, PSCDs do not seem to occur more often than expected by chance alone.
A complicating issue in the analysis of synteny is the occurrence of many-to-many orthologs. The Inparanoid database defines so-called inparalogs as paralogs arising through gene duplication after speciation. These can form a group of genes that together are orthologous to a gene in the other species. As a result, there can be many to many, many to one and one to one relationships. Individual member genes in many-to-many or many-to-one relationships may not be the main orthologs. Interestingly, there is one many-to-one case in which four Arabidopsis genes are all orthologs of the same single rice gene (Os07g43560.1). These 4 Arabidopsis genes are: At4g23140.2, At4g23150.1, At4g23230.1 and At4g23270.1. The first two, At4g23140.2, At4g23150.1, form a local coexpressed pair. The other two genes, At4g23230.1 and At4g23270.1, are not more than ten genes away from the previous two genes on the same chromosomal region. The latter two genes are separated from each other by a few genes. Further analysis shows that gene At4g23270.1 has a duplicated neighbor, At4g23280.1, but is not coexpressed with it. It is, however, coexpressed with its other neighbor At4g23260.1, but is not duplicated with it. Orthology is established between At4g23270.1 and the rice gene Os07g43560.1, but not between any of the neighbors of the Arabidopsis genes. The rice gene Os07g43560.1 is also coexpressed with one of its neighboring genes Os07g43540.1, but it is not a duplicate of it, while this rice gene itself is a duplicate of another neighboring gene (Os07g43570), but it is also not coexpressed with it. A schematic representation of the resulting gene configuration is given in Fig. 3. Such detailed analyses may reveal local microsynteny in the twilight zone of statistical significance and evolutionary relevance.
Fig. 3Schematic representation of the chromosomal regions covering genes involved in a four-to-one orthology between Arabidopsis and rice. Top part of the figure is the chromosomal region from rice (from gene locus Os07g43540.1 to gene locus Os07g43570.1). Bottom part of the figure is the chromosomal region from Arabidopsis, representing 23 genes (from gene locus At4g23120 to At4g23340; the numbers in the picture do not carry “At4g”). Black arrows represent the four Arabidopsis and the one rice gene involved in this orthology, and dashed curved connecting lines show the orthology relationships. Black bracket-like lines depict duplication and genes connected and included within by black bracket line are duplicated to each other. Dotted lines depict coexpression relationship and genes connected and included by dotted line are coexpressed with each other
Discussion
Local coexpression domains represent only a small part of the genome
Setting stringent criteria for coexpression using MPSS and microarray expression data, the rice genome was found to contain a small but significant number of local coexpression domains that range from predominantly two, up till 4, genes, irrespective of the expression platform used for analysis. This is similar to the situation in Arabidopsis (Ren et al. 2005). It shows that a genome is essentially not a random entity with respect to the occurrence of local coexpression domains. Our results agree with other coexpression studies where strong coexpression was shown to occur only within close proximity of several genes (Cohen et al. 2000; Hershberg et al. 2005; Lercher et al. 2003; Semon and Duret 2006). Although coexpression was shown to extend to regions covering up to 30 genes and more (Spellman and Rubin 2002) and to cover chromosomal regions up to 100 kb (Ma et al. 2005; Spellman and Rubin 2002; Williams and Bowles 2004) and more, there appears to exist a decrease in the strength of coexpression with increasing distances. The local coexpression domains described here represent ∼4–5% of the potential coexpression fraction in the whole genome as found in other studies (Semon and Duret 2006). Larger but looser coexpression domains might cover up to ∼10% (Cohen et al. 2000; Williams and Bowles 2004) till 20% (Spellman and Rubin 2002) of the genome. The difference in occurrence between local and longer-range weaker but still statistically significant coexpression domains is highly dependent on the method used (Semon and Duret 2006). Moreover, the expression platforms used focus on snapshots of gene expression at the RNA level. Such data ignore various post-transcriptional regulatory mechanisms that can for example result in poor correlations between RNA and protein levels. Although we have shown before that in a transgenic set-up the creation of an artificial local coexpression domain results in markedly improved RNA/protein correlations (Mlynarova et al. 2002), this does not need to be the case for all endogenous local coexpression domains now identified.
The terms cluster or chromosomal domain and associated terms such as neighboring are generally based on a (much) more loose definition compared to the definition used here to identify local coexpression domains. Local coexpression domains require a pair-wise correlation between the expressions of ALL adjacent genes above 0.7. The larger domains are defined on the basis of the use of a sliding window of either a given sequence length (number of nucleotides) or of a given number of genes (Spellman and Rubin 2002; Williams and Bowles 2004). In such a window, the average correlation is calculated and compared with simulated sets. This allows for the presence of genes within a domain that are not strongly (co)expressed but are “carried along for a ride” in the open chromatin domain (Spellman and Rubin 2002).
The two expression data sets here analyzed show very little overlap in coexpressed pairs (only 10 out of 4,643 all common pairs). The low number of shared pairs is thought to reflect the biological background of the data sets. The MPSS data concern a broad range of tissues and experimental conditions, while the MA data only cover crown and growing point tissue under control and salt stress condition (Walia et al et al. 2007). The number of libraries taken into account, as well as their biological background, obviously influences the possibility of identifying significant numbers of coexpression domains above the number of domains expected by chance. Two genes evaluated in four comparably similar conditions (MA data) are predicted to have a higher likelihood of exhibiting a similar expression pattern, than when examined in 18 different (MPSS) conditions. When considering genome-wide local coexpression of genes, a wide diversity of tissues and/or conditions should be taken for analysis. Highly tissue-specific coexpression may be masked in this approach and should be analyzed by other means.
Parameters shaping local coexpression domains
The existence of local coexpression domains in rice could not be explained solely by gene orientation, such as tandemly, divergently or convergently oriented gene pairs. No relative enrichment of the proportion of coexpressed pairs was seen. The fraction of coexpressed genes in the divergent orientation was even lower than for the other two orientations (Table 3). So shared promoter regions (for divergent pairs) and transcriptional read-through (for tandem pairs) do not explain the local coexpression domains in rice, similar to what we have concluded for Arabidopsis (Ren et al. 2005). This is in contrast to some other studies in which shared promoter region (for divergent pairs) and transcriptional read-through established coexpression domains (Semon and Duret 2006). Whereas we do not detect any preferred orientation to result in coexpression in rice, other studies show a higher degree of coexpression in divergent and tandemly oriented gene pairs (Williams and Bowles 2004; Zhan et al. 2006). The differences in conclusions are most likely due to the different methods used, such as the definition of coexpression of neighboring genes as well as the dataset and/or expression platform used.
Gene distance is not the explanatory factor for the occurrence of local coexpression domains in rice. No significant decrease in the fraction of coexpressed genes was observed with increasing intergenic distance (Fig. 2). The fraction of coexpressed pairs does not decrease even with gene distances up to 12 kb. It shows that at a relatively large distance, neighboring genes can still be coexpressed. Another study reported that when genes >12 kb apart were taken into account, the negative correlation between coexpression and gene distance was gone (Williams and Bowles 2004). In a comparative study of 6 eukaryotic genomes, coexpression was shown to vary at chromosomal distances above 100 kb (Fukuoka et al. 2004). This suggests that considerable coexpression of neighboring genes can occur even at large gene distance, although the coexpression may not be related to the physical distance anymore. While gene distance itself is not predictive for coexpression (Cohen et al. 2000; Kruglyak and Tang 2000), the likelihood of coexpression would favor short gene distances (Hershberg et al. 2005; Hurst et al. 2002; Lercher et al. 2003; Semon and Duret 2006). It should be kept in mind that the rice data set now analyzed is far from complete in terms of its annotation, so what are now far-apart neighboring genes may be no longer directly neighboring the moment the annotation is improved. Because the characteristics of the local coexpression domains in Arabidopsis and rice are remarkably alike, the actual numbers and possibly even genes involved in local coexpression domains may be subject to change, but not the occurrence of such domains in these genomes.
With the gene ontology developed for plants (GOslim), there is no evidence that coexpressed genes are more enriched in the same functional category in comparison to non-coexpressed genes (Table 4). Previous studies suggested that clustering of functionally related genes would occur in all metazoans (Cohen et al. 2000; Lercher et al. 2003). Recent studies demonstrated a significant enrichment for coexpressed genes in the same metabolic pathway (Williams and Bowles 2004) or the same biological processes (Zhan et al. 2006), although this appeared not to be the explanation for the coexpression of neighboring genes (Williams and Bowles 2004). In worm, clusters of similarly expressed genes cover similar biological functions (Roy et al. 2002). In human, coexpression over the whole genome was shown to correlate with functional relationships between the genes (Lee et al. 2004). Our study found no enrichment of coexpressed gene pairs in the same functional category than non-coexpressed pairs, suggesting that it is not necessarily true that the natural selection maintained regional coexpression by keeping genes with similar functions in adjacent positions (Cohen et al. 2000; Semon and Duret 2006).
The genomic context of genes is supposed to play an important role in the regulation of gene expression (van Drunen et al. 1997). In a number of coexpression studies in various organisms, the occurrence of coexpression domains, whether small (local) or larger (global), sometimes independent of gene orientation and gene distance, were all supposed to be regulated at the level of higher-order chromosomal structure (Cohen et al. 2000; Hershberg et al. 2005; Ren et al. 2005; Spellman and Rubin 2002; Williams and Bowles 2004; Zhan et al. 2006). It will be difficult to formally exclude the possibility of transcriptional regulation as a cause of local coexpression. Co-regulated transcription could occur through shared promoter elements between the neighboring genes in coexpression domains. However, previous experience with transgene expression data indicated that the particular position of neighboring genes in a genome affects the expression of that gene considerably (Mlynarova et al. 1994, 1995). Two physically neighboring transgenes could only be made to into an artificial local expression domain, that is, show correlated expression, when chromatin-organizing elements were placed around the genes (Mlynarova et al. 2002). Although this experimental result indicates the importance of chromatin organization in establishing local coexpression in a plant genome, this result does not need to be the case for all coexpression domains identified. With this caveat, this study of local coexpression domains in rice that are independent of duplication, gene orientation, or gene distance strengthen the notion that the regulation of genes in such domains resides at the level of higher-order chromatin structures. Future studies using for example advanced fluorescence in situ hybridization (FISH) technology on interphase chromosomes (Walter et al. 2006), or the transgenic approach outlined above (Mlynarova et al. 2002), will generate further experimental support for the location and characteristics of local coexpression domains. In addition, the new approaches in and insights from epigenetics and epigenomics (Henderson and Jacobsen 2007), such as the genome-wide mapping and analysis of DNA methylation (Zhang et al. 2006; Zilberman et al. 2007) and future histone modification maps (Esteller 2007) of plants, possibly in combination with genetics (Jansen and Nap 2001), will be useful to get more insight into the occurrence and function of local coexpression domains in plant genomes.
Lack of microsyntenic coexpression
From an evolutionary point of view, syntenic regions between species reveal genes for conserved and important traits. Macrosynteny is generally not easily detectable after a long evolutionary time, as colinearity erodes by various mechanisms, such as transposon activity, intra or inter-chromosomal rearrangements, duplications, translocations, inversions and/or individual divergence after speciation (Salse et al. 2002). While macrosynteny may not be detectable any more for genomes that diverged more than 100 million years ago (mya), microsynteny, i.e., conservation of local gene order and orientation, may still exist and be informative (Devos et al. 1999; Salse et al. 2002). Arabidopsis and rice are thought to have diverged about 120–200 mya (Salse et al. 2002). Microsyntenic local coexpression domains between Arabidopsis and rice would indicate the importance of the evolutionary conservation of regulatory systems beyond sequence similarity after the divergence of dicotyledonous and monocotyledonous plants. Analyses show that there is not a single coexpressed pair in either Arabidopsis or rice of which both genes are orthologous to a gene in a coexpressed pair in the other species. Therefore, there are no syntenic local coexpression domains between Arabidopsis and rice. Although the analyses were performed for only one monocot and one dicot and should be extended to many more genomes, the results could be taken to suggest that maintenance of coexpression has not been an important driving force in genome conservation during or after the divergence of dicotyledonous and monocotyledonous plants. Although individual genes in local coexpression domains in either rice or Arabidopsis may have an ortholog in the other species, establishing so-called partially syntenic coexpression domains (PSCDs), this does not seem to occur above chance in the context of whole-genome configurations. Without statistical significance, the occurrence of such PSCDs is unlikely to have any evolutionary relevance on a genome-wide scale. Detailed analyses of individual cases and gene locations may suggest the occurrence of local microsynteny and point to chains of evolutionary events in which the conservation of coexpression could be involved. However, more detailed studies are required to assess the functional relevance, if any, of such genomic constitutions.
Electronic supplementary material
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coexpression domain
rice
microsynteny
arabidopsis
higher-order chromatin structures


Accuracy of dual-source CT coronary angiography: first experience in a high pre-test probability population without heart rate control

The aim of this study was to assess the diagnostic accuracy of dual-source computed tomography (DSCT) for evaluation of coronary artery disease (CAD) in a population with extensive coronary calcifications without heart rate control. Thirty patients (24 male, 6 female, mean age 63.1±11.3 years) with a high pre-test probability of CAD underwent DSCT coronary angiography and invasive coronary angiography (ICA) within 14±9 days. No beta-blockers were administered prior to the scan. Two readers independently assessed image quality of all coronary segments with a diameter ≥1.5 mm using a four-point score (1: excellent to 4: not assessable) and qualitatively assessed significant stenoses as narrowing of the luminal diameter >50%. Causes of false-positive (FP) and false-negative (FN) ratings were assigned to calcifications or motion artifacts. ICA was considered the standard of reference. Mean body mass index was 28.3±3.9 kg/m2 (range 22.4–36.3 kg/m2), mean heart rate during CT was 70.3±14.2 bpm (range 47–102 bpm), and mean Agatston score was 821±904 (range 0–3,110). Image quality was diagnostic (scores 1–3) in 98.6% (414/420) of segments (mean image quality score 1.68±0.75); six segments in three patients were considered not assessable (1.4%). DSCT correctly identified 54 of 56 significant coronary stenoses. Severe calcifications accounted for false ratings in nine segments (eight FP/one FN) and motion artifacts in two segments (one FP/one FN). Overall sensitivity, specificity, positive and negative predictive value for evaluating CAD were 96.4, 97.5, 85.7, and 99.4%, respectively. First experience indicates that DSCT coronary angiography provides high diagnostic accuracy for assessment of CAD in a high pre-test probability population with extensive coronary calcifications and without heart rate control.
Introduction
Recent advances in multi-detector-row computed tomography (CT) technology have continuously improved the quality of non-invasive coronary artery imaging. As a result, various studies have demonstrated a high accuracy of coronary angiography with 64-slice CT for the diagnosis of coronary artery disease (CAD) [1–8]. In particular, the high negative predictive value has made non-invasive coronary angiography using 64-slice CT a modality that allows significant coronary stenoses to be reliably excluded. Consequently, the Task Force on the Management of Stable Angina Pectoris of the European Society of Cardiology has recently recommended in their guidelines that CT coronary angiography be performed in patients with stable angina who have a low pre-test probability of CAD, and an inconclusive exercise electrocardiogram (ECG) or stress imaging test [9].
To date, many studies have been published assessing the diagnostic performance of CT coronary angiography with different generations of scanners. Four-slice CT coronary angiography showed limited resolution and required long breath-hold times of nearly 40 s, resulting in a high number of vessel segments that could not be assessed [10]. Sixteen-slice CT coronary angiography showed improved diagnostic accuracy because of reduced breath-hold times and an increased temporal and spatial resolution [11, 12]. Sixty-four-slice CT scanners were the first to allow highly accurate identification of significant coronary artery stenoses [1–8]. However, data analysis in several of these studies could not be performed in all coronary artery segments, with a percentage of non-assessable segments of up to 12% [2]. The main reasons were severe coronary artery wall calcifications and motion artifacts, the latter occurring especially with higher heart rates. Calcifications are known to complicate the assessment of coronary arteries [10], however, some authors have suggested that substantial coronary calcification should not be used as a reason to defer CT coronary angiography [13].
In order to reduce motion artifacts, the administration of either intravenous or oral beta-blocker medication for heart rate control has been proposed even for 64-slice CT systems [2–6, 8, 11, 14]. In most studies, target heart rates for scanning were below 60–65 beats per minute (bpm). Another concept to comply with higher coronary velocities at higher heart rates is the use of multi-segment reconstruction algorithms that combine data from two or more consecutive cardiac cycles to improve temporal resolution at certain heart rates [15]. However, the latter approach relies on a complete periodicity of heart motion, limiting its use in patients with variable heart rates; does not take into account the inter-heartbeat variability of coronary artery position; and requires low pitch factors, prolonging data acquisition times [16]. In line with this, a recent 64-slice CT study demonstrated no significant overall benefit of multi-segment reconstruction with regard to image quality of coronary arteries [17].
The recently introduced dual-source CT (DSCT) scanner is characterized by two x-ray tubes and two corresponding detectors mounted onto the rotating gantry with an angular offset of 90° [18]. Regarding cardiac imaging capabilities, the new scanner system offers a high temporal resolution of 83 ms in a mono-segment reconstruction mode. Temporal resolution is independent of the heart rate, which is a major difference from single-source CT systems that rely on multi-segment reconstruction techniques. The first feasibility studies have shown promising results with DSCT coronary angiography regarding image quality of coronary arteries, cardiac valves, and left ventricular myocardium independent of the actual heart rate of the patient [19, 20]. However, to date no study has investigated the diagnostic accuracy of DSCT coronary angiography for the diagnosis of CAD.
The purpose of our study was to determine the performance of DSCT coronary angiography in diagnosing significant stenoses in comparison to invasive coronary angiography (ICA) in a high pre-test probability patient population without heart rate control.
Materials and methods
Patients
In 30 patients (6 women, 24 men; mean age 63.1±11.3 years; age range 35–86 years) who had undergone ICA for evaluation of suspected CAD, CT coronary angiography was performed within 30 days of catheterization (mean 14±9 days). Exclusion criteria for DSCT were allergy to iodine-containing contrast medium, renal insufficiency (creatinine level>120 μmol/L), pregnancy, hemodynamic instability, and previous stent graft placement or bypass surgery. Symptoms and cardiovascular risk factors of all patients are reported in Table 1. Based on a previously published clinical pre-test score [21], each patient in this population had a high pre-test probability of CAD. Patients with elevated or irregular heart rates were not excluded from the study. The study protocol was approved by our local ethics committee and all participating patients gave written informed consent.
Table 1Synopsis of cardiovascular risk factors and symptomsFrequency (%)Risk factorsFamily history16 (53%)Obesity23 (77%)Dyslipidemia18 (60%)Diabetes19 (63%)Smoking25 (83%)Hypertension23 (77%)SymptomsAngina pectoris21 (70%)Probable angina pectoris7 (23%)Atypical chest pain2 (7%)
Dual-source CT scan protocol
All CT examinations were performed on a DSCT scanner (Somatom Definition, Siemens Medical Solutions, Forchheim, Germany). The patients were centrally placed in the scanner to ensure that the entire heart was covered with the smaller field-of-view of the second tube detector array. Irrespective of the individual heart rate and the heart rate variability, no beta-blockers were given prior to the scan. Three patients took oral beta-blockers as part of their baseline medication.
Nonenhanced DSCT for calcium scoring was performed from 1 cm below the level of the tracheal bifurcation to the diaphragm in a cranio-caudal direction using the following scanning parameters: detector collimation 32×0.6 mm, slice acquisition 64×0.6 mm by means of a z-flying focal spot, gantry rotation time 330 ms, pitch of 0.2–0.39 adapted to the heart rate, tube current 80 mAs per rotation, and tube potential 120 kV.
Thereafter, all patients received a single dose of 2.5 mg isosorbiddinitrate s. l. (Isoket, Schwarz Pharma, Monheim, Germany) [22]. After 2 min, the coronary angiography scan was started by continuously injecting a bolus of 80 ml of iodixanol (Visipaque 320, 320 mg/mL, GE Healthcare, Buckinghamshire, UK) followed by 30 ml saline solution into an antecubital vein via an 18-gauge catheter (injection rate 5 mL/s). Contrast agent application was controlled by bolus tracking. A region of interest (ROI; mean diameter 10.1±5.6 mm, range 7.5–17.0 mm) was placed into the aortic root, and image acquisition started 5 s after the signal attenuation reached the predefined threshold of 100 Hounsfield units (HU). Data acquisition was performed from 1 cm below the level of the tracheal bifurcation to the diaphragm in a cranio-caudal direction with a detector collimation of 32×0.6 mm, slice acquisition 64×0.6 mm by means of a z-flying focal spot, gantry rotation time 330 ms, pitch of 0.2–0.43 adapted to the heart rate, tube current 400 mAs per rotation, and tube potential 120 kV. ECG-pulsing for radiation dose reduction was applied in all patients. At mean heart rates below 60 bpm, full tube current was applied from 60 to 70%, at 61–70 bpm from 50 to 80%, and at heart rates above 70 from 30 to 80% of the RR-interval. The electrocardiogram (ECG) was digitally recorded during data acquisition and was stored with the unprocessed CT dataset.
Dual-source CT image reconstruction
A retrospective gating technique was used to synchronize the data reconstruction with the ECG signal. A mono-segment reconstruction algorithm that uses the data from a quarter rotation of both detectors was used for image reconstruction [18]. In each patient, images were first reconstructed at 60 and 70% of the RR-interval. If considered necessary, additional images were reconstructed in 5% steps of the RR interval within the full tube current window. In case of irregular heart rates, the temporal variability in the reconstruction phase was compensated by manual ECG editing. In case of premature heart beats, the temporal window past the premature heart beat was deleted, and the next diastolic window was filled with one to three temporal windows to avoid data gaps. All ECG editing was performed by one experienced cardiovascular radiologist not involved in data read-out. For calcium scoring, non-overlapping images with a slice width of 3 mm were reconstructed using a medium-sharp convolution kernel (B35f). For DSCT coronary angiography, images were reconstructed from the contrast-enhanced DSCT scan with a slice thickness of 0.75 mm, a reconstruction increment of 0.5 mm, and using a medium soft-tissue convolution kernel (B26f). Depending on the individual anatomy, the reconstructed field-of-view (FoV) was adjusted to encompass the heart exactly (mean FoV 167±19 mm; range 131–187 mm; image matrix 512×512 pixels). After removing patient and ECG information, all reconstructed images were transferred to a dedicated workstation (Wizard, Siemens Medical Solutions) equipped with dedicated cardiac post-processing software (Syngo Circulation, Siemens Medical Solutions).
Dual-source CT data analysis
The mean Agatston score was calculated for each patient from the non-enhanced DSCT data with a detection threshold of 130 HU by using semi-automated software (Syngo Calcium Scoring, Siemens Medical Solutions).
For analysis of DSCT coronary angiography data, coronary arteries were segmented according to the guidelines of the American Heart Association [23]. The right coronary artery (RCA) was defined to include segments 1–4, the left main and left anterior descending artery (LM-LAD) to include segments 5–10, and the left circumflex artery (LCX) to include segments 11–15. The intermedial artery was designated as segment 16, if present. All segments with a diameter of at least 1.5 mm at their origin were included. Diameter measurements were performed with an electronic caliper tool. Segments distal to an occluded vessel were excluded from analysis.
All reconstructed images were evaluated and classified by two independent readers using axial source images, multi-planar reformations (MPR), and thin-slab maximum intensity projections (MIP) on a per-segment basis. Both readers semi-quantitatively assessed the image quality of each coronary segment on a four-point ranking scale as previously published [1]: 1, excellent (no artifacts, unrestricted evaluation); 2, good (minor artifacts, good diagnostic quality); 3, adequate (moderate artifacts, still acceptable and diagnostic), and 4, not assessable (severe artifacts impairing accurate evaluation). Causes of image degradation were noted by both observers as arterial wall calcifications, motion artifacts, or others. Motion artifacts were defined as any impairment of image quality caused by vessel movement resulting in blurred or doubled vessel contours. Other reasons included low vessel opacification, low signal-to-noise ratio, and disturbing adjacent structures such as contrast-enhanced cardiac veins.
In addition, both readers assessed all coronary artery segments for the presence of hemodynamically significant stenoses. Significant stenosis was defined as narrowing of the coronary luminal diameter exceeding 50%. The vessel diameters were measured on reconstructions perpendicularly oriented to the vessel course.
For any disagreement in data analysis, consensus agreement was achieved.
Invasive coronary angiography
ICA was performed according to standard techniques, and multiple views were stored on a CD-ROM. The angiograms were evaluated by one experienced observer who was blinded to the results of the DSCT coronary angiography. Coronary artery segments were defined according to the same guidelines mentioned above [23]. Each vessel segment was scored as being significantly stenosed, defined as a diameter reduction of >50% or not. Coronary artery analysis was performed in all vessels with a luminal diameter of at least 1.5 mm, excluding those vessels distal to complete occlusions.
Statistical analysis
Statistical analysis was performed using commercially available software (SPSS 12.0, SPSS, Chicago, IL, USA). Quantitative variables were expressed as mean±SD and categorical variables as frequencies or percentages. In a subanalysis, patients were subdivided into mean heart rates of <70 bpm and ≥70 bpm and into Agatston scores of <400 and ≥400. We took into account the clustered nature of the data (i.e., the fact that there were not 420 independent vessel segments but instead clusters of segments in 30 patients). Inter-observer agreements for image quality read-out and assessment of significant coronary artery stenoses were interpreted according to the guidelines of Landis and Koch [24] using the clustered data. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated from chi-squared tests of contingency, and the 95% confidence intervals (CI) were calculated from binomial expression on a per-segment basis. Because of the interdependencies between different segments, the statistics were also calculated on a per-patient basis (presence of at least one significant coronary artery stenosis or absence of any significant stenosis in each patient). ICA was considered the standard of reference.
Results
DSCT and ICA were successfully performed in all patients without complications. The DSCT protocol was well tolerated by all patients, and all were able to hold their breath during data acquisition. The average HR during scanning was 70.3±14.2 bpm (range 47–102 bpm). Seventeen patients (56.7%) had a heart rate below 70 bpm (mean 59.7±5.9 bpm, range 47–66 bpm), while 13 patients (43.3%) had a heart rate of ≥70 bpm (mean 84.2±8.4 bpm, range 72–102 bpm).
Prevalance of coronary artery stenosis and calcium score
A total of 56 coronary artery stenoses with a luminal narrowing of more than 50% in diameter were identified in 15 patients (50.0%) using ICA. Single-vessel disease was present in 13.3% (4/30), two-vessel disease in 10.0% (3/30), and three-vessel disease in 26.7% (8/30). Significant coronary artery stenoses could be excluded in 15 patients (50.0%).
Calcified vessel wall deposits were present in 24 patients (80%). Fourteen of these patients (58.3%) had significant coronary artery stenoses, while 10 patients (41.7%) had calcifications without significant stenoses. The mean Agatston score was 821±904 (range 0–3,110). Agatston score was <400 in 15 patients (50%, mean score 85±118) and ≥400 in the other 15 patients (50%, mean score 1,483±893).
Image quality and image reconstruction intervals with DSCT
A total of 420 coronary artery segments with a diameter ≥1.5 mm were evaluated (11 segments were missing because of anatomical variants, 26 segments had a diameter less than 1.5 mm at their origin, and 9 segments were distal to an occluding stenosis). An intermedial artery was present in 16 patients (53.3%). Inter-observer agreement for image quality rating using clustered data was moderate (kappa=0.68). The initially used reconstruction time-points of 60 and 70% provided excellent image quality (score 1) in 21.9% (92/420) and 60.2% (253/420), respectively, while additional reconstructions were necessary in 17.9% (75/420) to improve image quality. Whereas in the subgroup of patients with heart rates ≥70 bpm additional reconstructions at 30 to 50% were considered necessary in 27.4% (48/175), in patients with heart rates <70 bpm, the 60 and 70% interval provided excellent image quality (score 1) in 90.2% of the segments [30.2% (74/245) and 60.0% (147/245), respectively].
Using the individual reconstruction interval with best image quality for each segment, image quality was excellent (score 1) in 47.4% of the coronary segments (199/420), good (score 2) in 37.9% (159/420), adequate (score 3) in 13.3% (56/420), and poor/not assessable in 1.4% (6/420). Non-assessable (score 4) coronary artery segments (segment 4, n=2; segment 8, n=1; segment 10, n=2; and segment 14, n=1) were present in three patients with heart rates of 72, 77, and 86 bpm, and Agatston scores of 30, 616, and 0, respectively.
In the 221 segments with image quality rated other than excellent (score 1), reasons for impaired image quality were arterial wall calcifications in 48.9% (108/221), motion artifacts in 46.6% (103/221), or other in 4.5% of segments (10/221; low signal-to-noise ratio, n=5; low vessel opacification, n=4; overlaying adjacent structures, n=1). At heart rates ≥70 bpm, minor to moderate motion artifacts accounted for 68.2% (73/107) of image quality impairment, while in patients with an Agatston score ≥400, severe arterial wall calcifications were mainly responsible for decreased image quality [76.4% (94/123)].
Diagnostic accuracy of DSCT in comparison to ICA
The kappa value for coronary artery stenosis detection with DSCT was 0.83 indicating a good inter-observer agreement. DSCT coronary angiography correctly recognized 54 of the 56 significant stenoses (96.4%) detected with ICA. For both readers, nine false-positive (FP) and two false-negative (FN) ratings occurred with DSCT coronary angiography. Causes of FP and FN ratings were massive calcifications in nine segments (eight FP, one FN) and motion artifacts in two segments (one FP, one FN). The two FN ratings occurred in segments 8 and 9. Examples of three patients with suspicion of CAD who underwent DSCT coronary angiography are provided in Figs. 1, 2, and 3.
Fig. 1a, bDual-source CT coronary angiography in a 62-year-old woman with suspected coronary artery disease (mean heart rate during scanning 76 bpm, Agatston score 0). a Curved thin-slab maximum-intensity projections through the centerline of the right coronary (RCA), left anterior descending (LAD), and left circumflex artery (LCX). Slight blurring of the mid-RCA and mid-LAD rendered image quality as good (score 2) in these segments, while image quality was rated excellent (score 1) in all other segments. Coronary artery disease could be reliably excluded in this patient. b Volume-rendered image of the left coronary arteries and of the proximal RCA (insert) demonstrates accurate depiction of the coronary artery treeFig. 2a, bDual-source CT coronary angiography in a 69-year-old man with suspected coronary artery disease (mean heart rate during scanning 77 bpm, Agatston score 1,316). a Curved-planar reconstruction of the right coronary artery demonstrates high-grade coronary artery stenosis of the proximal segment (arrow) and non-significant stenoses of the mid and distal segments (arrowheads). b Invasive coronary angiography in a left anterior oblique projection (45°) confirms significant stenosis of the proximal segment of the right coronary artery (arrow) and non-significant stenoses of the mid and distal segments (arrowheads)Fig. 3a, bDual-source CT scan in a 73-year-old man with suspected coronary artery disease (mean heart rate during scanning 86 bpm, Agatston score 259). a Thin-slab maximum-intensity projection shows stenosis of the distal right coronary artery near the origin of the posterior descending artery. This stenosis was rated significant (i.e., >50% luminal diameter narrowing) by both readers. b Invasive coronary angiography in a right anterior oblique projection shows irregular luminal narrowing in the distal right coronary artery that was qualitatively graded as less than 50% luminal diameter narrowing, resulting in a false positive diagnosis in DSCT coronary angiography
On a per-segment analysis, overall sensitivity was 96.4% (54/56; 95% CI: 87.7–99.6), specificity was 97.5% (355/364; 95% CI: 95.4–98.9), positive predictive value was 85.7% (54/63; 95% CI: 74.6–93.3), and negative predictive value was 99.4% (355/357; 95% CI: 98.0–99.9). Table 2 summarizes demographic data, overall image quality, and diagnostic accuracy of DSCT.
Table 2Demographic data, overall image quality, and diagnostic accuracy  Mean heart rateAgatston score Total<70 bpm≥70 bpm<400≥400No. of patients3017131515Age (years)63.1±11.363.2±10.162.9±13.362.8±13.763.4±8.9Male/female24/615/29/410/514/1BMI (kg/m2)28.3±3.928.9±4.327.6±3.528.1±3.528.5±4.4Mean heart rate (bpm)70.3±14.259.7±5.984.2±8.470.6±13.770.0±15.1Agatston score821±904901±991674±78085±1181,483±893Overall image qualitya1.68±0.751.60±0.731.81±0.771.59±0.751.79±0.75 Score 147.4% (199/420)51.8% (131/245)38.9% (68/175)54.2% (116/214)40.3% (83/206) Score 237.9% (159/420)32.4% (82/245)44.0% (77/175)35.5% (76/214)40.3% (83/206) Score 313.3% (56/420)11.9% (30/245)14.9% (26/175)7.5% (16/214)19.4% (40/206) Score 41.4% (6/420)0.8% (2/245)2.2% (4/175)2.8% (6/214)–Sensitivity96.4% (54/56)97.2% (35/36)95.0% (19/20)100% (5/5)96.1% (49/51)  95% CI87.7–99.685.5–99.975.1–99.947.8–10086.5–99.5Specificity97.5% (355/364)97.1% (203/209)98.0% (152/155)99.5% (208/209)94.8% (147/155) 95% CI95.4–98.994.2–98.894.5–99.697.4–10090.1–97.8PPV85.7% (54/63)85.4% (35/41)86.4% (19/22)83.3% (5/6)86.0% (49/57) 95% CI74.6–93.370.8–94.465.1–97.135.9–99.674.2–93.7NPV99.4% (355/357)99.5% (203/204)98.8% (152/153)100% (208/208)98.7% (147/149) 95% CI98.0–99.997.3–10096.4–10098.2–10095.2–99.8aApplied scores were 1 excellent (no artifacts), 2 good (minor artifacts, good image quality), 3 adequate (moderate artifacts, acceptable image quality), and 4 not assessable (severe artifacts impairing image evaluation). A score of 1–3 was considered acceptable for diagnosis.BMI Body mass index, CI confidence interval, PPV positive predictive value, NPV negative predictive value
On a per-patient analysis, sensitivity was 93.3% (14/15; 95% CI: 68.1–99.8), specificity was 100% (15/15; 95% CI: 78.2–100), positive predictive value was 100% (14/14; 95% CI: 76.8–100), and negative predictive value was 93.8% (15/16; 95% CI: 69.8–99.8).
In both heart-rate subgroups, diagnostic accuracy for the assessment of coronary artery stenosis was similar and the rate of false ratings was comparable (one FN and six FP at heart rates <70 bpm; one FN and three FP at heart rates ≥70 bpm). In contrast, the subgroup of patients with an Agatston score ≥400 included more false ratings (two FN and eight FP) than the subgroup of patients with an Agatston score <400 (zero FN and one FP). Consequently, sensitivity and specificity were worse in severely calcified vessels (see Table 2).
Discussion
Four main conclusions can be drawn from this study. First, DSCT coronary angiography provides a high diagnostic accuracy for the evaluation of CAD. Second, this high diagnostic performance of DSCT could be achieved in a patient population with extensive calcifications and in whom no heart rate control using beta blocker medication prior to CT was performed. Third, taking into account these circumstances, only six (1.4%) segments had to be excluded from data analysis, all in the distal part of the coronary artery tree with small vessel diameters. Fourth, false ratings were primarily due to severe vessel wall calcifications rather than motion artifacts.
Temporal resolution better than 100 ms in combination with submillimeter isotropic spatial resolution and examination times no longer than 10 s are considered prerequisites for successful implementation of cardiac CT into routine clinical algorithms [18]. DSCT scanners with 0.33-s gantry rotation time and 32×0.6 mm collimation in combination with double z-sampling (i.e., simultaneous acquisition of 64 overlapping 0.6-mm slices) fulfill these requirements.
Early feasibility studies confirmed the technical capacity of DSCT to provide diagnostic image quality of coronary arteries in patients with high heart rates [19, 20]. This study is, to the best of our knowledge, the first to demonstrate a high diagnostic accuracy of DSCT coronary angiography for the diagnosis of CAD in comparison to ICA. By including a patient population with a high prevalence of coronary calcification and without heart rate control during scanning, we found an overall sensitivity of 96.4% and specificity of 97.5% for the detection of significant coronary stenoses. These results are comparable to those previously reported with 64-slice CT; however, beta-blocker administration for strict heart rate control was needed in those studies to compensate for motion artifacts. By including patients with heart rates up to 102 bpm, we found minor to moderate vessel wall blurring due to motion artifacts to be present at high heart rates; however, only 1.4% of all segments had to be excluded from analysis because they were non-diagnostic.
Further confirmation of our preliminary results in larger patient populations may broaden the clinical indications for CT coronary angiography from a modality that is recommended for ruling-out CAD in patients with a low pre-test probability [9] to use in populations with intermediate- or even high pre-test probabilities of the disease. This might also include the implementation of CT coronary angiography as a tool for preoperative planning before cardiac bypass surgery [25] and as a gatekeeper for invasive angiography in patients referred for cardiac valve surgery [26].
Our patient population had a high prevalence of coronary wall calcification, with a mean Agatston score of 821—being higher than the 75% percentile in an age- and gender-matched control population [27]. With 64-slice CT, Raff et al. [2] reported a considerable decline in diagnostic accuracy in patients with Agatston scores >400 with a sensitivity of 93%, a specificity of 67%, and positive and negative predictive values of 93 and 67%, respectively. Using the same cut-off Agatston score, we found a sensitivity of 96%, a specificity of 95%, and positive and negative predictive values of 86 and 99%, respectively. Considering that the spatial resolution of DSCT is the same as that of the single-source 64-slice CT scanner, this apparent difference in calcification-dependency could indicate that the blooming artifact of severely calcified vessel walls may be sometimes superimposed by additional motion artifacts.
We acknowledge the following study limitations. First, we included a relatively small group of only 30 patients. Certainly, future studies with larger patient populations are needed to confirm our first experience. Second, our study patients were a high pre-test probability population, which may have resulted in an overestimation of the ability of DSCT to detect and to rule-out stenoses. In particular the predictive value of positive and negative diagnostic tests is known to be strongly influenced by disease prevalence and pre-test probability. Third, we did not apply the multi-segment reconstruction mode, which possibly may further improve the image quality at elevated heart rates. Fourth, CT coronary angiography is associated with substantial irradiation to the patient. However, using the DSCT system allowed the variable use of ECG-pulsing for dose-saving purposes [28] in all of our patients. In the protocol applied in this study, the ECG-pulsing window width was chosen according to the mean heart rate during scanning, i.e., a relatively narrow window width at low and a larger window width at higher heart rates. As previously performed for 64-slice CT [17, 29], studies analyzing optimum reconstruction intervals for DSCT coronary angiography are mandatory to reduce the width of the pulsing window and the applied radiation dose further. Finally, the ability to use the two x-ray tubes simultaneously with different voltages to improve plaque composition characterization [30] and thus potentially to improve the accuracy of stenosis detection has not been investigated.
Conclusion
First experience indicates that DSCT coronary angiography provides high diagnostic accuracy for assessment of CAD in a high pre-test probability population with extensive coronary calcifications and without heart rate control. Further studies are needed to confirm our results in appropriate clinical settings with larger patient populations.

coronary angiography
dual-source computed tomography
coronary stenosis


Microbiological evaluation of a new growth-based approach for rapid detection of methicillin-resistant Staphylococcus aureus

Objectives Recently, a rapid screening tool for methicillin-resistant Staphylococcus aureus (MRSA) has been introduced that applies a novel detection technology allowing the rapid presence or absence of MRSA to be determined from an enrichment broth after only a few hours of incubation. To evaluate the reliability of this new assay to successfully detect MRSA strains of different origin and clonality, well-characterized S. aureus strains were tested in this study.
Introduction
The increasing numbers of multidrug-resistant Gram-positive pathogens have generated worldwide concern in the medical community. In particular, methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of disease and healthcare expenditures in almost every continent. The emergence and spread of MRSA has been shown to be associated with both hospital- and community-acquired infections. Effective treatment options for these infections are limited and the situation may become more severe soon. For these reasons, a proactive management of MRSA in healthcare facilities is needed.1,2
Active screening and compliance to appropriate infection control activities have been shown to play an important role in the control of MRSA.1 Rapid diagnostic tests have the potential to make efforts even more effective. Thus, infection prevention has taken a step forward with the introduction of various tests for rapid identification of MRSA carriers.1,2
In 2006, a new rapid method based on a novel bioluminescence detection technology for the rapid detection of MRSA directly from specimens was published.3 An improved version of this assay, the 3M™ BacLite™ Rapid MRSA Test (3M Company, Maplewood, MN, USA), was recently introduced, which allows the presence or absence of MRSA to be determined within 5 h. Although the clinical performance has been previously analysed,4,5 so far no data are available on the detection of MRSA strains with highly diverse genetic backgrounds.
The aim of this study was to assess the reliability of this assay to successfully detect MRSA strains circulating currently in Germany and other parts of Europe on the basis of several well-characterized S. aureus strain collections. For this purpose, S. aureus strains of different origin and clonality comprising 724 methicillin-susceptible and methicillin-resistant strains comprising >90% of all registered European MRSA spa types within the SeqNet network (www.SeqNet.org) were tested in this study.
Materials and methods
All staphylococcal strains were freshly isolated from clinical material at the University of Münster or during the course of various multicenter studies. One hundred and sixty-four strains isolated from patients with S. aureus bacteraemia (including 12 MRSA strains) as well as 50 isolates from the anterior nares of patients who did not subsequently develop S. aureus bacteraemia in a subsequent observation period (4 MRSA strains), were included into this study. Apart from 28 isolates from 14 patients (S. aureus first recovered from the anterior nares and subsequently from blood, one infected with MRSA), only one isolate per patient was tested. In addition, 96 MRSA strains, each presenting a unique spa type, were selected from our institutional collection (Table 1). Furthermore, four hundred MRSA isolates were collected during the course of a recent multicenter study, also including community-acquired MRSA. In total, the 724 S. aureus strains tested comprised 211 methicillin-susceptible S. aureus (MSSA) and 513 MRSA strains.
Table 1
Grouping of spa types tested in this study into spa clonal complexes (spa-CCs)
spa-CCa
spa typesb
Putative MLST typesc
001
t001, t002, t003, t010, t035, t039, t041, t045, t055, t057, t066, t105, t106, t109, t110, t143, t149, t151, t264, t265, t422, t820, t892, t1018
ST-5, ST-45, ST-46, ST-222, ST-225, ST-228, ST-231, ST-111, ST-228
004
t004, t028, t029, t033, t040, t043, t061, t065, t141, t142, t266, t911
ST-45, ST-45, ST-46
015
t015, t031, t069, t073, t102, t116, t133
ST-45
024
t008, t009, t024, t036, t051, t052, t068, t113, t115, t139, t146, t243, t305
ST-8, ST-235, ST-247, ST-250, ST-254, ST-247, ST-250
032
t005, t022, t032, t107, t290, t379, t432, t794
ST-22, ST-23, ST-60
037
t011, t030, t037, t047, t108, t135, t137, t138
ST-30, ST-239, ST-240, ST-241, ST-246
038
t038, t161, t247
ST-45
044
t042, t044, t131
ST-80
Singletons
t012, t091, t101, t104, t163, t321, t372, t417, t431, t907
ST-7, ST-30, ST-22
aNaming of spa-CC was derived from the group founder.
bt026, t103, t111, t132, t145, t282, t322 and t1007 were excluded from determination of the spa-CCs due to low number of repeats.
cPutative MLST types were taken from the public SeqNet.org spa server database.
If the biochemical identification of staphylococcal isolates using the ATB 32 Staph gallery (bioMérieux, Marcy l’Étoile, France) was ambiguous or categorized as unacceptable, partial 16S rRNA gene and RNA polymerase B (rpoB) gene sequencing was performed as described previously.6 Isolates were confirmed to be methicillin-resistant by detection of the mecA gene.
To determine the clonal lineages of MRSA strains, the x region of the spa gene was amplified by PCR with primers 1095F (5′-AGACGATCCTTCGGTGAGC-3′) and 1517R (5′-GCTTTTGCAATGTCATTTACTG-3′). DNA sequences were obtained with an ABI 377 sequencer (Applied Biosystems, Foster City, CA, USA). spa types were determined using the Ridom StaphType software version 1.3 (Ridom GmbH, Würzburg, Germany), and spa clonal complexes (spa-CCs) were assigned by using the BURP algorithm.7
The 3M™ BacLite™ Rapid MRSA Test is a novel culture-based test for the detection of MRSA performed on a semi-automated system comprising a sample processor and a luminometer. The assay consists of three successive selectivity steps: selective enrichment in a proprietary broth containing cefoxitin (2 mg/L) and colistin (50 mg/L), immuno-magnetic extraction using a highly specific anti-S. aureus monoclonal antibody and selective lysis using lysostaphin. The selectivity steps are followed by a detection step using a highly sensitive cell marker, adenylate kinase (AK). AK is an essential enzyme found in all living cells, which regulates energy provision by catalysing the equilibrium reaction ATP + AMP = 2 ADP. By supplying a continual source of purified ADP, this assay drives the AK reaction to generate up to 40 000 ATP molecules per min. These amplified levels of ATP are measured using the luminometer supplied with the system.
For the present study, test isolates were streaked on sheep blood agar plates and grown at 37°C to confirm purity. For each isolate, one to three colonies were picked and suspended in sterile saline (0.9%) to achieve a turbidity equivalent to that of a 0.5 McFarland standard. Usually, 43 isolates were processed in each run. The 3M™ BacLite™ Rapid MRSA Test was performed according to the instructions of the manufacturer. In brief, 10 µL of each prepared bacterial suspension was transferred into a vial containing 1 mL of selective enrichment broth. Vials were incubated at 37°C for 2 h before 150 µL aliquots of each test sample were transferred into two adjacent wells of a 96-well assay plate, each containing 20 µL of capture reagent. MRSA cells bound to the monoclonal antibody on the capture reagent were then extracted from the sample matrix using the sample processor and concentrated in 100 µL of broth. One well for each sample was processed automatically for a baseline signal (T0) in the BacLite luminometer. After a further incubation period of 2 h at 37°C, the second well for each sample was processed in the same way (T2 reading). Results are expressed in relative light units and interpreted as either a positive or negative screen result by a software embedded algorithm. Positive and negative controls as well as reagent and broth controls were included in each test run. A run took ∼5 h with a total hands-on time of 45–50 min (<1.5 min per tested isolate).
Results and discussion
Analysing a total of 724 S. aureus strains, all 513 MRSA strains tested were recognized as MRSA, whereas none of the 211 MSSA strains was detected positive by the 3M™ BacLite™ Rapid MRSA Test. These results are particularly impressive as the institutional MRSA strain collection used in this study represents more than 90 different spa types covering >90% of all registered European MRSA spa types within the SeqNet network (Table 1). Beside several singletons, the MRSA enrolled in this study were grouped into eight spa clonal complexes (Table 1). Thus, a very large number of MRSA strains with different genetic backgrounds were recognized as MRSA using this novel method.
Hospitals and other healthcare facilities across the world are faced with alarming rates of infections caused by MRSA. Continuous spread of these pathogens requires efficient strategies for infection control. Early identification of MRSA carriers among hospitalized patients is crucial to prevent its spread.1 Therefore, rapid availability of laboratory results is of utmost importance. However, conventional screening methods require prolonged incubation and confirmatory testing. During this time, MRSA-negative patients may be held in unnecessary isolation, whereas unidentified MRSA-positive individuals remain a hidden reservoir for cross-infection. A rapid negative result should allow more effective use of hospital isolation resources, whereas a rapid positive result should help reduce the spread of the infection and MRSA infection rates.2
In the past few years, several in-house and commercial rapid MRSA assays based on molecular techniques have been introduced for the detection of this pathogen directly from the specimen, mostly from the anterior nares. The first molecular assays developed were based on the detection of an S. aureus-specific sequence and the mecA gene, which encodes methicillin resistance.8 These tests are difficult to use for the direct detection of MRSA from non-sterile specimens, such as nasal samples, because of the likely co-presence of MSSA and methicillin-resistant coagulase-negative staphylococci.9 In a setting of low prevalence of MRSA, a molecular test targeting the mecA and an S. aureus-specific gene in parallel applied directly to clinical specimens would result in a high number of false positives and unacceptable performance.9
This technical limitation has been overcome in some assays, by linking detection of the presence of the mecA gene with detection of the neighbouring chromosome-borne orfX gene.10,11 In that approach, regions near the integration site of SCCmec were targeted as surrogate markers instead of the mecA gene itself. However, these flanking regions are known to be more heterogeneous than assumed so far.10 Thus, false-negative results due to variations within the primer binding sites may occur. Moreover, false-positive results due to the detection of DNA from non-viable MRSA, deletions or replacement of the mec region in vivo or ‘ghost sequences’ such as partial SCCmec sequences in MSSA can occur, albeit their incidence in the routine clinical setting is as yet unclear.12 Despite the technical improvements in recent molecular-based assays, their high costs and relatively high operator skill requirement remain obstacles to their widespread routine use.
The ability of a test to detect a broad range of MRSA clones is particularly important for an assay that may be used across a wide geographic region as tests with gaps in detection could potentially ‘select out’ strains whose spread would be uncontrolled.
Beside a variety of method-inherent limitations, rapid DNA-based methods amplify the nucleic acid and not the organism, which means the MRSA strain is unavailable for further characterization, such as determination of the resistance profile and strain typing. In contrast, applying the rapid growth-based assay tested here, any further examinations to characterize the respective strains will be possible from the enrichment broth.
For the 3M™ BacLite™ Rapid MRSA Test, a diagnostic sensitivity of 94.6% and diagnostic specificity of 96.9% for nasal screening swabs and 95.9% sensitivity and 88.8% specificity for groin screening swabs, respectively, have been reported.4,5 The analytical limit of detection of the assay was shown to be ≤94 cfu (3M™ BacLite™ Rapid MRSA Test, Instructions for Use).4,5
Here, the new growth-based rapid MRSA assay was shown to detect without exception all MRSA strains of large collections of strains comprising highly diverse genetic backgrounds. Such a phenotypic test might be potentially more likely to cope with new strains. Further studies are warranted to evaluate this method using clinical specimens.
Funding
This work was supported in part by a grant from the Bundesministerium für Bildung und Forschung (BMBF), Germany (Pathogenomic Plus Network PTJ-BIO/0313801B). The device and the respective assay consumables were given to us by 3M for this study.
Transparency declarations
Two of the authors (A. W. F. and K. B.) delivered independent scientific presentations at 3M-supported symposia. C. v. E. is a member of 3M’s Scientific Advisory Board. All other authors: none to declare.

mrsa
infection control
spa typing
staphylococci