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A BAC-based physical map of the Hessian fly genome anchored to polytene chromosomes

Background The Hessian fly (Mayetiola destructor) is an important insect pest of wheat. It has tractable genetics, polytene chromosomes, and a small genome (158 Mb). Investigation of the Hessian fly presents excellent opportunities to study plant-insect interactions and the molecular mechanisms underlying genome imprinting and chromosome elimination. A physical map is needed to improve the ability to perform both positional cloning and comparative genomic analyses with the fully sequenced genomes of other dipteran species. Results An FPC-based genome wide physical map of the Hessian fly was constructed and anchored to the insect's polytene chromosomes. Bacterial artificial chromosome (BAC) clones corresponding to 12-fold coverage of the Hessian fly genome were fingerprinted, using high information content fingerprinting (HIFC) methodology, and end-sequenced. Fluorescence in situ hybridization (FISH) co-localized two BAC clones from each of the 196 longest contigs on the polytene chromosomes. An additional 70 contigs were positioned using a single FISH probe. The 266 FISH mapped contigs were evenly distributed and covered 60% of the genome (95,668 kb). The ends of the fingerprinted BACs were then sequenced to develop the capacity to create sequenced tagged site (STS) markers on the BACs in the map. Only 3.64% of the BAC-end sequence was composed of transposable elements, helicases, ribosomal repeats, simple sequence repeats, and sequences of low complexity. A relatively large fraction (14.27%) of the BES was comprised of multi-copy gene sequences. Nearly 1% of the end sequence was composed of simple sequence repeats (SSRs). Conclusion This physical map provides the foundation for high-resolution genetic mapping, map-based cloning, and assembly of complete genome sequencing data. The results indicate that restriction fragment length heterogeneity in BAC libraries used to construct physical maps lower the length and the depth of the contigs, but is not an absolute barrier to the successful application of the technology. This map will serve as a genomic resource for accelerating gene discovery, genome sequencing, and the assembly of BAC sequences. The Hessian fly BAC-clone assembly, and the names and positions of the BAC clones used in the FISH experiments are publically available at .

Background
The Hessian fly (Mayetiola destructor) is an important pest of wheat (Triticum spp.) and a member of one of the largest and most economically important families of insects, the gall midges (Cecidomyiidae). Its genetic tractability and short generation time (~28 days) make it especially attractive as an experimental model for investigating insect-plant interactions [1]. This capacity was first demonstrated when it was the first insect shown to have a gene-for-gene interaction with its host plant [2]. Previously, it was used to study chromosome elimination and the function of the germ-line-limited "E" chromosomes that characterize all gall midge species [3][4][5]. Later investigations demonstrated that it has other biological attributes that are compatible with genomic analyses, including polytene chromosomes in the larval salivary glands [6] and a small genome (158 Mb) [7]. The development of a physically anchored genetic map [8], a syntenic analysis of a BAC-based contig [9], and transcriptomic analyses of the first instar salivary glands [10,11] also demonstrated the potential of this insect for comparative genomics with other dipteran species.
Improved genomic maps are essential to further develop this insect as an experimental organism. However, certain biological characteristics make fine-scale genetic mapping in the Hessian fly problematic. The first problem is the relatively low number of offspring produced by single females, typically ranging from 50 to 200, which limits the resolution of a genetic map. The second problem is the small size (2 to 3 mm in length) of the insect. This severely limits the yield of the genomic DNA extracted from individuals, and the number of molecular markers that can be genetically mapped. A third problem, the insect's unusual chromosome cycle and mechanism of sex determination, makes the construction of inbred strains difficult. Because this phenomenon is both central to the genetics of the insect and worthy of genomic investigation, it is briefly described below.
The germ line of the Hessian fly contains both maternally and paternally derived copies of each of two autosomes (A1 and A2) and two X chromosomes (X1 and X2) [12]. It also contains a variable number (~32) of maternally inherited germ-line-limited "E" chromosomes (A1A2X1X2/A1A2X1X2; E). The E chromosomes are eliminated from all future somatic cells during the fifth cleavage division of embryogenesis [4]. This post-zygotic division also determines sex. Embryos that eliminate the paternally inherited X chromosomes, together with the E chromosomes, possess a male determining somatic karyotype (A1A2X1X2/A1A2OO) and develop as males. Those that retain both sets of X chromosomes possess a female determining somatic karyotype (A1A2X1X2/A1A2X1X2) and develop as females. Maternal genotype determines whether the paternally derived X chromosomes are eliminated or retained in the soma [13]. As a consequence, an unusual mating structure is established that is resistant to inbreeding; females produce either all-female or all-male families. Chromosome elimination also occurs during spermatogenesis. The result is that all sperm carry only the maternally derived autosomes and X chromosomes (A1A2X1X2). Genetic recombination between the homologous autosomes and X chromosomes occurs only during meiosis of oogenesis. All ova carry a single copy of each autosome and each X chromosome and a full complement (~32) of E chromosomes (A1A2X1X2; E). There is no heterogametic sex since all sperm and all ova are genetically equivalent and sex is determined after fertilization.
In contrast to the problems that complicate genetic analyses, the Hessian fly polytene chromosomes make physical mapping relatively simple. Although they lack the banding resolution of Drosophila polytene chromosomes, they still provide a relatively high mapping resolution when bacterial artificial chromosomes (BACs) are used as probes for fluorescence in situ hybridization (FISH) [8]. This facility suggested that the development of a dense physical map of the Hessian fly's polytene chromosomes should precede high-resolution genetic mapping. We therefore applied the high-information content fingerprinting (HICF) method developed by Luo et al. [14] to construct a BAC contig map using three Hessian fly BAC libraries (Table 1). The result was a first generation FPCbased physical map of the Hessian fly genome that is anchored to the polytene chromosomes. It is based on restriction fingerprints of 13,614 BAC clones. The largest 266 contigs were positioned on the chromosomes by fluorescence in situ hybridization (FISH). These contigs contain 4,563 BAC clones, and cover ~60% of the genome. The BAC end sequences (BES) of the clones used in this analysis were determined to provide the capacity to develop sequenced tagged site (STS) markers evenly distributed over the genome. BAC-end sequence data were also analyzed to improve our understanding of Hessian fly genome organization.

BAC fingerprinting
We subjected 13,614 BACs to high-information content fingerprinting ( Table 2). The DNA fingerprint profile of each BAC clone was then converted into an FPC compatible format using the FP Miner 1.1 software (BioinforSoft LLC, OR). The standard deviation associated with the size of two vector bands present in the fingerprints of each BAC (157.4 ± 0.01 bp and 369.5 ± 0.02 bp) indicated that fragment size reproducibility was high [15]. The FP Miner software was used to remove background signals and vector bands, and to assign a quality score to each BAC DNA fingerprint. We examined all of the fingerprints and removed 1662 that failed to meet each of the following quality standards: 1) 30 to 250 fragments, 2) = 10 fragments of each of the four colors, 3) a size standard match quality score that was >0.8, and 4) a fingerprint editing quality score that was >10. The fingerprints of 11,952 BACs (88% of the total) remained available for FPC assembly. The percentage of successfully fingerprinted clones compared favorably with the HICF maps developed for maize (Zea mays; 87%) [15], Nile tilapia (Oreochromis niloticus; 87%) [16], channel catfish (Ictalurus punctatus; 91.5%) [17], and Brassica rapa (94.2%) [18]. DNA fragment sizes ranging from 50 to 500 bp were imported into the FPC v8.5.1 [19,20] to build contigs. Among the successfully fingerprinted clones, 5,766 were derived from the Hf library, 6,108 were derived from the CL library, and 78 were derived from the Mde library. BAC clones from the Hf library had an average insert size of 150 kb and the CL library had an average insert size of 130 kb. The average number of scored bands per clone in the Hf library (90.6) and the CL library (87.6) was slightly less than the average observed during the construction of the maize map (107) [15], but slightly greater than the averages of the two BAC libraries used to construct the Nile tilapia map (54 and 70) [16]. Combined, the BACs in the Hf and CL libraries provided an estimated 10.5-fold cov-erage of the Hessian fly genome (Table 1). This was greater coverage than that used to construct either the Nile tilapia (5.6-fold) [16] or the catfish (6.8-fold) [17] maps, but considerably less than the coverage used to construct the maps of B. rapa (15.2-fold) [18] and maize (22-fold) [15].

Contig assembly
Contigs were assembled from the fingerprint data using the computer program FPC version 8.5.1 [15,19,20]. FPC parameters were adjusted for the HICF technique as described by Lou et al. [14] and Nelson [15]. In each assembly, we used a tolerance (5) that restricted two bands with lengths greater than 0.5 bp from being considered the same band. In the initial assembly, we used a cutoff (the threshold for the probability score that matching bands are a coincidence) of 1e-35. Using these settings, FPC built 1258 contigs containing from 2 to 126 BAC clones per contig. The DQer function was then used to identify contigs with ≥ 10% questionable clones (Qs). These contigs were then gradually split using three more stringent cutoffs (1e-38, 1e-41, and 1e-44). This produced 1477 contigs containing from 2 to 67 BAC clones per contig. The assembly was then end merged at a cutoff of 1e-29. This produced the first generation HICF Hessian fly map, which consisted of 1377 contigs, containing 7,716 BACs, and 4,236 (35%) singletons. The map had an average of 5.6 BACs per contig and the largest contig contained 73 BACs (Table 2). FPC analysis estimated that the clones used to produce the map had 1,061,478 unique bands, averaging 88.8 bands per clone. We used the known lengths of 23 BAC clones and the total number of labeled fragments for each of these clones to estimate average band size (1182 bp). Using this value, the assembled contigs had an estimated length of 283,555 kb, providing approximately 1.8-fold coverage of total genome length. The average contig length was 206 kb and the longest contig covered 1166 kb of the genome. FPC identified a total of 441 questionable clones (Qs) in the assembly. There were 88 contigs with >10% Qs. However, the average number of Qs per contig was only 0.32. Furthermore, 1212 contigs (88%) had no Qs, no contig had  In addition, the percentage of Qs in the Hessian fly assembly (0.32) was lower than that observed in maize (11.0) [15], B. rapa (15.0), catfish (7.3), and Nile tilapia (9.6). Thus, it appeared that the Hessian fly contigs provided reasonable coverage with few questionable clones. This suggested that an abundance of RFLPs is not an absolute barrier to the construction of a HICF-based physical map.
Because the BAC clones were largely derived from two different libraries (Table 1), we performed an FPC assembly of each library separately using the same parameters that were used to assemble both libraries combined. The BACs in the CL library assembled into a greater number of contigs (888 vs. 795) with a greater average number of BACs per contig (4.74 vs. 3.78) than the BACs in the Hf library. In addition, the percentage of singletons in the CL assembly (31%) was fewer than the singletons in the Hf assembly (48%), but the number of contigs with >10% Qs was nearly the same (CL = 33 and Hf = 30). Thus, it appeared that if RFLPs were interfering with contig assembly, they were more abundant in the Hf library. Interestingly, the sum of the total number of contigs assembled using the CL (888) and Hf BACs (793) separately was only 22% greater than the total number of BACs in the combined assembly (1377), and the sum of the coverage provided by the CL (159,451 kb) and Hf BACs (149,099 kb) was only 9% greater than that of the combined assembly (283,555 kb). Thus, it appeared as if the contigs of one library only occasionally overlapped with the contigs of the other library and this lowered contig size more than total coverage when the libraries were combined. This possibility was also evident in the contigs of the FPC map where CL BACs tended to stack with other CL BACs and Hf BACs tended to stack with other Hf BACs [21]. It is also consistent with the suggestion that using libraries prepared with different restriction enzymes reduces the number of gaps in the assembly [22][23][24].

Contig validation
Two different approaches were used to validate contigs in the first generation Hessian fly HICF map. The first examined the FPC assembly of two contigs that had been constructed by chromosome walking (Figure 1). This was the only region of the Hessian fly genome in which chromosome walking had been previously performed. The construction and composition of one walk (walk-124) was described previously [9]. It consisted of 64 BAC clones covering a 7.5-cM genetic distance that was marked with four STS markers and a physical distance of nearly 1 Mb. The other chromosome walk began from STS marker 134 (walk-134) and was composed of 60 BAC clones. Together, the contigs constructed by walk-124 and walk-134 contained 73 Mde BAC clones that were analyzed by FPC. The 134-walk was deeper and contained a greater proportion of Hf clones. Neither chromosome walk contained clones from the CL library because that library had not yet been prepared when the walking experiments were performed. Only 5 clones in the 124-walk were discovered in the contigs of the FPC assembly ( Figure 1). Those clones were present in 3 small contigs (contigs #837, #938, and #1176) containing a combined total of 7 clones. FPC assembly of the 134-walk was more successful. FPC assembled 52 (87%) of the clones in the 134walk into six contigs ( Figure 1). The majority of those clones were assembled into two contigs: one (contig #12) containing 24 134-walk clones and the other (contig #56) containing 19 134-walk clones. An additional 28 CL clones were identified in these contigs. Reducing the stringency of FPC assembly (cutoff of 1e-15) merged all six contigs associated with walk-134 into a single contig. However, the assembly of the 124-walk was not improved. This analysis suggested that, as expected, regions of the genome with relatively deep coverage (walk-134) were well represented in the map whereas regions with less depth (walk-124) were poorly represented.
The second approach of assessing contig validity was to test the continuity of the 196 longest FPC assembled contigs using FISH. In these experiments, a BAC clone at one extreme of each FPC assembled contig was labeled with biotin and a BAC clone at the opposite extreme was labeled with digoxigenin. Separate FISH experiments were BAC contigs established by chromosome walking performed for each contig, in which both BAC clones were hybridized to the same polytene chromosome preparations ( Figure 2). The FPC assembly was judged "valid" when both probes from the same contig co-localized on the Hessian fly polytene chromosomes ( Table 3). The contigs examined contained from 9 to 73 BAC clones per contig and ranged in length from 172 to 1157 kb. Using the banding patterns of the four polytene Hessian fly chromosomes, we divided the Hessian fly genome into 26 chromosomal segments (Figure 2), and determined which segments contained each valid contig (Table 4). Among the 196 contigs tested, 169 contigs (86%) were scored as valid (Table 3). In 63 of these valid contigs (32% of the total) it was possible to discern which of the two BAC clones was the most proximal ( Figure 2). It was, therefore, possible to suggest the orientation of those 63 contigs on the chromosomes. There were 27 contigs (14%) that were determined to be "invalid." These had a greater number of Qs than the valid contigs (Table 3). Moreover, they were easily separated into two groups: A "repetitive" group of 11 contigs, and a "non-repetitive" group of 16 contigs. The contigs in the repetitive group had at least one probe that hybridized to multiple locations ( Figure 3). Seven of these had a probe that hybridized to pericentromeric heterochromatin. The nonrepetitive group had probes that hybridized to different, but unique, genomic locations. This group of contigs had a greater number of Qs per contig than both the valid group of contigs and the invalid-repetitive group of contigs (Table 3). We reexamined all invalid contigs by performing FISH with additional BAC clones selected from the same contigs. In each experiment, these clones hybridized in the same position as one of the previous BAC clones ( Figure 3). These data were then used to assign the unreliable contigs to a chromosomal segment (Table 4).
Using cytogenetic data to validate a physical map has been performed previously in Drosophila species [25,26]. However, to our knowledge, this is the first time it has been used to anchor the physical map of an insect of agricultural importance. Our method was analogous to performing linkage analysis with the two most terminal BACs in the contigs. Using that approach, 18 of the 19 longest contigs (95%) in the catfish HICF map were validated [17]. Therefore, although the two approaches are not entirely comparable, our results suggest that the Hessian fly map may have a few more errors. Nevertheless, our results also clearly indicate that the Hessian fly assembly was largely valid and that most errors are associated with the contigs containing the greatest number of Qs. Moreover, they demonstrated that the Hessian fly map can be easily improved using FISH as a manual contig-editing tool.

Genome coverage
To further evaluate the coverage of the assembled contigs, FISH was used to position 70 additional contigs. In these experiments, only one BAC was used as probe, but we avoided invalid contigs by selecting those that had an average of only 0.1 ± 0.1 Qs per contig. These experiments brought the total number of BACs used as probes to 489 and the total number of contigs that had been FISHmapped to 266 (Table 4). Of these contigs, 257 were assigned to single chromosome segments. Nine contigs hybridized to the pericentromeric heterochromatin of all four chromosomes, and could not be assigned to a single chromosome segment. The relative length of each chromosome segment was used as a measure of genome content so that the amount of coverage provided to each segment could be compared (Table 4). Except for the nucleolar organizing region (NOR), segment E, every chromosome segment contained at least one contig. The Contigs in which the probes co-localized were assessed as valid. Contigs in which the probes failed to co-localize were assessed as invalid. Invalid contigs were further divided into contigs that either had one or more repetitive BAC probes (Invalid-R) or no repetitive BAC probes (Invalid-NR). *Means significantly different from the Valid mean (Student's t; p < 0.10). **Means significantly different from the Valid mean (Student's t; p < 0.05). † Means significantly different from the Invalid-R mean (Student's t; p < 0.10). † † Means significantly different from the Invalid-R mean (Student's t; p < 0.05).
subtelomeric segments A, P, and Q, as well as segments B, F, and H, had a slightly greater proportion of contigs than their relative lengths. In the remaining segments, the proportion of contigs and BACs approximated relative length. Thus, the contigs appeared to be evenly distributed. Disregarding the FPC assembly errors previously discovered, and ignoring the centromeric contigs, 95,668 kb of genome length was covered by the FISH mapped contigs. We observed no evidence suggesting that any contig was exclusively associated with the E chromosomes. This coverage therefore represents approximately 60% of the total content of the Hessian fly autosomes and X chromosomes. Moreover, the heterochromatin of these chromosomes is restricted to the centromeres [27]. These contigs therefore clearly appeared to be distributed in the gene rich regions of these chromosomes.
The FPC assembly was developed into a publically available WebFPC archive that shows the BAC clones that were used as probes to physically anchor each contig [21].
Ordering the contigs that are currently grouped into chromosome segments will be one of the most immediate improvements we make to the map. The segments on the autosomes and the long arm of chromosome X1 are expected to provide sufficient resolution to order the contigs using FISH. However, morphologies of the short arm of chromosome X1 and all of chromosome X2 are problematic. We therefore expect that genetic mapping will be necessary to order the contigs in the corresponding segments.

BAC-end sequence
In order to fully utilize the physical map as a resource in genetic investigations, comparative analyses, and whole genome sequence assembly, we end sequenced the 13  Hessian fly polytene chromosomes and BAC clone co-locali-zations Figure 2 Hessian fly polytene chromosomes and BAC clone co-localizations. Chromosome segments (A-Z) are indicated on geimsa stained chromosomes (a, f, k, and p). White bars (10 μm) indicate the relative lengths of corresponding DAPI stained chromosomes (b, g, l, and q). Four examples of co-localizing BAC clones are shown in color overlay images (e, j, o, and t). Contig #320 BAC clones CL4h4 (c) and CL31e21 (d) hybridized to chromosome A1 segment G (e). Contig #199 BAC clones Hf30g15 (h) and Hf4e2 (i) hybridized to chromosome A2 segment L (j). Note that Hf30g15 hybridization (green) was more distal than Hf4e2 hybridization (red), allowing contig orientation. Contig #320 BAC clones CL25e22 (m) and Hf10j14 (n) hybridized to chromosome X1 segment U (o). Contig #320 BAC clones CL29m24 (r) and Hf10j14 (s) hybridized to chromosome X2 segment X (t).
Resolving "invalid" contigs using FISH Figure 3 Resolving "invalid" contigs using FISH. (a) The hybridization of BACs CL24B4 (green arrow, A2 segment L) and Hf15M12 (red arrows, pericentromeric heterochromatin) defined contig #36 as invalid and repetitive. (b) The hybridization of a third contig #36 BAC (CL24N4, yellow arrow) resolved the position of contig #36 to A2 segment L. (c) The hybridization of BACs CL17J4 (green arrow, A2 segment L) and Hf7B18 (red arrows, A1 segment J) defined contig #234 as invalid but non-repetitive. (d) Hybridization of a third contig #234 BAC clone (Hf13K2, yellow arrow) with BAC clone CL17J4 resolved the position of contig #234 to A2 segment L.
A combination of a custom repeat library and RepBase database [28] was used to identify repetitive DNA sequence. Because the BACs were derived from heterogeneous strains, this analysis required parameters that limit the possibility of confusing alleles with gene families. Therefore, only repetitive sequences that were >40 bp and present in = 5 copies were selected. These criteria made identification of gene families more likely, given the low coverage of BES. Using these settings, we found that 17.91% of the BES contained repetitive elements (Table  5). There was, however, a 1.48% overestimate of repetitive content due to the difficulty of assigning boundaries to different repeat classes. Transposable Elements (TEs) composed only 4.53% of the repetitive DNA and 0.81% of total BES (Table 5). Combined, Helicases, ribosomal DNA (including tRNA, rRNA and 60S and 40S ribosomal proteins), simple sequence repeats (SSRs), and sequence of low complexity composed 15.79% of the repetitive fraction. The largest fraction of repetitive DNA (79.68%) consisted of multigene families and unclassified TEs. This observation was consistent with previous investigations of the Hessian fly salivary gland transcriptome [10,11,29] that discovered over 200 gene families of small putatively secreted proteins with no sequence similarities to other genes in GenBank. In the BES, the most prominent multigene families identified appeared to be tandem copies of the calponin homology domain (an actin binding domain), thrombospondin type1 repeats (which bind and activate TGF-beta), U1 small nuclear ribonucleoprotein C proteins (involved in mRNA splicing), and SMChinge family genes (involved in the structural maintenance of chromosomes).
Within the Class I TEs, LTR retrotransposons were more prominent than the non-LTR retrotransposons ( Table 5). The most prominent Class II TE was the Mos1 mariner-like DNA transposable element, which accounted for 1.7% of the repetitive fraction. The remaining Class II TEs composed 0.24% of the repetitive sequence, which had sequence similarities to the Tigger2, Blackjack, Looper, MER45R, and Zaphod TEs. Previous investigation found an abundance of mariner-like elements in Hessian fly pericentromeric heterochromatin [30]. Therefore, it was interesting to note that the combined proportions of TEs and low-complexity sequence in the BES (2.6%) was virtually the same as the proportion of the BACs that hybridized to pericentromeric heterochromatin in the FISH experiments that anchored the physical map to the chromosomes (2.4%). These observations were consistent with a low abundance of TEs in the euchromatin of the small Hessian fly genome, and they suggested that the BACs that hybridized to heterochromatin contained one or more TEs. This in turn suggests that seven of the eleven contigs that were initially classified as "invalid-repetitive" (Table 3) would have been classified as "valid" if not for the presence of one or more TEs in the BACs used as probe.
SSRs, which have a wide range of applications [31], formed 0.81% of the total BES and 4.51% of the total repetitive fraction. The di-and tri-nucleotide repeats were 29.1% and 33.4% of the SSRs, respectively, and were the most frequent types of SSRs found in this analysis. The most abundant di-and tri-nucleotide motifs were (TC/ GA)n and (TAA/TTA)n, which composed 49.9% of the total di-nucleotides and 43.7% of the total tri-nucleotides, respectively. The value of 'n' ranged from 10 to 56 in the (TC/GA)n motif and from 7 to 76 in the (TAA/TTA)n motif.
To identify putative genic sequences in the BES, both the repeatmasked and unmasked BESs were searched against the NCBI non-redundant database using BLASTX [32]. Interestingly, fewer annotated sequences were observed in the repeatmasked set for almost all categories. This observation indicated that a proportion of the predicted gene products were derived from the repetitive sequences, which when masked, resulted in lower numbers of annotations. This was also consistent with the earlier observation that multigene families composed a relatively large proportion of the repetitive BES. To examine this further, we unmasked the SSRs so that they were excluded from the repeatmasked set [see Additional file 2]. Again, we observed fewer annotations with that data set than with unmasked BES. Therefore, multigene families and transcripts from transposable elements probably accounted for the observed differences between the two data sets.

Conclusion
Hessian fly's small genome and the polytene chromosomes provided an excellent opportunity to determine if BAC libraries constructed from heterogeneous strains would unduly limit the mapping abilities of the HICF and FPC technologies. Although less heterogeneity would have been preferred, the results have clearly improved the Hessian fly as an experimental model. A first generation map now exists that includes 266 FPC contigs containing 4,563 BAC clones and their associated BES positioned to 26 segments of the Hessian fly genome. The BES provided new knowledge regarding Hessian fly genome organization, and the mapped BES constitutes an abundant resource for the development of physically mapped DNA markers. We expect that these improvements will be made as genetic investigations focus on the discovery of avirulence genes, the genes that are involved in plant-gall formation, and the mechanisms of chromosome elimination. In the near future, we also expect the map to facilitate the assembly of whole-genome shotgun sequences and the assignment of sequenced scaffolds to chromosome segments.

BAC libraries
Three BAC libraries were utilized in this investigation. Each library was constructed using a separate heterogeneous source of genomic DNA. The Hf library, previously described by Liu et al. 2004 [33], was prepared from DNA isolated from a Kansas "Great Plains" (GP) population. To supplement the Hf library, we used clones derived from the MD_Bb library developed by the Clemson University Genomics Institute [34]. Herein referred to as the "CL" library, these BACs were prepared from DNA isolated from an Indiana "L" Hessian fly population. From a third library (Mde), 78 BACs were fingerprinted. Seventy-three of these clones were present in two contigs that were previously developed by chromosome walking [9]. These clones therefore served as an internal control while developing contigs from the fingerprint data using FPC software [19]. The Mde library was constructed with DNA isolated from the Georgia derived "vH13" Hessian fly population as described previously [8]. Plates and filters of all of these clones are available on a cost-recovery basis by request from the Purdue Genomics Center [35].
Detection was performed using Alexa Fluor 488-conjugated anti-biotin and rhodamine-conjugated anti-digoxigenin (Molecular Probes-Invitrogen). When testing for the co-localization of BACs in the same contig, both labeled BACs (one labeled with biotin and the other labeled with digoxigenin) were hybridized simultaneously to the same polytene chromosome preparations with the expectation that the probes would co-localize if the contigs were genuine. Digital images were taken using UV optics on an ORCA-ER (Hammanmatsu) digital camera mounted on an Olympus BX51 microscope, and Met-aMorph (Universal Imaging Corp.) imaging software.

Chromosome walking
Two BAC contigs were used to test the reliability of the FPC-based Hessian fly contigs. These contigs were constructed by chromosome walking from two markers flanking the Hessian fly Avirulence gene, vH13. The walk from marker 124 was previously described [9]. The walk from marker 134 is described above (Figure 3). Chromosome walking experiments consisted of BAC library screens using 32 P-dCTP-labeled probes prepared from markers and BAC-end DNA sequence. Probe labeling and the isolation of BAC-end DNA were performed as described previously [9].

BAC-end Sequence Analysis
BAC clone DNA was prepared and sequenced in an automated process using a 384-well format. BAC clones were grown in 375 μl of sterile TB supplemented with chloramphenicol (12.5 μg/ml) for 20 hours at 30°C. Alkaline lysis was used to isolate BAC DNA. Sequencing was performed in 7.5 μl volumes containing 0.5 μl of Big Dye Terminator v3.1 (Applied Biosystems), 1.8 μl of Big Dye Terminator v1.1/v3.1 5× buffer (Applied Biosystems), 3 pmoles of primer, and 100 to 250 ng of BAC DNA. Oligonucleotides T7-ZL (TAATACGACTCACTATAGGG) and BES-HR (CACTCATTAGGCACCCCA) were used to prime separate reactions from opposite ends of each BAC template. Those reactions were performed in a GeneAmp PCR System 9700 (Applied Biosystems). Using primer T7-ZL, sequencing reactions underwent 120 cycles of 96°C for 10 s, 45°C for 5 s, and 60°C for 8 min. Using primer BES-HR, sequencing reactions underwent 120 cycles of 96°C for 10 s, 45°C for 5 s, and 52°C for 8 min. The sequencing products were cleaned using ethanol precipitation and then resuspended in 15 μl of ddH2O. Sequences were then determined using a 3730XL Genetic Analyzer (Applied Biosystems).
Analysis of repetitive sequence in BES was performed using a custom repeat database combined with RepBase database version 20061006 [28]. The custom repeat database was constructed using RECON [37]-a de novo approach to identify repetitive sequences. PERL scripts were used to select repetitive sequences greater than 40 bp in length and present in 5 or more copies, which were then annotated using BLASTX [32] at e = 10 -5 to the NCBI non-redundant database. The annotated repeat database was used as a custom repeat library for RepeatMasker [38]. RepeatMasker version 3.1.9 was used at a default mode with WU-BLAST as the search engine (blastp version 2.0 MP-Washington University) to mask the repetitive sequences in the BESs. Another round of RepeatMasker using the RepBase update 20061006 (RepBase database version 20061006) was also run on the same set of sequences. Results from both runs were combined after manually removing the overlaps. The masked sequences were then categorized into different classes of repetitive sequences.
To identify potential genic sequences and to look for differences between the repeat-masked and unmasked BESs, we used both sets of sequences for GO analysis. The two sets of sequences were used as queries to the NCBI nonredundant database using BLASTX (e = 10 -5 ). The BLAST output in the XML format was imported into BLAST2GO (B2G) for GO analysis and functional annotation of gene or protein sequences [39]. The resulting annotations were converted into 'GO-Slim' format and retrieved for the three GO categories (biological process, molecular func-


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What Is the Metabolic Role of Phosphoenolpyruvate Carboxykinase?*

The enzyme phosphoenolpyruvate carboxykinase (GTP; EC 4.1.1.32) (PEPCK) 2 has the unusual distinction of being very well studied but metabolically misunderstood. As we will doc-ument in this minireview, the enzyme has been almost exclu-sively linked to gluconeogenesis to the point that changes in the levels of PEPCK mRNA or its activity are associated with the control of hepatic glucose output and, more recently, with alterations in life span. That a tissue such as brown adipose tissue, which does not make glucose, has more PEPCK activity on a protein basis than is present in the liver is largely ignored. In addition, all eukaryotes have a gene for both a mitochondrial (PEPCK-M) and cytosolic (PEPCK-C) form of the enzyme. In the livers of most mammals studied to date (including humans), 50% of the total PEPCK activity is PEPCK-M. However, for reasons to be discussed, only PEPCK-C has been studied in any detail in mammals. Thus, the “strange case of PEPCK-M” deserves our attention. This minireview is an attempt to broaden our prospective on the metabolic role of this enzyme by reviewing the body of literature that has accumulated dem-onstrating that PEPCK plays a key role in a several metabolic processes associated with cataplerosis.

PEPCK Is a Cataplerotic Enzyme and as Such Is a "Feeder Reaction" for Downstream Metabolic Processes
Our understanding of the metabolic role of PEPCK has greatly expanded in recent years. PEPCK should be viewed as a cataplerotic enzyme because it plays the important role of removing citric acid cycle anions for either the biosynthetic process or the subsequent complete oxidation of the these compounds to carbon dioxide in the citric acid cycle. Thus, the downstream pathways in which PEPCK participates are linked to cataplerosis. There are four major pathways in which PEPCK plays a key role (Fig. 1). These are (a) gluconeogenesis, (b) glyceroneogenesis, (c) the synthesis of serine, and (d) the conversion of the carbon skeletons of amino acids, such as glutamine and glutamate, to P-enolpyruvate (via PEPCK) and then to pyruvate (via pyruvate kinase) for subsequent oxidation in the citric acid cycle as acetyl-CoA.

Gluconeogenesis
The role of PEPCK-C in hepatic and renal gluconeogenesis has been extensively reviewed (6), as have the factors that control transcription of its gene in these tissues (7). By removing citric acid cycle anions (i.e. in the conversion of oxalacetate to P-enolpyruvate), this enzyme links cataplerosis with a major biosynthetic pathway: glucose synthesis. Deletion of the gene for PEPCK-C in mouse liver leads to ablated gluconeogenesis in that tissue (8,9), whereas a total body deletion of the enzyme results in profound hypoglycemia and death. It is well established that changes in the rate of transcription of the gene for PEPCK-C is a critical step in establishing the overall activity of the enzyme in rodent liver and kidney cortex, tissues that synthesize glucose (7). After years of study of PEPCK-C, the general consensus is that alterations in PEPCK-C gene transcription regulate the total activity of the enzyme is these tissues. It is unusual for an enzyme at a branch point in a metabolic pathway not to be subject to some type of allosteric regulation. Recently, Lin et al. (10) reported that PEPCK-C in yeast was acetylated at Lys 19 and Lys 514 and that Sir2 deacetylated the enzyme both in vitro and in vivo. In addition, PEPCK-C in HepG23 cells, a human hepatoma line, was inhibited by acetylation, and the amount of glucose produced by the cells was markedly decreased. The regulatory significance of acetylation of PEPCK-C in mammalian tissues where the enzyme is active remains to be established. In the absence of identified allosteric regulation or covalent modification, it has been assumed that flux through PEPCK-C is regulated by the concentration of key intermediates, such as oxalacetate and GTP. Rapid alterations in the activity of key regulatory steps in gluconeogenesis, such as fructose-1,6-bisphosphatase and pyruvate kinase, by allosteric activation or covalent modification are critical in setting the overall flux rate for glucose synthesis. PEPCK-C would thus serve as a feeder reaction for the overall process by removing carbon from the citric acid cycle for utilization by biosynthetic and oxidative pathways (Fig. 1). We thus consider that PEPCK-C provides substrates for a number of downstream processes. Although gluconeogenesis is quantitatively the major route of carbon flux in the liver and kidney cortex, it is only one of the metabolic pathways that depend on PEPCK for a source of carbon derived from citric acid cycle intermediates.

Glyceroneogenesis as a Source of Glyceride-Glycerol
Most textbooks of biochemistry teach that the glycerideglycerol found in the triglyceride of mammals is derived from the triose phosphate that is generated by the metabolism of glucose via glycolysis. This is an attractive concept because it links the intake of glucose with the deposition of fat as triglyceride. Because insulin stimulates glucose transport into adipose tissue, the picture is complete; a diet high in carbohydrate will stimulate fatty acid synthesis and deposition in adipose tissue, resulting in obesity. However, fatty acids are synthesized to a negligible extent in the adipose tissue of adult mammals, especially in humans (11); this is the job of the liver. Newly synthesized fatty acids are transported from the liver to the adipose tissue as triglycerides that are packaged in very low density lipoprotein; the triglycerides are subsequently degraded by lipoprotein lipase to their component fatty acids and used to resynthesize triglyceride. The carbon source of glycerol 3-phosphate required to support the synthesis of the triglyceride in the liver comes largely from glyceroneogenesis (12). In fact, two studies that determined the rates of glyceroneogenesis compared with glycolysis in overnightfasted human subjects found that the majority (50 -60%) of the glyceride-glycerol in triglyceride isolated from circulating very low density lipoprotein was derived from glyceroneogenesis, with a smaller contribution from glucose (13,14). The overall rate of glyceroneogenesis was measured in rats subjected to a variety of dietary conditions. Very high rates of glyceroneogenesis were noted in the liver, adipose tissue, and skeletal muscle, despite the fact that the rats were fed a diet high in carbohydrate and received an intravenous infusion of glucose during the experiment (12). A more detailed review of this subject can be found in Ref. 15.
Glyceroneogenesis as a Source of Glycerol in the Rainbow Smelt, Schistosomes, and Caenorhabditis elegans-The glycerol found in the blood of mammals is generated from the hydrolysis of triglyceride in adipose tissue by the action of lipases; the concentration of glycerol in the blood is often used as a measure of lipolysis. Glycerol becomes an important source of carbon for gluconeogenesis during prolonged starvation, when the breakdown of triglyceride is accelerated. However, the concentration of glycerol in human blood does not exceed 0.25 mM (16). In contrast, the blood of Osmerus mordax, the rainbow smelt, which lives in the waters of the North Atlantic, contains a concentration of glycerol that can reach 0.4 M in the winter, when the ambient sea temperatures approach 0°C; the glycerol is acting as an "antifreeze" in these fish. The work of Driedzic, Ewart, and colleagues (17)(18)(19)(20)(21)(22) provides a fascinating picture of the metabolic adaptations that are responsible for the synthesis of such high concentrations of glycerol in the blood of these fish. They noted that the levels of PEPCK-C mRNA in rainbow smelt liver rise as the sea temperature falls in the winter months; this parallels an increase both in glycerol in the blood and in the rate of glyceroneogenesis from amino acids (Fig. 2). Glyceroneogenesis is of special importance because the diet of the rainbow smelt consists largely of fat and protein (they eat small marine invertebrates), and the carbon skeletons of amino acids are thus the chief source of glycerol (17,22). The direct synthesis of glycerol by a process that does not involve the lipolysis of triglyceride requires glycerol 3-phosphatase, an enzyme that has not been extensively studied in mammals. It seems likely, however, that the direct synthesis of glycerol also occurs in mammals. Jensen et al. (23) noted that ϳ15-20% of systemic glycerol in fasted humans was not formed from the lipolysis of adipose tissue triglycerides. As with the rainbow smelt, this would require glycerol 3-phosphatase to catalyze the direct dephosphorylation of the glycerol 3-phosphate.
Schistosoma mansoni is the parasite responsible for schistosomiasis, a chronic and debilitating disease that affects large numbers of individuals in tropical countries. The life cycle of the parasite includes propagation in the freshwater snail Biomphalaria glabrata, where it undergoes asexual multiplication of sporocysts and the generation of cercariae, nematode worms in the second stage of larval life that infect humans. Development of the parasite through its two larval stages requires considerable energy, which, in adult worms, is provided by glucose from the infected human host. However, glucose is limiting in the hemolymph of snails, and glutamine has been suggested as a major source of energy for the organism in this stage of development (24). PEPCK mRNA (at the present time, it is not clear which isoform) is present at 60 times the adult level in miracidia (the free-swimming larval stage) and 10 times the adult level in sporocysts. Assuming that changes in the level of PEPCK mRNA parallel its activity, these findings suggest that the organism can make glucose via either gluconeogenesis or glycerol, via glyceroneogenesis, presumably from glutamine as the source of carbon. Khayath et al. (24) reported that glutamine is converted mainly to free glycerol by sporocysts; this process does not involve the lipolysis of triglyceride in the organism. Treatment of the sporocysts with 3-mercaptopi-colinate, an inhibitor of PEPCK (24), resulted in decreased synthesis of both glucose and glycerol, supporting the key role of the enzyme in this process. It thus seems likely that schistosomes, like the rainbow smelt, carry out glyceroneogenesis, which results in the direct generation of glycerol (i.e. the lipolysis of triglyceride is not required).
C. elegans embryos from mothers that were exposed to medium containing 0.3 M NaCl for 24 h were resistant to 0.5 M NaCl; embryos from mothers that were not preconditioned to hyperosmotic shock as larvae did not survive (25). Embryos from conditioned adults had 10 times the glycerol content found in control embryos (25). Worms with a mutation in the gene for PEPCK did not survive conditioning in 0.3 M NaCl, suggesting that this enzyme is required for the formation of glycerol. The mobilization of glycogen in the worm was not sufficient to account for the levels of glycerol formed in response to hyperosmotic conditions. Perfusion of rat liver with a hyperosmotic medium markedly increased PEPCK-C mRNA, suggesting an effect on gene transcription (26), so that the role of this enzyme in response to hyperosmolarity is more general in organisms. As in the case of the rainbow smelt discussed above, C. elegans must have a glycerol 3-phosphatase to generate the glycerol formed from either glycogen mobilization or glyceroneogenesis. Although the rate of glyceroneogenesis has not been measured in C. elegans, it is likely that is an important process for glycerol formation.
Glyceroneogenesis, PEPCK, and Energy Metabolism in Developing Drosophila-The genes for both PEPCK-C and PEPCK-M are present in tandem on chromosome 2R of Drosophila. This contrasts with many mammalian species in which the genes are on separate chromosomes; for example, human PEPCK-C is on chromosome 20, and human PEPCK-M is on chromosome 14. Okamura et al. (27) noted that knockdown of the transcription factor ATF-2 by RNA interference resulted in a 70% decrease in the level of PEPCK mRNA in the fat bodies of flies compared with controls. This caused a 30% drop in the rate of glyceroneogenesis in the tissue as measured by the incorporation of [ 14 C]pyruvate into glyceride-glycerol. This correlated directly with the level of triglyceride found in the fat bodies of the genetically modified flies as well as with a decreased body weight. Interestingly, transgenic flies with an extra copy of the gene for ATF-2 had 60% more triglyceride compared with controls.

PEPCK and the Synthesis of Serine
Rats fed a low protein diet (6% of calories) have a 30-fold increase in hepatic mRNA for 3-phosphoglycerate dehydro-  (20). The activity of hepatic PEPCK is presented as a gray bar graph, the ocean temperature is shown in blue, and the concentration of glycerol in the blood of the rainbow smelt is presented as a red line.
genase, 3 an NAD-linked enzyme that converts 3-phosphoglycerate to 3-phosphohydroxypyruvate, the first step in the biosynthesis of serine from glycolytic intermediates. Although serine is not an essential amino acid, it is an important source of carbon for the one-carbon pool and for the synthesis of cysteine in the transsulfuration pathway. The induction of 3-phosphoglycerate dehydrogenase gene expression in the livers of rats fed a low protein diet suggests that the synthesis of serine is of special significance for the survival of the animals in situations in which dietary protein is limiting (Fig. 1). It is noteworthy that the pathway of glyceroneogenesis can also generate 3-phosphoglycerate, so it is possible that serine can be synthesized in the livers of starved animals using carbon derived from the P-enolpyruvate that was generated by PEPCK.

PEPCK-C and Recycling/Oxidation of the Carbon Skeletons of Amino Acids
A major cataplerotic function of PEPCK-C is the recycling of citric acid cycle anions back into the cycle to generate energy. This process involves the entry of amino acid carbon into the citric acid cycle, which will be further metabolized in the mitochondria to malate; malate exits the mitochondria and is oxidized to oxalacetate. PEPCK-C converts the oxalacetate to P-enolpyruvate, which is subsequently converted to pyruvate via pyruvate kinase and then to acetyl-CoA in the mitochondria by the pyruvate dehydrogenase complex; the acetyl-CoA is a substrate for complete oxidation in the citric acid cycle or can be used for fatty acid synthesis in the liver (Fig. 1). The same type of carbon cycling via PEPCK-C has been suggested as the fate of some of the glutamine carbon utilized by the kidney cortex (28). By this scenario, the conversion of citric acid cycle anions (i.e. via oxalacetate) to P-enolpyruvate (by PEPCK) provides a high degree of metabolic flexibility because tissues such as the small intestine or skeletal muscle can convert the P-enolpyruvate to pyruvate and derive energy by the subsequent oxidation of this compound as acetyl-CoA in the citric acid cycle. A tissue such as the liver makes glucose from citric acid cycle anions rather than recycling them back into the cycle for energy. However, some recycling of pyruvate into oxalacetate via pyruvate carboxylase and thus back into the citric acid cycle does occur in the liver. The rate of "futile cycling" was estimated to account for as much as 25% of the carbon flow during hepatic gluconeogenesis in fasted rats (29).

Overexpression of the Gene for PEPCK-C in Mouse Skeletal Muscle and the Interaction of Glyceroneogenesis and Cataplerosis
Skeletal muscle has a small but significant level of PEPCK-C activity (30). There have been several proposals regarding the metabolic role of this enzyme in muscle (30,31). These include providing pyruvate (from P-enolpyruvate formed by PEPCK-C, as discussed above) for the synthesis of alanine by alanine aminotransferase. This is a potentially important process because alanine constitutes a considerable fraction of the amino acids released by skeletal muscle during fasting, requiring the net synthesis of this amino acid in the muscle. Another possible metabolic role of PEPCK-C in skeletal muscle is glyceroneogenesis. Skeletal muscle contains triglyceride, and glyceroneogenesis is the major source of carbon for the glyceride-glycerol in the triglyceride in this tissue (12). PEPCK-C also has a cataplerotic function in skeletal muscle. The concentration of citric acid cycle anions greatly increases in the mitochondria during strenuous exercise and then falls when the muscle is at rest (32) and removed after exercise.
To determine the metabolic role of PEPCK-C in skeletal muscle, transgenic mice were generated using the cDNA for PEPCK-C, which was linked to the ␣-skeletal actin gene promoter (PEPCK-C mus mice) and the 3Ј-end of the bovine growth hormone gene. By crossbreeding two founder lines, a line of mice was created that had ϳ100 times the activity of the enzyme in their skeletal muscle as was noted in control littermates (33). These mice were hyperactive; they ran up to 5 km at 20 m/min without stopping and were eight times more active than controls in their home cages. To support this high level of activity, the PEPCK-C mus mice ate twice as much as control animals but weighed about half as much. During strenuous exercise, the PEPCK-C mus mice used fatty acid as the primary fuel, and the concentration of lactate in the blood did not increase to the same extent as in control animals after the mice ran to exhaustion. The reason for this high level of aerobic activity in the PEPCK-C mus mice is most likely the greatly increased number of mitochondria noted in their skeletal muscle. They also had markedly elevated levels of triglyceride in their skeletal muscle (33). The concentrations of various metabolic markers in the blood, such as glucose, free fatty acids, ketone bodies, and cholesterol, were not significantly different in fasted PEPCK-C mus mice compared with controls. However, there was markedly less insulin and leptin in the blood of PEPCK-C mus mice compared with controls. 4 This is consistent with the effect of hyperactivity on glucose utilization by skeletal muscle (increased insulin sensitivity) and the reduced adipose tissue (reduced leptin formation) in the PEPCK-C mus mice.
The mechanism by which overexpression of PEPCK-C in skeletal muscle causes such a marked increase in activity in the mice is not clear at present. We assume that the increased concentration of triglyceride in the skeletal muscle of the PEPCK-C mus mice is caused by the elevated activity of PEPCK-C, which increases the rate of glyceroneogenesis. The fact that the concentration of triglyceride in the skeletal muscle of the PEPCK-C mus mice was proportional to the activity of PEPCK-C in their muscle suggests that carbon flow over this pathway is an important determinant of the increased triglyceride levels in the muscle (33). The role of PEPCK in muscle metabolism is further supported by a recent study of rats bred for running endurance; these rats were also leaner, more active, and aggressive (34). There was a 2-fold higher PEPCK activity in the quadriceps of the more active rats. It was concluded that PEPCK may be an important factor in the enhanced running endurance noted in the lean rats (34).

Why Are There Two Isoforms of PEPCK?
The current wisdom is that PEPCK-M is involved in gluconeogenesis from lactate (see Ref. 35 for a detail review). Because the oxidation of lactate to pyruvate via lactate dehydrogenase generates NADH in the cytosol, it is important to balance the cytosolic redox state by synthesizing P-enolpyruvate directly in the mitochondria. This obviates the need to transport malate, which must be oxidized to oxalacetate in the cytosol, generating NADH. Only one NADH is consumed per triose phosphate converted to glucose; the two molecules of NADH that could be potentially produced during gluconeogenesis from lactate would skew the cytosolic redox state. The synthesis of P-enolpyruvate directly in the mitochondria and its subsequent transport to the cytosol (36) bypass this problem. Support for this concept is based largely on studies with birds (they have PEPCK-M and no PEPCK-C activity in their livers), whose livers synthesize glucose largely from lactate (37,38). However, lactate is derived from glucose metabolism in the large flight muscles of birds, so lactate carbon does not result in the net formation of glucose via gluconeogenesis; this is provided by gluconeogenesis from amino acids in the kidney. However, the metabolic role of PEPCK-M in other organisms has not been rigorously studied and is largely ignored.
In contrast to PEPCK-C, an extensive study of the tissue distribution of PEPCK-M activity in the rat and mouse has not been carried out. Recently, Stark et al. (39) reported the presence of PEPCK-M in mouse and rat pancreatic islets. They proposed that the enzyme is involved in cataplerosis by shunting excess oxalacetate as P-enolpyruvate from the mitochondria to the cytosol. The P-enolpyruvate would then be converted to pyruvate and recycled by ␤-cells. This process is critical for the complete metabolism of citric acid cycle intermediates formed from either pyruvate via pyruvate carboxylase or from the carbon skeletons of amino acids. Recycling from the mitochondria via PEPCK-M can contribute up to 40% of the cytosolic P-enolpyruvate pool. In addition, the GTP synthesized by succinyl-CoA synthetase is converted to GDP by PEPCK-M (39). These findings underline the importance of the direct delineation of the role of PEPCK-M in a wide variety of mammalian tissues.


Immunosedation: a consideration for sepsis

In a recent issue of Critical Care, Qiao and colleagues showed in a rat model of sepsis that dexmedetomidine and midazolam suppress the generation of pro-inflammatory mediators but the effects vary between agents. While dexmedetomidine limited apoptosis to a greater extent than midazolam, both agents significantly reduced short-term mortality compared with saline. This study, in addition to those by others, suggests there are disparate immunomodulating effects between sedatives. Clinical studies are warranted to investigate whether these effects impact outcomes of septic patients. Perhaps one day the choice of sedative in septic patients will not be based solely on sedative properties but rather immunosedative profiles.

In a recent issue of Critical Care, Qiao and colleagues [1] used a cecal ligation model of sepsis to compare the immunomodulating effects of dexmedetomidine, midazolam, and saline (placebo). They demonstrated that, over the course of an 8-hour infusion period, both sedatives significantly reduced the production of tumor necrosis factor-alpha (TNF-α) but that only dexmedetomidine decreased interleukin (IL)-6 generation. Similarly, dexmedetomidine limited the splenic expression of caspase 3, a marker of apoptosis, to a greater extent than midazolam when compared with placebo. Mortality rates at 24 hours were similar between sedatives and were significantly reduced compared with placebo.
In vitro and other animal studies of sepsis have shown that dexmedetomidine suppresses the expression of cyclooxygenase-2 (COX-2), inducible nitric oxide synthase (iNOS), TNF-α, IL-1β, IL-6, and interferon-gamma (IFN-γ) [2][3][4]. These effects appear to be dose-dependent across the range of commonly used doses and time-dependent in that greater anti-inflammatory effects are observed if dexmedetomidine is started earlier after the exposure of endotoxin [3][4][5]. Reversal of these effects occurs when alpha-2 antagonists are concomitantly administered with dexmedetomidine, suggest-ing that neural-immune modulation involving alpha-2 stimulation is essential to the anti-inflammatory mechanism of dexmedetomidine [2]. Of note, iNOS expression is enhanced at supratherapeutic concentrations of dexmedetomidine and further suggests that alpha-2 selectivity contributes to the anti-inflammatory action of dexmedetomidine [2]. Perhaps most intriguing is that hemodynamic stability and short-term survival rate emulate the dose-dependent and timedependent anti-inflammatory effects of dexmedetomidine in these animal models of sepsis [3][4][5].
Benzodiazepines have also demonstrated dose-dependent suppression of COX-2, iNOS, and pro-inflammatory mediators in models of sepsis [6][7][8][9][10]. These studies suggest that the mechanism is mediated by inhibiting nuclear translocation of nuclear factor-kappa-B, reducing phosphorylation of p38 mitogen-activated protein, and stabilizing mast cells. The results of animal studies have shown conflicting outcomes with benzodiazepines, in contrast to dexmedetomidine, as survival rate is lower and organ function unaffected but bactericidal effect enhanced with benzodiazepine therapy [10].
Few studies have investigated the immunomodulating effects of sedatives in critically ill patients. In surgical patients, dexmedetomidine 0.2 to 2.5 μg/kg per hour reduced IL-6 over the course of an 8-hour period to a greater extent than propofol 1 to 3 mg/kg per hour [11]. Also in surgical patients, midazolam 0.02 to 0.06 mg/kg per hour reduced TNF-α, IL-1β, and IFN-γ after 48 hours whereas propofol 0.5 to 1.5 mg/kg per hour increased the production of these proinflammatory cytokines [12]. A direct comparison of dexmedetomidine 0.2 to 2.5 μg/kg per hour and midazolam 0.1 to 0.5 mg/kg per hour in septic patients showed that only dexmedetomidine suppressed the expression of TNF-α, IL-1β, and IL-6; however, both agents improved oxygenation

Robert MacLaren
University of Colorado Denver School of Pharmacy, Academic Office 1, C238-L15, 12631 East 17th Avenue, Aurora, CO 80045, USA as assessed by gastric mucosal pH [13]. In a subgroup of 39 septic patients from the MENDS (maximizing efficacy of targeted sedation and reducing neurological dysfunction) trial, the risk of dying was lower in the group that received dexmedetomidine compared with lorazepam (hazard ratio = 0.3, 95% confidence interval = 0.1 to 0.9, P = 0.036) [14]. The SEDCOM (Safety and Efficacy of Dexmedetomidine Compared With Midazolam) study compared dexmedetomidine and midazolam and reported a lower overall infection rate in the dexmedetomidine group (10.2% versus 19.7%, P = 0.02), but this may be attributed to a shorter ventilator requirement in this group rather than different immunomodulating properties of the sedatives [15]. Mortality rates at day 30 were similar between groups.
Should a particular class of sedative be preferred when sedating the septic patient? At present, the answer to this question is 'no' or at least 'not yet'. What is evident is that sedatives have immunomodulating properties and that autonomic activity influences cytokine expression. Indeed, adrenergic catecholamines are known to influence immune responses and the process of inflammation [16]. Animal models of sedatives in sepsis rarely administered vasopressors for hemodynamic support, but it is conceivable that a particular vasopressor-sedative combination may be preferentially chosen to counterbalance their immunomodulating effects or enhance a specific effect. Septic patients frequently receive other immunomodulating therapies, including corticosteroids, drotrecogin, opioid analgesics, propofol, or immunonutrients [17,18]. How these agents interact with alpha-2 agonists or benzodiazepines is unknown, but presumably the use of these modalities was distributed equally in the few clinical studies conducted to date. Hypotension is a particular concern of using an alpha-2 agonist in sepsis. Data from animal studies, however, demonstrate improved hemodynamic profiles as the pro-inflammatory process subsides with dexmedetomidine administration [3][4][5].
Whether sedatives possess dose-dependent immunomodulating effects has not been studied in critically ill patients. Animal data, however, suggest that dexmedetomidine may possess an optimal dose for its immunomodulating activity [3][4][5]. This dose would likely vary between patients and within the same patient over time. The logical question if such a dose does exist is what to do if additional sedation is required. Or is this, in addition to shorter ventilator requirements and improved neurologic recovery, justification for minimizing sedation? Another intriguing observation is the time-dependent immunomodulating effect of dexmedetomidine because, in theory, dexmedetomidine may be an ideal sedative to initiate in early sepsis but other sedatives may be preferred later [3][4][5]. Does this necessitate changing the sedative agent as the sepsis process progresses? This is not unlike the scenario of the patient perceived to be unresponsive to a particular antibiotic who is changed to another class of antibiotic for greater response. Obviously, these queries are speculative. The studies conducted to date in animal models of sepsis show that there are disparate immunomodulating effects and possibly therapeutic outcomes between sedatives [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Perhaps one day the choice of sedative in septic patients will not be based solely on sedative properties but rather immunosedative profiles.