Modification of neuropathic pain sensation through microglial ATP receptors

Neuropathic pain that typically develops when peripheral nerves are damaged through surgery, bone compression in cancer, diabetes, or infection is a major factor causing impaired quality of life in millions of people worldwide. Recently, there has been a rapidly growing body of evidence indicating that spinal glia play a critical role in the pathogenesis of neuropathic pain. Accumulating findings also indicate that nucleotides play an important role in neuron-glia communication through P2 purinoceptors. Damaged neurons release or leak nucleotides including ATP and UTP to stimulate microglia through P2 purinoceptors expressing on microglia. It was shown in an animal model of neuropathic pain that microglial P2X4 and P2X7 receptors are crucial in pain signaling after peripheral nerve lesion. In this review, we describe the modification of neuropathic pain sensation through microglial P2X4 and P2X7, with the possibility of P2Y6 and P2Y12 involvement.
Introduction
There is a type of pain that does not go away even though the tissue has already healed. One type of this pain is called neuropathic pain that typically develops when peripheral nerves are damaged such as through surgery, bone compression in cancer, diabetes, or infection. Neuropathic pain is a major factor causing impaired quality of life in millions of people worldwide and is frequently resistant to all known analgesic drugs. Over the last decade, accumulating evidence concerning how peripheral nerve injury creates neuropathic pain has suggested that nerve injury produces molecular and cellular alterations that result in multiple forms of neuronal plasticity and anatomical reorganization in the dorsal horn of the spinal cord. These alterations have been proposed to be crucial in the pathogenesis of neuropathic pain [1, 2]. While the dominant theme in research on neuropathic pain has been to understand the roles of neurons in the peripheral nervous system and the dorsal horn, there is a rapidly growing body of evidence indicating that spinal glial cells play a critical role in the pathogenesis of neuropathic pain.
Recently, growing evidence has indicated that neuron-glia interaction is a key idea to understand functions of the central nervous system (CNS). Especially glia play important roles in pathophysiological situations of the CNS including psychiatric disorders, physical trauma, and infections [3]. Glia consist of three members: astrocytes, oligodendrocytes, and microglia. Accumulating findings also indicate that nucleotides play an important role in neuron-glia communication through P2 purinoceptors, even though ATP is recognized primarily to be a source of free energy and nucleotides are key molecules in cells. Microglia, which are thought to be residential macrophages in the CNS, express P2 purinoceptors, mainly P2X4 and P2X7 as well as P2Y2, P2Y6, and P2Y12 [4]. Damaged neurons release or leak nucleotides including ATP and UTP to stimulate microglia [5, 6]. It is clear that these nucleotides trigger the release of various neurotoxic and neuroprotective cytokines and growth factors via different purine receptors [4, 7] or induce P2Y12-dependent chemotaxis in cultured cells [8, 9]. It was shown in an animal model of neuropathic pain that microglial P2X4 and P2X7 receptors are crucial in pain signaling after peripheral nerve lesion [10–12]. Other purine receptors are upregulated in microglia in response to neuronal injury, as was recently demonstrated for P2Y6 [6]. The P2Y6 receptor triggers microglial phagocytosis [6]. In this paper, we review the modification of pain sensation through microglial P2X4 and P2X7, and also point out the possibility of P2Y6 and P2Y12 involvement in pain signaling.
P2X4 in neuropathic pain
We found that the marked tactile allodynia that develops following nerve injury was reversed by acutely administering TNP-ATP intrathecally but was unaffected by administering PPADS [10]. From the pharmacological profiles of TNP-ATP (blocking P2X4 at high concentration) and PPADS (not blocking P2X4), it was suggested that tactile allodynia depends upon P2X4 in the spinal cord. The expression of P2X4 protein, normally low in the naïve spinal cord, progressively increased in the days following nerve injury with a time course parallel to that of the development of tactile allodynia. Double immunolabeling analysis demonstrated that not neurons or astrocytes but activated microglia in the dorsal horn were intensely positive for P2X4 protein [10]. Moreover, intrathecally administered antisense oligodeoxynucleotide for P2X4R reduced the expression of P2X4 protein in spinal microglia and prevented the development of the nerve injury-induced tactile allodynia. In naïve rats, intrathecal administration of cultured microglia that were preincubated with ATP to activate P2X4 produced tactile allodynia over the 3–5 h after the administration [10].
Since it was already reported that the nerve injury-induced tactile allodynia depends on a depolarizing shift in the Eanion of spinal lamina I (LI) neurons in the dorsal spinal cord, resulting in converting the GABAA receptor- and glycine receptor-mediated inhibition to excitation [13], it was considered that microglia may affect Eanion in LI neurons. To investigate this possibility, microglia were administered to the lumbar spinal level of naïve rats by an intrathecal catheter as described [11]. Administering microglia stimulated with ATP caused a progressive tactile allodynia over the 5 h after injection. Eanion in LI neurons from rats administered ATP-stimulated microglia was shifted to −61.6 mV from −68.3 mV of normal rats. In addition, we found that GABA response switched from hyperpolarizing in control rats to depolarizing in microglia-treated rats. Activated microglia secrete various biologically active molecules, one of which, BDNF, was implicated in the hypersensitivity of dorsal horn neurons that follows sensitization and inflammation [14–16] and in anion gradient shifts in the hippocampus [17]. Indeed, intrathecal administration of recombinant BDNF produced tactile allodynia comparable to that produced by ATP-stimulated microglia [11]. Eanion of LI neurons in slices treated with BDNF (>90 min, in vitro) was significantly less negative than that of LI neurons from control slices. The rise in [Ca2+]i was prevented by the GABAA receptor blocker bicuculline, confirming that the effect was mediated by GABAA receptors. Thus, acute administration of BDNF in slices caused a depolarizing shift in Eanion and caused GABA to produce net excitation [11]. Moreover, a function-blocking antibody against the TrkB receptor (anti-TrkB) and a BDNF-sequestering fusion protein (TrkB-Fc) acutely inhibited the allodynia and the shift of Eanion of LI neurons [11]. The administration of ATP-stimulated microglia with either anti-TrkB or TrkB-Fc did not develop tactile allodynia. After pretreatment of microglia with double-stranded short interfering RNA directed against BDNF (BDNF siRNA), the ATP-stimulated microglia injected intrathecally into normal rats did not cause the allodynia [11]. Anti-TrkB and BDNF siRNA prevented the shift in Eanion induced by ATP-stimulated microglia. ATP stimulation caused release of BDNF from microglia in culture. This effect of ATP was blocked by treating the cultures with the P2X receptor blocker TNP-ATP. In addition, pretreatment of the microglia with BDNF siRNA prevented release of BDNF by ATP stimulation. By bath application of TNP-ATP to spinal slices taken from allodynic rats 2 weeks after nerve injury, Eanion of LI neurons was returned to normal value [11]. These findings indicate that P2X4-dependent release of BDNF from microglia is necessary to sustain both the tactile allodynia and the depolarizing shift in Eanion in LI neurons that result from nerve injury (Fig. 1).
Fig. 1Schematic illustration of potential mechanisms by which P2X/Y receptors in activated microglia modulate neuropathic pain signaling in the dorsal horn. Activated microglia in the spinal cord after nerve injury express ionotropic ATP receptors [e.g., P2X4 receptor (P2X4R) or P2X7R]. P2X4R or P2X7R activation leads to the release of bioactive diffusible factors such as BDNF and other proinflammatory factors (cytokines and chemokines). BDNF causes a collapse of transmembrane anion gradient in dorsal horn lamina I neurons presumably through the downregulation of KCC2, which in turn renders GABA and glycine effects depolarizing, rather than hyperpolarizing, in these neurons. Microglial factors may also interact with excitatory synapses of neighboring dorsal horn neurons and enhance the excitability in dorsal horn neurons. The net hyperexcitability in the dorsal horn pain network by these factors from activated microglia may be responsible for neuropathic pain. Microglia also express G protein-coupled ATP receptors [e.g., P2Y6 receptor (P2Y6R) and P2Y12R]. Activating P2Y12R and P2Y6R leads to chemotaxis and phagocytosis, respectively, but their functional relevance to neuropathic pain remained to be determined
P2X7 in neuropathic pain
P2X7 purinoceptors are a ligand-gated nonselective cationic channel and are expressed predominantly on immune cells [18]. Stimulation of the P2X7 receptors on microglia is associated with release of cytokines including tumor necrosis factor-α (TNF-α) [5, 19], interleukin-6 (IL-6) [20], and interleukin-1β (IL-1β) [21–23]. Several cytokines such as IL-1β, IL-6, and TNF-α in the dorsal horn are increased after nerve lesion [24–26] and have been implicated in contributing to neuropathic pain [24–27]. Recent evidence indicates the relationship between TNF-α and neuropathic pain [28–32], and TNF-α released after injury is proposed as an initiator of abnormal pain sensation. TNF-α is upregulated after nerve injury in both dorsal root ganglion (DRG) and spinal cord [33]. The inhibition of TNF-α reduces the hyperalgesia in neuropathic pain models [34]. Recent evidence indicates the relationship between inflammatory cytokines including IL-1β and neuropathic pain [25, 26, 28]. The expression of IL-1β is upregulated in the spinal cord of several rat neuropathy models [24–26]. These findings further support a role for central IL-1β in the development and maintenance of neuropathic pain through induction of a proinflammatory cytokine cascade (Fig. 1).
Recently, it was reported that in mice lacking P2X7 inflammatory and neuropathic hypersensitivity is completely absent to both mechanical and thermal stimuli, whilst normal nociceptive processing is preserved [12]. Contribution of P2X7 receptor to neuropathic pain is also demonstrated by using the recently developed selective antagonist for P2X7 receptor A-740003 [35]. The knockout animals were unimpaired in their ability to produce mRNA for pro-IL-1β, and cytometric analysis of paw and systemic cytokines from knockout and wild-type animals following adjuvant insult suggested a selective effect of the gene deletion on release of IL-1β and IL-10, with systemic reductions in adjuvant-induced increases in IL-6 and MCP-1. In addition, P2X7 receptors were upregulated in human dorsal root ganglia and injured nerves obtained from chronic neuropathic pain patients [12]. It was hypothesized that the P2X7 receptor plays a common upstream transductional role in the development of pain of neuropathic and inflammatory origin via regulation of mature IL-1β production.
In addition, a recent study has shown that activation of P2X7 receptors expressed on satellite glia that enwrap each DRG neuron leads to a release of TNF-α, which in turn increases the excitability of DRG neurons [36]. P2X7 receptors are upregulated in surrounding satellite glial cells in the DRG in humans [12]. Implication of TNF-α has been reported in neuropathic pain [28–32]. Therefore, the contribution of P2X7 receptor to neuropathic pain might be related not only to the activation of microglial P2X7 receptors, but also to P2X7 receptors on other cell types such as satellite glia.
P2Y6 and P2Y12 in neuropathic pain?
When neurons are injured or dead, microglia are activated, resulting in their interaction with immune cells, active migration to the site of injury, release of proinflammatory substances, and the phagocytosis of damaged cells or debris. For such activation of microglial motilities, extracellular nucleotides have a central role. Extracellular ATP functions as a chemoattractant. Microglial chemotaxis by ATP via P2Y12 receptors was originally found by Honda et al. [8] and has recently been confirmed in vivo in P2Y12 receptor knockout animals [37]. Neuronal injury results in the release or leakage of ATP that appears to be a “find-me” signal from damaged neurons to microglia to cause chemotaxis. In addition to microglial migration by ATP, another nucleotide, UDP, an endogenous agonist of the P2Y6 receptor, greatly activates the motility of microglia and orders microglia to eat damaged neurons. UDP does not cause chemotaxis, but instead causes phagocytosis by microglia [6]. Phagocytosis, a specialized form of endocytosis, is the uptake by the cell of relatively large particles (>1.0 μm) into vacuoles and has a central role in tissue remodeling, inflammation, and the defense against infectious agents [38]. Phagocytosis is initiated by the activation of various cell surface phagocytosis receptors, including Fc receptors, complement receptors, integrins, endotoxin receptors (CD18, CD14), mannose receptors, and scavenger receptors [39], which are activated by corresponding extracellular ligands. In the CNS, a full innate immune system, i.e., Fc receptors, complement system, scavenger receptors, and Toll-like receptors etc., has been described, and microglia reveal related roles as dedicated phagocytes. Since recognition is the first and the most important step for phagocytosis, extensive studies on phagocytosis receptors have been reported. It is well-known that dying cells express so-called eat-me signals such as phosphatidylserine on their surface membrane [39], by which microglia recognize the apoptotic cells to catch and remove them [39]. We first found that exogenously applied UDP caused microglial phagocytosis in a concentration-dependent manner, which was P2Y6 receptor-dependent. We found that neuronal injury caused by kainic acid (KA) upregulated P2Y6 receptors in microglia, the KA-evoked neuronal injury resulted in an increase in extracellular UTP, which was immediately metabolized into UDP in vivo and in vitro. Moreover, UDP leaked from injured neurons caused P2Y6 receptor-dependent phagocytosis in vivo and in vitro. These results suggest that UDP could be a molecule that signals the crisis of damaged neurons to microglia, triggering phagocytosis. It should be noted that nucleotides could be both “find-me” and “eat-me” signals. Cells release ATP, and we also found that KA caused an increase in extracellular UTP/UDP. Therefore microglia might be attracted by ATP/ADP [8, 40, 41] and subsequently recognize UDP, leading to the removal of the dying cells and their debris.
Are P2Y6 and P2Y12 receptors involved in neuropathic pain? For this question, there is a report indicating that the UDP-sensitive P2Y6 receptor produces inhibitory effects on spinal pain transmission in a neuropathic pain model [42]. In the neuropathic pain model, in which the sciatic nerves of rats were partially ligated, UDP (30 and 100 nmol/rat) produced significant antiallodynic effects. UDP (100 nmol/rat) caused no motor deficit in the inclined plane test [42]. The mechanism of this UDP-evoked inhibitory effect on neuropathic pain is unknown. There is no evidence to show the site (neuron or glia) of action of UDP. We are now investigating the involvement of P2Y12 in pain signaling and have obtained evidence suggesting the role of P2Y12 in the neuropathic pain state.
Conclusion
Neuropathic pain is a major factor causing impaired quality of life in millions of people worldwide. We have to try to reveal the mechanism of this pain in order to develop effective drugs against the pain. Recently, there has been a rapidly growing body of evidence indicating that spinal glia, especially microglia, play a critical role in the pathogenesis of neuropathic pain and that nucleotides play an important role in neuron-glia communication through P2 purinoceptors. We described the modification of neuropathic pain signaling through microglial P2X4 and P2X7, with the possibility of P2Y6 and P2Y12 involvement (Fig. 1). It was shown in an animal model of neuropathic pain that microglial P2X4 and P2X7 receptors are crucial in pain signaling after peripheral nerve lesion. Microglial P2Y6 and P2Y12 play very interesting roles for phagocytosis and chemotaxis, respectively. Besides, there is a possibility that these receptors are involved in pain sensation. Additional experiments are needed to clarify the possibility.

neuropathic pain
atp
microglia
p2x4
spinal cord
allodynia


Positively Selected Codons in Immune-Exposed Loops of the Vaccine Candidate OMP-P1 of Haemophilus influenzae

The high levels of variation in surface epitopes can be considered as an evolutionary hallmark of immune selection. New computational tools enable analysis of this variation by identifying codons that exhibit high rates of amino acid changes relative to the synonymous substitution rate. In the outer membrane protein P1 of Haemophilus influenzae, a vaccine candidate for nontypeable strains, we identified four codons with this attribute in domains that did not correspond to known or assumed B- and T-cell epitopes of OMP-P1. These codons flank hypervariable domains and do not appear to be false positives as judged from parsimony and maximum likelihood analyses. Some closely spaced positively selected codons have been previously considered part of a transmembrane domain, which would render this region unsuited for inclusion in a vaccine. Secondary structure analysis, three-dimensional structural database searches, and homology modeling using FadL of E. coli as a structural homologue, however, revealed that all positively selected codons are located in or near extracellular looping domains. The spacing and level of diversity of these positively selected and exposed codons in OMP-P1 suggest that vaccine targets based on these and conserved flanking residues may provide broad coverage in H. influenzae.
Introduction
Vaccination with Haemophilus influenzae type b polysaccharide (in the form of Hib conjugate vaccines) has proven to be highly effective in preventing invasive H. influenzae disease and has effectively reduced serotype b in many parts of the world (Black et al. 1992). Nontypeable H. influenzae strains, however, remain a serious problem (Zeckel et al. 1992). These strains are generally less invasive than their encapsulated type b counterpart but are a major cause of ear infections (otitis media) and sinusitis in children. They are also associated with respiratory tract infections such as pneumonia in infants, children, and adults. Ongoing efforts to develop a vaccine against nontypeable H. influenzae have mainly focused on immunogenic surface-exposed proteins (Bolduc et al. 2000). This approach clearly has potential, as immune responses against surface proteins have been shown to aid the recovery from otitis media (Shurin et al. 1980) and vaccination with surface-exposed domains of a major outer membrane protein (OMP-P1) provides protection in animal models (Bolduc et al. 2000). However, this effect is strain-specific (Gonzales et al. 1987) and, hence, not sufficient to provide broad protection. This problem may be overcome by the inclusion of less variable immunogenic protein regions in the vaccine.
Identification of candidate vaccine antigens is classically achieved via immunization studies with isolated or recombinant antigen and via epitope mapping, often in combination with analysis of correlates of protection. Vaccine development is often hampered by antigenic diversity within the surface-exposed regions. This limits a broad cross-reactivity of the elicited immune response. This holds also for proteins such as OMP-P1 in which large conserved domains separate relatively small variable domains (Munson and Grass 1988; Chong et al. 1995; Bolduc et al. 2000). On the other hand, the genetic diversity of vaccine candidates can be used to choose OMP-P1 variants for use in immunological assays (Bolduc et al. 2000). Nowadays, instead of an arbitrary selection of vaccine candidates, novel and rigorous computational approaches can be used to identify codons whose diversity is driven by the immune system. These codons are characterized by a higher rate of nonsynonymous substitutions (dN) relative to the synonymous substitution rate (dS) (Yang et al. 2003; Fitzpatrick and McInerney 2005). The codons are referred to as positively selected, although they may typically reflect immune selection (Fitzpatrick and McInerney 2005). To avoid confusion with the results of immunological studies, we refer here to codons with a higher rate of nonsynonymous substitution than synonymous substitution (dN > dS) as positively selected.
Knowledge of the position of positively selected codons in a protein would constitute an excellent starting point for immunization studies and epitope mapping, not only because of their biological function, but also because the number of variable sites of proteins that are candidates for inclusion in a vaccine can be reduced. In the present study, we identified codons that evolved more rapidly through nonsynonymous than through synonymous substitutions in a sample of 36 OMP-P1 sequences. We compared the location of these codons with the location of peptides that were used in epitope mapping and with B- and T-cell OMP-P1-specific antigens to examine the congruence among these techniques and to identify regions that could be important for vaccine design. Finally, we localized stretches with positively selected codons in secondary structures and three-dimensional (3D) models of OMP-P1. Our computational approach led to the identification of several novel domains with positive selected codons within the OMP-P1 protein that may be attractive targets in future vaccine design.
Materials and Methods
Evolutionary Analysis of the Selection Pressure on OMP-P1
We used the nr database for BLASTP searches under standard settings to collect closely related OMP-P1 sequences of H. influenzae using accession 9716616 as a query. The GenBank accession numbers and the data set of OMP-P1 sequences are provided as Supplementary Information. We aligned the sequences using the standard alignment parameters using Clustalx (Thompson et al. 1997) and checked the amino acid alignment with DNASP version 4.0 (Rozas et al. 2003). Codons comprised in insertions and deletions and in extremely variable stretches were removed from the alignment and are referred to in this work as hypervariable domains. A phylogenetic tree was reconstructed using PAUP* (Swofford 2003) with the maximum likelihood algorithm (100 random additions of taxa, TBR branch swapping) under the optimal nucleotide substitution model based on modeltest version 3.06 (Posada and Crandall 1998). Bootstrapping based on maximum likelihood was used to assess support for internodes using 100 random additions, SPR branch swapping, steepest descent, chuckscore = 0.1, and nchuck = 1. We used the alignment and tree topology of one of the maximum likelihood (ML) trees as input for PAML (Yang 1997). This program implements the currently most widely used algorithms to detect selection on codons, which take into account the transition–transversion ratio and codon usage bias and allow identification of codons that accumulate nonsynonymous mutations more rapidly than synonymous ones. To detect such sites, the rates of nonsynonymous and synonymous substitutions are determined through ML analyses using the model of Goldman and Yang (1994). For immune-exposed proteins of pathogens, a dN/dS ratio >1 indicates that nonsynonymous substitutions are driven by selection pressures exerted by the immune system (Derrick et al. 1999; Jiggins et al. 2002; Fitzpatrick and McInerney 2005). Because it is most likely that only a subset of codons in any protein-coding gene evolves under positive selection, several distributions of dN/dS that reflect neutral (M1, M7) and selection (M2, M8) models can be fitted to the data using PAML (Yang 1997). The neutral models assume a single estimate of ω (0<ω<1; M1) and a discrete beta distribution of ω with 10 categories (0<ω<1; M7). The dN/dS ratio under the neutral models is constrained to values ≤1 (Yang et al. 2003; Yang and Swanson 2002). The selection models M2 and M8 have an additional class of ω values that exceed unity. This latter codon category is believed to comprise codons that code for amino acids that are affected by the immune system (Yang et al. 2000) and that, consequently, evolve rapidly at the amino acid level. The cross-category ω’s under M7 and M8 were estimated by averaging the ω estimates of individual ω classes weighted by their frequency. We used multiple starting values for the selection models M2 and M8, because these models may have multiple local optima (e.g., Wong et al. 2004). The fit of M1 and M7 to the data was compared with selection models (M2 and M8, respectively). The ratio of the fit of neutral and selection models mentioned above follows a chi-square distribution with two degrees of freedom, i.e., for significance at the 1% significance level, two times the difference of the likelihood of neutral and selection models should be >9.21. We examined the evidence for positive selection in the entire alignment (excluding codons with ambiguity codes and gaps; see above), in a data set comprising 30 nontypeable isolates and in a data set of 36 sequences from which five variable domains were deleted. The latter data set enables the detection of positively selected codons in highly conserved domains. The selection models may give false-positive results in the case of a poor fit of the beta distribution to the true distribution of ω in the interval between 0 and 1 (Swanson et al. 2003). The test advocated by Swanson et al. (2003) to guard against such false positives of the ML methods implemented in PAML involves the comparison of M8 (ω2 > 1) and M8A (ω2 = 1). This likelihood ratio test (LRT) is asymptotically distributed as a 50:50 mixture of a point mass at 0 and a chi-square with one degree of freedom, which corresponds to 2.71 units difference between the likelihoods of M8A and M8 at the 1% level. If the fit of the selection models is significantly better than that of the corresponding neutral models, the selection models can be used to identify individual codons under positive selection (Yang et al. 2005). For inferring which sites are under positive selection, the Bayes theorem was used (Yang et al. 2005), which assumes that given the proportion of sites across ω classes as estimated from the ML analyses, assigns priors to model parameters to integrate over their uncertainties. The Bayes Empirical Bayes (BEB) approach used here was specifically advocated for small data sets, because it takes into account sampling errors of the proportions and ω ratios of site classes (Yang et al. 2005).
The rigorous identification of codons under positive selection is an important step when linking evolutionary and structural data, and when testing individual codons, p-values should ideally be confirmed by independent tests and corrected for multiple tests. To these ends, we applied the conservative tests of positive selection based on parsimony (Suzuki and Gojobori 1999) implemented as the Single Likelihood Ancestor Counting (SLAC) analysis in the datamonkey web server (Kosakovsky Pond and Frost 2005). In contrast to the original version of the Suzuki-Gojobori test, the modified version allows selection of a nucleotide substitution model. We also used a new ML method developed by Massingham and Goldman (2005), which does not assume an underlying distribution of ω. Instead, their sitewise likelihood ratio (SLR) procedure, which uses the same codon substitution model as in Goldman and Yang (1994), performs a LRT on a sitewise basis, testing the null model (neutrality, ω = 1) against an alternative model (ω ≠ 1). In the SLR method, tree topology, branch lengths, equilibrium codon frequencies, and transition/transversion rate ratio are assumed to be common to all sites in an alignment. Under the null model, all parameters except ω are allowed to vary freely. Under the alternative model, all parameters vary freely. The SLR method applies Hochberg’s step-up procedure to correct for multiple tests (Massingham and Goldman 2005). Both the parsimony method and the SLR method also allow the identification of negatively selected sites.
Secondary Structure of OMP-P1
Following the localization of positively selected codons in the primary sequence, we determined the higher-order structure of amino acid stretches in OMP-P1 using TMHMM version 2.0 (Krogh et al. 2001) to determine whether stretches with positively selected codons take secondary structures typical of transmembrane domains. The exposition of regions of OMP-P1 was assessed by means of the solvent accessibility algorithm as implemented in Jpred (Cuff et al. 1998). We identified exposed residues using the secondary structure algorithm implemented in SSPRO 4.5 (Cheng et al. 2005) and Jnet5 (Cuff and Barton 1999). These attributes may suggest whether regions with positively selected codons are exposed to the immune system.
Tertiary Structure of OMP-P1
We examined the 3D structure of OMP-P1 using x-ray crystallography or NMR data on structurally related proteins. We used the precompiled Vector Alignment Search Tool (VAST) and NCBI’s structural database to identify closely related structural variants that could serve as a template for homology modeling of OMP-P1. The entries in VAST contain experimentally verified information on secondary structure elements such as type, relative orientation, and connectivity. Structurally related proteins can be fitted to each other on a residue-by-residue basis using VAST’s transformation matrices and a Gibbs sampling algorithm. In addition, alternative alignments can be generated which take into account the information in secondary structure, thereby increasing the confidence one can have in amino acid alignments of relatively divergent proteins. The VAST program was used for one randomly chosen H. influenzae OMP-P1 sequence (GenBank accession no. 9716616 of the nontypeable strain 1512A). Superimposed models of template and target protein were constructed to illustrate the position of gaps and insertions, hypervariable domains, and positively selected codons.
As an alternative to the identification of structural neighbors as implemented in VAST, homology modeling can be used. This always starts by looking for close relatives using standard PSI-BLAST or other similarity searches. We used homology modeling as implemented in GENO3D (Combet et al. 2002) and SWISS-MODEL (Peitsch 1996; Schwede et al. 2003) to assess the congruence with the VAST searches and to collect additional data on liability of portions of the 3D models of OMP-P1 and its structurally closest neighbors. The 3D models of OMP-P1 were visualized using Cn3d version 4.1, a free 3D model viewing and selection program. To judge the quality of the 3D reconstruction, we collected the percentage identity between template and target, the VAST P-value, the score reported by this program, and the squared root of mean square deviations (RMSD) of the backbone atoms between the template and the target proteins in angstroms (Å) using a sliding window approach. To assess whether hypervariable domains were robustly assigned to their exact coordinates in the 3D structures, we also examined the reports of loop reconstructions by the CSP (Constrained Space Programming) routine as implemented in Swiss-Model (Schwede et al. 2003).
To further quantify the degree of similarity in β-strands between OMP-P1 and a structural template, we compared the distribution of amino acids maintaining β-barrels of transmembrane proteins using the TBB-PRED program (Liu et al. 2003), which uses atomic data to assign the most likely amino acid stretches that contribute to β-barrels. We used the Support Vector Machines (SVM) algorithm to determine the location of β-barrels.
Results
Alignment of OMP-P1 Sequences
BLASTP searches used cutoffs of 0 (E-value) and 727 (score) to identify closely related OMP-P1 sequences. Thirty-six hits were obtained, including 33 protein-coding sequences (27 nontypeable, 1 a, 3 b, 1 e, and 1 f) that have previously been analyzed as part of a population genetics study of OMP-P1 (Bolduc et al. 2000; six sequences were left out because they were not among the top hits of the BLASTP search). The average pairwise amino acid similarity in the entire data set was 94%. The sequences of the nontypeable and typeable OMP-P1 protein variants comprised three hypervariable portions with many insertions and deletions (nucleotides 262–282, 610–621, plus 625–633 and 1288–1302 plus 1309–1317) and a few codons with ambiguity codes or gaps (646–651, 730–732, 850–852, 877–879, and 1360–1362). In addition, 22 codons missed from the 5’ end of the OMP-P1 gene of isolate 88591, leading to a total of 50 codons that were deleted to ensure a correct alignment. The amino acid and the nucleotide alignments of the five most variable domains that remained after removal of the codons mentioned above can be viewed as Supplementary Information. The nucleotide alignment revealed that many of the underlying codons in the variable domain frequently shared nucleotides at two of three positions, indicating that the alignment of these regions was correct.
Identification of Positively Selected Codons
The computational approaches implemented in PAML version 3.14 (Yang 1997) determine which, if any, codons may have evolved under positive selection. On the basis of a single phylogenetic estimate using the preferred nucleotide substitution model derived from MODELTEST (the transversion model TVM, which has variable base frequencies, variable transversion rates, and equal transition rates) with a proportion of invariant sites (0.66) and gamma distributed substitution rates (shape parameter, 0.51), we found 14 ML trees, one of which is shown in Fig. 1 (ML –5104.27). Bootstrapping indicated that four branches received 100% support (Fig. 1). Support for the other branches was <95% (not shown in Fig. 1). Comparison of neutral and selection models using the OMP-P1 data set and the tree in Fig. 1 indicated that both selection models M2 (likelihood, –5169.38) and M8 (–5177.30) fitted the data significantly better than the neutral models M1 (–5260.86) and M7 (–5270.13), respectively (Table 1). Different starting values of ω did not lead to different parameter estimates after optimization (not shown). Both comparisons M1–M2 and M7–M8 exceeded by far the difference required for significance at the 1% level (see Materials and Methods). This suggested that some codons had a higher nonsynonymous than synonymous substitution rate. Similarly, the Swanson test indicated significant evidence for the occurrence of positive selection under M8 (Table 1). Both selection models M2 and M8 suggested that the same codons were under positive selection, i.e., codons 93, 94, 97, 99, 105, 198, 222, 284, and 329, and possibly also codon 96 (under model M8). The OMP-P1 alignment (Supplementary Information) further showed that in this sample of OMP-P1 sequences, positively selected codons mostly flank hypervariable domains. Limitation of the analyses to the 30 nontypeable strains resulted in very similar findings. In these analyses, both M2 (likelihood, –4802.93) and M8 (–4808.87) fitted the data significantly better than M1 (–4883.86) and M7 (–4895.19), respectively (not shown). Thus, both model comparisons were again significant. This held also true for the positively selected codons. For M2, relative to the analyses in Table 1, one additional positively selected codon (225) was found. For M8, no additional codons were found. After deletion of the five variable domains, no evidence for positive selection was left (M1, –3107.88; M2, –3107.77; M7, –3108.18; M8, –3106.35).
Fig. 1.Maximum likelihood tree based on the TVM model of nucleotide substitution with a proportion of invariant sites and rate heterogeneity (see text). The error bar indicates the number of substitutions per nucleotide. Asterisks indicate branches with 100% bootstrap support.Table 1.Positively selected codons in OMP P1 Heamophilus influenza according to neutral models (M1 and M7) and selection models (M2 and M8)LikelihoodTree lengthKappa κParametersCodonAmino acidProbability positive selection (BEB)dN/dS codon ± SEM1−5260.861.911.90ω0 = 0.02, ω1 = 1, p0 = 0.84, p1 = 0.16— —M2−5169.382.172.13ω0 = 0.02, ω1 = 1, ω2 = 6.42, p0 = 0.83, p1 = 0.14, p2 = 0.0393A1.006.523 ± −0.83394S1.006.523 ± 0.83396K0.573.979 ± 2.63297I1.006.523 ± 0.83399R1.006.523 ± 0.834105Q1.006.523 ± 0.833198A1.006.522 ± 0.835222K1.006.523 ± 0.834225T0.795.293 ± 2.349284K1.006.519 ± 0.844329H1.006.493 ± 0.919M7−5270.132.071.91B(p = 2.14, q = 0.04)——M8A−5260.961.921.91B(p = 2.14, q = 99), p0 = 0.84, p1 = 0.16, ω2 = 1M8−5177.302.172.12B(p = 0.07, q = 0.38), p0 = 0.97, p1 = 0.38, ω2 = 5.7993A1.005.445 ± 0.58594S1.005.445 ± 0.58595V0.844.661 ± 1.72696K0.955.214 ± 1.09997I1.005.445 ± 0.58599R0.744.191 ±2.015100N1.005.445 ± 0.585105Q1.005.445 ± 0.585151I0.904.929 ± 1.480198A1.005.445 ± 0.585222K1.005.445 ± 0.585225T0.965.238 ±1.098284K1.005.445 ± 0.586329H1.005.442 ± 0.596371Y0.764.279 ± 2.030The likelihood indicates the fit of the data to the models of Yang (1997). Tree length is measured as the number of mutations per codon. Kappa is the transversion – transition ratio. pi denotes the proportion of sites falling in site class ωi. The parameters p and q are the shape parameters of the beta distribution which underlies M8. The probability that codons were under positive selection was determined using Bayes Empirical Bayes (Yang et al. 2005), with the proportion, ω, and standard error indicated per codon. Model 8A refers to the model of Swanson et al. (2003), in which ω2 is fixed at one under M8. The reference sequence is 9716616.
The modified Suzuki-Gojobori method indicated that four sites evolved under positive selection (substitution model 012010; codons 93 [P= 0.01], 94 [P= 0.01], 97 [P= 0.03], and 105 [P= 0.03]; likelihood, –5548.10; tree length per site, 0.59, dN/dS over all codons = 0.32; middle row in Fig. 2). Using the SLR method, we found eight positively selected codons. Relative to the results of PAML (top row in Fig. 2), all but one positively selected codon found by the SLR method remained significant upon correction for multiple tests (except for codon 329, P= 0.14; bottom row in Fig. 2 and Table 1). Overall, the strongest evidence for positive selection based on the parsimony and ML methods was found for residues 93, 94, 97, and 105.
Fig. 2.Codons under positive selection as inferred from the site models in PAML (top panel), the modified Suzuki-Gojobori method implemented as the SLAC procedure in the datamonkey server (middle panel), and the sitewise likelihood ratio test (bottom panel) of 36 OMP-P1 sequences of H. influenzae. Codons with significant evidence for positive selection have probabilities >0.95 (PAML and SLR methods) or <0.05 (modified Suzuki-Gojobori method). The spacing between codons is adjusted for the insertions and deletions in OMP-P1.
Topographic Localization of the Amino Acids Encoded by Putatively Positively Selected Codons and Their Relation to Immunological Data of OMP-P1
A priori, positively selected codons in B-cell and T-helper determinants in the OMP-P1 protein are suitable vaccination targets, because these sites can be expected to be highly immunogenic and involved in the clearance of OMP-P1 variants. The most promising broadly protective regions of OMP-P1 based on epitope mapping studies are mainly located outside the stretch of closely spaced positively selected codons (Fig. 3). Peptide-specific antisera directed against these regions did not invoke protective immunity, although strong IgG and T-cell responses were obtained (Proulx et al. 1991; Chong et al. 1995). Only in rare cases was targeting of the conserved regions associated with protection (one epitope located on peptide HIBP1-4 and recognized by MAb 7C8 was protective [Panezutti et al. 1993]). Furthermore, two of five positively selected sites are present in a synthetic P1 peptide (HIBP1-12) that has been used in immunization studies (Proulx et al. 1992; Panezutti et al. 1993; Chong et al. 1995). The peptide, which was not identified as a conserved B-cell epitope across typeable and nontypeable H. influenzae isolates, did not carry an immunodominant epitope in animal models. Another peptide that was used for epitope mapping and immunological assays (Proulx et al. 1992; Chong et al. 1995) fully overlaps with the stretch with positively selected amino acids. This HIBP1-2 (Chong et al. 1995) or 2H (Proulx et al. 1992) peptide covers amino acids 60–88 of 1H in H. influenzae serotype b strain MinnA (Munson and Grass 1988), which correspond to residues 82–108 in the reference sequence 9716616. However, anti-P1 antisera isolated from rabbits, guinea pigs, and humans did not react with HIBP1-2 (Chong et al. 1995), and vice versa, anti-HIBP1-2 specific sera did not recognize the intact P1 protein. Despite these negative immunological data, our computational analysis suggests that the stretches with positively selected codons are likely under immune selection in vivo. It may be worthwhile to study these stretches in more detail at the immunological level.
Fig. 3.Diagram of different immunological and genetic attributes of OMP-P1 of Haemophilus influenzae. From top to bottom: linear B-cell epitopes in different hosts (murine, guinea pig, and rabbit), B-cell epitopes conserved among H. influenzae strains, murine T-helper determinants, positively selected codons, distribution of amino acids with a potentially extended (black) or a helical configuration (gray; Jpred) typical of α-helices and β-sheets, respectively, and distribution of amino acids with <5% solvent accessibility (Jnet5), location of hypervariable domains, and location of 13 synthetic peptides against OMP-P1, which were used for immunological studies (Proulx et al. 1992; Chong et al. 1995). The approximate locations of peptides HIBP1-2 and HIBP1-12 are boxed and dotted, respectively (see text).
Five of the nine codons for which significant evidence for positive selection was obtained in both PAML selection models M2 and M8 cluster tightly in the primary sequence of OMP-P1 (amino acids 93–105) and four of these are confirmed as positively selected sites using the other types of analyses of positive selection (Fig. 2). Chong et al. (1995) found that some of these codons were part of a transmembrane domain based on secondary structure analysis, hydrophobicity, and reactivity with monoclonal antibodies. The nature of the variability of the first hypervariable domain has also been questioned on the basis of a lack of hydrophilic residues (Munson et al. 1992). TMHMM version 2.0 was used to determine whether stretches with positively selected codons involved a transmembrane domain or adopted secondary structures typical of such domains (Krogh et al. 2001). No secondary structures such as α-helices and β-sheets typically associated with transmembrane domains were found in this stretch (not shown). Investigation of the exposition of this region using the solvent accessibility algorithm as implemented in Jpred (Cuff et al. 1998) classified this portion of OMP-P1 as an exposed stretch with high solvent accessibility (Fig. 3), which exceeded 25% for the entire stretch with the five positively selected codons. Thus, the positively selected codons are likely to be exposed. The above findings deviate from previous suggestions that these amino acids are part of a putative transmembrane domain (Chong et al. 1995), which would render this stretch unsuitable for vaccine purposes.
Localization of the Amino Acids Encoded by Putative Positively Selected Codons as Assessed by 3D Modeling
The apparent ambiguity about the putative transmembrane domain of OMP-P1 led us to examine the 3D topology of the protein. BLASTP searches identified one 3D structure with high similarity to OMP-P1 of H. influenzae; the FadL outer membrane protein of E. coli (39% similar in primary amino acid sequence). This protein has been analyzed at a resolution of 2.60 and 2.80 Å using two x-ray crystallography methods (van den Berg et al. 2004). Apart from its role in the transport of long-chain fatty acids across the outer membrane, FadL is also a receptor for the bacteriophage T2. OMP-P1 and FadL are both classified in PFAM03349.10, which also includes TodX from Pseudomonas putida F1 and TbuX from Ralstonia pickettii PKO1. The latter are membrane proteins of uncertain function that are involved in toluene catabolism. The VAST database identified the same structural neighbors (1T1A-B, 1T16A-B) as the BLASTP search and the homology modeling programs GENO3D and SWISS-MODEL (not shown).
There are many indications that the reconstructed model of OMP-P1 (Fig. 4) is reliable. The percentage identity of the OMP-P1 sequences to the target based on secondary elements was approximately 43%, which, in principle, may allow confident 3D models as evidenced by the lower boundary for which homology modeling is used (25%; http://www.expasy.org/swissmod/SWISS-MODEL.html). Although VAST provides no easily interpretable confidence parameters (the score of this structure pair was 48.5 using an alignment length of 391 residues and the VAST P-value was 10−43), the level of similarity and the root-mean-square deviation (RSMD) of the fit across OMP-P1 and 1T16A indicate a high degree of structural similarity (RMSD < 3 Å; Fig. 5) (T. Madej, NCBI, personal communication). A large number of secondary structures such as helices, strands, and loops are shared between OMP-P1 and FadL as evident from VAST searches (Figs. 4A and B). This interpretation is supported by the congruence among models of secondary structure to assign coiled regions that are exposed as determined through the use of SSPRO version 4.5 (not shown) and by the agreement of regions with a β-barrel structure between FadL and OMP-P1 as judged from the results of the TBB-PRED server based on atomic data of β-barrel proteins (Fig. 5). Clearly, the fit of the overall structure of the reconstructed 3D model of OMP-P1 does not imply that the model is stable and robust for every region of the protein. The hypervariable domains introduce many differences in exposed portions of the 3D models between model and target in the superimposed models (Fig. 4, bottom row). Further, it is important to note that only a single OMP-P1 sequence was used to model the 3D structure based on FadL. Slightly different loops may be obtained when using different OMP-P1 sequences. Not surprisingly, the CSP algorithm implemented in SWISS-MODEL had considerable difficulties in finding and optimally position loops (not shown). Many of the loops examined by the program could not be confidently reconstructed, and in many cases, the accepted loops (by the Spare Part procedure) were found only after shorter ones had not been accepted. These regions in many instances corresponded to hypervariable and looping regions (Fig. 4A) that had much higher RMSD values than other regions of the OMP-P1 molecule. However, as judged from the secondary structure analyses these regions likely do not contribute much to the overall stability of the β-barrel structure of OMP-P1 (Fig. 5).
Fig. 4.Three-dimensional model of OMP-P1 of H. influenzae (A), FadL of E. coli (B), and superimposed models of both proteins (C). Brown arrows and threads mark strands, yellow threads mark regions with positively selected codons, green regions mark helical regions, and blue regions mark coiled regions. E, extracellular side; M, membrane; P, periplasm. In the superimposed models, a splitting of the two models indicates gaps or insertions.Fig. 5.Alignment and structural analysis of OMP P1 9716616 of H. influenzae and FadL of E. coli based on the VAST 3D structural database. The deviation (Å) of the 3D model of OMP P1 relative to the template FadL is shown for windows of five amino acids (above alignment). The contribution of individual residues to the β-barrel structure of OMP P1 as analyzed with TBB-PRED is marked by “b” (barrel) and “n” (nonbarrel). Asterisks above the alignment mark conserved amino acid positions; asterisks below the alignment mark positively selected amino acid positions. The alignment of OMP P1 9716616 starts at residue 23.
The most relevant conclusion of the 3D reconstruction was that the regions that comprised positively selected codons (yellow in Fig. 4) were always on the extracellular side of the membrane, indicating that the positively selected codons are potential candidates for vaccine development. The positions of insertions and deletions between FadL and OMP-P1 that are longer than a few amino acids occurred—in agreement with expectation—mostly in these exposed regions of OMP-P1. Looping regions with insertions and deletions are indicated by the splitting of the two molecules in superimposed 3D models (Fig. 4C). Overall, the superimposed 3D models agree with the models based on individual 3D structures in that, as expected for a β-barrel protein, particularly the main chain hydrogen-bonding patterns characteristic of strands contribute to the structural maintenance of OMP-P1.
Discussion
Positive Selection on OMP-P1
Identification of positive selection on OMPs is generally hampered by extremely high levels of sequence divergence and the frequent occurrence of insertions and deletions in hot spots. These domains are, however, potentially interesting for vaccine studies, as they may be the targets of immune selection (Smith et al. 1995). To enable identification of positively selected codons, we focused on codons directly adjacent to hypervariable and unalignable regions (cf. Supplementary Information). The hypervariable regions were excluded, as it is simply not possible to identify homologous nucleotide positions for these domains. Our strategy to detect positively selected codons is still feasible, as the inference of positive selection is not very sensitive to the use of slightly different trees resulting from, for example, the deletion of hypervariable codons, if sufficient phylogenetic signal remains (Derrick et al. 1999). Our results indeed indicate that after deletion of the hypervariable regions, a substantial number of substitutions remains for OMP-P1 (see Supplementary Information), which allows the identification of positively selected codons.
Another important issue to consider in this type of study is how recombination affects the inferences of positive selection. Recombination generates different tree topologies when based on different regions of a gene, and it has been shown that—depending on the level of recombination—the LRT may be affected (Anisimova et al. 2003). In general, recombination is considered most problematic for analysis of selection pressures using divergent sequence data (cf. viral sequence data [Anisimova and Yang 2004]), but it has generally been difficult to determine its quantitative importance. Simulation studies provide information on the circumstances in which recombination may affect evolutionary analysis of selection pressures. For example, in spite of the congruent results between M2 and M8 in this study, these models are not equally affected by recombination (Anisimova et al. 2003). In addition, the Bayesian analysis that was used to identify positively selected residues is less affected by the presence of recombination than the LRTs, presumably because it does not depend strongly on the entire gene tree topology but, instead, reconstructs the numbers of synonymous and nonsynonymous mutations of individual codons. Finally, the proportion of correctly classified sites for the BEB procedure in simulations under positive selection and recombination increases with selection pressure (Anisimova et al. 2003). The higher ω values of positively selected codons in this study (Table 1) relative to those of the simulation study mentioned above (ω2 = 2.55) and the large number of positively selected sites in OMP-P1 suggest that the inference of positive selection and sites under positive selection is valid.
Use and Value of 3D Reconstruction of OMP-P1
The alternation of relatively conserved and hypervariable stretches in OMP-P1 and other OMPs also impacts the 3D reconstruction. Hypervariable amino acid stretches may render a 3D structure unstable, despite the fact that more conserved parts may share considerable similarity between target and template. Thus, although the level of congruence between the experimental 3D structure of FadL and the computational 3D structure of OMP-P1 may vary considerably, the most probable and approximate location of stretches with positively selected codons can be determined if the conserved portions of OMP-P1 can be confidently reconstructed. With this goal in mind, the identification of β-barrel regions and the degree of fit of stranded regions between model and template are most important. These attributes leave little doubt that the fit between FadL and OMP-P1 is sufficient to pinpoint the approximate localization of positively selected codons (Fig. 5). This is not surprising, as more divergent structural templates and targets such as OmpF and PhoE of E. coli have successfully been used for the reconstruction of 3D models of porins in Neisseria (Derrick et al. 1999). In sum, there is every reason to believe that the basic barrel structure is not profoundly affected by hypervariable loops, even if these experience frequent insertions and deletions that are potentially also under positive selection (Smith et al. 1995; Derrick et al. 1999).
Are Positively Selected Sites Useful for Vaccine Formulations?
The question arises why the peptides that partially (HIBP1-12) and fully (HIBP1-2) overlapped with the stretch of positively selected sites did not include immunodominant epitopes (Proulx et al. 1992; Panezutti et al. 1993; Chong et al. 1995). The stretches, however, were very long (29 and 27 amino acids, respectively) and they extended well into the interior of OMP-P1 (not shown). The potentially most interesting peptide (HIBP1-2) comprises the entire hypervariable domain of the Eagan strain (Chong et al. 1995), including residue 105 (SASQRNV is the C-terminus; Supplementary Information). Our analyses suggest that relatively short peptides starting at the C-terminal end of the first hypervariable domain would be suited for immunological experiments. This portion of OMP-P1 has not yet been explored.
Interestingly, one of the positively selected residues in OMP-P1 that flank the first hypervariable domain allows examination of the impact and prospect of positively selected residues for vaccine formulation in a considerable fraction of the H. influenzae population. The positively selected codons 93, 94, and 97 are flanked on one side by the first hypervariable region and are separated from codon 105 by a conserved amino acid stretch (see Supplementary Information). The latter residue, in turn, is flanked by a long region at the C-terminal side that is conserved in the collection of H. influenzae strains examined by Bolduc et al. (2000). In our study sample, only three amino acids are encoded by residue 105, and this number increases only marginally in the larger set of H. influenzae isolates (Bolduc et al. 2000). Consequently, judging from the spacing of positively selected and conserved residues, there are opportunities to combine sites under purifying and positive selection in immunological experiments of OMP-P1. If it can be demonstrated by, for example, epitope mapping that these positively selected and exposed residues of OMP-P1 are located in immunologically relevant domains, a large portion of the H. influenzae population can be targeted by short peptides from this region of OMP-P1. Relative to traditional approaches to vaccine development, this strategy may allow a much broader coverage of the H. influenzae population.
Another question is to what extent the isolates studied here reflect the evolutionary diversity of H. influenzae. The isolates analyzed by Bolduc et al. (2000) were selected for OMP-P1 sequencing, because they were phylogenetically diverse in a collection of more than 500 H. influenzae isolates. Given the increase in multilocus sequence typing (MLST) of human pathogenic bacteria, it is also of interest to compare the diversity of the strains of Bolduc et al. (2000) with the phylogenetic relationships based on housekeeping genes in the MLST database of H. influenzae. In the case of H. influenzae, seven housekeeping genes are widely used to characterize phylogenetic relationships. In a housekeeping tree of 131 isolates (Meats et al. 2003), most of the nontypeable isolates were comprised of two highly divergent clusters, and in contrast to the housekeeping genes of encapsulated isolates, the congruence among trees based on single housekeeping genes of nontypeable isolates was only marginal. The current view of H. influenzae evolution is that nontypeable isolates are genetically distinct from encapsulated variants, and that nontypeable isolates form distinct and diverse populations. Among the five strains that were used by both Bolduc et al. (2000) and Meats et al. (2003), the shared isolates (B-RM7109, d-Rd, a-7416, nt-667, and e-6181) were scattered over the MLST tree. Apart from issues of sampling, the congruence of gene tree topologies based on different housekeeping genes and OMP-P1 hinges on the level of recombination between genes. It is currently unknown how important recombination is in H. influenzae. Although information on the clinical symptoms caused by the isolates with OMP-P1 sequences is extremely limited, strains 667 (9716582), BCH-1 (9716566), BCH-2 (9716610), and BCH-3 (9716568) caused middle ear infection, pneumonia, and nasopharyngeal infection in children. In spite of the limited amount of information on the nontypeable isolates with OMP-P1 sequences, the intermingling of nontypeable and typeable isolates on the housekeeping tree and the clinical symptoms of some of the nontypeable isolates suggest that, in principle, the OMP-P1 diversity studied here might be a suited starting point for vaccine development with the potential of providing broad coverage.
Recent technological progress in vaccinology offers hope that epitopes based on moderately rapidly evolving portions of OMPs may turn out to be realistic vaccine material. Although it cannot be excluded that some epitopes will not be stable or immunogenic, or may not constitute stable constructs, extracellular loops in OMPs have been demonstrated to be immunodominant (Easton et al. 2005). Also, the inclusion of multiple OMPs in vaccine formulations is possible by using outer membrane vesicles or whole-cell vaccines based on recombinant bacteria (de Jonge et al. 2004) and the delivery of antigens based on OMPs of nontypeable H. influenzae based on fusion proteins (Riedmann et al. 2003) offer opportunities to target interesting vaccine candidate regions of OMPs. Finally, whole-cell vaccines of LPS mutants with strongly reduced toxicity are increasingly suited for vaccination purposes (Fisseha et al. 2005), thereby reducing the adverse effects of immune responses against these types of vaccines.
Impact of Computational Analyses on Vaccine Development
Positive selection on codons of exposed outer membrane proteins of pathogenic viruses and bacteria is common (Urwin et al. 2002; Yang et al. Yang 2003; Fitzpatrick and McInerney 2005). The lack of correspondence between interesting sites in OMP-P1 of H. influenzae in immunological and genetic diversity studies highlights the difficulty of finding the most promising epitopes, even among closely related sequences. The a priori identification of positively selected codons will normally greatly reduce the number of peptides that need to be screened using epitope mapping or immunization. Because the incorporation of antigenic diversity in the experimental setup of immunological and protection studies is a major bottleneck in terms of time and costs, the identification of a limited number of positively selected codons suggests that a multidisciplinary approach comprising evolutionary and structural data and tools can greatly improve the focus of ensuing empirical studies.
Electronic Supplementary Material
Supplementary material

vaccine
haemophilus influenzae
epitope
immune selection
outer membrane proteini
structural information


Perimembrane Aurora-A Expression is a Significant Prognostic Factor in Correlation with Proliferative Activity in Non-Small-Cell Lung Cancer (NSCLC)

Purpose Aurora-A, also known as STK15/BTAK, is a member of the protein serine/threonine kinase family, and experimental studies have revealed that Aurora-A plays critical roles in cell mitosis and in carcinogenesis. However, no clinical studies on Aurora-A expression in non-small-cell lung cancer (NSCLC) have been reported. Thus, the present study was conducted to assess the clinical significance of Aurora-A status.
Primary lung cancer is the leading cause of cancer deaths in most industrialized countries, and non-small-cell lung cancer (NSCLC) accounts for 75–80% of primary lung cancer. Surgery is the most effective therapeutic modality for the cure, but the postoperative prognosis remains poor.1–3 To improve the prognosis, establishment of biological markers other than tumor-node-metastasis (TNM) factors that determine prognosis and response to a particular treatment is essential. Although many possible biological markers such as mutations of p53 and k-ras genes have been reported, none of them has been established as a marker in decision-making of the treatment of NSCLC. In addition, most NSCLC patients present with advanced stages and are appropriate candidates for surgery. For inoperable patients, chemotherapy with or without radiotherapy may be given. Recently, tyrosine kinase inhibitors targeting epidermal growth factor receptor (EGFR), gefitinib and erlotinib, have been introduced into the therapy of NSCLC, and experimental and clinical studies have revealed that these agents induce dramatic antitumor effects for tumors with activating mutations within the EGFR kinase domain.4,5 Thus, other molecular abnormalities in NSCLC should be revealed in the development of useful prognostic markers and in the development of effective molecular targeting agents.
Aurora-A, also known as BTAK, STK15, Aurora-2, ARKI, or AIKI, is a member of the serine/threonine kinase family. As Aurora-A is involved in proper centrosome functions and chromosome segregation during normal cell mitosis,6–8 its abnormalities such as overexpression as well as gene amplification may contribute to development and progression of malignant tumors.9–11 In clinical studies, Aurora-A abnormalities have been reported in a variety of malignant tumor including glioma,12 medullobrastoma,13 gastric cancer,14 esophageal cancer,15,16 pancreatic cancer,17 ovarian cancer,18 breast cancer,19,20 bladder cancer,21 and hepatocellular carcinoma.22 In all these clinical studies, Aurora-A gene was amplified or Aurora-A expression was upregulated in tumor tissues compared with normal tissues, which also support experimental results suggesting that Aurora-A abnormalities are involved in carcinogenesis. In addition, many studies showed that Aurora-A abnormalities were positively correlated with aggressive tumor behavior such as poorly differentiated tumor grade, invasion, and nodal metastasis,15,16,19,20–22 but some studies showed no correlation12,17 or an inverse correlation.18 Some studies also assessed a prognostic significance of Aurora-A status; most studies showed that Aurora-A abnormalities were correlated with a significant poor prognosis,13,16,21,22 but one study failed to show.20 These results may suggest that clinical significant of Aurora-A status in malignant tumors remains controversial. Moreover, no clinical study on Aurora-A status in NSCLC has been reported. Thus, we conducted a clinical study on Aurora-A expression in resected NSCLC and assessed its clinical significance in correlation with other biomarkers including proliferative activity.
Patients and Methods
Patients
We retrospectively reviewed a total of 191 consecutive patients with pathologic (p)-stage I–IIIA NSCLC who underwent complete tumor resection and nodal dissection without any preoperative therapy at the Department of Thoracic Surgery, Kyoto University, between January 1, 1985 and December 31, 1990. The p-stage was reevaluated and determined according to the current tumor-node-metastasis (TNM) classification as revised in 1997.1 Histological type was also redetermined according to the classification by the World Health Organization (WHO) as revised in 2004. Two patients who experienced operation-related death were excluded from the study. Thus, a total of 189 patients were finally evaluated in the present study. For all these patients, the inpatient medical records, chest x-ray films, whole-body computed tomography (CT) films, bone and gallium scanning data, and records of surgery were reviewed without knowledge of the results of immunohistochemical staining (IHS) or the terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end-labeling (TUNEL) staining.
Details of postoperative adjuvant therapy were described in previous studies.23,24 In brief, cisplatin-based chemotherapy, radiation, and oral administration of tegafur (a fluorouracil derivative drug) were prescribed postoperatively for 47, 30, and 49 patients, respectively. Intraoperative therapy was not performed on any patient.
The day of thoracotomy was considered the starting day for counting postoperative survival days. The median and mean follow-up periods were 1697 and 1983 days, respectively (range, 50–6381 days). This study has been approved by the Ethics Committee of Faculty of Medicine, Kyoto University.
Tissue Preparation
All tumor specimens were fixed immediately in 10 vol% formalin, and then embedded in paraffin. Serial 4-μm sections were prepared from each sample and used for routine hematoxylin and eosin (HE) staining, the TUNEL staining to detect apoptotic cells, and IHS to determine Aurora-A expression status, p53 status, intratumoral microvessel density (IMVD), and proliferative index (PI).
Immunohistochemistry
Aurora-A expression was evaluated using a standard streptavidin-biotinylated horseradish peroxidase detection system. Dewaxed sections were incubated overnight with a rabbit polyclonal antibody (KR051, Transgenic Co. Ltd., Kumamoto, Japan; it had been produced and its reliability had been confirmed by western blotting on a commercial basis) diluted at 1:100. Diaminobenzidine-tetrahydrochloride (0.03%) containing 0.1% hydrogen peroxide was used as a chromogen, and sections were counterstained with hematoxylin. For negative control slides, rabbit immunoglobulin G was used instead of the primary antibody. Colon cancer sections that are known to express Aurora-A were used as positive control slides.
Aurora-A expression on tumor cells was first classified into negative or positive expression; a slide was judged to be Aurora-A positive expression, regardless of the staining intensity. For slides with positive Aurora-A expression, the staining pattern was, then, classified as follows: diffuse cytoplasmic staining (Fig. 1B), perimembrane staining (Fig. 1C). Slides were evaluated by two pathologists (E.O. and Y.T.) independently, without knowledge of any patient characteristic.
Fig. 1.Aurora-A expression in non-small-cell carcinoma (NSCLC) as detected with imuunohistochemical staining. A Negative staining. B diffuse cytoplasmic staining. C Peri-membrane staining.
PI, IMVD, and p53 status were also determined with IHS as described previously.23,25 Briefly, an anti-PCNA monoclonal antibody (PC-10, mouse immunogloblin G [IgG] 2a, kappa; DAKO Japan, Kyoto, Japan) diluted at 1:50 was used as the primary antibody to determine PI;23 PI was defined as the percentage of PCNA-positive staining tumor cells. IMVD was determined with an anti-CD34 monoclonal antibody QBEnd10 (mouse IgG1, kappa; DAKO Japan) diluted at 1:50; the 10 most vascular areas within a section were selected for evaluation of angiogenesis, and the average counts of CD34-positive vessels were recorded as IMVD in each case.25 An antihuman p53 monoclonal antibody DO-7 (mouse IgG2b, kappa, 250 μg/mL, DAKO Japan) was used to determine p53 status. When the percentage of positive-staining cells exceeded 5%, the slide was judged to exhibit aberrant expression of p53.23
Detection of Apoptosis
The TUNEL staining was performed using the In Situ Death Detection Kit, POD (Boehringer Manheim, Manheim, Germany) following the manufacture’s protocol as described previously.23 The specificity of the TUNEL staining of apoptotic cells was confirmed by making the negative and the positive control slides at every staining. As negative control slides, sections incubated with the TUNEL reaction mixture without TdT were used. Sections treated with DNase I (Stratagene, La Jolla, CA) before the TUNEL reaction were used as positive control slides. Apoptotic cells were determined with careful observation of TUNEL-staining sections and serial HE-staining sections, and TUNEL-positive staining cells, if they represented the histological features of necrosis in HE-staining sections, were not considered to be apoptotic cells. In each case, a total of 10,000 tumor cells, consisting of 1000 tumor cells each in 10 different fields, were evaluated at high magnification (400×). The apoptotic index (AI) was defined as the number of apoptotic cells per 1000 tumor cells.
Statistical Analysis
Counts were compared by the chi-square test. Continuous data were compared using the t test if the distribution of the samples was normal, or using the Mann-Whitney U test if the sample distribution was asymmetrical. The postoperative survival rate was analyzed by the Kaplan-Meier method, and differences in the survival rates were assessed by the log-rank test. Death from any cause was included in calculation of postoperative survival. Multivariate analysis of the prognostic factors was performed using Cox’s proportional hazard model. Differences were considered significant when the P value was less than .05. All statistical manipulations were performed using the SPSS ver.10 for the Windows software system (SPSS Inc., Chicago, IL).
Results
Expression of Aurora-A in NSCLC
Expression of Aurora-A was not observed in normal adjacent lung tissues in any patient. Expression of Aurora-A in tumor cells was negative in 31 patients (16.4%) and positive in the other158 patients. Among Aurora-A positive patients, 124 patients showed diffuse cytoplasmic staining, 6 patients showed pure perimembrane staining, and 28 patients showed both diffuse cytoplasmic staining and perimembrane staining (mixed staining). Because the number of patients showing pure perimembrane staining was very small (n = 6), both patients with pure perimembrane staining and patients with mixed staining were classified into perimembrane patients. Thus, there were 31 negative patients (16.4%), 124 diffuse cytoplasmic patients (65.6%), and 34 perimembrane patients (18.0%) (Table 1).
Table 1.Characteristics of patients and Aurora expressionNo. of patients (%)MAGE-D4 expressionP valueNegativePositiveDiffuse cytoplasmicPerimembraneAll patients189 (100)31 (16.4%)124 (65.6%)34 (18.0%)Age (mean ± SD) 62.4 ± 9.660.5 ± 10.262.3 ± 9.264.3 ± 9.10.256Sex.137    Male133 (70.4)26 (19.5%)86 (64.7%)21 (15.8%)    Female56 (29.6)5 (8.9%)38 (67.9%)13 (23.2%)Performance status (PS).353    0164 (86.8)28 (17.1%)109 (66.5%)27 (16.5%)    1–225 (13.2)3 (12.0%)15 (60.0%)7 (28.0%)Histologic type.302 (Sq vs Ad)    Squamous cell (Sq)64 (33.9)8 (12.5%)41 (64.1%)15 (23.4%)    Adeno (Ad)108 (57.1)19 (17.6%)73 (67.6%)16 (14.8%)    Large cell (La)10 (5.3)4 (40.0%)4 (40.0%)2 (20.0%)    Others7 (3.7)0 (0.0%)5 (85.7%)1 (14.3%)Tumor differentiation*.393    Well68 (37.4)9 (13.2%)49 (72.1%)10 (14.7%)    Moderately67 (36.8)13 (19.4%)43 (64.2%)11 (16.4%)    Poorly47 (25.8)9 (19.1%)26 (55.3%)12 (25.5%)Pathologic (p-) stage.007 (for all p-stage)    I104 (55.0)12 (11.5%)80 (76.9%)12 (11.5%)    II22 (11.6)4 (18.2%)11 (50.0%)7 (31.8%)    IIIA63 (33.3)15 (23.8%)33 (52.4%)15 (23.8%)* Other histologic types were excluded in the analyses.
The incidence of perimembrane staining was significantly higher in higher p-stages; perimembrane staining was seen in only 11.5% (12 of 104) of p-stage I patients, but seen in as high as 25.8% (22 of 85) of p-stage II–IIIA patients (Table 1). There was no significant correlation between Aurora-A expression status and any other patients’ characteristic (Table 1).
The mean PI for perimembrane Aurora-A expression patients high tumor was significantly higher than that for Aurora-A negative or that for diffuse cytoplasmic Aurora-A expression patients (PIs: 63.5, 49.2, and 41.7, respectively; P < .001) (Table 2). There was no significant difference in the mean IMVD or in the mean AI according to the status of Aurora-A expression; there was no significant correlation between Aurora-A expression status and p53 status (Table 2).
Table 2.Correlation between Aurora expression and other biomarkersAurora expressionP valueNegativePositiveDiffuse cytoplasmicPerimembraneIntratumoral microvessel density (IMVD)*191.2 ± 16.3174.7 ± 8.9167.6 ± 14.6.584Proliferative index (PI) (%)49.2 ± 4.841.7 ± 2.563.5 ± 3.8<.001Apoptotic index (AI) (/1000 tumor cells)13.5 ± 3.220.5 ± 2.015.9 ± 2.5.154p53-status: Aberrant expression (+)12/31 (38.7%)55/124 (44.4%)15/34 (44.1%).848Values were shown as the mean ± standard error (SE).* Evaluated with an anti-CD34 antibody.
Expression of Aurora-A and Postoperative Survival
For all 189 patients, 5-year survival rates of Aurora-A negative patients, diffuse cytoplasmic Aurora-A patients, and perimembrane Aurora-A patients were 67.8%, 66.7%, and 47.6%, respectively; perimembrane Aurora-A patients showed the poorest postoperative survival (P = .033) (Table 3 and Fig. 2).
Table 3.Aurora expression and postoperative survival in non-small-cell lung cancer (NSCLC) patients5-year survival rate (%)P valueAURORA expressionNegativePositiveDiffuse cytoplasmicPerimembraneAll patients67.8%66.7%47.6%.033Subset analysis, histologic type    Squamous cell (Sq)75.0%74.2%34.5%.007    Adeno (Ad)64.2%60.8%62.5%.894Pathologic (p-)stage    I91.7%72.9%58.3%.098    II75.0%68.6%57.1%.579    IIIA50.4%50.9%28.9%.088Fig. 2.Survival curves of patients who underwent complete resection for pathologic (p-) stage I–IIIA non-small-cell lung cancer. Comparison according to Aurora-A expression status.
Subset analyses revealed that perimembrane Aurora-A expression was a significant factor to predict a poor prognosis in squamous cell carcinoma patients, not in adenocarcinoma patients (P = .007 and P = .894, respectively) (Table 3).
A multivariate analysis of prognosis factors using a Cox proportional hazard model confirmed that perimembrane Aurora-A expression was an independent and significant factor to predict a poor prognosis in NSCLC (Table 4).
Table 4Multivariate analysis of prognostic factors (Cox’s proportional hazard model)FactorsBetaP valueHazard ratio (95% confidence interval)Gender (male/female)−0.539.0960.583 (0.309–1.101)Age0.006.7141.006 (0.977–1.035)Performance status (0/1/2)0.183.5711.201 (0.637–2.264)Histologic type (Nonadenocarcinoma/adenocarcinoma)−0.050.1620.951 (0.887–1.020)Pathologic stage (I/II/IIIa)0.551.0011.734 (1.311–2.295)Perimembrane Aurora expression (No/Yes)−0.362.0320.343 (0.169–0.699)
Discussion
The present study is the first clinical study on Aurora-A expression in NSCLC and has revealed the clinical significance as follows: (1) positive Aurora-A expression is exclusively observed in tumor cells, not in normal cells; (2) there are two Aurora-A expression patterns, diffuse cytoplasmic staining and perimembrane staining; (3) perimembrane staining is significantly correlated to higher p-stage and higher proliferative activity; and (4) perimembrane staining is a significant and independent factor to predict a poor prognosis, especially in squamous cell carcinoma patients. As mentioned in the introduction section, previous experimental and clinical studies have demonstrated that Aurora-A is overexpressed and upregulated in tumor cells and/or tissues,8–20,22,26 which is also true in NSCLC as demonstrated in the present study. The centrosomes play important roles in the maintenance of genomic instability by establishing bipolar spindles during mitosis and by ensuring equal segregation of replicated chromosome into two daughter cells.27 Aurora-A is involved in normal centrosome functions, and its abnormalities such as gene amplification and overexpression may lead to centrosome function disorder, chromosomal instability, and carcinogenesis.26Aurora-A gene amplification usually contributes to overexpression of Aurora-A gene and Aurora-A protein, but also common is overexpression without gene amplification.22 These results may suggest that Aurora-A expression is regulated not only by gene amplification but also by other mechanisms.22 In the present study, only Aurora-A protein expression was assessed immunohistochemically, and comparative study of overexpression and amplification should be conducted in future.
In previous clinical studies on Aurora-A expression assessed with IHS, Aurora-A expression was classified simply into negative or positive, and its clinical significance was examined.13,15–18,20 Some studies showed a significantly positive correlation between Aurora-A overexpression and aggressive tumor behavior such as poor differentiation and nodal metastasis,15,16,20 but others showed no17 or an inverse correlation.18 In addition, among three studies where prognostic significance of Aurora-A overexpression was assessed, two studies showed a significantly poor prognosis in patients with Aurora-A overexpression13,16 and one failed to show.20 These conflicting results on clinical significance of Aurora-A expression status might be partly due to the evaluation system. In fact, there was no difference in the proliferative activity as represented as PI or in the prognosis between negative Aurora-A tumors and diffuse cytoplasmic Aurora-A tumors in the present study (Tables 2 and 3); when perimembrane Aurora-A expression was separated and analyzed, tumors with perimembrane Aurora-A expression showed an aggressive tumor phenotype correlated with a higher PI and a poor prognosis. The biochemical implication of perimembrane Aurora-A expression is unknown, staining patterns should be taken into consideration in evaluation of Aurora-A expression status in clinical samples.
Another reason for conflicting results of clinical significance of Aurora-A expression status might be difference in the histologic type. Two clinical studies on esophageal squamous cell carcinoma showed that Aurora-A overexpression was significantly correlated with aggressive tumor behavior.15,16 In contrast, two clinical studies on adenocarcinoma, one on pancreatic cancer17 and one on breast cancer,20 failed to show a significant correlation between Aurora-A status and tumor differentiation or prognosis. In fact, a subset analysis according to histologic types in the present study showed that perimembrane Aurora-A expression was a significant factor to predict a poor prognosis in lung squamous cell carcinoma, but not in lung adenocarcinoma (Table 3). These results may suggest that clinical impact of Aurora-A expression status is enhanced in squamous cell carcinoma and reduced in adenocarcinoma. Further clinical studies on a variety of malignant tumors will give some answers to the issue.
We analyzed correlations between Aurora-A status and other biomarkers such as proliferative activity and p53 status. There has been no clinical study where Aurora-A status is assessed in correlation with proliferative activity. Considering experimental results that Aurora-A is a centrosome-associated kinase that contributes to regulation of cell mitosis,8,26 Aurora-A expression status may be correlated with proliferative activity in clinical samples as shown in the present study (Table 2). However, it remains unknown why a specific Aurora-A expression pattern, the perimembrane expression, is correlated with higher proliferative activity. Experimental studies have also suggested an interaction between Aurora-A and p53,28–30 suggesting that the p53 inhibit oncogenic activity of Aurora-A28 and that Aurora-A, especially when overexpressed, inactivate tumor-suppressor activity of p53.29,30 One clinical study also documented that Aurora-A overexpression was significantly correlated with p53 mutation in hepatocellular carcinoma.22 These results strongly suggest that Aurora-A in concert with p53 contributes to the development and progression of malignant tumors. In the present study, we failed to show a significant correlation between Aurora-A expression status and p53 status as assessed with IHS. In a future study, p53 gene mutation status in correlation with Aurora-A expression should be examined.
More recently, increasing studies on Aurora-A gene polymorphisms and risk of malignant tumors have been reported.31–36 Two common single nucleotide polymorphisms in the coding regions are 91T->A transversion and 169G->A transversion, which result in phenylalanine/isoleucine substitution at amino acid 31 (F31I) and valine/isoleucine substitution at amino acid 57 (V57I), respectively. Several studies have shown such polymorphisms are associated with increased risk of occurrence of a variety of malignant tumors including esophageal,31,35 ovarian,32 and breast33,34 cancers. In future, correlation between Aurora-A polymorphism and risk of development of primary lung cancer should be investigated. In conclusion, Aurora-A expression status was a significant prognostic factor in correlation with proliferative activity in NSCLC. To confirm the clinical significance, future prospective studies should be conducted.

aurora-a
lung cancer
surgery
prognosis
proliferation