s2fieldsofstudy: Biology Medicine

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Direct Inhibition of T-Cell Responses by the Cryptococcus Capsular Polysaccharide Glucuronoxylomannan

The major virulence factor of the pathogenic fungi Cryptococcus neoformans and C. gattii is the capsule. Glucuronoxylomannan (GXM), the major component of the capsule, is a high-molecular-weight polysaccharide that is shed during cryptococcosis and can persist in patients after successful antifungal therapy. Due to the importance of T cells in the anticryptococcal response, we studied the effect of GXM on the ability of dendritic cells (DCs) to initiate a T-cell response. GXM inhibited the activation of cryptococcal mannoprotein–specific hybridoma T cells and the proliferation of OVA-specific OT-II T cells when murine bone marrow–derived DCs were used as antigen-presenting cells. Inhibition of OT-II T-cell proliferation was observed when either OVA protein or OVA323–339 peptide was used as antigen, indicating GXM did not merely prevent antigen uptake or processing. We found that DCs internalize GXM progressively over time; however, the suppressive effect did not require DCs, as GXM directly inhibited T-cell proliferation induced by anti-CD3 antibody, concanavalin A, or phorbol-12-myristate-13-acetate/ionomycin. Analysis of T-cell viability revealed that the reduced proliferation in the presence of GXM was not the result of increased cell death. GXM isolated from each of the four major cryptococcal serotypes inhibited the proliferation of human peripheral blood mononuclear cells stimulated with tetanus toxoid. Thus, we have defined a new mechanism by which GXM can impart virulence: direct inhibition of T-cell proliferation. In patients with cryptococcosis, this could impair optimal cell-mediated immune responses, thereby contributing to the persistence of cryptococcal infections.

Introduction
Cryptococcosis is an invasive fungal infection that is caused by species of Cryptococcus, most commonly Cryptococcus neoformans or C. gattii. Following inhalation of the organism, the immune response is initiated in the lungs by alveolar macrophages and dendritic cells (DCs) [1,2]. Clinical and experimental data have established that T cells are required for resistance to C. neoformans. Disease occurs mainly in those with impaired cell-mediated immunity (CMI), including persons with AIDS, although infections can occur in immunocompetent persons as well [3].
The cryptococcal capsule is composed primarily of the polysaccharide glucuronoxylomannan (GXM), and also contains galactoxylomannan (GalXM). It is the structure of GXM that imparts serotype specificity to the organism. C. neoformans is composed of two varieties: C. neoformans var. grubii (serotype A isolates) and var. neoformans (serotype D isolates), while C. gattii includes serotypes B and C. GXM is a large polysaccharide; a study of GXM from four cryptococcal strains concluded that the molecular size ranged from 1,700 to 7,000 kDa [4]. A second polysaccharide, galactoxylomannan (GalXM), is also present but at approximately 10% of the mass of GXM [5].
Capsule is thought to contribute to the virulence of the yeast both by inhibiting phagocytosis and by being shed. In patients with cryptococcosis, GXM circulates in the blood and cerebrospinal fluid (CSF) at high concentrations [6] and can often be detected in body fluids for months to years after successful antifungal therapy ( [7] and unpublished data).
GXM has a number of immunomodulatory properties, including downregulating proinflammatory cytokine secretion from human monocytes and inhibiting leukocyte migration [8].
GXM in the brain of patients with cryptococcal meningitis is associated with macrophages/microglial cells, and it is possible that these cells serve as a reservoir for the polysaccharide once the organism is cleared [9,10]. Supporting a role for macrophages in uptake of GXM in vivo, studies in mice have found injected GXM localized primarily to marginal zone macrophages in the spleen and Kupffer cells in the liver [11,12]. In vitro, human monocytes and neutrophils and murine peritoneal macrophages internalize GXM, with GXM accumulating in the human monocyte-derived macrophages (MDMs) for up to 1 wk in culture [13,14]. The functional consequences of GXM internalization on phagocyte function include decreased human neutrophil anticryptococcal activity [13] and modulation of MHC class II and costimulatory molecule expression on MDMs [15].
DCs are powerful antigen-presenting cells (APCs) that recognize a wide variety of microbes and microbial products via their pattern-recognition receptors and subsequently initiate and direct T-cell responses [16]. Because of the importance of DCs in orchestrating immune responses, numerous pathogens have evolved ways of interfering with their functions [17]. Few studies have examined the effects of GXM on DCs. While acapsular C. neoformans cells induced the activation and maturation of human monocyte-derived DCs (HDCs), encapsulated organisms did not. In addition, soluble GXM did not affect the expression of MHC class I, MHC class II, CD40, or CD86 on HDCs [18].
Given the importance of the CMI response in the defense against Cryptococcus, we hypothesized that the organism's major virulence factor, GXM, would interfere with the ability of DCs to activate T cells. We found that DCs internalize GXM and the presence of GXM impairs antigen-specific Tcell responses. Surprisingly, the dominant mechanism responsible for this effect was a direct, inhibitory effect of GXM on T-cell proliferation.

Results
The ability of DCs to internalize GXM was measured by flow cytometry using an anti-GXM antibody. We found GXM is taken up by both murine bone marrow-derived DC (BMDCs) and HDCs ( Figure 1). GXM internalization increased over time, up to at least 2 d in culture. At all time points, the vast majority of the GXM was intracellular, as opposed to surface-bound (unpublished data).
Having demonstrated that GXM is internalized by DCs, we next studied the effects of GXM on the ability of DCs to activate antigen-specific T cells. In the first set of experiments, BMDCs were incubated with varying concentrations of GXM in the presence of cryptococcal mannoprotein (MP) and MP-specific hybridoma T cells. Activation of the T cells was determined by assaying interleukin (IL)-2 production. GXM inhibited the activation of T cells in a dose-dependent manner, with GXM at the highest concentration tested inhibiting activation by approximately 45% (Figure 2). We next utilized the OT-II system to examine whether GXM also inhibited the response of primary antigen-specific T cells. BMDCs were pretreated with varying concentrations of GXM before the addition of antigen, either whole OVA (contained in endotoxin-free egg white) or OVA 323-339 peptide. Naïve OVA-specific CD4 þ T cells, purified from OT-II transgenic mice, were added and proliferation was measured. GXM at 30, 100, and 300 lg/ml significantly inhibited T-cell proliferation induced by BMDC plus egg white, with GXM at 300 lg/ml inhibiting proliferation by almost 50% (Figure 3). Similar results were obtained when OVA contaminated with endotoxin was used as antigen (unpublished data). To determine if GXM was affecting antigen uptake and/or processing, we activated OT-II T cells with BMDC plus OVA 323-339 peptide. This peptide directly binds to MHC class II, thus bypassing the need for antigen uptake and processing [19]. GXM inhibited proliferation induced by the peptide to a similar extent as observed with egg white, indicating the mechanism of inhibition is not interference with antigen uptake or processing ( Figure 3). In support of this, inhibition of proliferation was similar whether GXM was added 2 h before or 2 h after egg white

Synopsis
Infections due to the pathogenic yeast Cryptococcus are a significant cause of morbidity and mortality in persons with impaired T-cell functions, particularly those with AIDS. The major virulence factor of Cryptococcus is its capsule, which is composed primarily of the polysaccharide glucuronoxylomannan (GXM). The capsule not only surrounds the organism but also is shed during cryptococcosis. GXM is taken up by macrophages in vitro and in vivo; however, little is known about the interaction between GXM and dendritic cells, which are the most potent cells capable of activating T cells. Because of the importance of T cells in the anticryptococcal response, the authors investigated the effect of GXM on the ability of dendritic cells to initiate a T-cell response. They found the polysaccharide was internalized by dendritic cells and inhibited antigen-specific T-cell responses. Furthermore, GXM had a direct, inhibitory effect on T-cell proliferation, independent of the effect on dendritic cells. These findings may help explain the persistence of cryptococcal infections and suggest that GXM could be therapeutic in situations where suppression of T-cell responses is desired.
(unpublished data). Reduced T-cell proliferation could also be the result of impaired DC maturation, but we found GXM did not inhibit lipopolysaccharide-or tumor necrosis factor a-induced upregulation of MHC class II or CD86 on BMDC (unpublished data), suggesting GXM does not interfere with DC maturation.
After determining that GXM inhibited the proliferation of antigen-specific T cells activated by BMDC, we sought to examine if the effect was specific to BMDCs or would be observed if macrophages were used as the APCs. We first established that bone marrow-derived macrophages (BMM/) internalize GXM, with uptake increasing over time (unpublished data), similar to uptake by MDMs [13] and BMDCs ( Figure 1). Purified CD4 þ OT-II T cells were activated by BMM/ plus egg white, in the presence or absence of varying concentrations of GXM. While GXM at the lower concentrations tested (30 and 100 lg/ml) had no significant effect on T-cell proliferation, GXM at 300 lg/ml inhibited proliferation by 57 6 9% (p , 0.01, n ¼ 2). Thus, GXM-mediated inhibition of T-cell proliferation is observed whether DCs or macrophages present the antigen.
GXM was present for the duration of the coculture in the T-cell proliferation experiments described above. Accordingly, although some GXM was undoubtedly taken up by the APCs, there was likely GXM present extracellularly in contact with the T cells. Therefore, we questioned whether intracellular GXM would be sufficient to impair the ability of BMDCs to activate T cells. To investigate this, BMDCs were incubated with GXM and then washed to remove any GXM not bound or internalized before the addition of antigen and T cells. Pulsing of BMDCs with 100 lg/ml GXM before the addition of egg white had no effect on subsequent T-cell proliferation, and pulsing with 300 lg/ml had a minimal, inhibitory effect on proliferation (unpublished data). Thus, GXM-pulsing of BMDCs slightly reduced T-cell proliferation but not to the extent observed when GXM was added directly to the cocultures, suggesting GXM was having a direct effect on T cells. To investigate direct effects of GXM on CD4 þ T cells, we first demonstrated that GXM bound to CD4 þ T cells (unpublished data) and then used three stimuli that activate T cells independent of APC: concanavalin A (Con A), platebound anti-CD3e antibody, and phorbol-12-myristate-13acetate (PMA)/ionomycin. GXM at 100 lg/ml added to anti-CD3e-activated T cells modestly increased proliferation; however, higher concentrations of GXM (300 and 1,000 lg/ ml) significantly inhibited proliferation ( Figure 4). GXM at 100 lg/ml had no effect on Con A-mediated T-cell proliferation, yet 300 lg/ml GXM inhibited proliferation by 60% ( Figure 4). We also found GXM inhibited the proliferation of PMA/ionomycin-activated T cells in a dose-dependent fashion ( Figure 4). These data demonstrate that GXM has a direct, inhibitory effect on T-cell proliferation. Moreover, inhibition is observed regardless of whether T cells are activated in a physiologic or pharmacologic manner.
To further investigate the reduced proliferative response, we sought to determine if GXM inhibits the upregulation of T-cell activation markers. Surface expression of CD69, which  is an early activation antigen [20], was evaluated. GXM did not affect the upregulation of CD69 on T cells activated with anti-CD3e or BMDCs plus egg white (unpublished data). Similarly, GXM did not inhibit the upregulation of CD25 (unpublished data), which is the a-chain of the high-affinity IL-2 receptor and another activation marker of T cells [21].
The decreased T-cell proliferation observed in the presence of GXM could be due to induction of necrosis or apoptosis. Therefore, we assessed the effect of GXM on T-cell viability by Annexin V/propidium iodide (PI) staining. GXM at 300 lg/ml induced a small decrease in the percentage of live cells and a corresponding increase in the percentage of late apoptotic or dead cells when T cells were activated by anti-CD3e or BMDCs plus egg white ( Figure 5). The addition of 1,000 lg/ml GXM to anti-CD3e-activated T cells also resulted in a minimal, statistically insignificant increase in the percentage of late apoptotic or dead cells (unpublished data). However, this effect of GXM on T-cell viability is minimal and does not appear to be sufficient to account for the impaired proliferative responses. The effect of GXM on viability was also examined by adding the polysaccharide to cell lines and measuring proliferation. GXM at 300 lg/ml had no significant effect on the incorporation of [ 3 H]thymidine by a number of T cell lines and a B cell line, demonstrating that GXM is not directly cytotoxic ( Figure S1). Taken together, these data indicate GXM-mediated inhibition of T-cell proliferation is not primarily due to cell death.
We next sought to determine the effect of GXM on cytokine production by primary T cells. IL-2, IL-10, and interferon (IFN)-c, but not IL-4, were detected in supernatants of BMDCs/egg white/OT-II T-cell cocultures and T cells directly activated with anti-CD3e ( Figure 6 and unpublished data). The presence of GXM resulted in reduced IL-2 levels when T cells were activated with anti-CD3e or BMDCs plus egg white ( Figure 6). However, GXM had no significant effect on IL-10 or IFN-c levels (unpublished data). Because IL-2 is a T-cell growth factor and is produced by activated T cells, the effect of GXM on IL-2 production was investigated further. Reduced levels of IL-2 in the presence of GXM could be due to a direct inhibition of IL-2 production or a result of fewer T cells due to an impaired proliferative response. To address the former possibility, we added exogenous IL-2 to T cells activated with anti-CD3e. IL-2 at concentrations up to 100 U/ml did not restore the proliferative response of cells activated in the presence of 1,000 lg/ml GXM (68 6 5% inhibition with GXM alone and 62 6 13% with GXM plus 100 U/ml IL-2; mean 6 SEM of two independent experiments performed in triplicate). We also examined the effect of GXM on the amount of IL-2 produced by phytohemagglutinin (PHA)-activated Jurkat T cells and found GXM had no significant effect on IL-2 production (PHA plus 300 lg/ml GXM ¼ 110 6 24% of IL-2 induced by PHA alone; mean 6 SEM of three independent experiments done in triplicate). Together, these data reveal the inhibitory effect of GXM on T-cell proliferation cannot solely be attributed to impaired IL-2 production.
To ascertain whether the effect of GXM on murine T-cell proliferation would be replicated using human cells, in the final set of experiments, we investigated whether the polysaccharide affected the proliferation of peripheral blood mononuclear cells (PBMCs) in response to the recall antigen, tetanus toxoid. In addition, as all of the above studies were performed using GXM from serotype A C. neoformans strain Cn6, in these experiments we also tested the effects of GXM isolated from other cryptococcal serotypes. Accordingly, PBMCs were activated with tetanus toxoid in the presence Figure 4. Effect of GXM on T-Cell Proliferation Induced by Anti-CD3, Con A, or PMA/Ionomycin Purified CD4 þ murine T cells were activated with plate-bound anti-CD3e antibody, Con A, or PMA/ionomycin, in the presence or absence of GXM (100, 300, or 1,000 lg/ml). The data are expressed as the percent proliferation compared with the stimuli alone (À) (set at 100%) 6SEM of two to five independent experiments done in triplicate. The mean CPM for anti-CD3, Con A, and PMA/ionomycin were 224,440, 41,820, and 118,875, respectively. *p , 0.01. doi:10.1371/journal.ppat.0020120.g004 or absence of GXM isolated from serotype A C. neoformans strain H99, serotype B C. gattii strain R265, serotype C C. gattii strain Cn18, and serotype D C. neoformans strain B3501, as well as the strain Cn6 GXM. GXM from all serotypes significantly inhibited PBMC proliferation, although the GXM preparations varied in their potencies (Figure 7). GXM isolated from the serotype A strains inhibited proliferation to the greatest extent; serotype D GXM was the least potent, and GXM from the serotype B and C strains were intermediate in their ability to suppress proliferation.

Discussion
The data presented herein establish a new mechanism by which the capsular polysaccharide GXM imparts virulence upon C. neoformans and C. gattii: direct inhibition of T-cell proliferation. While GXM is taken up by DCs, inhibition of Tcell responses occurs in an APC-independent manner. Thus, GXM-mediated inhibition is seen regardless of whether T cells are activated by APC plus antigen or directly by mitogens. Moreover, GXM inhibits both murine and human T-cell responses.
GXM inhibited T-cell proliferation induced by BMDCs presenting the immunodominant cryptococcal MP antigens. Assuming these effects occur in vivo during cryptococcosis, the anticryptococcal CMI response may be dampened once a cryptococcal infection gets firmly established and GXM titers rise. As GXM also impairs humoral immune responses and phagocytic defenses [22], this could lead to a situation where the host is left without effective innate or acquired immunologic defenses. Indeed, prior to the introduction of effective antifungal drugs, cryptococcal meningoencephalitis was nearly always fatal.
In addition to its effect on antigen-specific T-cell responses, GXM had a direct effect on primary, naïve T cells, as demonstrated by its capacity to inhibit the proliferation of T cells activated with anti-CD3e or Con A. GXM also has been reported to inhibit the proliferation of Con A-activated rat spleen mononuclear cells [23]. In those studies, the impaired proliferation may have been due to increased cell death, as GXM was found to induce apoptosis in these cells [24]. In contrast, the impaired proliferative responses we measured could not be attributed to GXM-mediated cytotoxicity, as the polysaccharide did not significantly affect T-cell viability.
GXM also inhibited the proliferation of T cells activated with the chemical mitogens PMA and ionomycin, indicating GXM interferes with T-cell signaling downstream of T-cell receptor activation. GXM did not inhibit the early activation of T cells, as measured by CD25 and CD69 expression. Similarly, Salmonella inhibits T-cell proliferation without inhibiting the upregulation of T-cell activation markers, including CD25 and CD69 [25]. Additionally, HIV has been shown to inhibit CD4 þ T-cell cycle progression without affecting the expression of early activation markers [26].
The presence of GXM resulted in less IL-2 detected in cultures of T cells activated with BMDC and egg white or anti-CD3e. Likewise, GXM impaired the response of MP-specific T cells as measured by IL-2 production. However, exogenous IL-2 could not restore the proliferative response of T cells activated in the presence of a high concentration of GXM, and GXM did not inhibit IL-2 release by PHA-activated Jurkat cells. Although it is possible GXM exerts different effects on cell lines and primary T cells, these data indicate GXM impairs T-cell proliferation independently of inhibiting IL-2 production or IL-2 receptor expression. Taken together, our results suggest that GXM exerts its effect at a later stage of Tcell activation. In fact, GXM may have a direct, inhibitory effect on cell cycle progression in primary T cells, similar to the action of Helicobacter pylori vacuolating cytotoxin, VacA, which inhibits IL-2-driven T-cell proliferation [27]. Studies from other laboratories have found that GXM induces the production of the immunosuppressive cytokine, IL-10, from human monocytes [28], and identified a role for IL-10 in GXM-mediated immunosuppression in vitro [23] and in vivo [29,30]. However, we do not observe an induction of IL-10 by human monocytes or BMDCs incubated with GXM (unpublished data). Furthermore, GXM did not enhance IL-10 production from T cells activated with BMDCs plus egg white or anti-CD3e. Thus, we conclude that GXM-mediated inhibition of T-cell proliferation in our studies likely is IL-10 independent.
In addition to the direct effect of GXM on T cells, we found DCs internalized the polysaccharide continuously over time, yet GXM-pulsed BMDCs were only minimally impaired in their ability to activate T cells. This is not surprising, as GXM treatment does not appear to inhibit BMDC maturation (unpublished data). Similarly, it was recently demonstrated that pneumococcal capsular polysaccharides are internalized by DCs yet do not inhibit LPS-induced maturation [31]. Interference with antigen uptake or processing is also an unlikely mechanism for the inhibitory effect of GXM, as GXM effectively inhibited proliferation when T cells were activated with BMDCs plus OVA 323-339 peptide.
Importantly, the inhibitory effect of GXM on murine T cells was also observed using human cells. We show that GXM inhibited proliferation of PBMCs activated with tetanus toxoid in a dose-dependent manner. A recent study demonstrated the other capsular component of Cryptococcus, GalXM, inhibits human T-cell proliferation by inducing T-cell apoptosis [32]. Consistent with our results, the researchers found a low concentration of GXM did not affect T-cell proliferation induced by PHA or anti-CD3. They did not examine the effects of higher concentrations of GXM, however.
Interestingly, our studies revealed that GXM isolated from all four cryptococcal serotypes inhibited proliferation of tetanus toxoid-activated PBMCs, although not to the same extent. GXM from the serotype D strain B3501 was less inhibitory than GXM from the other strains. Whether this is due to a size or structure difference is unknown. Differences in GXM structure affect virulence, inhibition of neutrophil migration, and tissue accumulation of GXM [33][34][35]. GXM from B3501 is larger than GXM from H99 [4]; however, the molecular sizes of GXM from strains Cn6, R265, and Cn18 are unknown. Our results suggest differences in virulence between cryptococcal strains may be due in part to the varying capacities of GXM to inhibit T-cell proliferation.
The concentrations of GXM we used that consistently inhibited T-cell proliferation (300 and 1,000 lg/ml) are high yet still biologically relevant. One study found 68% of AIDS patients with cryptococcosis had serum GXM titers of at least 1:1,024 (approximately 10 lg/ml), and 21% had titers of 1:10,000 or higher (approximately 100 lg/ml) [6]. In experimental cryptococcosis, serum GXM levels in the mg/ml range have been observed [36]. However, GXM concentrations in infected tissues, particularly at foci of infection, are undoubtedly much higher. Our findings suggest that such high concentrations of GXM could interfere with the generation of T-cell responses. In this regard, while the osmolarity of these high concentrations of GXM has not been determined, hyperosmotic stress has been reported to have direct effects on T-cell signaling pathways [37].
In conclusion, our data reveal a novel inhibitory property of GXM. Inhibition of T-cell proliferation could have severe PBMCs were activated with tetanus toxoid (TT) in the presence or absence of GXM (100, 300, or 1,000 lg/ml) isolated from serotype A strain Cn6, serotype A strain H99, serotype B strain R265, serotype C strain Cn18, or serotype D strain B3501. Proliferation was measured by [ 3 H]thymidine incorporation 6 d later. Data are expressed as the percent proliferation compared with TT alone (À) (set at 100%) and are expressed as the mean 6 SEM of four donors (or two donors for strain Cn6 GXM) assayed in triplicate. The mean CPM for TT in the absence of GXM was 63,550. *p 0.005; **p , 0.0001. doi:10.1371/journal.ppat.0020120.g007 Figure 6. Effect of GXM on IL-2 Production Purified CD4 þ T cells from OT-II mice were activated with anti-CD3e antibody or BMDCs plus egg white in the presence or absence of GXM (100 or 300 lg/ml) for 48 h. IL-2 levels were determined by ELISA or bioassay. Data are expressed as the percent of IL-2 compared with stimuli alone (À) 6SEM of two to four independent experiments done in triplicate. The mean IL-2 (pg/ml by ELISA) for egg white and anti-CD3 were 210 and 320, respectively. The mean IL-2 (U/ml by bioassay) for egg white was 0.65. *p , 0.001. doi:10.1371/journal.ppat.0020120.g006 consequences during cryptococcosis due to the importance of T cells in the anticryptococcal response. Furthermore, the ability of GXM to persist in patients after successful antifungal therapy underscores how T-cell responses during subsequent infections or neoplasia may be affected as well. Finally, GXM, or products derived from GXM, could prove to be of therapeutic value in situations where suppression of Tcell responses is desired, such as transplantation or autoimmunity.

Materials and Methods
Materials. All chemical reagents were purchased from Sigma-Aldrich (St. Louis, Missouri, United States) unless otherwise specified. All tissue culture media were purchased from Invitrogen Life Technologies (Carlsbad, California, United States) unless otherwise noted.
Isolation and purification of GXM. GXM from serotype A C. neoformans strain Cn6 (62066; American Type Culture Collection [ATCC], Manassas, Virginia, United States), serotype A C. neoformans strain H99 (208821; ATCC), serotype B C. gattii strain R265 (a gift from Dr. J. Heitman, Duke University Medical Center, Durham, North Carolina, United States), serotype C C. gattii strain Cn18 (24066; ATCC), and serotype D C. neoformans strain B3501 (34873; ATCC) was prepared as described [38]. In brief, cryptococcal culture supernatants were precipitated with ethanol and cetyltrimethylammonium bromide. GXM was dialyzed against 1 M NaCl, followed by dH 2 O for 1 wk. The amount of polysaccharide was quantified by the phenolsulfuric acid method [39]. The strain Cn6 GXM used in Figures 1 through 6 and Figure S1 was in 13 PBS, whereas the GXM preparations used in Figure 7 were lyophilized and resuspended in RPMI 1640. All GXM preparations had undetectable levels (less than 0.03 endotoxin U/ml) of endotoxin as measured by Limulus Amebocyte Lysate assay (Associates of Cape Cod, East Falmouth, Massachusetts, United States).
Mice. C57BL/6 and OT-II mice were purchased from The Jackson Laboratory (Bar Harbor, Maine, United States). OT-II TCR-transgenic mice have CD4 þ T cells specific for OVA amino acids 323-339 in the context of MHC class II (I-A b ) [40]. The mouse experiments were approved by the Boston University Institutional Animal Care and Use Committee.
Isolation and culture of Human DCs. Human DCs were obtained as described [41]. Briefly, peripheral blood was obtained by venipuncture from healthy volunteers using a protocol approved by the Boston University Medical Center Institutional Review Board. Blood was anticoagulated with heparin (American Pharmaceutical Partners, Los Angeles, California, United States) and diluted 1:1 with Hanks' balanced salt solution (Cambrex BioScience Walkersville, Walkersville, Maryland, United States). PBMCs were separated by density gradient centrifugation using Lymphoprep (AXIS-SHIELD; PoC AS, Oslo, Norway), and 1 to 3 3 10 5 PBMCs were added to the wells of a six-well plate. After a 2-h incubation at 37 8C, the wells were washed to remove nonadherent cells. Human DCs were obtained by culturing the adherent cells for 7 d in HDC media (RPMI 1640, 5% heatinactivated [HI] FBS [Tissue Culture Biologicals, Los Alamitos, California, United States], 100 U/ml penicillin, 100 lg/ml streptomycin, 2 mM L-glutamine, 55 lM 2-mercaptoethanol, and 10 mM HEPES [Sigma]) supplemented with 50 ng/ml rhIL-4 (BD PharMingen, San Diego, California, United States) and 150 ng/ml rhGM-CSF (Immunex Corporation, Thousand Oaks, California, United States).
Generation of BMM/. Bone marrow was harvested as described above, and cells were seeded at 5 3 10 5 cells/ml in a tissue culture flask. Cells were cultured in RPMI 1640, 100 U/ml penicillin, 100 lg/ml streptomycin, 2 mM L-glutamine, and 10% HI FBS (Tissue Culture Biologicals). Media was supplemented with 30% L cell (CCL-1; ATCC)-conditioned medium as a source of M-CSF. On day 2, nonadherent cells were harvested and seeded at 10 5 /ml in Petri dishes (BD Falcon) in media containing 30% L cell-conditioned medium. The media was changed on day 6, and macrophages were harvested on day 8 using Versene.
Flow cytometric analysis of GXM binding/internalization. BMDCs, HDCs, BMM/, or murine CD4 þ T cells (2 3 10 5 ) were incubated in their respective media with GXM (40 lg/ml) for varying lengths of time at 37 8C. Cells were harvested, washed two times, fixed with 1% paraformaldehyde, permeabilized with 0.1% saponin, and stained with the anti-GXM monoclonal antibody, 3C2 (a gift from Dr. T. Kozel, University of Nevada School of Medicine, Reno, Nevada, United States), followed by a donkey anti-mouse Cy2-congugated secondary antibody (Jackson ImmunoResearch, West Grove, Pennsylvania, United States). This procedure allowed for detection of total GXM, both surface bound and intracellular. To detect only surface bound GXM, cells were not fixed or permeabilized before staining.
Activation of cryptococcal MP-specific T cells. BMDCs (10 5 ) were plated in 96-well flat-bottom plates. Some wells received a 2-h GXM pretreatment before the addition of cryptococcal MP (10 lg/ml). MP was isolated from culture supernatants of C. neoformans acapsular strain Cap67 (52817; ATCC) as described [44]. The cryptococcal MPspecific T-cell hybridoma, P1D6, has been described previously [45]. This hybridoma secretes IL-2 when activated with cryptococcal MP in a MHC class II-restricted manner. P1D6 T cells (2 3 10 5 ) were added to the BMDC 1 h after MP. Supernatants were collected 24 h later, freeze-thawed, and incubated with the IL-2-dependent cell line CTLL-2 [46]. AlamarBlue (10 ll; BioSource, Camarillo, California, United States) was added 24 h later for 8 h. Absorbance was determined using a plate reader, and the results were compared with an IL-2 standard curve.
Purification of murine CD4 þ T cells. Inguinal, brachial, axial, cervical, and mesenteric lymph nodes and spleens were harvested from 6-to 14-week-old C57BL/6 or OT-II mice. Lymph nodes and spleens were disrupted using sterile frosted glass slides, and RBCs were lysed using RBC lysis buffer (eBioscience, San Diego, California, United States). CD4 þ T cells were enriched using magnetic bead positive selection (CD4 þ [L3T4] beads; Miltenyi Biotec, Auburn, California, United States) according to the manufacturer's instructions.
Proliferation of OVA-specific T cells. Mitomycin C (50 lg/ml)treated BMDCs were plated in 96-well U-bottom plates, and GXM (30, 100, or 300 lg/ml) was added 2 h before egg white (100 lg/ml) or OVA 323-339 peptide (0.1 lg/ml; Invitrogen). The endotoxin-free egg white (which contains approximately 54% ovalbumin [47]) was a gift of Dr. T. Singleton, Boston University School of Medicine (Boston, Massachusetts, United States). Naïve purified CD4 þ OT-II T cells (10 5 ) were added to the wells 2 h later. The wells were pulsed with 1 lCi [ 3 H]thymidine (PerkinElmer, Wellesley, Massachusetts, United States) on day 3, and the cells were harvested and freeze-thawed 20 h later. Proliferation was measured on a beta scintillation counter.
Proliferation of Con A-, anti-CD3-, or PMA/ionomycin-activated murine T cells. For anti-CD3 activation, 96-well flat-bottom plates were coated with anti-mouse CD3e antibody (clone 145-2C11; BD Biosciences PharMingen) diluted in PBS for 2 h at 37 8C. Wells were washed with PBS, and 10 5 CD4 þ purified T cells were added to each well. Some wells then received varying concentrations of GXM. In some experiments, IL-2 (Cetus, Emeryville, California, United States) was added to some wells. For Con A or PMA/ionomycin activation, 10 5 CD4 þ purified T cells were added to 96-well U-bottom plates and stimulated with Con A (0.5 lg/ml) or PMA (3 ng/ml) and ionomycin (0.15 lM; Calbiochem, La Jolla, California, United States). GXM was then added to some wells. Proliferation was measured by [ 3 H]thymidine incorporation as described above.
Proliferation of PBMCs. Freshly isolated PBMCs (10 5 ) were added to the wells of a 96-well U-bottom plate. Some wells received varying concentrations of GXM isolated from serotype A strain Cn6, serotype A strain H99, serotype B strain R265, serotype C strain Cn18, or serotype D strain B3501. Tetanus toxoid (TT; Calbiochem) was added for a final concentration of 1 lg/ml. The wells were pulsed with [ 3 H]thymidine on day 6 and harvested 20 h later, and proliferation was determined as described above.
Detection of cytokines. T cells were activated with BMDCs plus egg white or anti-CD3e as described above. Cell-free supernatants were harvested and frozen at 48 h. Levels of IL-2, IL-4, IL-10, and IFN-c were determined by ELISA (eBioscience) according to the manufacturer's instructions; however, in one experiment, IL-2 levels were measured by bioassay using the CTLL-2 cell line as described above. IL-2 produced by PHA (5 lg/ml)-activated Jurkat cells (TIB-152; ATCC) was measured using the CTLL-2 assay.
Analysis of T-cell viability. OT-II CD4 þ purified T cells were activated by plate-bound anti-CD3e antibody or BMDCs plus egg white. Cells were left untreated or treated with GXM (300 lg/ml) for 48 h. At 24 h, staurosporine (0.5 lM) was added to some wells containing anti-CD3e-activated T cells as a positive control for apoptosis induction. At 48 h, T cells were harvested and stained with Annexin V/FITC and PI (BD PharMingen), and analyzed by flow cytometry. AnnexinÀPIÀ cells are defined as live, AnnexinþPIÀ as early apoptotic, and AnnexinþPIþ as late apoptotic or dead.
Cell line proliferation. Murine P1D6 T cells, CTLL-2 T cells, J558L B cells, and human Jurkat T cells were plated at 3 3 10 4 per well in a 96-well flat-bottom plate. GXM (300 lg/ml) was added to some wells. Wells were pulsed immediately with [ 3 H]thymidine, and proliferation was measured 24 h later on a scintillation counter.
Statistical analysis. The mean and SEM values were compared using the Student's t test. Values of p , 0.05 were considered significant. Bonferroni's correction was applied when making multiple comparisons. Figure S1. Effect of GXM on [ 3 H]Thymidine Incorporation by Cell Lines Murine P1D6 T cells, CTLL-2 T cells, J558L plasmacytoma B cells, and human Jurkat T cells were incubated in the presence or absence of GXM (300 lg/ml) for 24 h, and proliferation was measured by [ 3 H]thymidine incorporation. Data are expressed as the mean 6 SEM of one of two independent experiments with similar results done in triplicate. Found at doi:10.1371/journal.ppat.0020120.sg001 (36 KB PPT).


extfieldsofstudy:
Biology
Medicine
Computer Science

text:
Machine learning approaches to supporting the identification of photoreceptor-enriched genes based on expression data

Background Retinal photoreceptors are highly specialised cells, which detect light and are central to mammalian vision. Many retinal diseases occur as a result of inherited dysfunction of the rod and cone photoreceptor cells. Development and maintenance of photoreceptors requires appropriate regulation of the many genes specifically or highly expressed in these cells. Over the last decades, different experimental approaches have been developed to identify photoreceptor enriched genes. Recent progress in RNA analysis technology has generated large amounts of gene expression data relevant to retinal development. This paper assesses a machine learning methodology for supporting the identification of photoreceptor enriched genes based on expression data. Results Based on the analysis of publicly-available gene expression data from the developing mouse retina generated by serial analysis of gene expression (SAGE), this paper presents a predictive methodology comprising several in silico models for detecting key complex features and relationships encoded in the data, which may be useful to distinguish genes in terms of their functional roles. In order to understand temporal patterns of photoreceptor gene expression during retinal development, a two-way cluster analysis was firstly performed. By clustering SAGE libraries, a hierarchical tree reflecting relationships between developmental stages was obtained. By clustering SAGE tags, a more comprehensive expression profile for photoreceptor cells was revealed. To demonstrate the usefulness of machine learning-based models in predicting functional associations from the SAGE data, three supervised classification models were compared. The results indicated that a relatively simple instance-based model (KStar model) performed significantly better than relatively more complex algorithms, e.g. neural networks. To deal with the problem of functional class imbalance occurring in the dataset, two data re-sampling techniques were studied. A random over-sampling method supported the implementation of the most powerful prediction models. The KStar model was also able to achieve higher predictive sensitivities and specificities using random over-sampling techniques. Conclusion The approaches assessed in this paper represent an efficient and relatively inexpensive in silico methodology for supporting large-scale analysis of photoreceptor gene expression by SAGE. They may be applied as complementary methodologies to support functional predictions before implementing more comprehensive, experimental prediction and validation methods. They may also be combined with other large-scale, data-driven methods to facilitate the inference of transcriptional regulatory networks in the developing retina. Furthermore, the methodology assessed may be applied to other data domains.

Background
Retinal photoreceptor cells, the specialized cells involved in light detection and phototransduction, are essential for mammalian vision. Many retinal diseases occur as a result of inherited dysfunction of the rod and cone photoreceptor cells. Photoreceptor degeneration, for example, constitutes an important cause of visual impairment affecting all age groups and ethnic backgrounds [1]. Development and maintenance of photoreceptor function in the retina requires appropriate regulation of gene expression, especially for genes specifically or highly expressed in photoreceptor cells during retinal development (photoreceptorenriched genes). Comprehensive identification of photoreceptor-enriched gene expression patterns may have important implications in neurobiology, leading to a better understanding of molecular mechanisms of retinal development, the improvement of diagnosis of complex retinal diseases, and the identification of potential therapeutic targets [2].
Over the last decades, different experimental approaches have been developed to identify retinal disease genes. Using microarray data analysis, for example, Yoshida et al. [3] revealed that 43 genes, which are differentially expressed in the absence of Nrl (neural retina leucine zipper protein), are either associated with or are candidates for retinal diseases involving rod or cone photoreceptor dysfunction. Katsanis et al. [4] positioned 925 expressed sequence tags (ESTs) likely to be specifically or preferentially expressed in the retina. They also identified positional candidate genes for 42 of 51 uncloned retinopathies. The quality of the results was assessed by reverse transcriptase-polymerase chain reaction (RT-PCR). Recently, Blackshaw et al. [2] presented a comprehensive genomic analysis of mouse retinal development using serial analysis of gene expression (SAGE), followed by in situ hybridization (ISH) validation. Libraries were obtained from microdissected mouse photoreceptors from the retinal outer nuclear layers (ONL), retina from various mouse developmental stages and retina from the paired-homeodomain transcription factor Crx knockout mouse (Crx -/-) and its wild type counterpart (Crx +/+ ) at postnatal day (P)10, and from NIH3T3 mouse fibroblasts.
The SAGE-based expression analysis performed by Blackshaw et al. [2] has advantages over other RNA analysis methods. This technique uses a unique sequence tag of 13 or more bases isolated from a defined position within each transcript [5]. The basic concept of SAGE rests on two principles: (1) a short nucleotide sequence tag contains sufficient information to uniquely identify a transcript and (2) concatenated short sequence tags can be cloned to facilitate efficient sequencing analysis. Unlike RNA blotting and RT-PCR, SAGE is not limited to examining only a few known genes at a time. Unlike DNA microarray tech-nology, the SAGE approach allows the simultaneous analysis of a large number of transcripts without prior, complete knowledge of the sequence of the genes [6].
In the study by Blackshaw et al. [7], the candidate photoreceptor-specific genes were selected by comparative analysis between SAGE libraries on the basis of four chosen criteria (see Results Section). This method, however, has shown relatively low true positive and true negative prediction rates. For example, out of 196 newly-identified photoreceptor-enriched (PR-enriched) tags, only approximately 20% meet all the four classification criteria and about 44% meet more than three of the four criteria. To further identify true PR-enriched tags, Blackshaw et al. experimentally validated the candidate tags through exhaustive ISH analysis. In this paper, we explore the feasibility of using computational approaches to support large-scale analysis of photoreceptor gene expression data.
The main purpose of this study is to implement several in silico models to detect complex features and relationships encoded in the SAGE data, which may be used to predict functional associations. By way of illustration, this paper focuses on the classification of two functional classes of genes, which were studied and experimentally validated by Blackshaw et al. [7]: PR-enriched and non-PR-enriched genes. The main question was: Can machine learningbased classifiers be built to accurately distinguish PRenriched from non PR-enriched genes solely based on patterns in the SAGE data? The potential benefits of this approach are two-fold. In situations for which there is insufficient biological knowledge, machine learningbased classifiers could be used to predict functional classes of genes. Secondly, if classifiers are demonstrated to effectively predict specific gene types, these empirically derived relationships could be used to derive biological significance.
In order to study temporal patterns of photoreceptor gene expression during retinal development, a two-way cluster analysis: clustering of tags and clustering of libraries were performed. While SAGE libraries were clustered using traditional hierarchical clustering method, SAGE tags were analysed by a newly developed Poisson model-based kmeans algorithm (PoissonC) specifically designed for SAGE data [8]. To address one of the limitations of Pois-sonC, a Figure of Merit (FOM)-based approach [9] to estimating the number of clusters was proposed. The FOM is computed by first removing one experiment (library in our case), clustering genes based on the remaining data, and then measuring how the left-out library fits the expression patterns obtained from the other libraries. Such adaptations represent one of the aspects distinguishing our investigation from traditional clustering-based analyses. To explore the feasibility of machine-learning approaches to predicting functional associations encoded in the SAGE data generated from different developmental stages in mouse retina, three supervised classification methods were tested to predict the two functional classes investigated by Blackshaw et al. [7]: PR-enriched and non-PR-enriched genes. To deal with the problem of functional class imbalance occurring in the dataset, two data re-sampling techniques were studied. To adequately evaluate the performance of the supervised classifiers in imbalanced dataset, three predictive quality indicators that are independent of the class prior probabilities were implemented. To further assess the statistical significance of the computational approaches, a 100-run permutation test was implemented. The reader is referred to the section of Methods for a more detailed description of the data sets and techniques studied. The following section summarises relevant results.   This hierarchical tree provides further insights. The split at birth is less marked. The P10.5 Crx-/-library is clustered with the P6.5 library rather than with its wild type counterpart.

Clustering of SAGE libraries
reflecting the effect of the physiological changes occurring at birth on gene expression. Moreover, the 8 postnatal libraries are clustered into two groups, 4 of which are grouped together after P6.5. This might suggest that a critical time point occurs between P6.5 and P10.5 for the development of photoreceptor cells. In order to focus on photoreceptor cells, 14 SAGE libraries were clustered using the 261 PR-enriched tags, as shown in Figure 2. This provides further insights, for example the split at birth is less marked, which is consistent with evidence showing that terminal differentiation and functional activation of photoreceptor cells occurs at later stages [7]. The P10.5 Crx-/-library now clusters with the P6.5 library rather than with its wild type counterpart, as would be predicted from the essential function of Crx in photoreceptor cell type specification. This suggests that its loss has a greater effect on PR-enriched genes and their profile more closely resembles that of the immature P6.5 wild type retina. Figure 3 shows 5-runs adjusted FOM values for the kmeans algorithm with Euclidean distance on normalised SAGE data from 1 to 20 clusters. The value of adjusted FOM steeply decreases until the number of clusters is equal to 10. Afterwards the rate of decline with respect to the number of clusters is reduced. The results for a 10-cluster analysis using the PoissonC algorithm are shown graphically in Figure 4. Table 1 lists the number of tags within each cluster, the number of tags previously known to be PR-enriched/non-PR enriched, as well as the number of tags identified as PR-enriched/non-PR enriched by Blackshaw et al. [7]. The description of the cluster profiles is also given in Table 1. The clustering results from a Euclidean-based clustering model are given in the supplementary materials.

Clustering of SAGE tags
In general, the temporal expression patterns observed in this SAGE data reflect previously characterized photoreceptor gene expression patterns in the developing retina. For example, genes associated with tags in Clusters 2 and The mean values of 5-runs adjusted FOM calculations against the number of clusters Figure 3 The mean values of 5-runs adjusted FOM calculations against the number of clusters. An open-source implementation of FOM provided by the Institute for Genomic Research (TIGR) [22] was used to calculate the adjusted FOM for kmeans algorithm with Euclidean distance on normalised SAGE data. For a given tag, the abundance in each SAGE library was rescaled to make the sum of tag counts across all 14 libraries equal to one.
6, whose expression consistently increases throughout postnatal development and reaches their highest value in the adult retina, are highly likely to be expressed in developing photoreceptors. Sixty out of 63 tags previously known to be PR-enriched and 134 out of 197 tags identified to be PR-enriched by Blackshaw et al. [7] fell within these two clusters, highlighting that this is an important feature which can be used to identify PR-enriched genes. A closer examination of these two clusters revealed that many tags were mapped to genes with known functions relevant to phototransduction and visual formation. For example, 8 tags associated with rhodopsin gene and 2 tags linked to rod photoreceptor were grouped together in Cluster 6. Four tags mapped to guanine nucleotide binding proteins (G-protein) [10], which are involved as modulators or transducers in various transmembrane signalling systems, were all found in Cluster 2. Apart from those tags directly related to the visual process, some tags associated with genes with other functions such as peripherin-2 and rod outer segment membrane protein 1 (ROM1) were also found. It has been shown that these genes are involved in maintaining the integrity of pho-toreceptor outer segment and are therefore critical for rod photoreceptor viability and regulation of disk morphogenesis [11].
Interestingly, 8 out of the 10 known non-PR-enriched tags were found in Cluster 9, having peak expression values occurring within 3T3 fibroblast cells. This might confirm that those genes that have higher expression levels in nonretina tissues are unlikely to become PR-enriched genes. Cluster 7 exhibits similar expression profiles of genes strongly expressed in adult hypothalamus. Although the characteristics of all 27 tags in this cluster were not investigated by Blackshaw et al. [7], our analysis indicates that these genes are unlikely to be selectively expressed in photoreceptors based on the observations derived from Cluster 9.
Clusters 3 and 5, whose peak expression values occurred during embryonic development, also offered relevant insights. Genes that fell within these two clusters generally have higher expression levels in the mouse retina before P6.5 with expression gradually decreasing throughout later postnatal development. Examples includes macrophage migration inhibitory factor gene in Cluster 5, which plays an important role in T-cell activation and may contribute to regulation of retinal inflammation and its local immunity [12]. Its expression starts at E12.5, peaks at E16.5, and significantly decreases in the adult. In clusters 3 and 5, a total of 12 tags were examined by Blackshaw et al. [7], ten out of 12 resulted non-PR enriched. The expression profile exhibited by these two clusters may serve as template for the detection of non-PR enriched genes.
Unlike the other gene clusters, the expression levels of genes within Clusters 1, 4, 8 and 10 did not follow a consistent trend throughout retinal development. Forty-six tags associated with various PR-enriched genes were found in these four different clusters, reflecting the heterogeneity of photoreceptor gene expression patterns on the basis of the onset and peak time of expression [2]. Unlike most of the photoreceptor genes (e.g. Rhodopsin) whose expression values are very low at birth and dramatically increase throughout postnatal retinal development, some of the PR-enriched genes exhibit totally different expression patterns. In Cluster 10, for example, expression of NeuroD is high at early embryonic stages, peaking at P4.5 and decreases significantly in the adult. This might reflect a dual function of NeuroD in cell specification and in regulation of rod photoreceptor survival [13]. In Cluster 8, Mertk, a c-mer proto-oncogene tyrosine kinase, exhibits an embryonic expression profile with peak at E14.5 and is selectively expressed in mature photoreceptor cells. Previous studies have shown that mutations in Mertk are responsible for retinal dystrophy [14]. The diversity of photoreceptor expression profiles may reflect the fact that some of PR-enriched genes are involved in more than one biological process.

Supervised functional classification of tags
The goal was to determine whether a tag represents a PRenriched or a non-PR-enriched gene given a set of SAGE libraries associated with each tag. Among the 1118 tags representing at least 0.01% of the total expression in the ONL library, 261 tags have been identified as PR-enriched genes, which exhibit diverse and complex expression patterns. This highlights the difficulties in using in silico methods to detect key relationships encoded in the SAGE data. Such complexities are further stressed when the original data are projected on 3-dimensional space using wellknown mapping methods, i.e. Principal Component Analysis and Sammon's mapping, which clearly indicate that the two classes are not linearly separable from each other [see Additional file 1].
Based on a comprehensive analysis of the expression of tags previously known to be PR-enriched, Blackshaw et al. [7] introduced four criteria for the selection of candidate PR-enriched genes: 1. Tissue specific (criterion 1): the number of tags in either the hypothalamus or 3T3 libraries is less than 2.
3. Crx dependent (criterion 3): tags are present at a level greater than 1.6-fold higher in the P10.5 crx+/+ library compared to the library of crx-/-mice. We encoded the SAGE dataset using these criteria to study significant associations between the two functional classes and these criteria. The Apriori algorithm was applied to extract association rules from the SAGE data. This algorithm, which was proposed by Agrawal and Skrikant [15], is a well-known association rule learning algorithm. Given a dataset the Apriori algorithm is able to generate association rules that have support and confidence levels greater than user-specified values. A list of all association rules induced from the 324 tags, together with their support and confidence levels, are given in Table 2. The distribution of these 324 tags on the basis of their compliance with the four criteria is given in the additional file [see Additional file 1].
The results obtained showed that a high rate of true positives was observed for genes corresponding to tags meeting more than two of the four criteria. However, when applying these criteria to perform supervised classification, poor prediction results were obtained. For example, only 57 out of 261 PR-enriched tags met all four criteria. From the 196 tags identified by Blackshaw et al. [7] as PRenriched, only about 20% met all four criteria and around 44% met more than three criteria. When applying the four criteria to the data individually, a relatively low rate of true negatives was obtained. For instance, 22 out of 63 non-PR-enriched genes met criterion 1, 11 met criterion 2, 28 met criterion 3, and 40 met criterion 4. Therefore, these classification criteria do not represent accurate and robust rules for the classification. Tables 3 and 4 show the prediction results from 10-fold cross validation for three supervised classifiers using random over-and under-sampling methods respectively. The section of Methods provides a description of these models. For each classifier, the overall classification accuracy (Ac), along with the precision (Pr), Se and Sp for each class, were calculated. Precision is defined as the proportion of predictions that are correct. The mathematical definitions of these metrics are given in the additional file [see Additional file 1]. The corresponding ROC graphs are depicted in Figure 5. For each ROC graph, the area under ROC curve (AUC) was calculated. The AUC has been suggested as a reliable and robust measure for classification performance [16]. A higher value of AUC is associated with a classifier that is both effective and robust, i.e. it presents a better average classification performance across different prediction (decision) thresholds.
Tables 3 and 4 indicate that the classifiers built on data derived from over-sampling methods provided better results than those derived from under-sampling methods in terms of Ac, Se and Sp. The advantages of random oversampling techniques can be further demonstrated by the AUC values shown in Figure 5. This may be explained by the fact that a random under-sampling strategy may throw away potentially useful data. In addition, we found that the relatively simple KStar [17] algorithm can outperform more complex models such as MLP. To investigate the effect of class distribution on the classifier, we varied the class distribution using the data oversampling technique. To further assess the statistical significance of our computational approaches and their predictive performance, a 100-run permutation test was implemented. For each permutated dataset, the results were significantly worse than the one generated using the original data in terms of Ac, Se, and Sp, strongly indicating that the relationship between the data and the labels may be reliably learned by the proposed classifiers. For example, when implementing the permutation test for the KStar model on a balanced dataset and 10-fold cross validation, the random classifiers never performed better than the (original) prediction model built. The obtained average values of Ac, Se and Sp (for class PR-enriched) were significantly lower than the results shown in

Discussion
As the most accessible part of the central nervous system (CNS) and as a highly ordered laminar structure, the ret-ina offers unique opportunities to study both the development and physiology of the CNS. This paper described several in silico approaches, including unsupervised and supervised models, to supporting large-scale analysis of photoreceptor gene expression by SAGE.
By clustering SAGE libraries, a hierarchical tree reflecting the relationship between the libraries was obtained. Libraries from adjacent developmental periods were generally grouped together as expected. However, significant discontinuities were identified at the time of birth and between P6.5 and P10.5, highlighting important developmental periods. By clustering SAGE tags, a more comprehensive expression profile for photoreceptor cells was revealed. It confirmed that most of the PR-enriched genes may be successfully clustered. These genes have lower expression levels before birth with expression dramatically increasing throughout postnatal development. Nevertheless, a closer examination of the clustering results revealed that photoreceptor expression patterns are highly heterogeneous and not separable by linear methods. The diversity of photoreceptor expression profiles reflects the variability in onset of expression which can occur early in development or when photoreceptors undergo terminal differentiation.
With regard to the clustering of SAGE tags, different algorithms with different distance metrics have been previously proposed. For example, it has been suggested that by modelling SAGE data with Poisson statistics better results can be achieved. However, it is relatively computationally expensive and Poisson-based distance has only been assessed as part of the k-means algorithm, which  To demonstrate the usefulness of machine learning-based models in predicting functional associations from the SAGE data, a comprehensive comparative assessment of three supervised classification models was presented. The results indicated that a relatively simple instance-based model (KStar model) performed significantly better than relatively more complex algorithms, e.g. neural networks. This may be partly explained by the fact that neural network-based prediction models typically require larger amounts of high quality training data. Given the limited The ROC curves for three classifiers using random over-sampling and under-sampling methods Figure 5 The ROC curves for three classifiers using random over-sampling and under-sampling methods. Figure  amount of SAGE data available, neural network-based algorithms may not be recommended. Nevertheless, the application of different types of machine learning approaches, including state-of-the-art classifiers such as Support Vector Machine, deserves further investigations.
Due to the imbalanced class distribution of the SAGE data, two re-sampling techniques: random over-sampling and under-sampling methods were studied. The results indicated that over-sampling strategies may provide more accurate predictions than under-sampling methods. This result seems to contradict some studies previously published in the literature [18]. However, other studies have suggested that when there is a significant disproportion in the number of samples belonging to each class, random under-sampling methods could actually ignore many potentially relevant data. Investigations of more sophisticated re-sampling techniques [16] will be part of our further research. We also intend to further address some of the limitations exhibited by such techniques, such as the predictive bias imposed by the incorporation of partially disjoint data sets during cross-validation.
The results suggest that, machine learning approaches such as KStar model may be useful for many purposes. For example, it can be applied as an inexpensive, user-friendly technique to support functional predictions in the retina before applying more comprehensive validation methods. It can be used to effectively select candidate genes for studies of retinal development and function.

Conclusion
The methodology assessed represents an efficient and relatively inexpensive approach for supporting functional predictions. The techniques discussed in this paper can be in principle regarded as a generic framework, scalable to other types of data and biological functions. They can support functional predictions prior to the application of more comprehensive, integrative validation methods.
They can be used to effectively select candidate genes for further studies and may also be combined with other large-scale, data-driven methods to facilitate the inference of transcriptional regulatory networks in the developing retina.

The dataset under study
The database under study was generated by the Cepko group at Harvard Medical School [7]. This database comprises a total of 14 murine SAGE libraries from different tissues and developmental stages, including mouse NIH-3T3 fibroblast cells, adult hypothalamus, developing retina at 2 day intervals from embryonic day (E) 12.5 to postnatal day (P) 6.5, P10.5 retinas from the pairedhomeodomain gene crx knockout mouse (crx-/-) and from wild type (crx+/+) littermates, adult retina and microdissected outer nuclear layer (ONL). A total of 50000 -60000 tags were sequenced from each tissue library, resulting in a dataset large enough to encompass all genes expressed at moderate or high levels in photoreceptor cells.
In order to control for sampling variability and to allow expression examination via ISH, we focused on 1118 tags whose abundance levels represent at least 0.01% of the total mRNA expression in the ONL library as done by Blackshaw et al. [7]. The distribution of these tags within the two retinal functional classes is given in  The class distribution is obtained based on random over sampling method. A 10-fold cross validation was carried out to estimate the true classification error. Class distribution is represented as the number of PR-enriched tags against the number of non-PR-enriched tags. There are 261 PR-enriched and 63 non-PR-enriched tags in the original dataset. In all resampled datasets, the number of PR-enriched tags is as also equal to 261.

Clustering methods
A central problem in the design of clustering models is the selection of a distance function to measure differences between expression profiles. Traditional approaches include the Euclidean distance and Pearson correlation coefficient. It has been shown that with regard to classification of SAGE libraries, Pearson correlation-based clustering analysis may detect significantly similar groups of genes [19]. On the other hand, for clustering of SAGE tags, different algorithms with different distance metrics have been proposed. Buckhaults et al. [20] adopted hierarchical cluster analysis with centered correlation similarity metric to support the identification of diagnostic SAGE tags. Becquet et al. [21] used self-organising tree algorithm to perform clustering analysis on human SAGE data. Based on the implementation of several distance metrics into the kmeans procedure, Cai et al. [8] argued that Poisson-based distances are more appropriate and reliable for analysing SAGE data than traditional approaches. Thus, in the present study, SAGE libraries were clustered by hierarchical clustering with Pearson correlation as a measure of similarity, while SAGE tags were clustered using a k-means clustering algorithm based on the Poisson distance function (PoissonC) specifically designed for SAGE data [8].
The reader is referred to [8] for a detailed description of this algorithm.
Like other k-means models, one limitation of the Pois-sonC algorithm is that it requires users to specify a priori the number of clusters to be detected in the data. To deal with this problem, we used the FOM to estimate the optimal number of clusters encoded in the data [9]. The lower the FOM value is, the higher the predictive power of the algorithm. To compensate for a possible statistical bias when using many clusters, an adjusted FOM was implemented. A detailed description of the calculation of the adjusted FOM can be found in [9].
Due to the computational cost and characteristics of the PoissonC algorithm, the calculation of the adjusted FOM based on PoissonC may be a time-consuming process. It has been suggested that the patterns encoded in the SAGE data revealed by the clusters under different algorithms roughly agree with each other. Moreover, the performance (i.e. classification effectiveness) of clustering algorithms can be improved when the algorithms are applied to normalized data [8]. Thus, we proposed the following framework to estimate the appropriate cluster numbers for PoissonC: 1. Calculate the adjust FOM value using traditional kmeans algorithm, in which the Euclidean distance was used to measure similarity on normalised SAGE data. For a given tag, the abundance in each SAGE library was rescaled to make the sum of tag counts across all 14 libraries equal to one. In our application the adjusted FOM was calculated for a range of numbers of clusters, from 1 to 20.
2. Draw the FOM value against the number of clusters graph.
3. Estimate the optimal number of clusters based on the graph 4. Use the number obtained in last step as an input to perform PoissonC-based clustering analysis.
We adopted an open-source implementation of FOM provided by the Institute for Genomic Research (TIGR) [22] Supervised classification methods Three different classification models were implemented using the freely available Weka package [23]: KStar, C4.5 decision tree, and multilayer perceptron (MLP) neural network model. KStar is an instance-based classifier [17]. Based on information theory, it uses an entropy-based distance function to compute the similarity between two cases. The use of entropy as a distance measure provides a robust approach to handling different types of attributes such as symbolic and real-valued data [17]. We tested MLP models with different architectures without observing prediction performances significantly different to the results reported in this paper. The representative MLP results included here were obtained from a model with one hidden layer consisting of 8 neurones. The learning epochs for MLP was set to 500. For C4.5 algorithm, the minimum number of instances per leaf was equal to 2. A more detailed description of learning parameters for these models can be found in additional file [see Additional file 1].
These three models were assessed as classifiers for PRenriched genes on the basis of SAGE data. To estimate the true classification error rate, a 10-fold cross validation was applied. To further assess the statistical validity of our computational approaches, 100-run permutation tests were performed in this study, i.e.: the labels for each tag were randomly shuffled, classifiers were then implemented, their prediction quality was assessed and this process was repeated for a number of permuted datasets. By counting the times the permuted datasets produced better results than the classifier built on the original dataset, the statistical significance was then established.
A key challenge was to address the class imbalance exhibited by the dataset available -as demonstrated by the highly skewed distribution of tags ( Table 6). The predictive performance of traditional machine learning models may be significantly compromised when dealing with this type of data [24]. The problem is how to effectively distinguish patterns belonging to the minority class, i.e. non-PRenriched class, from the majority class under consideration. To deal with this problem, two data re-sampling techniques were studied: random under-sampling and random over-sampling. The former method randomly eliminates majority class tags to achieve a balanced dataset. The latter randomly replicates minority class samples until a balanced class distribution is reached [25].
Another crucial problem is how to evaluate the performance of classifiers in imbalanced dataset. Traditional techniques include the calculation of classification accuracy based on a confusion matrix. However, it is known that when classes are imbalanced, these two metrics may offer misleading conclusions because they are strongly biased to favour the majority class [16]. For example, if we suppose that all non-PR-enriched tags were incorrectly classified as PR-enriched, a classifier would still be able to achieve a high classification accuracy (around 80%). Such a classifier, however, would be irrelevant. Thus, what is needed is a classification quality indicator that is independent of the class prior probabilities. It has been suggested that the true negative rate (also known as specificity, Sp), true positive rate (also known as sensitivity, Se), and Receiver Operating Characteristic (ROC) graphs are three appropriate metrics to assess the quality of a classifier in the presence of class imbalances [16]. It is evident that a true-positive prediction in the PR-enriched category is a true-negative in the non-PR-enriched category. Therefore, the values of Se and Sp for non-PR-enriched were omitted in Tables 3 to 5.