# Simultaneous gene expression profiling in human macrophages infected with Leishmania major parasites using SAGE

Fatma Guerfali, Dhafer Laouini, Lamia Guizani-Tabbane, Florence Ottones, Khadija Ben-Aissa, Alia Benkahla, Laurent Manchon, David Piquemal, Sondos Smandi, Ons Mghirbi, et al.

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Fatma Guerfali, Dhafer Laouini, Lamia Guizani-Tabbane, Florence Ottones, Khadija Ben-Aissa, et al. Simultaneous gene expression profiling in human macrophages infected with Leishmania major parasites using SAGE. BMC Genomics, 2008, 9 (1), pp.238. 10.1186/1471-2164-9-238. hal-02867264

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# Simultaneous gene expression profiling in human macrophages infected with *Leishmania major* parasites using SAGE

Fatma Z Guerfali¹,², Dhafer Laouini*¹,², Lamia Guizani-Tabbane¹,², Florence Ottones²,³, Khadija Ben-Aissa¹,², Alia Benkahla¹,², Laurent Manchon⁴, David Piquemal⁴, Sondos Smandi¹,², Ons Mghirbi¹,², Thérèse Commes²,³, Jacques Marti²,³ and Koussay Dellagi¹,²

Address: ¹Laboratoire d'Immuno-Pathologie, Vaccinologie et Génétique Moléculaire (LIVGM), WHO Collaborating Center for Research and Training in Leishmaniasis, Institut Pasteur de Tunis, 13 place Pasteur, BP 74, 1002 Tunis-Belvédère, Tunisia, ²Laboratoire International Associé (LIA) "Ingénierie Biomoléculaire", Centre National de Recherche Scientifique (CNRS), France, ³Groupe d'Etude des Transcriptomes (GET), Institut de Génétique Humaine, UPR CNRS 1142, 141, rue de la Cardonille, Montpellier cedex5, 34396, France and ⁴Skuld-Tech, 88, cour des Camisards, Montpellier, 34080, France

Email: Fatma Z Guerfali - fzguerfali@yahoo.fr; Dhafer Laouini* - dhafer_l@yahoo.ca; Lamia Guizani-Tabbane - lamia.guizani@pasteur.rns.tn; Florence Ottones - florence.ottones@u-bordeaux2.fr; Khadija Ben-Aissa - benaissk@mail.nih.gov; Alia Benkahla - alia.benkahla@pasteur.rns.tn; Laurent Manchon - laurent@skuldtech.com; David Piquemal - piquemal@skuldtech.com; Sondos Smandi - smandisondos@yahoo.fr; Ons Mghirbi - mghirbions@yahoo.fr; Thérèse Commes - commes@univ-montp2.fr; Jacques Marti - jmarti@univ-montp2.fr; Koussay Dellagi - koussay.dellagi@ird.fr

Published: 21 May 2008

Received: 5 October 2007

BMC Genomics 2008, 9:238 doi:10.1186/1471-2164-9-238

Accepted: 21 May 2008

This article is available from: http://www.biomedcentral.com/1471-2164/9/238

© 2008 Guerfali et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## Abstract

**Background:** *Leishmania* (L) are intracellular protozoan parasites that are able to survive and replicate within the harsh and potentially hostile phagolysosomal environment of mammalian mononuclear phagocytes. A complex interplay then takes place between the macrophage (MΦ) striving to eliminate the pathogen and the parasite struggling for its own survival.

To investigate this host-parasite conflict at the transcriptional level, in the context of monocyte-derived human MΦs (MDM) infection by *L. major* metacyclic promastigotes, the quantitative technique of serial analysis of gene expression (SAGE) was used.

**Results:** After extracting mRNA from resting human MΦs, *Leishmania*-infected human MΦs and *L. major* parasites, three SAGE libraries were constructed and sequenced generating up to 28,173; 57,514 and 33,906 tags respectively (corresponding to 12,946; 23,442 and 9,530 unique tags). Using computational data analysis and direct comparison to 357,888 publicly available experimental human tags, the parasite and the host cell transcriptomes were then simultaneously characterized from the mixed cellular extract, confidently discriminating host from parasite transcripts. This procedure led us to reliably assign 3,814 tags to MΦs' and 3,666 tags to *L. major* parasites transcripts. We focused on these, showing significant changes in their expression that are likely to be relevant to the pathogenesis of parasite infection: (i) human MΦs genes, belonging to key immune response proteins (e.g., IFNγ pathway, S100 and chemokine families) and (ii) a group of *Leishmania* genes showing a preferential expression at the parasite's intra-cellular developing stage.

<footnote>* Corresponding author</footnote>
**Conclusion:** Dual SAGE transcriptome analysis provided a useful, powerful and accurate approach to discriminating genes of human or parasitic origin in *Leishmania*-infected human MΦs. The findings presented in this work suggest that the *Leishmania* parasite modulates key transcripts in human MΦs that may be beneficial for its establishment and survival. Furthermore, these results provide an overview of gene expression at two developmental stages of the parasite, namely metacyclic promastigotes and intracellular amastigotes and indicate a broad difference between their transcriptomic profiles. Finally, our reported set of expressed genes will be useful in future rounds of data mining and gene annotation.

**Background**

Co-evolution of humans and pathogens has exerted a dual selective pressure on the immune system of the host that strives to control infection and on the pathogens, which have developed various strategies to circumvent the host's immune responses.

*Leishmania* (*L*) parasites are obligate intracellular pathogens that preferentially invade macrophages (MΦs) where they replicate, ultimately causing a heterogeneous group of diseases that affects millions of people mainly in subarid, tropical and subtropical areas [1]. In view of their wide distribution, leishmaniasis remain embedded in impoverished populations and represent a paradigm of neglected diseases [2].

To establish infection, the flagellated metacyclic promastigotes must enter MΦs and avoid triggering host responses. Since MΦs play a dual function in infection, acting as a safe shelter for parasites but also as their ultimate killer, these cells are the alpha and the omega for host resistance or susceptibility to *Leishmania* infection. Cellular events occurring early during MΦ-parasite interactions are likely to influence the fate of infection. MΦs are able to secrete a remarkably diverse set of regulators known to influence the physiological functions and differentiation of neighboring cells. Thus, activation of the innate immunity is required, by migrating parasitized dendritic cells to trigger an adaptive immune response of the Th1-type. The latter induces interferon (IFN) γ-activated MΦs to kill *Leishmania* parasites, promote disease healing and regulate resistance to re-infection as well as vaccine-induced immunity [3].

*Leishmania* have developed a range of sophisticated mechanisms to subvert the leishmanicidal activities of MΦs, by altering gene expression for cytokines, chemokines, transcription factors, membrane receptors and molecules involved in signal transduction in infected cells [4,5]. Although a wealth of crucial information has already been reported on the matter, it generated only a segmented view that hardly recognizes the full value of the biological consequences of this host-parasite conflict on a more global scale.

There is obviously a need for a high-throughput approach that generates a global view, in order to identify the salient modifications of the biological pathways triggered by intracellular parasitism. Applying transcriptomics to study host-pathogen interactions has already contributed important insights to the understanding of the mechanisms of pathogenesis, and it is expanding further with the accumulation of genomic sequences of host organisms (e.g., human) and their pathogens [6]. Indeed, several studies analyzing the human MΦ transcriptome upon viral [7], bacterial [8] or fungal [9] infections have been published. However, to our knowledge, only one study, using the microarray technique has described the effect of *L. major* infection on the transcriptome of human MΦs [10]. More recently, a paper has described at the global scale the abrogation in the human monocytic THP1 cell line of IFNγ gene expression by this parasite species [11].

Gene expression profiling has also been used in several studies of different pathogenic microorganisms, including protozoan parasites [12]. These studies were applied to *L. major*, *L. donovani*, *L. infantum* and *L. mexicana* spp., using differential display or array probes amplified from either cDNA or randomly sheared genomic DNA; these techniques identified differentially expressed genes at different developmental stages [13-20].

Compared to other transcriptomic methods, Serial Analysis of Gene Expression (SAGE) technology has proved to be a powerful tool for the quantitative cataloguing and comparison of genes expressed in cells or tissues from various physiological and pathological conditions. Additionally, SAGE allows one to study the expression profiles of both known and unknown genes and as a result contributes to better genome annotation [21]. This technology was successfully applied to study the transcriptome of different parasites e.g., *Plasmodium falciparum* [22]. *Schistosoma mansoni* [23] and *Trypanosoma congolense* [24], among others [21].

As far as we know, this is the first study using the SAGE strategy that provides a high-throughput simultaneous analysis of gene expression in the context of the *Leishmania*-human MΦ encounter. Although the impact of the
parasite on the human transcriptome appeared globally marginal, we identified several genes corresponding to diverse functional pathways that were differentially expressed upon infection, suggesting their likely involvement in the infectious process. Interestingly, we individualized genes involved in complement or IFNγ pathways, and others belonging to S100 proteins, MHC molecules, apoptosis, cytokines and chemokines families. Concurrently, our SAGE analysis unveiled a deep variation in parasite transcript abundance; such characterized transcripts could contribute to understanding the dynamics of gene expression in the intracellular parasite-stage.

## Results

Three SAGE libraries were generated from: (i) resting human MΦs (monocyte-derived MΦs or MDMs), (ii) human MΦs infected with *L. major* metacyclic promastigotes ("MDM+Lm", infection rates ranged from 87% to 92%) and (iii) *L. major* metacyclic promastigotes (Lm). A total of 28,173, 57,514 and 33,906 tags (corresponding to 12,946, 23,442 and 9,530 unique tags) were obtained from each library respectively (Table 1). Additional file 1 (Unique SAGE tags as a function of total sequenced tags in the different constructed libraries) shows the unique SAGE tags as function of total sequenced tags (for all tags panel A and for tags present at least twice, panel B). The entire dataset discussed in this publication has been deposited in NCBI Gene Expression Omnibus [25] and is

<table><caption>Table 1: Distribution of sequenced tags from different SAGE libraries.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Libraries</th></tr><tr><th>MDM</th><th>MDM+Lm</th><th>Lm</th></tr></thead><tbody><tr><td>No of tags</td><td>28,173<br>(12,946)</td><td>57,514<br>(23,442)</td><td>33,906<br>(9,530)</td></tr><tr><td>1 copy</td><td>10,494<br>(10,494)</td><td>17,172<br>(17,172)</td><td>6,564<br>(6,564)</td></tr><tr><td>2 copies</td><td>2,206<br>(1,103)</td><td>5,512<br>(2,756)</td><td>2,746<br>(1,373)</td></tr><tr><td>3 copies</td><td>1,233<br>(411)</td><td>3,549<br>(1,183)</td><td>1,668<br>(556)</td></tr><tr><td>4 - 10 copies</td><td>3,906<br>(686)</td><td>10,019<br>(1,794)</td><td>5,113<br>(901)</td></tr><tr><td>11 - 20 copies</td><td>1,833<br>(127)</td><td>4,020<br>(284)</td><td>2,163<br>(149)</td></tr><tr><td>21 - 100 copies</td><td>4,432<br>(107)</td><td>8,463<br>(211)</td><td>6,149<br>(291)</td></tr><tr><td>&gt; 100 copies</td><td>4,069<br>(18)</td><td>8,774<br>(42)</td><td>9,700<br>(42)</td></tr><tr><td>Occurrence up to</td><td>689</td><td>720</td><td>753</td></tr></tbody></table>

This table shows number of tags present at 1, 2, 3, 4 to 10, 11 to 20, 21 to 100 and > 100 copies and number of occurrences in each library. Numbers in parentheses indicate different unique tags in each category.

accessible through GEO series accession number [GSE10442].

### Data analysis allows good discrimination between human and parasite tags generated in the same "MDM+Lm" mixed SAGE library

To identify the transcripts that were modulated upon infection, we compared the three libraries that were constructed. We found that the MDM and Lm libraries had 194 tags in common and 3,857 tags were shared by the "MDM+Lm" and Lm libraries. In addition, 2,535 tags were common between MDM and "MDM+Lm" libraries (Figure 1A). Unexpectedly, this initial analysis showed that a large number of tags were specifically present in "MDM+Lm" library.

In order to more confidently assign these tags to a human origin, they were compared to an assembled composite matrix containing a total of 26,176 unique tags and built from (i) nine publicly available leukocyte SAGE libraries, that were generated from freshly isolated monocytes, M-CSF differentiated, GM-CSF differentiated and LPS activated cells, immature and mature monocyte-derived dendritic cells and un-fractionated populations of leukocytes, (ii) the non-infected MDM library (this paper) and (iii) a second in-house-generated MDM-M library (Ottones et al., unpublished data; see Methods section for details).

As shown on the Venn diagram in Figure 1B, only 191 tags were shared by all libraries (ABC subset) and 196 tags (AC subset) were shared by the Lm library (referred to as C) and the non-infected human leukocyte libraries (referred to as A). This low level (1.4%) of synonymy between human and parasite tags indicated that we could accurately discriminate human transcripts from parasite transcripts in the infected "MDM+Lm" sample (referred to as B). It is not excluded that some of the 3,814 human tags sorted in AB might correspond to parasite tags specifically expressed by the amastigote stage and absent in the promastigote-derived library. However, in this case we assumed that their number was likely to be in the same range as in AC and ABC, so that most AB tags could be reasonably considered as MΦ-specific.

It must be stressed here that the apparently large number of "MDM+Lm"-specific tags must be interpreted with caution because most of them were observed only once and may result from sequencing errors, the major source of noise inflating the number of unique tags. Indeed, when we reanalyzed the data excluding tags that appeared only once (unless present at least twice in another library), we ended up with 3,506 human tags sorted in AB, 2,960 tags common to the Lm and "MDM+Lm" libraries (BC subset, Figure 1D). Interestingly, when excluding unique tags, number of these present only in "MDM+Lm" library
<figure><img src="image_3.png" /></figure>

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<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>**Figure 1**</figcaption></figure>

Venn diagram comparing the parasite-infected MDM and L. major SAGE libraries with the MDM non-infected library or with other publicly available leukocyte libraries. This figure is not drawn to scale. All unique tags species detected within the "MDM+Lm" library were compared to unique tags from the Lm and MDM libraries (A), or from the Lm library and a collection of publicly available human leukocyte tags (B). Panels C and D show comparisons after withdrawing tags found only once (unless present twice in another library).

dropped from 15,771 to 1,678 tags. In fact, most of the tags occurring at high frequencies in the "MDM+Lm" library were sorted into the AB or BC subsets. Since these tags could be safely considered as identifying human (AB) or parasite (BC) transcripts, their respective frequencies could be taken as representative of the actual figure of the two species-specific transcripts in the initial mRNA inputs. Thus, we estimated that human and parasite mRNAs contributed to 51% and 49% (54% and 46% for tags > 1), respectively, of the "MDM+Lm" sample.

**Impact of L. major infection on MΦ transcriptome**

To investigate MΦ tags that were modulated by *Leishmania* infection, we compared total tags present in the MDM library to those present in the "MDM+Lm" library, after withdrawing tags of parasitic origin. Most tags were expressed at similar levels between resting and *Leishmania*-infected MDM. A semi-logarithmic plot (Figure 2) showed that both up- and down-modulated tags were distributed within a bell-shaped symmetric curve, though tailed for the tags up-regulated 12–16 times. This ratio profile
<figure><img src="image_7.png" /><figcaption>**Figure 2**
Comparison of gene expression modulation in the *L. major*-infected "MDM+Lm" library to MDM library. A semi-logarithmic plot shows that both up- and down-modulated tags were decreased within a bell-shaped curve except for a tail corresponding to tags upregulated 12 to 16 times. The relative expression of each transcript was determined by dividing the number of tags observed in the MDM library by the number of the same tags observed in the "MDM+Lm" library. To avoid division by 0, we used a tag value of 1 for any tag that was not detectable. These ratios are plotted on the abscissa. The number of tag species comprising each ratio is plotted on the ordinate.</figcaption></figure>

clearly indicates that only some transcripts (1.4% down-modulated, 1% up-modulated) were altered by *Leishmania* infection. A scatter plot showing statistically scaled modulated transcripts is shown in Additional file 2: Scatter plot showing the comparison of MDM versus "MDM+Lm" SAGE libraries.

Starting with the matrix registering initial SAGE data, we recalculated tag frequencies in each of the 11 libraries, replaced by the nearest integer of tag frequencies for 10,000 counts. For every tag, the sum of normalized frequencies was calculated and tags were discarded for values less than 2. The resulting matrix (2,918 rows) was split into two parts: the first registering the 500 tags with the highest sum of frequencies (Top500) and the second registering tags with lower frequencies (2,418).

Using Principal Component Analysis, observable either on 2D (not illustrated) or 3D graphs (Figure 3), landscapes generated with the Top500 dataset showed that the closest relationship was between "MDM+Lm" and their MDM control, and both were in the vicinity of the MDM-M sample. For the 2,418 dataset, the closest relationship was between the MDM and MDM-M libraries. Data analysis by Hierarchical Clustering using various modules of the TIGR MultiExperiment Viewer Package (MeV 4.0, 2006) led to similar conclusions (Additional file 3: Hierarchical Clustering raised with the 500 most abundant tags or with the next 2,418 tags).

Taking these data sets as a whole, MDM infected with *L. major* parasites showed a transcriptional profile closer to that of non-infected cells but clearly different from that of
<figure><img src="image_8.png" /><figcaption>**Figure 3**
Covariance Analysis of Gene expression profiles raised with the 500 most abundant tags (A) or with the next 2418 tags (B). This figure shows similarities of transcriptome profiles between different SAGE tag collections. "MDM+Lm" corresponds to MDM-specific genes extracted from the *Leishmania*-infected sample, MDM to the library built with the same MDM preparation and MDM-M to a second in-house library prepared independently with MDMs raised in similar conditions from another pool of donors (Ottones et al., unpublished data). Mono, LPS, GM-CSF, M-CSF, IDC, MADC, leuk, WBC-N and WBC-Bc correspond to publicly available SAGE libraries (see Methods' section).</figcaption></figure>

LPS-activated MΦs. In addition, the expression profiles of MDM, whether infected or not, were the closest to those of GM-CSF- and M-CSF-elicited cells.

These results globally indicate that the internalization of viable *Leishmania* parasites in macrophage and their intracellular multiplication appear to induce only minor changes in the basal transcriptome profile with no indication of an obvious inflammatory response. Nevertheless, a detailed comparison of MDM and "MDM+Lm" profiles revealed changes that might be biologically relevant to the infectious process.

**Quantitative PCR experiments confirmed the changes in gene expression detected in human SAGE libraries**

Human tags were assigned to their corresponding genes using Preditag® software [26] and BLAST and then to their related biological processes using Gene Ontology [27]. Quantitative real-time PCR was then used to assess the accuracy of the generated data. Several candidate families of genes showing differential expression patterns in our human SAGE libraries were selected. To compare the Q-PCR and SAGE data, "MDM+Lm"/MDM, expression ratios were calculated (Table 2).

On the whole, data generated by SAGE or Q-PCR showed a good concordance between the trends (up- or down-regulation) of expression ratios for 83% of the genes tested,

although the response measured by the two techniques might differ in magnitude. The best correlations between SAGE and Q-PCR data were observed for the genes that were abundantly expressed.

**L. major infection induces a discreet but selective change in human MΦ transcripts**

Following tag annotation, we used the STRIPE software [28] to screen for any spatial clustering across the human genome (Additional file 4: Spatial Clustering across the Human genome of tags extracted from MDM and "MDM+Lm" libraries). Statistical analysis of transcripts did not show any specific up- or down-modulated gene clustering across the human chromosomes. Further analysis showed that the response of MDM to *Leishmania* infection is characterized by the expression of genes encoding for proteins involved in several biological processes (Figure 4 and Additional file 5: Extended names of abbreviated genes).

**Complement activation**

We first focused our analysis on genes involved in innate immunity such as complement components. *In vivo* opsonization of *Leishmania* promastigotes by C3b and C3bi permits the interaction with the MΦ complement receptors 1 (CR1) and 3 (CR3), respectively. In addition, it is known that C1qA and C1qB molecules are highly up-regulated by activation. Our results showed a drastic inhibition of gene transcription of these latter two proteins upon MDM infection. Several other transcripts, such as C5R1, C2, C1qG or RGC32, were also down-regulated after *L. major* infection.

**S100 proteins**

Recently, a novel group of calcium-binding molecules, namely the phagocytic S100 proteins, was described as pro-inflammatory factors. These endogenous damage-associated molecular pattern (DAMP) molecules, also called alarmins, play an important role in innate immunity. Our results showed that S100A6, S100A8, and S100A9 transcripts were repressed upon *L. major* infection. The S100A8/A9 complex has been shown to play an important role in phagocyte NADPH oxidase activation, which contributes to intracellular parasite killing. Their inhibition could be way for *L. major* to avoid reactive oxygen intermediate (ROI) killing. Two other S100 family members were up-regulated (i.e., S100A10 and S100A11). These two proteins are described as interacting with the N-termini of annexins A1 and A2, forming a sophisticated Ca2+ sensing system. The annexin A2, which acts as a receptor of plasmin, a potent pro-inflammatory activator of human monocytes, was down-regulated twice. The identification of several transcripts of this family modulated by *Leishmania* suggests a novel mechanism of inflammation and tissue damage in infected MΦs.
<table><caption>Table 2: Quantitative RT-PCR validation of human gene transcripts.</caption><thead><tr><th>Gene symbol</th><th>Gene name</th><th>Fold Change</th><th>MDM+Lm/MDM Ratio&amp;occurrences</th><th>2<sup>-ΔΔCT</sup></th></tr></thead><tbody><tr><td colspan="5"><strong>Chemokine activity</strong></td></tr><tr><td>IL-8</td><td>Interleukin 8</td><td>7.5 (15:2)</td><td></td><td>20</td></tr><tr><td>CXCL3</td><td>Chemokine (C-X-C motif) ligand 3</td><td>8 (8:1)</td><td></td><td>11.67</td></tr><tr><td>CXCL9</td><td>Chemokine (C-X-C motif) ligand 9</td><td>0.25 (2:8)</td><td></td><td>0.19</td></tr><tr><td>CXCL10</td><td>Chemokine (C-X-C motif) ligand 10</td><td>0.25 (0:4)</td><td></td><td>0.15</td></tr><tr><td colspan="5"><strong>Cytoskeleton</strong></td></tr><tr><td>GSN</td><td>Gelsolin (amyloidosis, Finnish type)</td><td>0.44 (12:27)</td><td></td><td>0.29</td></tr><tr><td>ADFP</td><td>Adipose differentiation-related protein</td><td>13 (13:1)</td><td></td><td>2.43</td></tr><tr><td>ACTG1</td><td>Actin, gamma 1</td><td>0.62 (5:8)</td><td></td><td>0.40</td></tr><tr><td colspan="5"><strong>Extra-cellular space</strong></td></tr><tr><td>MMP12</td><td>Matrix metallopeptidase 12 (macrophage elastase)</td><td>0.46 (2:9)</td><td></td><td>0.22</td></tr><tr><td>PTPNS1</td><td>Protein tyrosine phosphatase, non-receptor type S1</td><td>0.12 (1:8)</td><td></td><td>0.57</td></tr><tr><td>FNI</td><td>Fibronectin 1</td><td>0.05 (1:19)</td><td></td><td>0.28</td></tr><tr><td colspan="5"><strong>Inflammatory Response</strong></td></tr><tr><td>FCGR3</td><td>Fc fragment of IgG, low affinity IIIb, receptor (CD16)</td><td>0.22 (2:9)</td><td></td><td>0.22</td></tr><tr><td>C5R1</td><td>complement component 5 receptor 1 (C5a ligand)</td><td>0.21 (3:14)</td><td></td><td>0.65</td></tr><tr><td>CHIT1</td><td>chitinase 1 (chitotriosidase)</td><td>0.33 (0:3)</td><td></td><td>0.86</td></tr><tr><td>TNFSF10</td><td>tumor necrosis factor (ligand) superfamily, member 10</td><td>8 (8:1)</td><td></td><td>0.66</td></tr><tr><td colspan="5"><strong>Membrane-associated protein</strong></td></tr><tr><td>AP2S1</td><td>Adaptor-related protein complex 2, sigma 1 subunit</td><td>0.50 (1:2)</td><td></td><td>0.38</td></tr><tr><td>GPNMB</td><td>Glycoprotein (transmembrane) nmb</td><td>0.81 (36:44)</td><td></td><td>1.07</td></tr><tr><td>CD164</td><td>CD164 molecule, sialomucin</td><td>0.16 (1:6)</td><td></td><td>0.79</td></tr><tr><td>SCARB2</td><td>Scavenger receptor class B, member 2</td><td>0.25 (0:4)</td><td></td><td>0.66</td></tr><tr><td colspan="5"><strong>Processing, folding and targeting</strong></td></tr><tr><td>SRP9</td><td>signal recognition particle</td><td>0.16 (1:6)</td><td></td><td>0.49</td></tr><tr><td>RPL13A</td><td>Ribosomal protein L13a</td><td>0.44 (12:27)</td><td></td><td>0.28</td></tr><tr><td>LGMN</td><td>Legumain</td><td>7 (7:1)</td><td></td><td>0.90</td></tr><tr><td>HSPA8</td><td>Heat shock 70 kDa protein 8</td><td>0.33 (5:15)</td><td></td><td>0.58</td></tr><tr><td colspan="5"><strong>Response to stress</strong></td></tr><tr><td>SOD2</td><td>Superoxide dismutase 2, mitochondrial</td><td>2.5 (15:6)</td><td></td><td>2.17</td></tr><tr><td>GLRX</td><td>Glutaredoxin (thioltransferase)</td><td>0.66 (4:6)</td><td></td><td>0.78</td></tr><tr><td>MX1</td><td>Myxovirus resistance1, IFN-inducible protein p78</td><td>3 (9:3)</td><td></td><td>2.21</td></tr><tr><td>P4HB</td><td>Procollagen-proline, 2-oxoglutarate 4-dioxygenase</td><td>0.27 (3:11)</td><td></td><td>0.63</td></tr><tr><td colspan="5"><strong>Signal transduction events</strong></td></tr><tr><td>S100A8</td><td>S100 calcium binding protein A8 (calgranulin A)</td><td>0.36 (18:50)</td><td></td><td>0.15</td></tr><tr><td>SH3BGRL3</td><td>SH3 domain binding glutamic acid-rich protein-like 3</td><td>0.12 (0:8)</td><td></td><td>0.44</td></tr><tr><td>MAPKAPK3</td><td>Mitogen-activated protein kinase-activated protein kinase3</td><td>0.11 (0:9)</td><td></td><td>0.56</td></tr><tr><td>ILK</td><td>Integrin-linked kinase</td><td>1 (4:4)</td><td></td><td>0.64</td></tr><tr><td>PLCB2</td><td>Phospholipase C, beta 2</td><td>0.16 (0:6)</td><td></td><td>0.40</td></tr><tr><td>PTPRC</td><td>Protein tyrosine phosphatase, receptor type, C</td><td>0.5 (3:6)</td><td></td><td>0.41</td></tr></tbody></table>
<table><caption>**Table 2: Quantitative RT-PCR validation of human gene transcripts. (Continued)**</caption><thead><tr><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>SH3BP2</td><td>SH3-domain binding protein 2</td><td>0.44 (4:9)</td><td>0.66</td></tr><tr><td colspan="4"><strong>Transcription-related activity</strong></td></tr><tr><td>IRF1</td><td>Interferon regulatory factor 1</td><td>0.28 (2:7)</td><td>0.50</td></tr><tr><td>IRF7</td><td>interferon regulatory factor 7</td><td>8 (8:1)</td><td>1.37</td></tr><tr><td>STAT1</td><td>Signal transducer and activator of transcription 1</td><td>0.22 (4:18)</td><td>0.63</td></tr><tr><td>DDX3X</td><td>DEAD (Asp-Glu-Ala-Asp) box polypeptide 3</td><td>0.81 (9:11)</td><td>0.66</td></tr><tr><td>CEBPB</td><td>CCAAT/enhancer binding protein (C/EBP), beta</td><td><strong>12 (12:1)</strong></td><td><strong>0.81</strong></td></tr><tr><td>EGFR</td><td>Early growth response 1</td><td><strong>4.5 (18:4)</strong></td><td><strong>0.61</strong></td></tr><tr><td colspan="4"><strong>Lysosomal-associated protein</strong></td></tr><tr><td>CTSS</td><td>Cathepsin S</td><td>0.15 (3:20)</td><td>0.58</td></tr><tr><td>LAPTM5</td><td>Lysosomal-associated protein</td><td><strong>1 (17:17)</strong></td><td><strong>0.52</strong></td></tr><tr><td>IFI30</td><td>Interferon, gamma-inducible protein 30</td><td>0.58 (67:115)</td><td>0.53</td></tr></tbody></table>

The expression of 42 gene transcripts, identified by SAGE analysis, were tested using quantitative PCR. Results show the occurrences (between parentheses) and the ratio of "MDM+Lm"/MDM tags. The last column shows the 2-ΔΔCt values, obtained by quantitative PCR.

### MHC class I and class II molecules

We then investigated major histocompatibility complex (MHC) genes after *L. major* infection. Class II antigen-processing genes, including some cathepsins (i.e., *cathepsin S* or *cathepsin C*) and genes involved in class II presentation such as *CD74*, *Human Leukocyte Antigen (HLA)-DP*, *HLA-DQ*, *HLA-DR* and *HLA-DM*, were repressed in MDM cells relatively to samples from uninfected cells.

In contrast to the class II pathway, genes involved in antigen processing and presentation via the MHC class I pathway (i.e., *β2-microglobulin*, *tapasin*, *HLA-C*, *HLA-F* and *HLA-G*) were not altered by parasite infection at 24 h, except for *HLA-B* and *calnexin* genes, which were down-regulated, and *HLA-A*, which was up-regulated.

### Interferon (IFN)γ pathway

Inhibition of the IFNγ pathway appears to be a mechanism that is widely used by different pathogens to subvert the host responses [29]. IFNγ, a potent inducer of MHC class II expression in MΦs and hence of antigen presentation, once bound to its receptor, leads to STAT1 phosphorylation and translocation to the nucleus and to IFN regulatory factor (IRF) activation, which play a key role in the induction of a large set of MΦ effector molecules involved in host defense and inflammation. Our results showed a significant decrease in *IFNGR2*, *STAT1* and *IRF1* transcripts in parasite infected MDM.

### Apoptosis

Programmed cell death plays a pivotal role in normal tissue development and in pathological conditions [30]. Interestingly, *Leishmania* inhibits host cell apoptosis pathways in order to favor its own multiplication [31].

We annotated several tags as apoptotic and anti-apoptotic family members. Transcripts of *caspase 3* (CASP3), *Acyl coenzyme A-binding protein (DBI)*, *death inducer-obliterator-1* (DIDO1) and *Bcl2-related protein A1* (BCL2A1), are pro-apoptotic proteins or induced by apoptosis, and were down-modulated upon infection. In addition, an anti-apoptotic gene transcript called *defender against cell death-1* (DAD1) was slightly induced.

### Cytokines and chemokines

We finally focused on cytokine and chemokine transcripts. Several were up-regulated upon infection e.g., *IL-8*, *CXCL2*, *CXCL3* or *NFIL3*. On the other hand, we noted that the mRNA expression levels of different chemokines and their ligands, i.e., *CCR2*, *CCL5*, *CCL17*, *CXCL9*, *CXCL10*, *CCL4L2* or *CKLFSF3*, were drastically inhibited upon infection. As expected, the transcripts of the pivotal cytokine IL10 were strongly up-regulated (from 101 to 233 occurrences), even though the assigned tag did not correspond to the tag directly following the poly-A signal. However, we were not able to unambiguously assign tags corresponding to other cytokines, classically reported to be altered after *Leishmania* infection (i.e., *IL12*, *IL18* or *TNFα*).

### Transcriptome analysis of extra- and intracellular specific stages of L. major parasites

Our analysis also included the study of *Leishmania* transcriptome alterations, once parasites were exposed to the phagolysosomal intracellular environment and transform into amastigotes. We focused on (i) the most highly expressed transcripts at the metacyclic stage and (ii) differentially expressed tags between the intracellular and extra-cellular stages of *L. major* parasite.
Complement
Components

<figure><img src="image_9.png" /></figure>

IFNγ pathway

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

HLA Class I and
II Presentation

<figure><img src="image_12.png" /></figure>

Cytokines and
Chemokines

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

Apoptosis
signalling

<figure><img src="image_15.png" /></figure>

S100 Proteins

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /><figcaption>**Figure 4**</figcaption></figure>

**Examples of gene transcripts categorized into functional classes involved in defense MΦ programs.** This figure shows some gene transcripts related to defense pathways in human macrophages extracted from a list of down- or up-regulated transcripts after exposure to Leishmania infection and clustered into functional families (data not shown). Data are reported according to their scale of fold expression values ranging from -10 (green color) to +10 (red color). For extended names of genes abbreviated see Additional file 5.
## Annotation of L. major tags from the metacyclic parasite SAGE library

A total of 33,906 tags corresponding to 9,530 unique tags were generated from the metacyclic stage of the *L. major* library (Table 1). The 106 most abundant ones represented 1.1% of the total number of unique tags (106/9,530) but totaled up to 40% of the entire collection of parasite tags (13,636/33,906).

Tag-to-gene mapping was done for these most abundant (Table 3) and total tags of the *L. major* GLC94 library transcripts using BLAST against the Friedlin *L. major* genome. This snapshot of the major parasitic transcripts showed that 35 out of 106 tags (33%) mapped unambiguously to their genes with 100% sequence identity (downstream stop codon). Twenty-seven tags mapped to a unique gene

and 8 mapped to two or more genes belonging to the same family. Among these assigned tags, 32 tags were located in the 3' region, downstream the stop codon and three matched inside the CDS. Finally, from the tags present at least twice (3,163 tags), we were able to assign 1,068 tags to their genes (Additional file 6: "Tag to gene assignation" of all parasitic tags present at least twice and extracted from Lm library).

Through this annotation, several transcripts were found encoding for ribosomal proteins including 40S, 60S, L1a, L27, I3, S25 and S27a ribosomal proteins. This analysis also revealed the abundant expression of mRNA encoding for histones H1, H2A and H3, for ubiquitin related proteins, tubulin and microtubule associated protein among others (Table 3).

<table><caption>Table 3: The most abundant and annotated transcripts expressed in metacyclic L. major grown in culture.</caption><thead><tr><th>Tag</th><th>Absolute Occurrences in Lm library</th><th>Access number</th><th>Protein name</th></tr></thead><tbody><tr><td>CATGAGCGACCACC</td><td>329</td><td>LmjF31.2030;LmjF31.1900</td><td>ubiquitin-fusion protein</td></tr><tr><td>CATGATGGGGCGCT</td><td>266</td><td>LmjF35.1890</td><td>60S ribosomal protein L5, putative</td></tr><tr><td>CATGTCATTTCTCG</td><td>206</td><td>LmjF22.0030</td><td>60S ribosomal protein L11 (L5, L16)</td></tr><tr><td>CATGTATGTGCGCC</td><td>163</td><td>LmjF36.0600</td><td>ubiquitin/ribosomal protein S27a</td></tr><tr><td>CATGAGGCACTGTG</td><td>149</td><td>LmjF16.0600</td><td>histone h3, putative</td></tr><tr><td>CATGCGCGGCAGAC</td><td>135</td><td>LmjF13.0280 to 13.0390</td><td>alpha tubulin</td></tr><tr><td>CATGGCAACTGTCG</td><td>127</td><td>LmjF36.3740</td><td>60S ribosomal protein L34, putative</td></tr><tr><td>CATGTTATTTGGCC</td><td>123</td><td>LmjF36.2860;LmjF36.2870</td><td>40S ribosomal protein S24e</td></tr><tr><td>CATGTCAAATTTGT</td><td>115</td><td>LmjF33.0792 to 33.0819</td><td>beta-tubulin</td></tr><tr><td>CATGTAATTTGACTC</td><td>113</td><td>LmjF29.2370</td><td>60S ribosomal protein L39, putative</td></tr><tr><td>CATGCGTCCACCGC</td><td>111</td><td>LmjF24.2080</td><td>40S ribosomal protein S8, putative</td></tr><tr><td>CATGGTTCGCGTGT</td><td>110</td><td>LmjF35.0600</td><td>60S ribosomal protein L18a, putative</td></tr><tr><td>CATGCCGCATCACT</td><td>108</td><td>LmjF36.0990</td><td>40S ribosomal protein S10, putative</td></tr><tr><td>CATGGTGTGCAGGT</td><td>99</td><td>LmjF28.2560</td><td>40S ribosomal protein S17, putative</td></tr><tr><td>CATGTGACCCGTAT</td><td>98</td><td>LmjF31.0900</td><td>Hypothetical protein, conserved</td></tr><tr><td>CATGGCGTGCATTG</td><td>92</td><td>LmjF11.1110;LmjF11.1130</td><td>60S ribosomal protein L28, putative</td></tr><tr><td>CATGTGTGCGGATC</td><td>84</td><td>LmjF25.1190</td><td>ribosomal protein S25</td></tr><tr><td>CATGCCACTTGTTT</td><td>83</td><td>LmjF35.2190</td><td>60S ribosomal protein L12, putative</td></tr><tr><td>CATGAAGCTTCTGT</td><td>81</td><td>LmjF35.3290</td><td>60S ribosomal subunit protein L31</td></tr><tr><td>CATGGACGGTAGGC</td><td>69</td><td>LmjF29.1090</td><td>ribosomal protein L1a, putative</td></tr><tr><td>CATGGACCCGGACG</td><td>67</td><td>LmjF15.0950</td><td>40S ribosomal protein S3, putative</td></tr><tr><td>CATGCGCGGCCAGA</td><td>64</td><td>LmjF32.2690</td><td>ribosomal protein L27, putative</td></tr><tr><td>CATGCAAGCGAGGA</td><td>62</td><td>LmjF21.1070</td><td>40S ribosomal protein S23, putative</td></tr><tr><td>CATGCCCGCAGTAC</td><td>59</td><td>LmjF27.1190</td><td>histone H1, putative</td></tr><tr><td>CATGAATGCATCTT</td><td>59</td><td>LmjF34.2900</td><td>ribosomal protein L3, putative</td></tr><tr><td>CATGTGTACAGCCC</td><td>57</td><td>LmjF19.0820 to 19.0900</td><td>microtubule associated protein-like protein</td></tr><tr><td>CATGTGCAAGACTC</td><td>51</td><td>LmjF34.0440</td><td>ribosomal protein S25</td></tr><tr><td>CATGGCTTGCTGTG</td><td>50</td><td>LmjF12.0340</td><td>Hypothetical protein, unknown function</td></tr><tr><td>CATGCGACGAAAGA</td><td>50</td><td>LmjF29.1730</td><td>histone H2A, putative</td></tr><tr><td>CATGTATGCGTTTT</td><td>49</td><td>LmjF36.3620</td><td>Hypothetical protein, conserved</td></tr><tr><td>CATGGGCGGTCTCT</td><td>49</td><td>LmjF27.1390;LmjF27.1380</td><td>60S acidic ribosomal subunit protein</td></tr><tr><td>CATGAGTGGCGAGG</td><td>42</td><td>LmjF21.1550</td><td>40S ribosomal protein S11, putative</td></tr><tr><td>CATGCGCAGCATCC</td><td>41</td><td>LmjF27.1380</td><td>60S acidic ribosomal subunit protein</td></tr><tr><td>CATGACAAATAGTC</td><td>41</td><td>LmjF13.0450</td><td>Hypothetical protein, conserved</td></tr><tr><td>CATGATGCTGCCGC</td><td>41</td><td>LmjF06.0410;LmjF06.0415</td><td>60S ribosomal protein L19, putative</td></tr></tbody></table>

This table shows annotated tags from the 106 most abundant tags, their occurrences in the Lm library, their corresponding access numbers and protein names in GeneDB. A table with all annotated *Leishmania* tags are available as Additional file 6.
Preferential expression of transcripts in L. major parasites at the intracellular stage

When comparing libraries, we found 3,666 tags (2,960 with more than one copy) of them co-expressed in the "MDM+Lm" and Lm, but absent in other human libraries (Figure 1B and 1D). Statistical analysis and fold increase levels revealed that 697 of these tags were differentially expressed between metacyclic promastigotes (Lm library) and intracellular parasites ("MDM+Lm" library), with 420 tags preferentially expressed by intramacrophagic para-

sites (p < 0.05 for 193 and p ≥ 0.05 for 227 of them but with a fold increase greater than 3.5-fold).

Tag-to-gene mapping of these 420 tags showed that 113 (27%) of them were unambiguously mapped to their genes. Among them, only 105 tags mapped to a unique gene and eight mapped to genes within the same family.

This analysis also revealed differential expression of mRNA encoding for different proteins including an amas-

<table><caption>Table 4: L. major annotated transcripts preferentially expressed in human infected MDMs.</caption><thead><tr><th>Tag</th><th>Occurrence</th><th>IP 2-ΔΔCT</th><th>AP 2-ΔΔCT</th><th>Accession number</th><th>Protein name</th></tr></thead><tbody><tr><td>CATGACAATCTTGT</td><td>1:4</td><td>ND</td><td>ND</td><td>LmjF01.0490</td><td>Long chain fatty acid CoA ligase, putative</td></tr><tr><td>CATGGCAGTTATCT</td><td>18:36</td><td>ND</td><td>ND</td><td>LmjF04.0750</td><td>60S ribosomal protein L10, putative</td></tr><tr><td>CATGGGCGTCGCGC</td><td>32:48</td><td>ND</td><td>ND</td><td>LmjF08.0670to08.0760</td><td>amastin-like protein</td></tr><tr><td>CATGCCGGTGGGCC</td><td>4:14</td><td>ND</td><td>ND</td><td>LmjF15.1040 to 15.1160</td><td>tryparedoxin peroxidase</td></tr><tr><td>CATGCCAGCGGGAT</td><td>5:16</td><td>3.2 ± 0.11</td><td>9.4 ± 0.46</td><td>LmjF17.1220</td><td>Histone H2B</td></tr><tr><td>CATGGAATCCAAA</td><td>1:6</td><td>ND</td><td>ND</td><td>LmjF20.0870</td><td>ATP-dependent RNA helicase, putative</td></tr><tr><td>CATGGTGTAGAGGA</td><td>2:6</td><td>ND</td><td>ND</td><td>LmjF22.0110</td><td>GMP synthase, &amp; glutamine transferase</td></tr><tr><td>CATGAGCGCCTGAA</td><td>1:5</td><td>ND</td><td>ND</td><td>LmjF24.1090</td><td>Predicted multipass transmembrane</td></tr><tr><td>CATGACGGTACCGT</td><td>1:5</td><td>ND</td><td>ND</td><td>LmjF26.0210</td><td>Silent information regulator 2</td></tr><tr><td>CATGCGCGAACTAG</td><td>1:4</td><td>ND</td><td>ND</td><td>LmjF26.1700</td><td>Fatty acid desaturase, putative</td></tr><tr><td>CATGCTAACGTTCT</td><td>1:21</td><td>1.3 ± 0.09</td><td>0.8 ± 0.16</td><td>LmjF26.1710</td><td>Cytochrome c oxidase subunit V, putative</td></tr><tr><td>CATGCGACTTCGCT</td><td>1:4</td><td>ND</td><td>ND</td><td>LmjF26.2220</td><td>Ribosomal protein I38, putative</td></tr><tr><td>CATGGCTGTATCTC</td><td>2:21</td><td>ND</td><td>ND</td><td>LmjF26.2220</td><td>Ribosomal protein I38, putative</td></tr><tr><td>CATGGCTTCCCTCG</td><td>27:44</td><td>ND</td><td>ND</td><td>LmjF26.2330</td><td>60S ribosomal protein L35, putative</td></tr><tr><td>CATGGCGCAGTCCC</td><td>1:4</td><td>ND</td><td>ND</td><td>LmjF26.2400</td><td>Peroxisomal membrane protein 4, putative</td></tr><tr><td>CATGATGGCGCACC</td><td>1:5</td><td>ND</td><td>ND</td><td>LmjF27.1190to27.1240</td><td>histone H1, putative</td></tr><tr><td>CATGACGTGCTTGC</td><td>1:6</td><td>ND</td><td>ND</td><td>LmjF27.2320</td><td>Protein phosphatase-like protein</td></tr><tr><td>CATGCGTAGACGAT</td><td>25:64</td><td>1.6 ± 0.25</td><td>0.5 ± 0.05</td><td>LmjF28.2205</td><td>Ribosomal protein S29, putative</td></tr><tr><td>CATGCTTGCAGCAG</td><td>1:4</td><td>ND</td><td>ND</td><td>LmjF29.0630</td><td>BET1-like protein, putative</td></tr><tr><td>CATGACCGCTGCTA</td><td>8:22</td><td>ND</td><td>ND</td><td>LmjF30.0680</td><td>40S ribosomal protein S30, putative</td></tr><tr><td>CATGTTGTTGTATA</td><td>1:6</td><td>ND</td><td>ND</td><td>LmjF31.1960</td><td>Tryparedoxin-like protein</td></tr><tr><td>CATGGATGGTGCT</td><td>4:13</td><td>1.7 ± 0.2</td><td>5 ± 0.45</td><td>LmjF31.2250</td><td>3,2-trans-enoyl-CoA isomerase</td></tr><tr><td>CATGCGCGAATAGG</td><td>2:6</td><td>ND</td><td>ND</td><td>LmjF31.2850</td><td>Ribosomal protein I7/I12-like protein</td></tr><tr><td>CATGGCGTAGAGAA</td><td>1:5</td><td>ND</td><td>ND</td><td>LmjF31.3130</td><td>Methylcrotonoyl-coa carboxylase protein</td></tr><tr><td>CATGCGGAGTCAGG</td><td>1:7</td><td>3 ± 0.22</td><td>1.6 ± 0.09</td><td>LmjF33.0260</td><td>RNA binding protein rggm, putative</td></tr><tr><td>CATGGCCGGATAGA</td><td>1:4</td><td>ND</td><td>ND</td><td>LmjF33.2300</td><td>udp-glc 4'-epimerase, putative</td></tr><tr><td>CATGTCGGCGGTGA</td><td>1:8</td><td>2.3 ± 0.21</td><td>2.44 ± 0.16</td><td>LmjF34.3430</td><td>Cleavage &amp; polyadenylation factor</td></tr><tr><td>CATGGCCGCACCGG</td><td>2:9</td><td>ND</td><td>ND</td><td>LmjF34.365 &amp; 16.0460</td><td>60S ribosomal protein L21, putative</td></tr><tr><td>CATGCACACAGCGA</td><td>1:4</td><td>ND</td><td>ND</td><td>LmjF35.1470</td><td>choline/ethanolamine kinase, putative</td></tr><tr><td>CATGTGTGCTCTTA</td><td>2:8</td><td>0.9 ± 0.08</td><td>1.5 ± 0.08</td><td>LmjF35.1540</td><td>Reiske iron-sulfur protein precursor</td></tr><tr><td>CATGGGTAAGGATC</td><td>3:10</td><td>ND</td><td>ND</td><td>LmjF35.3790to35.3800</td><td>60S ribosomal protein L23, putative</td></tr><tr><td>CATGTCGATACCCG</td><td>1:8</td><td>1.9 ± 0.08</td><td>2 ± 0.13</td><td>LmjF35.4930</td><td>Tubulin tyrosine ligase, putative</td></tr><tr><td>CATGTCCAGTGGGA</td><td>2:8</td><td>ND</td><td>ND</td><td>LmjF35.5100</td><td>60S ribosomal protein L37</td></tr><tr><td>CATGTGTACGAGTC</td><td>1:11</td><td>ND</td><td>ND</td><td>LmjF36.0010&amp;25.2460</td><td>galactosyltransferase 1, 2, 3, 4 &amp; 6</td></tr><tr><td>CATGTGATCTTCCG</td><td>2:9</td><td>2.4 ± 0.06</td><td>2,1 ± 0.13</td><td>LmjF36.0070</td><td>Stress-inducible protein STII homolog</td></tr><tr><td>CATGCTGCGGTGTA</td><td>1:4</td><td>ND</td><td>ND</td><td>LmjF36.1050</td><td>RNA editing complex protein MP61</td></tr><tr><td>CATGCGCAAGAAGA</td><td>1:4</td><td>ND</td><td>ND</td><td>LmjF36.1070to36.1100</td><td>ribosomal protein L24, putative</td></tr><tr><td>CATGGGGACGCGTT</td><td>7:20</td><td>1.3 ± 0.07</td><td>0.5 ± 0.03</td><td>LmjF36.1250</td><td>40S ribosomal protein S9, putative</td></tr><tr><td>CATGCCACCACGC</td><td>17:28</td><td>ND</td><td>ND</td><td>LmjF36.3390</td><td>Ribosomal protein L29, putative</td></tr><tr><td>CATGCTTGTGTGAC</td><td>12:23</td><td>1.4 ± 0.19</td><td>0.9 ± 0.05</td><td>LmjF36.3760</td><td>60S ribosomal protein L10a, putative</td></tr><tr><td>CATGGTGGTGTATG</td><td>1:4</td><td>ND</td><td>ND</td><td>LmjF36.3810</td><td>Aminomethyltransferase</td></tr><tr><td>CATGCTGCGGCGGT</td><td>1:6</td><td>2.6 ± 0.71</td><td>2.3 ± 0.17</td><td>LmjF36.5880</td><td>Small GTPase, putative</td></tr></tbody></table>

This table shows annotated tags from the 420 differentially expressed tags at the intracellular L. major stage, their occurrences in the Lm and "MDM+Lm" SAGE libraries and their corresponding access numbers and protein names in GeneDB. Underlined accession numbers correspond to genes tested by quantitative RT-PCR; results are presented as mean 2-ΔΔCt values ± SD obtained with intracellular (IP) or with amastigote-like axenic (AP) parasites. ND: non-determined.
tin-like protein, histones H1 and H2B, tubulin tyrosine
ligase, reiske iron-sulfur protein precursor and several
ribosomal proteins (Table 4). Interestingly, 71 of the
assigned tags corresponded to hypothetical proteins with
conserved domains and/or unknown functions.

Stage-specific preferential expression of parasite
transcripts is confirmed by quantitative PCR experiments

We used quantitative real-time PCR to validate the accu-
racy of the SAGE data generated. Q-PCR was also per-
formed on cDNA obtained from amastigote-like axenic
parasites of *L. major*.

By comparing "MDM+Lm"/MDM tags ratios, Q-PCR
showed the same trend towards the up-regulated expres-
sion of all selected transcripts, but one, in intracellular par-
asites compared to *L. major* promastigote metacyclic
parasites (Table 4). Unexpectedly, 33% of the tested tran-
scripts that were up-regulated in intracellular amastigotes,
using SAGE and Q-PCR technologies, were down-regulated
in the amastigote-like parasites obtained by culture in
axenic conditions. This result suggests that the transcrip-
tome profile of *L. major* amastigote-like axenic parasites
may not reproduce the profile expressed by the naturally
induced intracellular amastigote stage and that the biologi-
cal results obtained with the former parasite should be cau-
tiously extrapolated to the latter parasite form.

Discussion

Genome-wide expression profiling offers new perspectives
for studying host-pathogen interactions to decipher, at the
transcriptional level, how host cells react to infection and
how pathogens adapt to their host's microenvironment. In
the present study, we took advantage of SAGE to analyze
the transcriptomes of both the infected MΦ and the intrac-
ellular parasite *Leishmania* using a one-step approach. Our
working hypothesis was that, having extracted the bulk of
mRNA molecules from a co-culture of parasites and
infected MΦs, it would be possible to separate, in the result-
ing SAGE library, the respective contributions of each
organism to the mixed collection of tags. The proportion of
ambiguous gene signatures was found to be lower than
1.5%, confirming the validity of this approach. Such unam-
biguous tag species identification would be more difficult
to reach using alternative high-throughput transcriptomic
methods, such as microarrays, due to the difficulties in
assessing the extent of cross-hybridization between the
human and the parasite transcripts.

Separating the contribution of both organisms in an
infected MΦsAGE library raised no technical problems
and could be performed on a desktop computer using the
functions of a commercial database management system
(MS-Access). To distinguish tags according to their origin,
we considered that merging all publicly available leuko-
cyte libraries would generate a set of tags that are repre-

sentative of human transcripts. Despite this extended
coverage, it is clear that the deconvolution of both tran-
scriptomes could not be complete, since unmatched tags
that could not be ascribed to either of the two species (*H.
sapiens* or *L. major*) may either correspond to very specific
human transcripts expressed only in *Leishmania*-infected
MΦs and never generated elsewhere or may reveal stage-
specific parasite transcripts strictly specific of the intracel-
lular stage. This problem was pointed out in a recent study
[32], suggesting that the human genome might actually
contain twice as many transcribed regions as currently
annotated. Moreover, the ENCODE project consortium
highlighted the number and complexity of the RNA tran-
scripts generated comparatively to the small number of
protein-coding genes (≈ 21,000) currently annotated on
the human genome [33,34].

SAGE was used as a quantitative approach, to evaluate the
expression levels of mRNAs and to calculate the respective
amount of material from human or parasite origin. With
the reasonable assumption that mRNAs originated only
from living cells, our data demonstrated the importance
(49%) of the parasitic load in infected cells.

In spite of this heavy parasitic burden, a salient feature
emerged from multivariate statistics: that parasite infec-
tion has, at the global level, an apparent marginal impact
(only 2.4% of the transcripts were found modulated) on
the expression profile of infected MΦs. Thus, the mRNA
profile of infected MΦs contrasted with that of monocytes
exposed to LPS because it revealed many fewer alterations
in gene expression.

However, although *Leishmania* parasites do not seem to
induce dramatic changes in the transcriptional remode-
ling program of MΦs, a closer analysis detected physiolog-
ically significant alterations in gene transcription. Despite
their discreteness, these alterations could harmfully
weaken macrophages' microbicidal defense task and
homing properties. Indeed, our analysis showed that sev-
eral MΦ antiparasitic pathways were altered at the level of
mRNA expression upon infection by *L. major* parasites. In
particular, we were able to show that several members of
the S100A family, among others, are up- or down-regu-
lated by infection. This is in contrast to a previous study
using microarray technology that reported almost stable
signals between non-infected MΦs as compared to *L.
major*-infected MΦs for this gene family [10]. Other differ-
ences in the expression levels of several chemokine family
members were observed between the two studies, except
for *CXCL3* and *IL8* transcripts, which were strongly up-
regulated.

Whether the discrepancies between the two approaches
reflect differences in the experimental protocols used by
the two studies (e.g., cell-parasite incubation time, para-
site strains or human genetic variability) or are attributa-
ble to differences in the sensitivity of the two techniques
to accurately quantitate the mRNA of expressed genes is
unclear. It is noteworthy that our results concerning the
IFNγ pathway, are in agreement with those obtained
recently by Dogra et al. in THP1-infected cells [11].
Indeed, transcripts of STAT-1, a key actor of this pathway,
were drastically down-regulated at 24 h after infection,
though there is no external activation by IFNγ. In addi-
tion, we found that several IFNγ-inducible chemokines
(CXCL9 and CXCL10) were down-modulated. Since key
proteins belonging to this pathway are also inhibited
upon L. major infection (K Ben-Aissa, Personal communi-
cation), such effects render the MΦ refractory to any
potential activation by IFNγ and obviously favor parasite
survival. Other genes, among those involved in antigen
presentation and implicated in the stabilization and the
recycling of classical MHC class II and in the binding and
the capture of antigens were also down-modulated by L.
major, as reported by Chaussabel et al. and Dogra et al.
[10,11].

Our results also show that several genes encoding pro-
inflammatory mediators were up-regulated, while other
family members were down-modulated. This indicates
that *Leishmania* have a remarkable capacity to specifically
inhibits the transcription of several molecules associated
with pro-inflammatory responses. It is notable that this
peculiarity of *L. major* infection does not completely fit –
in contrast to other pathogens (i.e., *Mycobacterium tubercu-
losis*, *Listeria monocytogenes*, *Escherichia coli*, *Bordetella per-
tussis*, *Candida albicans*, etc.) – with the so called "common
host-transcriptional response" [35], stressing the particu-
larity of this parasite. This is probably a survival mecha-
nism whereby the parasites can inhibit a harmful
inflammatory reaction in order to slip silently into the
MΦ and successfully establish inside the host.

In addition to the analysis of the MΦ transcriptome, in the
last 5 years, several studies have focused on the parasitic
transcriptome taking advantage of the availability of L.
major genome sequence [36]. Although this genome (35
Mb distributed across 36 chromosomes of varying lengths
i.e., 0,3 to 2,8 Mb, and coding for roughly 8,370 manually
annotated protein-coding genes), was declared to be fin-
ished in 2005 [36], only 2,191 L. major ESTs originating
from cDNA libraries of various sources, such as promas-
tigote or amastigote full length cDNA libraries, are
reported on NCBI.

Hence, our tag-to-gene mapping for parasite transcripts
was rather encouraging, compared to the number of
sequenced tags. Indeed, we were able to list up to 900 tags
expressed in at least two copies in the metacyclic promas-
tigote stage but totally absent from the intracellular amas-

tigote stage, generating useful data for better data mining.
In addition, among the tags common to L. major promas-
tigote and MΦ-infected libraries, 19% (697/3,666) were
differentially expressed. This led us to estimate (without
taking into account the tags that were specifically intracel-
lular and present only in the infected MΦ library) the tran-
scripts differentially expressed, between the two parasitic
stages, to roughly 1,600 tags, representing approximately
20% of transcripts if one considers the 8,370 annotated
Leishmania genes registered in the databases.

This figure is several-fold higher than those reported from
a variety of *Leishmania* species (i.e., *L. major* [13-15,18], *L.
donovani* [20], *L. infantum* [17] and *L. mexicana* [16]),
which clearly show limited differences using microarrays
(ranging from 0.2 to 5% of total genes) in stage-specific
gene expression between the promastigote and amastigote
life stages. These studies also show that the vast majority
of genes are constitutively expressed [18,20,37]. One
should note that these studies analyzed the amastigote
transcripts, either using amastigote parasites derived from
BALB/c lesions or axenic amastigotes obtained *in vitro*,
whereas our study used the amastigotes derived from
human MΦs.

However, while analyzing the functional significance of
gene expression in *Leishmania*, we should consider that it
is mainly regulated at the post-transcriptional level. As
highlighted by Cohen-Freue et al. [37], the alteration in
mRNA levels of regulated genes in *Leishmania* does not
necessarily correlate with subsequent protein abundance.
The functional significance is better manifested at the pro-
tein level, which is regulated by mechanisms such as
stage-specific translational control, RNA stability, process-
ing events and post-translational modifications. Nonethe-
less and despite these limitations, transcriptomic
approaches for *Leishmania* could mainly help to better
annotate its genome and to study the stability and trans-
lational regulation of its transcripts.

Conclusion

To our knowledge, we provide here for the first time a
large-scale gene expression profile of both the infected
human MΦ and the infective form of L. major using SAGE.
This set of expressed genes deserves future rounds of data
mining and experimental work, since it contains latent
information about proteins susceptible to behaving as
antigens and being evaluated as candidates in a vaccine
approach. These data also provide the basis for studies in
progress that aim to compare, at the molecular level, vari-
ous strains of *Leishmania* known to differ by their behavior
at the physiopathological level. Thus, comparing viscero-
tropic strains, e.g., L. infantum or L. donovani, to strictly
dermotropic strains e.g., L. major, may reveal differences at
the level of parasite-MΦ interactions that could indicate
to discard ditags that are duplicated or different from 20 bp between the two CATG. Then, for each concatemer sequence, DIGITAG generates the reverse complement, adds it to the initial sequence, and extracts all CATGs plus the 10 following bases to obtain the tag sequences and determine their copy number in each library. P-value calculations and identification of genes differentially expressed were performed according to the procedure described by Piquemal et al. [26]. Tag levels were compared between the two MDM libraries generated in the absence or presence of parasites, or between the MDM-infected library and metacyclic parasite library. Differentially expressed tags were selected for further analysis. Expression data were also analyzed using various modules of the TIGR MultiExperiment Viewer Package (MeV 4.0, 2006).

## Human leukocyte SAGE library collections

Following sequence analysis of SAGE libraries, data were assembled in a unique matrix. We also collected up to 357,888 experimental tags from nine publicly available human leukocyte SAGE libraries (retrieved from [44-46]) and from a second in-house non-stimulated MΦ generated library (raised in similar conditions but from a different pool of donors and noted MDM-M; Ottones et al., unpublished data). These SAGE libraries were generated from freshly isolated monocytes [47], M-CSF-differentiated [47], GM-CSF-differentiated [47] and LPS-activated [48] cells, immature [49] and mature [50] monocyte-derived dendritic cells and unfractionated populations of leukocytes [51], noted Mono, M-CSF, GM-CSF, LPS, IDC, MADC, leuk, WBC-N and WBC-Bc respectively. Data were assembled to build a matrix giving the expression levels of 26,176 unique tags.

## Human tag-to-gene mapping

Regular SAGE tags were identified as previously described [26]. Briefly, we constructed a reference database to compile tags predicted from collections of expressed sequences, including well-annotated sequences [52], reference sequences of UniGene clusters [53], SAGEmap tags [44] and the GenBank collection of human Alu sequences. This Preditag® software (Skuld-Tech, Montpellier, France) was also modified to register virtual tags matching the reverse complement of the sequences. We used its functions to automatically generate a table of results, by matching experimental tags to virtual ones. Tags matching with 100% sequence identity were then ranked based on the fidelity of the source sequence. The first positions starting from the 3'-most end of the transcript were kept.

## Parasite tag-to-gene mapping

In order to assign gene identity to each parasite tag, the experimental tag list from the purified metacyclic parasite SAGE library were matched against the *L. major* Friedlin genome (version 5.2) downloaded from GeneDB [54].

Since SAGE tags should be sitting in the untranslated regions of a given gene, tags that had a unique match, with 100% sequence identity, and that were found within 1 kb downstream of the stop codon of one gene or related genes within the same family and alternatively in the CDS, were assigned. These related genes were identified by blast, comparing the set of *L. major* proteins and selecting those that were sharing more than 85% identity.

## Validation of SAGE libraries by Q-PCR

The same pooled RNAs used for SAGE libraries were used for real-time reverse transcriptase-polymerase chain reaction (RT-PCR). For human tag validation analyses, predeveloped assay reagent probes, reagents, and Real-time PCR ABI-7900HT equipment were used for validation experiments as recommended by the manufacturer (Applied Biosystems, Fullerton, CA, USA). For parasite tag validation studies, reverse transcription and real-time PCR were performed using SYBR Green I Universal PCR MasterMix (PE Applied Biosystems, Foster City, CA, USA) and primers (Additional file 7: Parasite primer sequences for quantitative RT-PCR) were designed for each sequence, including endogenous controls, using Primer express Software (Version 1.5, PE Applied Biosystems). All PCR reactions were performed using the ABI PRISM 7700 sequence detection system. This technique is based on measuring PCR products in the logarithmic phase of the reaction by determining the CT [55], CT being the threshold cycle at which the fluorescence emission reaches the log phase of product accumulation.

Briefly, after defrosting at room temperature, total RNA was extracted using the Qiagen RNeasy Mini kit as indicated by the manufacturer (Qiagen, Courtaboeuf, France). The quality of the total RNA was determined by capillary electrophoresis analysis using an Agilent 2100 Bioanalyzer (Agilent, Palo Alto, CA, USA). cDNA was then synthesized using the High-Capacity cDNA Archive Kit (Applied Biosystems) according to the manufacturer's protocol. Samples were loaded on micro-fluidic plates and data were normalized by referring to the expression of an endogenous control which was highly homogeneous between used samples (Ct = 20.55 for MDM library and 20.46 for "MDM+Lm" library, i.e., human glyceraldehyde-3-phosphate dehydrogenase (GAPDH).

For parasite Q-PCR, each analysis was performed in triplicate and data were normalized by referring to the expression of an endogenous control (rRNA45; accession number: CC144545) described as equally expressed between procyclic, metacyclic, and amastigote stages of *L. major* using DNA microarrays, quantitative PCR and Northern blot experiments [14,56].

Finally, for each human target mRNA, results were expressed as a fold difference in MDM exposed to metacyclic
clic parasites of *L. major* promastigotes vs. non-infected MDM by calculating 2^ΔΔCT. For parasite target mRNA, results were expressed as a fold difference in *L. major* metacyclic promastigotes vs. *L. major*-infected MDM.

**Abbreviations**

CR: Complement receptors; L: *Leishmania*; MΦ: Macrophage; MDM: Monocyte-derived macrophages; MDM+Lm: MDM infected with *L. major*; Q-PCR: Quantitative-Polymerase Chain Reaction; SAGE: Serial Analysis of Gene Expression.

**Authors' contributions**

FZG and DL participated in the MDM preparation and infection experiments, to SAGE library construction, data analysis and manuscript writing. They did parasitic and human RT-PCR validation. LG-T contributed to the MDM preparation and infection experiments. FO participated in the construction of the two MDM SAGE libraries, the RT-PCR validation of human transcripts and data analysis. KB-A participated in the MDM preparation and infection experiments and parasitic SAGE library construction. OM worked on parasitic SAGE library construction. LM and DP extracted tags from raw sequence data and did statistical analysis. TC participated in the analysis of human RT-PCR data. ABK and SS designed parasitic annotation and realized parasite tag assignation. JM and KD contributed to data analysis and manuscript writing and supervised this work.

All authors read and approved the final version of the manuscript.

**Additional material**

**Additional File 1**

*Unique SAGE tags as a function of total sequenced tags in the different constructed libraries.* This figure shows the number of unique tags present in the MDM (black), "MDM+Lm" (red) or Lm (green) libraries as a function of sequenced tags in these libraries. Panel A represents the total sequenced tags and panel B represents tags present at least twice.

Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-2164-9-238-S1.tiff]

**Additional File 2**

*Scatter plot showing the comparison of the MDM versus "MDM+Lm" SAGE libraries.* This figure shows comparison between the MDM and "MDM+Lm" libraries of scaled tag frequencies (size dots relatively to the occurrences) and their statistical significance (color dots relatively to the p-values).

Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-2164-9-238-S2.tiff]

**Additional File 3**

*Hierarchical Clustering raised with the 500 most abundant tags (left panel) or with the next 2418 tags (right panel).* The clustering was done using various modules of the TIGR MultiExperiment Viewer Package (MeV 4.0, 2006). "MDM+Lm" corresponds to macrophage-specific tags extracted from the sample infected by Leishmania, MDM to the library built with the same MDM preparation and MDM-M to a second in-house MDM library prepared independently raised in similar conditions from another pool of donors. Mono, LPS, M-CSF, GM-CSF, IDC, MADC, leuk, WBC-N and WBC-Bc are publicly available SAGE libraries (see Methods' section).

Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-2164-9-238-S3.tiff]

**Additional File 4**

*Spatial Clustering across the Human genome of tags extracted from the MDM and "MDM+Lm" libraries.* This figure shows tags plotted as a function of position across the human genome sequence. Each of the 24 human chromosomes is depicted as a thin line in a 5' to 3' orientation. Down-modulated transcripts are shown as green bars, up-modulated transcripts as red bars and unchanged transcripts as black bars.

Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-2164-9-238-S4.tiff]

**Additional File 5**

*Extended names of abbreviated genes.* This file contains the extended names of genes abbreviated in figure 4 presenting examples of gene transcripts categorized into functional classes involved in defense MΦ programs.

Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-2164-9-238-S5.doc]

**Additional File 6**

*"Tag to gene assignation" of all parasitic tags present at least twice and extracted from Lm library.* This file contains the tag sequences, their occurrences in Lm library, their corresponding access numbers in GeneDB, their orientation across *L. major* genome and the number of genes they belong to (unique or multiple).

Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-2164-9-238-S6.xls]

**Additional File 7**

*Parasite primer sequences for quantitative RT-PCR.* This file contains the access numbers in GeneDB of the corresponding tag and the 5' to 3' sequences of the forward and reverse primers used in quantitative PCR experiments.

Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-2164-9-238-S7.doc]
## Acknowledgements

This study was supported by a joint action on the part of the Laboratoire International Associé (LIA) "Ingénierie Biomoléculaire" of the Tunisian Ministry for Higher Education, Research and Technology (MERST) and the Centre National de Recherche Scientifique (CNRS, Direction des Recherches Internationales, Paris, France). FZG was partly supported by UBS Optimus. The work of FO on the macrophage transcriptome was supported by ANRS and Sidaction grants.

We are grateful to Dr. M. Maamar, Ms. R. Dridi and Mr. A. Fatnassi from the Centre National de Transfusion Sanguine for their valuable help and to C. Cruaud and A. Roy from the Genoscope, Evry, France, for their technical assistance. We are thankful to Dr. B.A Lewis from NCI at the National Institutes of Health, USA and Dr. H. Abdulrazzak from The Faculty of Medicine at Imperial College, London, UK and Ms. L. Northrup for their English and grammar corrections.

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