Open Access

Transcriptional signatures of BALB/c mouse macrophages housing multiplying Leishmania amazonensis amastigotes

  • José Osorio y Fortéa1,
  • Emilie de La Llave1,
  • Béatrice Regnault2,
  • Jean-Yves Coppée2,
  • Geneviève Milon1,
  • Thierry Lang1 and
  • Eric Prina1Email author
Contributed equally
BMC Genomics200910:119

DOI: 10.1186/1471-2164-10-119

Received: 27 August 2008

Accepted: 20 March 2009

Published: 20 March 2009

Abstract

Background

Mammal macrophages (MΦ) display a wide range of functions which contribute to surveying and maintaining tissue integrity. One such function is phagocytosis, a process known to be subverted by parasites like Leishmania (L). Indeed, the intracellular development of L. amazonensis amastigote relies on the biogenesis and dynamic remodelling of a phagolysosome, termed the parasitophorous vacuole, primarily within dermal MΦ.

Results

Using BALB/c mouse bone marrow-derived MΦ loaded or not with amastigotes, we analyzed the transcriptional signatures of MΦ 24 h later, when the amastigote population was growing. Total RNA from MΦ cultures were processed and hybridized onto Affymetrix Mouse430_2 GeneChips®, and some transcripts were also analyzed by Real-Time quantitative PCR (RTQPCR). A total of 1,248 probe-sets showed significant differential expression. Comparable fold-change values were obtained between the Affymetrix technology and the RTQPCR method. Ingenuity Pathway Analysis software® pinpointed the up-regulation of the sterol biosynthesis pathway (p-value = 1.31e-02) involving several genes (1.95 to 4.30 fold change values), and the modulation of various genes involved in polyamine synthesis and in pro/counter-inflammatory signalling.

Conclusion

Our findings suggest that the amastigote growth relies on early coordinated gene expression of the MΦ lipid and polyamine pathways. Moreover, these MΦ hosting multiplying L. amazonensis amastigotes display a transcriptional profile biased towards parasite-and host tissue-protective processes.

Background

L. amazonensis are protozoan parasites belonging to the trypanosomatidae family. In natural settings, the L. amazonensis perpetuation relies on blood-feeding sand fly and rodent hosts. The development of promastigotes proceeds within the gut lumen of the sand fly hosts and ends with metacyclic promastigotes. The latter, once delivered into the mammal dermis, differentiate as amastigotes mainly within the resident dermal macrophage (MΦ) acting as bona fide host cells. Following the parasite inoculation and before the development of the more or less transient skin damages that characterize cutaneous leishmaniasis there is an asymptomatic phase lasting for several days or weeks during which the intracellular amastigote progeny expands. This expansion takes place within a compartment named parasitophorous vacuole (PV) that displays properties similar to late endosomes/lysosomes and the size of which grows significantly for Leishmania belonging to the mexicana complex [1, 2]. In this study we sought to analyze the transcriptional signatures of a homogeneous population of MΦ derived in vitro from BALB/c mouse bone marrow CSF-1 dependent progenitors and hosting amastigotes that are actively multiplying. The Affymetrix GeneChip technology was used to compare the gene expression profiles of L. amazonensis amastigotes-hosting bone marrow-derived MΦ and parasite-free ones. This in vitro transcriptomics approach was combined with the Ingenuity biological network analysis to highlight the mouse MΦ biological processes the multiplying L. amazonensis amastigotes rely on within their giant communal PV. Our findings suggest that MΦ hosting multiplying amastigotes contribute to carve a parasite-as well as a host tissue-protective environment.

Results and Discussion

L. amazonensis amastigotes subvert MΦ as host cells where they enter a cell-cycling phase lasting several days (Fig. 1A). We compared the transcriptomes of amastigote-free MΦ and amastigote-harbouring MΦ 24 h after the uptake of amastigotes carefully purified from nude mouse lesions. At this time-point amastigotes were multiplying within a huge PV (Fig. 1B) and their population size had almost doubled (Fig. 1A). Among the 45,101 probe-sets of the Mouse430_2 GeneChip, 1,248 (2.77%) were displaying features of differential expression at the 5% significance level (Fig. 2, see Additional file 1). Of these, 1,206 matched Ingenuity Pathway Analysis database version 5.5.1 which represented 898 genes with a known function. About 80% of these genes were incorporated into either Ingenuity's canonical pathway or biological network (i.e. their products interact with other molecules in Ingenuity's knowledge base). The symbols of the modulated genes are specified in the text (fold change [FC] values between brackets), while their full names are given in Additional file 1. Furthermore, comparable FC values were obtained between the Affymetrix technology and the Real Time quantitative Polymerase Chain Reaction (RTQPCR) method (Table 1) [3].
Table 1

List of differentially expressed genes between L. amazonensis-harbouring MΦ and parasite-free MΦ.

Symbol

Name

Probe-set

LocusLink

Affymetrix (RTqPCR)

P-value

abcD2

ATP-binding cassette, sub-family D (ALD), member 2

1438431_ata

26874

-2.11

4.40e-03

acaca

acetyl-Coenzyme A carboxylase alpha

1427595_at

107476

-1.32

4.79e-03

acsl3

acyl-CoA synthetase long-chain family member 3

1452771_s_at

74205

+2.09

1.48e-03

adhfe1

alcohol dehydrogenase, iron containing, 1

1424393_s_at

76187

+1.61

4.40e-02

akr1a1

aldo-keto reductase family 1, member A1 (aldehyde reductase)

1430123_a_at

58810

+1.13

1.22e-03

aldoA

aldolase 1, A isoform

1433604_x_ata

11674

+1.72

1.28e-02

aldoC

aldolase 3, C isoform

1451461_a_at

11676

+1.89

1.13e-02

anxA1

annexin A1

1444016_ata

16952

+2.68

4.47e-05

apoc2

apolipoprotein C-II

1418069_at

11813

-1.63

4.57e-02

arg2

Arginase 2

1418847_at

11847

NM (+1.91)

NS

atf1

activating transcription factor 1

1417296_at

11908

+1.84

4.20e-03

atf3

activating transcription factor 3

1449363_at

11910

+1.77

1.09e-02

atp6V0a1

ATPase, H+ transporting, lysosomal V0 subunit a isoform 1

1460650_ata

11975

+1.82

8.31e-03

atp6V0c

ATPase, H+ transporting, V0 subunit C

1435732_x_at

11984

+1.27

5.48e-13

atp6V0d2

ATPase, H+ transporting, V0 subunit D, isoform 2

1444176_ata

24234

+2.32

1.12e-05

atp6V1a

ATPase, H+ transporting, V1 subunit A1

1422508_at

11964

+1.57

3.96e-02

atp6V1c1

ATPase, H+ transporting, V1 subunit C, isoform 1

1419546_ata

66335

+2.31

1.10e-05

atp6V1d

ATPase, H+ transporting, V1 subunit D

1416952_ata

73834

+1.82

6.97e-03

atp6V1g1

ATPase, H+ transporting, V1 subunit G isoform 1

1423255_ata

66290

+1.84

3.78e-03

atp6V1h

ATPase, H+ transporting, lysosomal, V1 subunit H

1415826_at

108664

+1.69

2.39e-02

azin1

antizyme inhibitor 1

1422702_at

54375

+1.96

1.46e-03

brd8

bromodomain containing 8

1427193_at

78656

+1.08

3.75e-02

c1qa

complement component 1, q subcomponent, alpha polypeptide

1417381_at

12259

-1.48

3.15e-02

c1qb

complement component 1, q subcomponent, beta polypeptide

1417063_at

12260

-1.77

3.31e-04

c3

complement component 3

1423954_at

12266

-2.37

7.05e-06

c4b

complement component 4 (within H-2S)

1418021_at

12268

-1.76

4.55e-02

c5ar1

complement component 5a receptor 1

1439902_at

247623

-1.63

4.62e-02

ccr2

chemokine (C-C motif) receptor 2

1421187_ata

12772

-1.83 (-2.35)

6.42e-03

ccr3

chemokine (C-C motif) receptor 3

1422957_at

12771

-2.58 (-3.88)

2.49e-05

cd14

CD14 antigen

1417268_at

12475

-1.73

1.54e-03

cd200

CD200 antigen

1448788_at

17470

+4.14 (+6.52)

5.48e-13

cd274

CD274 antigen

1419714_at

60533

+1.93

1.61e-03

cd86

CD86 antigen

1420404_ata

12524

-1.83 (-1.03)

1.44e-02

cfh

complement component factor h

1450876_at

12628

-2.80

6.08e-06

c-fos

FBJ osteosarcoma oncogene

1423100_at

14281

-1.93

3.30e-03

ch25h

cholesterol 25-hydroxylase

1449227_at

12642

-6.57

1.39e-22

cmklr1

chemokine-like receptor 1

1456887_at

14747

-2.20

1.57e-04

cx3cr1

chemokine (C-X3-C) receptor 1

1450020_at

13051

-2.65 (-5.26)

2.39e-05

cyp51

cytochrome P450, family 51

1450646_ata

13121

+2.78

2.10e-07

dhcr24

24-dehydrocholesterol reductase

1451895_a_at

74754

+3.17

2.69e-09

dio2

deiodinase, iodothyronine, type II

1418937_ata

13371

+25.92 (+41.03)

0.00e+00

eno2

enolase 2, gamma neuronal

1418829_a_at

13807

+2.60

6.08e-06

fabp3

fatty acid binding protein 3

1416023_at

14077

+2.29

5.58e-05

fabp4

fatty acid binding protein 4

1417023_a_ata

11770

+6.42

0.00e+00

fabp5

fatty acid binding protein 5

1416022_ata

16592

+1.57

4.70e-08

fbp1

fructose bisphosphatase 1

1448470_at

14121

-2.16

4.68e-03

fdft1

farnesyl diphosphate farnesyl transferase 1

1438322_x_ata

14137

+2.62

4.00e-06

fdps

farnesyl diphosphate synthetase

1423418_at

110196

+3.59

9.78e-12

h-2ma

histocompatibility 2, class II, locus DMa

1422527_at

14998

-1.88

3.00e-03

h60

histocompatibility 60

1439343_at

15101

-2.07

5.30e-09

hk2

hexokinase 2

1422612_at

15277

+1.75

1.09e-02

hk3

hexokinase 3

1435490_at

212032

+2.03

3.72e-04

hmgcr

3-hydroxy-3-methylglutaryl-Coenzyme A reductase

1427229_at

15357

+1.95

2.34e-03

hmgcs1

3-hydroxy-3-methylglutaryl-Coenzyme A synthase 1

1433446_at

208715

+2.48

1.07e-06

hsd17b7

hydroxysteroid (17-beta) dehydrogenase 7

1457248_x_at

15490

+2.73

1.41e-05

icam1

intercellular adhesion molecule

1424067_at

15894

-1.75

1.43e-02

icam2

intercellular adhesion molecule 2

1448862_at

15896

-1.85

2.88e-02

idi1

isopentenyl-diphosphate delta isomerase

1451122_ata

319554

+2.72

2.77e-07

ifngr1

interferon gamma receptor 1

1448167_at

15979

-1.83 (-2.16)

4.66e-03

il10

interleukin 10

1450330_at

16153

-2.97 (-4.46)

1.11e-07

il10ra

interleukin 10 receptor, alpha

1448731_at

16154

-2.16 (-2.56)

4.40e-04

il11ra1

interleukin 11 receptor, alpha chain 1

1417505_s_at

16157

+2.24 (+3.55)

9.89e-05

il17rb

interleukin 17 receptor B

1420678_a_at

50905

-1.41

2.93e-02

il18

interleukin 18

1417932_at

16173

-1.77 (-2.12)

1.06e-02

il1b

interleukin 1 beta

1449399_a_at

16176

-3.09 (-5.17)

3.49e-07

il1rn

interleukin 1 receptor antagonist

1423017_a_ata

16181

+4.19 (+7.86)

0.00e+00

insig1

insulin induced gene 1

1454671_at

231070

+2.62

9.17e-08

itga4

integrin alpha 4

1456498_ata

16401

-2.06

2.37e-03

itgal

integrin alpha L

1435560_at

16408

-2.00

7.72e-03

klrk1

killer cell lectin-like receptor subfamily K, member 1

1450495_a_at

27007

-1.72

2.21e-02

ldhA

lactate dehydrogenase 1, A chain

1419737_a_at

16828

+1.79

2.71e-04

ldlr

low density lipoprotein receptor

1450383_ata

16835

+4.68

1.49e-13

lipe

lipase, hormone sensitive

1422820_at

16890

-2.20

2.90e-03

lpl

lipoprotein lipase

1431056_a_at

16956

-1.44

3.24e-02

lss

lanosterol synthase

1420013_s_at

16987

+2.05

2.29e-03

maoa

monoamine oxidase A

1428667_ata

17161

+2.56

4.71e-06

mapk14

mitogen activated protein kinase 14 (p38 mapk)

1416703_at

26416

-1.61

4.97e-02

mgll

monoglyceride lipase

1426785_s_at

23945

+3.40

3.75e-08

mvd

mevalonate (diphospho) decarboxylase

1417303_ata

192156

+2.15

6.33e-04

ncoa4

nuclear receptor coactivator 4

1450006_at

27057

+1.65

3.15e-02

nfkbia

nuclear factor of kappa light chain gene enhancer in B-cells inhibitor, alpha

1448306_at

18035

-1.83

5.53e-03

nos2

nitric oxide synthase 2, inducible, macrophage

1420393_at

18126

NM (+1.28)

NS

odc1

Ornithine decarboxylase 1

1427364_a_at

18263

NM (+1.18)

NS

p4ha2

procollagen-proline, 2-oxoglutarate 4-dioxygenase (proline 4-hydroxylase), α II polypeptide

1417149_at

18452

+2.27

1.96e-03

pfkl

phosphofructokinase, liver, B-type

1439148_a_at

18641

+1.68

2.32e-02

pkg1

phosphoglycerate kinase 1

1417864_at

18655

+1.70

8.88e-03

pkm2

pyruvate kinase, muscle

1417308_at

18746

+1.51

4.57e-02

ppap2B

phosphatidic acid phosphatase type 2B

1448908_ata

67916

+8.53

0.00e+00

pros1

protein S (alpha)

1426246_at

19128

-2.09

2.66e-03

relb

avian reticuloendotheliosis viral (v-rel) oncogene related B

1417856_at

19698

-1.91

1.94e-02

sat1

spermidine/spermine N1-acetyl transferase 1

1420502_at

20229

+1.47

2.30e-02

sc4mol

sterol-C4-methyl oxidase-like

1423078_a_at

66234

+2.28

1.61e-05

sc5d

sterol-C5-desaturase (fungal ERG3, delta-5-desaturase) homolog (S. cerevisae)

1451457_ata

235293

+2.57

2.37e-06

scd1

stearoyl-Coenzyme A desaturase 1

1415964_ata

20249

+2.68

4.50e-05

scd2

stearoyl-Coenzyme A desaturase 2

1415824_ata

20250

+2.45

1.32e-06

serping1

serine (or cysteine) peptidase inhibitor, clade G, member 1

1416625_at

12258

-1.35

4.84e-05

slc7a2

solute carrier family 7 (cationic amino acid transporter, y+ system), member 2

1436555_ata

11988

+4.14

6.07e-12

sms

spermine synthase

1434190_ata

20603

NM [-1.38b]

NS

socs6

suppressor of cytokine signaling 6

1450129_a_at

54607

+1.84

4.18e-03

sqle

squalene epoxidase

1415993_at

20775

+4.30

0.00e+00

srebf2

sterol regulatory element binding factor 2

1426744_at

20788

+1.84

1.26e-02

srm

spermidine synthase

1421260_a_at

20810

NM [-1.22b]

NS

stard4

StAR-related lipid transfer (START) domain containing 4

1429239_a_ata

170459

+2.31

2.43e-04

tlr2

toll-like receptor 2

1419132_at

24088

-3.11 (-1.58)

1.83e-08

tlr7

toll-like receptor 7

1449640_at

170743

-1.77 (-1.07)

4.61e-02

tlr8

toll-like receptor 8

1450267_at

170744

-1.79

1.00e-02

tollip

toll interacting protein

1423048_a_at

54473

+1.69

3.57e-02

This table is an excerpt from the table of the 1,248 significantly modulated probe-sets, available as online Additional file 1, and contains some genes tested by RTQPCR.

a when several probe-sets detect a target gene, data are only shown for the most modulated one. NM: No Modulation significantly detected with Affymetrix technology. NS: Not Significant p-value. b mean values obtained from the raw fluorescence intensities.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-119/MediaObjects/12864_2008_Article_2003_Fig1_HTML.jpg
Figure 1

Time course of intracellular amastigote population size increase and MΦ culture imaging. A: time course experiment showing the evolution of the amastigote population within MΦ. Mean number of amastigotes per MΦ were plotted against the time points selected. Ten microscope fields split up into biological duplicates were visualized and more than 200 MΦ nuclei were counted. B: L. amazonensis-housing bone marrow-derived MΦ imaged 24 h post amastigote (4 parasites per MΦ) addition. Nuclei were stained with Hoechst (blue) and amastigote with 2A3.26 mAb and Texas Red-labelled conjugate (red). Image acquisition was performed using an immunofluorescence and differential interference contrast inverted microscope (Zeiss Axiovert 200 M). Asterisk: Parasitophorous vacuoles; arrow heads: Amastigotes.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-119/MediaObjects/12864_2008_Article_2003_Fig2_HTML.jpg
Figure 2

Affymetrix outcome. A: Volcano plot. 1,248 probe-sets showed differential expression at the 0.05 threshold (green line): 605 positive and 643 negative FC values of which 454 in the right and 507 in the left upper corners (± 1.75 FC threshold, red lines, blue circles). B: Fold-change distribution of the 1,248 probe sets.

Though transcriptional changes due to the phagocytic uptake process per se – known to occur mostly within the first 2 hours post particle addition – cannot be completely excluded, the MΦ transcript modulation – detected at 24 h post the amastigote addition – very likely reflects MΦ reprogramming due to the presence of cell cycling amastigotes within giant PV. Indeed, in our experimental conditions, no extracellular amastigotes could be evidenced in the MΦ culture (a) after a brief centrifugation step and (b) one hour contact with adherent MΦ indicating that the phagocytic uptake of L. amazonensis amastigotes is a rapid and efficient process. Furthermore, it is worth mentioning that the size of the amastigote population hosted within the MΦ PV rapidly expands within the first 24 h (Fig. 1A) [4]. Using also mouse bone marrow-derived MΦ as host cells for Leishmania, Gregory and coworkers demonstrated that the gene expression profiles of control MΦ and MΦ that have phagocytosed latex beads 24 h before were very similar. They evidenced a statistically significant difference for only 15 probe sets. None of the 29 corresponding probe sets in the mouse 430 DNA Affymetrix gene chip was present in the list of 1248 modulated probe sets observed in presence of L. amazonensis amastigotes. Thus, these data strongly support our conclusion that the gene expression profile observed 24 h after the phagocytosis of L. amazonensis amastigotes was due to the presence of intracellular cell-cycling parasites.

L. amazonensis amastigotes set up an optimal sub cellular niche

Modulation of MΦ genes encoding vacuolar proton ATPase sub-units

Within their host cells, L. amazonensis amastigotes are known to multiply efficiently in the acidic environment of the MΦ PV [1]. In presence of amastigotes, we observed an up-regulation of the gene expression of eight isoforms of the V0 and V1 sub-units of the MΦ vacuolar proton ATPase (atp6V0a1, atp6V0c, atp6V0d2, atp6V1a, atp6V1c1, atp6V1d, atp6V1g1 and atp6V1h: +1.27 < FC < +2.32) [5]. This could contribute to the sustained acidification of the PV lumen which has been shown to be important at least for the optimal amastigote nutrient acquisition [6, 7].

Coordinated modulation of MΦ lipid metabolism

The most relevant biological networks fitting our dataset were strongly associated to the function "lipid metabolism", the most significant canonical metabolic pathway being "biosynthesis of steroids" (p-value = 1.31e-02). Indeed, several up-regulated genes (Fig. 3, Table 1) were involved i) in cholesterol uptake (ldlr: + 4.68), ii) in cholesterol transport (fabp4: + 6.42 and stard4: + 2.31) and iii) in sterol biosynthesis (hmgcs1, hmgcr, mvd, idi1, fdps, fdft1, sqle, lss, cyp51, sc4mol, hsd17b7, sc5d and dhcr24: +1.95 < FC < +4.30). Worth is mentioning the most up-regulated gene encoding type II deiodinase (dio2, + 25.92), an enzyme converting intracellular thyroxin (T4) to tri-iodothyronine (T3), the more active form of thyroid hormone. It has previously been demonstrated in mouse hepatocytes that the molecular basis for the connection of T3 and cholesterol metabolism involves the master transcriptional activator of the aforementioned genes, namely srebf2 (+ 1.84) the promoter of which contains a thyroid hormone response element [8]. Furthermore, thyroid hormone receptors can activate transcription of target genes upon T3 binding and this could be facilitated by co-activators ncoa4 (+ 1.65) and brd8 (+ 1.08). Interestingly, opposite to dio2, the most down-regulated gene was cholesterol-25-hydrolase (ch25h: -6.57), an enzyme acting downstream this pathway by breaking down cholesterol and by synthesizing a co-repressor of srebf2 transcriptional activation [9]. Upstream this pathway, several up-regulated genes involved in glycolysis could also contribute to increase the supply of acetate (acsl3, adhfe1, akr1a1, aldoa, aldoc, eno2, hk2, hk3, ldha, pfkl, pkg1 and pkm2: +1.13 < FC < +2.61). Of note was the down-regulation of genes encoding enzymes competing i) with hmgcs1 for acetate (acaca: -1.32) and ii) with aldoa and aldoc for fructose, 1-6, biphosphate, which is needed to produce glyceraldehyde-3-phosphate upstream the sterol biosynthesis pathway (fbp1: -2.16). In addition, the up-regulation of the transcription factor encoded by atf3 (+ 1.77) was consistent with the down-modulation of fbp1. These data suggest that the available intracellular pool of sterol-synthesis molecular intermediates was maintained by a gene expression program relying on a coordinated regulation at both the transcriptional level by srebf2, atf1 (+ 1.84) and atf3, and also most likely at the post-transcriptional level by insig1 (+ 2.62) encoding a sterol-sensing protein that regulates the intracellular cholesterol level [10].
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-119/MediaObjects/12864_2008_Article_2003_Fig3_HTML.jpg
Figure 3

Modulation of the sterol biosynthesis pathway in L. amazonensis -hosting MΦ. L. amazonensis-hosting MΦ display an up-regulation of several genes involved in sterol biosynthesis (*, at least 2 probe-sets modulated).

The expression of several genes involved in the fatty acid biosynthesis pathway was also up-regulated with the modulation of ppap2b (+ 8.53), scd1 (+ 2.68), scd2 (+ 2.45) and acsl3 (+ 2.09). Moreover, genes encoding fatty acid binding proteins that play a role in fatty acid uptake and transport were up-regulated (fabp3: + 2.29, fabp4: + 6.42 and fabp5: + 1.57). Extracellular lipolysis was down-modulated (lipe: -2.20, lpl: -1.44 and apoc2: -1.63), while intracellular catabolism of triglycerides mediated via mgll was up-regulated (+ 3.40). Fatty acid transport to peroxisome was diminished with abcd2 down-modulation (-2.11). Since this was not described neither for L. major nor L. donovani [11], this could be unique for the L. mexicana complex, all sub-species of which multiply within giant communal PV [1]. Indeed, previous experimental work performed with L. mexicana [12, 13], which is very close to L. amazonensis (both share the same distinctive feature to multiply within a communal PV), has shown that amastigotes could take advantage of the MΦ sterol biosynthesis pathway to produce ergosterol.

These data were in agreement with the sterol biosynthesis machinery of the MΦ host cell being exploited by the cell-cycling amastigotes for both their own cell membrane sterols, in particular ergosterol and the PV membrane sterol-dependent remodelling. Indeed, cholesterol availability might play a role in the formation of the PV lipid rafts [14] that could be involved in the control of fusion events leading to the sustained remodelling of the huge communal PV membrane where the aforementioned proton pump components are regularly delivered.

Modulation of MΦ polyamine metabolism

Polyamines (e.g. putrescine) derived from arginine catabolism are essential compounds for amastigote growth [15]. Using the Affymetrix technology we failed to detect, at the 5% significance threshold, arginase-2 (arg2) and ornithine decarboxylase-1 (odc1), two enzymes leading to the formation of polyamines through arginine catabolism. Indeed, while for arg2 the raw fluorescence intensity values were below or close to the background level, for odc1 the raw fluorescence intensities before data processing displayed only a slight increase (+ 1.21) in presence of amastigotes (see Additional file 1). However, the up-regulation of SLC7A2 (+ 4.14) in MΦ hosting amastigotes was a strong incentive for monitoring the abundance of arg2 and odc1 transcripts with a validated RTQPCR method. Using this method we did detect a slight variation of the expression of arg2 (+ 1.91) and odc1 (+ 1.18) (Table 1). Therefore, in presence of amastigotes, arg2 could favour arginine transformation into ornithine, the latter being catalyzed in turn by odc1 to generate putrescine (Fig. 4).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-119/MediaObjects/12864_2008_Article_2003_Fig4_HTML.jpg
Figure 4

Modulation of the polyamine biosynthesis pathways in L. amazonensis -hosting MΦ. L. amazonensis-hosting MΦ display a gene expression coordination of several genes involved in polyamine biosynthesis (*, at least 2 probe-sets modulated; blue values determined by RTQPCR).

ODC1-antizyme plays a role in the regulation of polyamine synthesis by binding to and inhibiting ODC1. The transcript abundance of azin1 encoding ODC1-antizyme inhibitor-1 was higher (+ 1.96) when amastigotes were present, so that this inhibitor might prevent antizyme-mediated ODC1 degradation. Of note, ornithine could also be generated from proline by p4ha2 (+ 2.27), and putrescine from spermine and spermidine by the successive action of sat1 (+ 1.47) and maoa (+ 2.56). Spermidine synthase (srm) and spermine synthase (sms), two enzymes catalyzing the reverse reactions leading to the formation of spermine from putrescine, were not detected with Affymetrix (5% threshold), although their transcript abundance decreased in presence of amastigotes (-1.22 and -1.38, respectively; see Additional file 1). No gene expression modulation was detected with Affymetrix for nos2 (5% threshold) that encodes a competing enzyme for arginine substrate leading to the production of microbe-targeting nitric oxide derivatives (fluorescence intensity was below the background level, see Additional file 1), and only a slight up-regulation was detected with RTQPCR (+ 1.28) (Table 1). The present data further extend former observations [15, 16], and highlight a coordinated gene expression modulation that sustains a metabolic flux leading to the biosynthesis of putrescine from arginine and proline via ornithine, and from spermine and spermidine.

L. amazonensis amastigotes set up an optimal dermis niche

Decreased expression of genes involved in the entry of non leishmanial micro-organisms as well as in the sensing and processing of microbial molecules

Several genes involved in classical and alternate complement component pathways were down-regulated (c1qa, c1qb, serping1, c3, c4b, cfh, c5ar1 and pros1: -2.80 < FC < -1.35) as well as some genes of the Toll-like receptor signalling pathway (tlr2, tlr7, tlr8, cd14, mapk14, c-fos and nfkbia: -3.11 < FC < -1.61. Furthermore, the negative regulator tollip also was up-regulated (+ 1.69). These pathways are known to contribute to the entry of micro-organisms and the sensing/processing of microbial molecules. In presence of the intracellular cell-cycling amastigotes these biological processes would be restricted, if not prevented. Indeed, it is conceivable that non-Leishmania micro-organisms or microbial molecules might trigger a different MΦ transcriptional program that could interfere with the one already set up by L. amazonensis amastigotes for their multiplication. Nevertheless, it has recently been demonstrated that the other L. amazonensis developmental stage, the promastigote, was still able to enter MΦ already hosting amastigotes, to transform into amastigote and to multiply efficiently within the PV [17].

The above data suggested that L. amazonensis amastigotes were able to control MΦ expression of the early complement components, the proteolytic products of which are known to be pro-inflammatory. This complement component pathway down-modulation was also recently described for human MΦ housing L. major parasites [18]. The down-modulation of the Toll-like receptor pathway also suggested prevention of the inflammatory process signalling. At this stage, although some anti-inflammatory genes were not up-modulated (il10: -2.97 and il10ra: -2.16) the gene expression modulation for the majority of the listed genes involved in inflammatory processes showed that the presence of cell-cycling amastigotes imposed an immune unbalance favouring the shaping of a counter-inflammatory and safe dermis niche for these parasites (il1rn, il1b, il11ra1, il17rb, il18, socs6, cd200, nfkbia, relB, c-fos and anxA1, an inhibitor of phospholipase A2 mediated-inflammation: 1.41 < | FC | < 4.19).

Decreased expression of genes involved in the chemokine-dependent MΦ traffic

The down-modulation of the expression of genes encoding chemokine receptors (ccr2, ccr3, cx3cr1 and cmklr1: -2.65 < FC < -1.83) suggested that amastigote-harbouring MΦ were less responsive to chemo-attractant gradients and thus less amenable to enter into the afferent lymphatics. This is consistent with the dominant residence of L. amazonensis-hosting MΦ in the skin. In favour of this possible reduced emigration of MΦ from the dermis niche was the down-regulation of itga4 (-2.06) encoding an integrin shown to contribute to the lymphatic adhesion/transmigration [19]. It is beyond the scope of this article to discuss about more than a dozen of chemokine receptor ligands the gene expression of which was modulated (see Additional file 1). Indeed, the interpretation is not that straightforward because of the complexity of their partial overlapping functions and/or common receptors.

Decreased expression of genes involved in the cellular communication with leukocytes prone to display parasite-damaging functions

The modulation of several transcripts indicated a prevention of MΦ communication with leukocytes that could be rapidly recruited such as NK lymphocytes, and T-lymphocytes. For instance, H60 is one of the ligand able to efficiently activate NK-lymphocytes by binding to the NKG2D receptor (encoded by klrk1). In presence of amastigotes, the h60 MΦ expression was down-modulated (-2.07), suggesting the prevention of this "immune synapse" by which parasitized MΦ and NK lymphocytes can communicate. Interestingly, NKG2D receptor engagement by H60 ligand in MΦ, that normally leads to the production of MΦ leishmanicidal molecules such as NO and TNF-α [20], could be impaired in MΦ hosting amastigotes since the expression of klrk1 gene was also down-modulated (-1.72). Besides, the gene expression of the co-stimulatory molecule CD86 was reduced (-1.83), while that of the inhibitory receptor CD274 (also referred to as B7-H1) was increased (+ 1.93). In addition, the transcript abundance of the co-stimulatory molecules ICAM1 (-1.75), ICAM2 (-1.85) and LFA-1 (or integrin-alpha L, – 2.0) was also reduced. The down-modulation of several genes involved in antigen presentation by MHC class II molecules was recently discussed for human MΦ housing L. major parasites [18]. This data suggested plausible reduced effectiveness of this other "immune synapse" involving TCR-dependent signalling by which MΦ and T-lymphocytes can communicate. Consistent with this was the reduced transcription level in MΦ hosting L. amazonensis amastigotes of h-2ma (-1.88) and of ifngr1 (-1.83 FC) that encodes the receptor for IFNγ, a cytokine secreted by both activated NK- and T-lymphocytes and involved upstream the MHC class II gene up-regulation.

Conclusion

The Affymetrix GeneChip technology has allowed – for many cell lineages – the global analysis of several thousand transcripts simultaneously to be carried out in a robust fashion [21]. The remarkable coordination of gene expression as well as coherent biological interaction networks displayed by MΦ subverted as host cells by the multiplying L. amazonensis amastigotes allow highlighting the power of this technology at two different levels: (a) the amastigote-hosting MΦ transcriptional features per se and (b) the features of MΦ hosting cell-cycling amastigotes which would have been captured within the dermal environment. Further in vivo quantitative analysis will have to be set up for validating or not the present transcriptional profile at early stage after the first wave of amastigote multiplication in the ear dermis of naïve BALB/c mice. Overall, the gene expression profile of MΦ hosting amastigotes did not strictly fall into either of the MΦ "activation" profiles, as it was also the case for L. chagasi [22]. Nevertheless, consistent with the multiplication of the amastigote developmental stage, some overlap with features of the alternative MΦ activation could be observed, such as the up-regulation of arg2 and il1rn, and the down-regulation of cd14 (-1.73 FC).

In addition to the conversion of the MΦ arginine metabolism from a parasite-damaging pathway to a parasite-supportive one, the most clear-cut and novel output of the present analysis was the up-regulation of the MΦ fatty acid biosynthesis pathway. Coupled to the polyamine biosynthesis the MΦ lipids could not only be a source of nutrients for the amastigotes but could also contribute to the PV unique membrane features [2, 23]. Lipids could not only influence the PV membrane curvature but also coordinate the recruitment and retention of key protein export to the PV where multiplying amastigotes are known to be attached [2]. This makes it conceivable that the multiplying amastigotes could take up trophic resources and sense non-trophic signals.

We have highlighted a promising set of transcripts accounting for the BALB/c mouse macrophage reprogrammed as cell-cycling amastigote hosting cells. We do not ignore that transcript modulation changes revealed by microarray analysis could be uncoupled to changes revealed by proteomic and phosphoproteomic analysis. We did not explore how these mRNA changes manifest at the level of the proteome but the present genomewide data will provide a unique resource (a) against which to compare any proteomic/phosphoproteomic data (b) to allow identifying novel small compounds displaying static or cidal activity towards cell-cycling amastigotes hosted within the macrophage PV. Indeed the readout assay we designed allows high content imaging in real time of (a) the amastigotes (b) the amastigotes-hosting PV as well as the macrophages per se [24] and can be up-scaled for high throughput screening of small compound libraries.

Methods

Mice, MΦ and amastigotes

Swiss nu/nu and BALB/c mice were used (following National Scientific Ethics Committee guidelines) for L. amazonensis (LV79, MPRO/BR/1972/M1841) amastigote propagation and bone marrow-derived MΦ preparation, respectively. Four amastigotes per MΦ were added. Parasite-harbouring MΦ (>98%) and parasite-free ones were cultured at 34°C either for 24 h for transcriptomic studies or for different time periods for microscopy analyses [25].

Kinetic study of the intracellular amastigote population size

At different time points post amastigote addition, MΦ cultures were processed for immunofluorescence and phase contrast microscopy. Briefly, MΦ cultures on coverslips were fixed, permeabilized, incubated with the amastigote-specific mAb 2A3.26 and Texas Red-labelled conjugate, stained with Hoechst 33342 and mounted in Mowiol for observation under an inverted microscope as previously described [25]. Ratios of amastigotes per MΦ (between 200 and 700 MΦ nuclei being counted) were calculated and expressed as mean numbers of amastigotes per MΦ at each time point.

GeneChip hybridization and data analysis

Total RNA were extracted from MΦ (RNeasy+ Mini-Kit, Qiagen), their quality control (QC) and concentration were determined using NanoDrop ND-1000 micro-spectrophotometer and their integrity was assessed [26] using Agilent-2100 Bioanalyzer (RNA Integrity Numbers ≥ 9). Hybridizations were performed following the Affymetrix protocol http://www.affymetrix.com/support/downloads/manuals/expression_analysis_technical_manual.pdf. MIAME-compliant data are available through ArrayExpress and GEO databases http://www.ebi.ac.uk/microarray-as/ae/, accession: E-MEXP-1595; http://www.ncbi.nlm.nih.gov/projects/geo/, accession: GSE11497). Based on AffyGCQC program QC assessment [27], hybridizations of biological duplicates were retained for downstream analysis. Raw data were pre-processed to obtain expression values using GC-RMA algorithm [28]. Unreliable probe-sets called "absent" by Affymetrix GCOS software http://www.affymetrix.com/support/downloads/manuals/data_analysis_fundamentals_manual.pdf for at least 3 GeneChips out of 4 were discarded, as well as probe-sets called "absent" once within samples plus once within controls. LPE tests [29] were performed to identify significant differences in gene expression between parasite-free and parasite-harbouring MΦ. Benjamini-Hochberg (BH) multiple-test correction [30] was applied to control for the number of false positives with an adjusted 5% statistical significance threshold. A total of 1,248 probe-sets showing significant differential expression were input into Ingenuity Pathway Analysis software v5.5.1 http://www.ingenuity.com to perform a biological interaction network analysis. Although a cross-hybridization study was performed by Gregrogy and coworkers (11) on a mouse U74av2 DNA Affymetrix gene chip (12,488 transcripts) with RNA from Leishmania donovani, it was important to also assess the absence of significant cross-hybridization in our experimental conditions. To this end, we compared the gene chip data obtained with MΦ RNA alone with those obtained with the same RNA preparation spiked with different amount of L. amazonensis RNA. Our data showed that L. amazonensis RNA did not interfere with mouse RNA hybridization onto GeneChips (data not shown). Indeed, fold-change values for a technical replicate of mouse RNA were not significantly different from those observed for mouse RNA spiked with up to 10% of L. amazonensis RNA taking the non-spiked mouse RNA as reference (one-sample one-sided Student's t-test P-values < 5% for all 45,101 probe-sets, the 1,248 significantly modulated probe-sets, the probe-sets of the 107 genes in Table 1 and the probe-sets of the 13 genes in Figure 3). Therefore, the observed over-expressions were not due to cross-hybridization between the mouse and the amastigote transcripts, thus providing valid information about the reprogramming of MΦ hosting cell-cycling amastigotes.

Real-time quantitative PCR

RTQPCR were performed on cDNA from various biological samples including those used for the hybridizations using a LightCycler®480 (Roche Diagnostics). Primer sequences are available upon request. Gene expression analysis using qBase [31] allowed determining the normalized relative quantities between parasite-free and parasite-harbouring MΦ.

Notes

Declarations

Acknowledgements

This research has received generous financial support from the Fonds Dédié Sanofi-Aventis/Ministère de l'Enseignement Supérieur et de la Recherche "Combattre les Maladies Parasitaires" (PI E. Prina, Co-PI T. Lang), from Institut Pasteur and from Programme de Recherche Pestis (PI E. Carniel, Co-PI G. Milon). We are grateful to Dr. R. Nunnikhoven for his gift that allowed purchasing the Affymetrix core facility and to Roche for providing us with the LightCycler-480.

Authors’ Affiliations

(1)
Département de Parasitologie et Mycologie, Institut Pasteur, Unité d'Immunophysiologie et Parasitisme Intracellulaire
(2)
Génopole Plate-Forme 2, Puces à ADN, Institut Pasteur

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© Fortéa et al; licensee BioMed Central Ltd. 2009

This article is published under license to 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.

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