Brain, Behavior, and Immunity 42 (2014) 123–137

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Brain, Behavior, and Immunity

journal homepage: www.elsevier .com/locate /ybrbi

Single episode of mild murine malaria induces neuroinflammation,alters microglial profile, impairs adult neurogenesis, and causesdeficits in social and anxiety-like behavior

http://dx.doi.org/10.1016/j.bbi.2014.06.0090889-1591/� 2014 Elsevier Inc. All rights reserved.

⇑ Corresponding authors. Address: Hutment Lab, Department of BiologicalSciences, Tata Institute of Fundamental Research, 1, Homi Bhabha Road, Mumbai400005, India. Tel.: +91 22 22782608 (office), +91 22 22782743 (lab) (V.A. Vaidya).Address: B-332, Department of Biological Sciences, Tata Institute of FundamentalResearch, 1, Homi Bhabha Road, Mumbai 400005, India. Tel.: +91 22 22782865(office), +91 22 22782570 (lab) (S. Pathak).

E-mail addresses: vvaidya@tifr.res.in (V.A. Vaidya), pathaksue@gmail.com(S. Pathak).

1 Present address: Department of Pharmacology, Perelman School of Medicine andInstitute of Translational Medicine and Therapeutics, University of Pennsylvania, USA.

2 Joint Corresponding Authors.

Suman K. Guha a, Rucha Tillu a, Ankit Sood a, Mandar Patgaonkar a, Ishira N. Nanavaty a,Arjun Sengupta b,1, Shobhona Sharma a, Vidita A. Vaidya a,⇑,2, Sulabha Pathak a,⇑,2

a Department of Biological Sciences, Tata Institute of Fundamental Research, Mumbai, Indiab Department of Chemical Sciences, Tata Institute of Fundamental Research, Mumbai, India

a r t i c l e i n f o

Article history:Received 18 November 2013Received in revised form 9 June 2014Accepted 13 June 2014Available online 19 June 2014

Keywords:PlasmodiumHippocampusNeural stem cellDoublecortinCytokineInfectionOpen-field testNeuroimmune

a b s t r a c t

Cerebral malaria is associated with cerebrovascular damage and neurological sequelae. However, theneurological consequences of uncomplicated malaria, the most prevalent form of the disease, remainuninvestigated. Here, using a mild malaria model, we show that a single Plasmodium chabaudi adamiinfection in adult mice induces neuroinflammation, neurogenic, and behavioral changes in the absenceof a blood–brain barrier breach. Using cytokine arrays we show that the infection induces differentialserum and brain cytokine profiles, both at peak parasitemia and 15 days post-parasite clearance. Atthe peak of infection, along with the serum, the brain also exhibited a definitive pro-inflammatory cyto-kine profile, and gene expression analysis revealed that pro-inflammatory cytokines were also producedlocally in the hippocampus, an adult neurogenic niche. Hippocampal microglia numbers were enhanced,and we noted a shift to an activated profile at this time point, accompanied by a striking redistribution ofthe microglia to the subgranular zone adjacent to hippocampal neuronal progenitors. In the hippocam-pus, a distinct decline in progenitor turnover and survival was observed at peak parasitemia, accompa-nied by a shift from neuronal to glial fate specification. Studies in transgenic Nestin-GFP reporter micedemonstrated a decline in the Nestin-GFP+/GFAP+ quiescent neural stem cell pool at peak parasitemia.Although these cellular changes reverted to normal 15 days post-parasite clearance, specific braincytokines continued to exhibit dysregulation. Behavioral analysis revealed selective deficits in socialand anxiety-like behaviors, with no change observed in locomotor, cognitive, and depression-like behav-iors, with a return to baseline at recovery. Collectively, these findings indicate that even a single episodeof mild malaria results in alterations of the brain cytokine profile, causes specific behavioral dysfunction,is accompanied by hippocampal microglial activation and redistribution, and a definitive, but transient,suppression of adult hippocampal neurogenesis.

� 2014 Elsevier Inc. All rights reserved.

1. Introduction each year, with more than 40% of the world’s population at risk

Malaria, caused by protozoan parasites of the Plasmodium spp.,results in 300–400 million clinical cases and 1–2 million deaths

(Kappe et al., 2010; Health Organization, 2011). Epidemiologicalevidence suggests that both non-cerebral and cerebral malaria areassociated with long-term neurological impairments (Carter et al.,2005; John et al., 2008; Kariuki et al., 2011; Nankabirwa et al.,2013; Kihara et al., 2006). Given that the malarial parasite is non-neurotropic, neurological sequelae are a consequence of a neuroin-flammatory response, with the specific case of cerebral malariainvolving cytoadhesion and brain sequestration of infected redblood cells (Hansen, 2012; Grau and Craig, 2012; Gay et al., 2012).However, the majority of malarial infections are of the non-cerebraluncomplicated type (World Health Organization, 2011). While wehave now gained insights into cerebral malaria and its effects onthe brain, the mechanisms underlying the neurological sequelae

124 S.K. Guha et al. / Brain, Behavior, and Immunity 42 (2014) 123–137

of non-cerebral, uncomplicated malaria still remain poorly eluci-dated. Given that patients never develop sterile immunity to theinfection and clinical immunity develops only after repeated expo-sures (Marsh and Kinyanjui, 2006), it is crucial that the neurologicalrepercussions of non-cerebral uncomplicated malaria, and itsmechanisms be better understood.

Experimental models of infection (Jurgens et al., 2012; Calsavaraet al., 2013; Lieblein-Boff et al., 2013; Buenz et al., 2006; Grangeret al., 2013; Evans et al., 2014; Vyas, 2013), including exposure tonon-neurotropic infectious agents, have been linked to cognitivedysfunction and mood-related abnormalities, with specific inflam-matory changes, structural and functional effects often reportedwithin the hippocampus, a brain region associated with cognition(Eichenbaum, 2004) and mood (Nestler et al., 2002; Vaidya andDuman, 2001). In particular, mouse models of cerebral malaria havebeen reported to exhibit enhanced anxiety-like behavior andsignificant cognitive impairment associated with increased pro-inflammatory cytokines and alterations in hippocampal structure(Desruisseaux et al., 2008; Reis et al., 2010, 2012; de Mirandaet al., 2011; Miranda et al., 2013). The hippocampus in addition toits important role in cognition and mood, also exhibits significantstructural plasticity including the ability to generate new neuronsthroughout life (Ming and Song, 2005). This process of adult hippo-campal neurogenesis is particularly responsive to environment andexperience, including neuroimmune cues (Glasper et al., 2012; Zivand Schwartz, 2008). Emerging evidence strongly suggests thatthe immune system contributes to the regulation of adult hippo-campal neurogenesis, and it may serve to integrate peripheral cuesand translate these into localized changes within the hippocampalneurogenic niche (Ziv and Schwartz, 2008; Rolls et al., 2007;Butovsky et al., 2006; Derecki et al., 2010). Here, using thePlasmodium chabaudi adami model which is one of the mildest mur-ine models, and is closest to the human uncomplicated disease(Langhorne et al., 2011; Craig et al., 2012) we investigated theeffects of mild malaria on the murine brain and specifically on thehippocampus, which is particularly vulnerable to neuroinflamma-tory insults (Eriksson et al., 1999).

2. Materials and methods

2.1. Mice

Wild type C57BL/6 male mice (6–8 weeks) and transgenicreporter male mice from a mixed C57BL/6 and CD2 background,expressing transgenic green fluorescent protein (GFP) under theNestin promoter (Yu et al., 2005), a kind gift of Prof. S.G. Kernie(Columbia University, USA), were bred and housed in specific path-ogen-free facility at the Tata Institute of Fundamental Research(TIFR). All experiments were approved by the TIFR animal ethicscommittee, and were in accordance with the guidelines of CPCSEA(Committee for the Purpose of Control and Supervision ofExperiments on Animals; registration No. 56/1999/CPCSEA).

Fig. 1. Mild malaria induces a reversible splenic immune response and does notinduce blood–brain barrier disruption or neuroanatomical changes. (A) Schematicrepresentation of the parasite kinetics. (B) Typical parasitemia profile of aP. chabaudi adami infection (N = 3, n = 3) induced in C57BL/6 mice. (C, D) Splenicresponse to parasitic infection (C) represented as a mean splenic index (ratio ofsplenic weight to body weight) and (D) as average splenic CD4+ T cells, CD8+ T cellsand CD19+ B cell numbers of uninfected (Control), peak infection point (Peak), and15 days post-parasite clearance (Recovery) mice (n = 6–7 mice per group). (E, F)Blood brain barrier integrity assessed by the absence of Evans Blue (EB) extrava-sation following its administration as revealed by (E) macroscopic images of thebrain and from (F) colorimetric measurement of the dye per gram of brain tissuefrom uninfected (Control) and infected (Peak) mice and at peak of parasitemia (n = 3mice per group). (G) Bright field photomicrographs of coronal brain sectionsrevealing absence of major anatomical alterations in brain tissue of control andinfected mice at peak. Individual 2.5� images were used to generate a singlecomposite image. Results are expressed as mean ± SEM. *p < 0.05 (Student’s t test).

2.2. Parasites and induction of blood stage malarial infection

P. chabaudi adami parasites, a kind gift from Prof. NirbhayKumar (Department of Tropical Medicine, Tulane University Schoolof Public Health and Tropical Medicine), were revived from frozenstocks. The infection was initiated by intraperitoneal (i.p.) injectionof 106–107 parasitized erythrocytes as previously described(Sanni et al., 2001). The progress of infection was monitored bymicroscopic examination of Giemsa-stained thin blood smearswhere at least 1000 erythrocytes were counted per slide. The pro-gress of infection was monitored by calculating percentageparasitemia {(number of infected erythrocytes/total number of

erythrocytes) � 100} and the maximum parasitemia attained postinfection was defined as the ‘peak’ (day 9); ‘recovery’ was day 25post infection and 15 days post parasite clearance (Fig. 1A).

2.3. Splenic index and flow cytometry

Spleens were isolated from control and infected euthanizedmice at the peak and recovery time points (n = 6–7 per group,

S.K. Guha et al. / Brain, Behavior, and Immunity 42 (2014) 123–137 125

per time point). Spleens were then weighed to establish the SplenicIndex (weight of spleen/weight of animal). Single cell suspensionswere prepared from these spleens as described previously (Seixasand Ostler, 2005). Briefly, the spleens were passed through a40 lm cell strainer and homogenized with 3 ml of FACS buffer(0.1 M phosphate buffered saline (PBS), 1% fetal bovine serumand 0.01% sodium azide). Erythrocytes were lysed using Gey’s buf-fer (Seixas and Ostler, 2005) and cell numbers determined using ahemocytometer. Aliquots of 106 cells were incubated for 15 min atroom temperature with FcR blocking antibody (anti-mouse CD16/32, BD Pharmingen, USA; Cat# 553142; clone 2.4G2) prior to incu-bating with anti-mouse CD3-FITC (BD Pharmingen; Cat# 561798;clone 17A2), anti-mouse CD4-PE (BD Pharmingen; Cat# 553652;clone H129.19), anti-mouse CD8-PE-Cy5 (BD Pharmingen; Cat#553034; clone 53-6.7), anti-mouse CD19-FITC (BD Pharmingen;Cat# 553785; clone ID3) antibodies and their respective isotypecontrols as specified by the manufacturer. The cells were washedwith PBS, the pellet was resuspended and fixed with 4% parafor-maldehyde followed by acquisition on a PAS Partec flow cytometer(Partec, Germany). At least 10,000 cells were acquired and ana-lyzed using FlowJo (Tree Star Inc, USA). The percentage of CD3+/CD4+ helper T cells, CD3+/CD8+ cytotoxic T cells and CD19+ B cellswere recorded, and the total number of each population wascalculated using total splenocyte numbers.

Brains were harvested at the peak from control and infectedmice (n = 4 per group), perfused with saline and collected in0.1 M PBS. Cells were isolated using the Neural Tissue DissociationKit-P (Miltenyi Biotech, Germany; Cat# 130-092-628) according tothe manufacturer’s instructions. Cells were stained using CD45-PE(BioLegend, USA; Cat# 103105; clone 30-F11), CD3-Pacific Blue(BioLegend, Cat# 100214; clone 17A2), CD8a-APC (BioLegend,Cat# 100712; clone 53-6.7) and CD4-FITC (eBiosciences, UK, Cat#11-0041-85; clone GK1.5) antibodies and their respective isotypecontrols and acquired on the BD LSR Fortessa flow cytometer (BDBiosciences). The CD45+ population was gated and 50,000 CD45+

cells were acquired. The number of CD45hi cells was recorded. Tcell population analysis was performed using the CD45+/CD3+/CD8+ gated cells.

2.4. Blood–brain barrier permeability

Mice from the control and infected cohorts (n = 3 per group)were injected intravenously with a 2% solution of Evans Blue(Sigma–Aldrich, USA) at the peak of infection, and were sacrificed1 h later using ice-cold saline perfusion as described (Manaenkoet al., 2011). The brains were dissected, fresh frozen in liquid nitro-gen, and stored at �80 �C until further analysis. The brain tissuewas homogenized, sonicated, and centrifuged at 15,000g for30 min at 4oC. The pellet was dried in a vacuum centrifuge toobtain the dry weight. The supernatant was treated with an equalamount of 50% trichloroacetic acid and incubated over night at4 �C. Subsequently, it was further sonicated and centrifuged at15,000g for 30 min at 4 �C. Evans Blue concentration in the super-natant was measured colorimetrically at 620 nm and quantifiedaccording to a standard curve. The results are presented as micro-grams of Evans Blue per gram dry weight of brain tissue. Prior tobeing subjected to the above protocol, images of all brains wereacquired using a Nikon D60 D-SLR (Nikon, Japan).

2.5. Cytokine array

Dissected brains from control and infected animals at peak andrecovery time points (n = 9 per group, per time point) and serasamples were fresh frozen in liquid nitrogen and stored at�80 �C. Brain lysates were prepared as per the manufacturer’sinstructions (R&D Biosystems, USA; Cat# ARY006). Briefly, brains

were homogenized in 1 ml lysis buffer (20 mM Tris pH 7.5,1.5 mM EDTA, 40 mM KCl, 5% glycerol, 0.5 mM DTT, 0.25% TritonX-100, and protease inhibitor) per 200 mg of tissue weight, in adounce homogenizer on ice, and incubated at 4 �C for 10 min withintermittent vortexing. Homogenates were centrifuged at 15,000gfor 10 min at 4 �C and the supernatant was snap frozen and storedat �80 �C. Pooled lysates or sera samples were incubated with thecytokine array blots and developed according to the manufac-turer’s instructions. The sensitivity of the blots did not allow indi-vidual blots per animal and lysates were pooled from 3 animals toget 3 biological replicates per treatment condition. Blots weredeveloped using an enhanced chemiluminescence kit (ThermoScientific Pierce, USA) and recorded on an X-ray film (Kodak,USA). The developed films were scanned on a TTL scanner (Bio-Rad,USA) and the signal intensities were quantified by densitometricmeasurement of individual spots in ImageJ (Schneider et al.,2012). The background corrected pixel density of each spot wasnormalized to the positive control. For generation of heatmapand hierarchical clustering, the data was normalized to baselineexpression by subtracting the mean value for each cytokine of con-trol animals from itself and the mean values of peak and recoverycohorts. The unsupervised data showed 6 major patterns of expres-sion and hence the program was instructed to provide the 6 mostaltered clusters. Heatmap and hierarchical clustering (Eisen et al.,1998) were performed using pheatmap package (Kolde (2013)) inR statistical programming language (R Core Team (2014)).

2.6. Quantitative PCR (qPCR)

To assess cytokine mRNA profiles in hippocampal tissue fromcontrol and infected mice at peak (n = 4 per group), quantitativereal-time PCR was performed. RNA isolation, cDNA synthesis andqPCR was performed as previously described (Benekareddy et al.,2010). Briefly, total RNA was isolated using Tri Reagent (Sigma),according to the manufacturer’s protocol. Isolated RNA was quan-tified and quality control was assessed with Nanodrop (Eppendorf,Germany) and equal quantity of RNA from each sample wasreverse transcribed using a cDNA Reverse Transcription kit(Applied Biosystems, USA; Cat# 4374966) and amplified withappropriate forward and reverse primers (Sigma–Aldrich) for thefollowing cytokine genes: IL-1b, IL-6, IFN-c, TNF-a, and fractalkine.The data from all genes was normalized to Hprt (hypoxanthine–guanine phosphoribosyltransferase), a housekeeping gene, foundto be unaltered across treatment groups. To compare the expres-sion of Hprt and target genes, the comparative threshold cycle(CT) method was used as described previously (Bookout andMangelsdorf, 2003). DCT = absolute CT value � endogenous CT -value; and DDCT = DCT infected � DCT control. Data were repre-sented as fold change ±SEM in mRNA expression levels ascompared to those of control. Listed are the primer sequences usedfor qPCR analysis: (IL-1b: Forward Primer (FP) - CTTCCTTGTGCAAGTGTCTG, Reverse Primer (RP) - GATTTGAAGCTGGATGCTCT; IL-6:FP - TGCAAGAGACTTCCATCCAGTTGCC, RP - TGTGAAGTAGGGAAGGCCGTGGT; IFN-c: FP - CAAAAGGATGGTGACATGAA, RP - TGACCTCAAACTTGGCAATA; TNF-a: FP-CTTGGAAATAGCTCCCAGAA, RP-CATTTGGGAACTTCTCATCC; Fractalkine: FP-ACGAATCCCAGTGGCTTTGCTCAT, RP-GTCACATTGTCCACCCGCTTCTCA; Hprt: FP-AGGAGTCCTGTTGATGTTGCCAGT, RP-GGGACGCAGCAACTGACATTTCTA.

2.7. BrdU labeling paradigms

To assess the effect of malarial infection on proliferation of hip-pocampal neuronal progenitors at the peak and recovery timepoints, age- and time point-matched controls along with infectedmice (n = 7 per group, per time point) were subjected to a singlei.p. injection of the mitotic marker 5-Bromo-20-deoxyuridine

126 S.K. Guha et al. / Brain, Behavior, and Immunity 42 (2014) 123–137

(BrdU, 100 mg/kg body weight; Sigma–Aldrich) and were sacri-ficed 2 h later (Fig. 4A). To assess the influence of malarial infectionon the postmitotic survival of BrdU-labeled hippocampal progeni-tors, treatment-naïve mice were first administered BrdU (100 mg/kg, once daily for three days), and parasite infection was induced2 days later in the treatment group (n = 5 per group). Animals weresubsequently sacrificed 21 days later by transcardial perfusionwith 4% paraformaldehyde (Fig. 4F). Brains were dissected outand further processed for immunohistochemistry.

2.8. Immunohistochemistry

Serial 50 lm coronal sections of the brains were preparedthrough the rostro-caudal extent of the hippocampus and subven-tricular zone using a Vibratome (Leica, Wetzlar, Germany) fromcontrol and infected animals at peak and recovery time pointsand free-floating sections were processed as described below.

To determine the number of BrdU+ cells, the sections were pro-cessed for BrdU immunohistochemistry as described previously(Desouza et al., 2005). After DNA denaturation and acid hydrolysis,sections were blocked by incubating in 10% horse serum in 0.1 Mphosphate buffer (PB) with 0.1% Triton X-100 prior to overnightincubation with rat anti-BrdU antibody (1:250, Accurate, USA;Cat# YSRTMCA2060GA). Following washes, the sections were incu-bated with biotinylated anti-rat IgG (1:500, Vector Laboratories,USA; Cat# BA9400), followed by avidin–biotin complex (VectorLaboratories) and were visualized with the substrate diam-inobenzidine (Sigma–Aldrich).

To determine expression of endogenous markers for microglia(ionized calcium binding adaptor molecule 1, Iba1), and immatureneurons (doublecortin, DCX), the tissue sections (n = 6–7 animalsper group) were blocked using 10% horse serum in 0.1 M PB and0.1% Triton-X100, prior to overnight exposure at 4�C to rabbitanti-Iba1 (1:1000, Wako Chemicals, Japan; Cat# 019-19741) andgoat anti-DCX antibodies (1:250, Santa Cruz Biotechnology, USA;Cat# SC8066). The sections were washed and then incubated withanti-rabbit IgG-DyLight 649 antibody (1:500, Jackson ImmunoRe-search, USA; Cat# 111-495-144) for Iba1 immunohistochemistry,and biotinylated anti-goat IgG antibody (1:500, Vector Laborato-ries; Cat# BA9500) followed by incubation with avidin–biotincomplex and the substrate diaminobenzidine for DCX immunohis-tochemistry. The Iba1-stained sections were counter stained withHoechst (1:2000, Sigma–Aldrich).

Immunohistochemistry for PCNA (n = 4 animals per group), anendogenous marker of proliferation, involved antigen retrieval in0.1 M PB for 1 h at 90 �C followed by blocking with 10% horseserum prior to overnight exposure to mouse anti-PCNA antibody(1:250, Chemicon, USA; Cat# MAB424R). Following washes,sections were incubated with biotinylated anti-mouse IgG (1:500,Vector Laboratories; Cat# BA2001), avidin–biotin complex andthe substrate diaminobenzidine.

For experiments to determine the neuronal and glial fate spec-ification, hippocampal sections (n = 5 animals per group) followingDNA denaturation and acid hydrolysis, were blocked with 10%horse serum in 0.1 M PB containing 0.1% Triton X-100, prior toovernight incubation with rat anti-BrdU (1:250, Accurate; Cat#YSRTMCA2060GA), mouse anti-NeuN (1:1000, Chemicon;Cat# MAB377) and rabbit anti-GFAP (1:300, Sigma–Aldrich; Cat#G9269) antibodies. Following washes, sections were incubatedwith biotinylated anti-rat IgG (1:500, Chemicon; Cat# AP183B),Alexa-555 donkey anti-mouse IgG (1:500, Molecular Probes, USA;Cat# A31570) and DyLight 649 anti-rabbit IgG (1:250, JacksonImmunoResearch; Cat# 111-495-144) and FITC-conjugatedstreptavidin (1:250, Molecular Probes; Cat# SA10002).

For identification of hippocampal stem/progenitor cell popula-tions, sections (n = 7 animals per group) were blocked by incubating

with 10% horse serum in 0.1 M PB containing 0.1% Triton X-100 priorto overnight incubation with biotinylated goat anti-GFP (1:200,Abcam, UK; Cat# AB6658) mouse anti-GFAP (1:300, Sigma–Aldrich;Cat# G3893) and rabbit anti-DCX (1:500, Abcam; Cat# AB18723)antibodies. Following washes, sections were incubated with Alexa555-conjugated anti-mouse IgG (1:250, Molecular Probes; Cat#A31570), Alexa 647-conjugated anti-rabbit IgG (1:250, MolecularProbes; Cat# A31573), and Alexa 488-conjugated streptavidin(1:500, Invitrogen, USA; Cat# S11223). The processed sections weremounted using VectaShield (Vector Laboratories) and imaged with aZeiss Axiovert confocal laser scanning microscope (510 LSM,Carl Zeiss, Germany).

2.9. Cell counting analysis

Quantitative cell counting analysis was performed on codedslides by an experimenter blind to the treatment conditions. Quan-titation of BrdU+ and Iba1+ cell numbers in the dentate gyrus (DG)subfield was performed using a modified unbiased stereology pro-tocol (Malberg et al., 2000). Sections spanned the rostro-caudalextent of the hippocampus (Bregma -1.34 to -3.80) (Paxinos,2004). Every 6th hippocampal section was processed for quantita-tion (8 sections per animal). BrdU+ and Iba1+ cells within DG werecounted as being in the subgranular zone (SGZ) when they werewithin a two-cell thickness from the granule cell layer (GCL).Iba1+ cells were defined as being in the GCL and hilus when theircell bodies were within these subregions. The total number ofBrdU+ and Iba1+ cells in the SGZ was calculated by multiplyingthe total number of BrdU+ and Iba1+ cells from the animal by thesection periodicity, 6. BrdU+ and Iba1+ cells were counted as beingin the SVZ (Bregma 1.18 to 0.14) when they were directly touchingthe distal border of the lateral ventricles. The total number ofBrdU+ and Iba1+ cells in the SVZ was calculated by multiplyingthe total number of BrdU+ and Iba1+ cells from the 4 sections peranimal by the section periodicity, 6. Percentage of cell types wasdetermined by analyzing at least 30 BrdU+ cells or Nestin-GFP+

cells using z-plane sectioning with 0.41 mm steps.Four sections per animal were observed to quantify the number

of DCX+ or PCNA+ cells in the DG. The results were expressed as thenumber of DCX+ or PCNA+ cells per section. Morphological status ofDCX+ cells was estimated by categorizing them as DCX+ cells with-out tertiary dendrites or DCX+ cells with complex tertiary arbors(Wang et al., 2008). Quantitation of both DCX+ cell numbers andmorphological category was performed using a Zeiss Axioskop at40�.

To assess gross anatomical changes, as well as to measure DGand GCL volume, sections (n = 5–6 animals per group) weremounted on poly-lysine coated slides and stained with Cresyl vio-let (Sigma–Aldrich; Cat# C5042-10G). Images were acquired on aZeiss Axioscope at 20� and the total area of the DG and GCL withinthe section was measured in ImageJ (Schneider et al., 2012). Thearea occupied by the GCL and the DG subfield was determinedby outlining the region using established boundary criteria(Paxinos, 2004). The total volume (V) of the GCL and DG was calcu-lated as per the formula V = �A � T � 6, where �A represents thetotal of area measurements, T is the section thickness, and 6 isthe sample periodicity. Pixels in each photomicrograph wereassigned a distance value using the internal calibration of themicroscope and cross-validated using a graticule.

2.10. Behavior analysis

All behavioral analysis was carried out with infected animals atpeak and their age and litter-matched controls (n = 5–8 per group).The same cohorts of mice were also behaviorally assayed at therecovery time point. All animals were handled for two days prior

S.K. Guha et al. / Brain, Behavior, and Immunity 42 (2014) 123–137 127

to initiation of the behavioral testing regime to minimize handling-induced stress. Locomotor and exploratory activities were mea-sured by allowing animals to freely explore a novel arena(40 cm � 40 cm) for 5 min on day 0, 2, 4, 6, 8, 10, 12, 15, and 24after malarial infection was induced. The animals were videotapedduring the duration of the test and videos were analyzed for totaldistance traveled and velocity using the tracking system Ethovision(Noldus, Wageningen, The Netherlands).

To analyze depression-like behavior and behavioral despair, theanimals were subjected to the tail suspension test (TST) (Castagnéet al., 2011). Using a tape, mice were suspended by their tail, from ametal rod, mounted 50 cm above the ground. The test was carriedout for 6 min and was recorded using a Sony Cybershot DSC HX9 V(Sony, Japan). Video analysis across the last 5 min of the TST wasassessed for the time spent immobile, by an experimenter blindto treatment conditions. Immobility was defined as absence ofany movement of the body or limbs except those caused byrespiration.

The novel object recognition (NOR) paradigm (Bevins andBesheer, 2006; Antunes and Biala, 2012) was used to assess objectrecognition memory. Control and infected animals at peak wereacclimatized to a novel arena (40 cm � 40 cm) by allowing freeexploration for 30 min each day for 3 days. On day 1 of NOR, ani-mals explored 2 identical objects placed at diagonally opposite cor-ners of the arena for 15 min. On day 2 of the test, object recognitionmemory was tested by replacing one of the familiar objects by anovel object of similar dimensions and the animals were allowedto explore the arena for 5 min. To rule out position bias in explora-tion, the position of the novel object relative to the familiar objectwas interchanged across animals in a randomized fashion. Thebehavior of the animals was recorded using Ethovision trackingsystem and discrimination index {[(time spent exploring the novelobject � time spent exploring the familiar object)/total objectexploration time] � 100} was used as a measure of object memory.Objects were previously tested on a neutral cohort of mice toassess no inherent object bias.

The test for social interaction was modified from Moy et al.(2004) and Bluthé et al. (2000) was carried out in the OFT arena(40 cm � 40 cm box). Prior to testing, the infected mice along withtheir age and time point matched controls were acclimatized to thearena for 3 days by allowing them to explore the area for 5 mineach day. During acclimatization an empty wire mesh cage(10 cm � 10 cm) was kept at one corner of the box and animalswere allowed to freely explore the entire area. On the day of test-ing, a juvenile mouse of postnatal (P) day P21�P25 was placedinside the wire mesh followed by introduction of the experimentalmouse into the arena. The time spent interacting with the juvenilemouse and the number of interaction events were recorded using aSony Cybershot. Number and duration of interaction events wasscored manually by an experimenter blind to treatment conditions.An interaction event was defined as when the experimental mouseactively sniffed the wire mesh cage containing the juvenile mouse.

The open field test (OFT) for measurement of anxiety-like behav-ior (Prut and Belzung, 2003) was performed in a 40 cm � 40 cmarena. The centermost 20 cm � 20 cm area was defined as the centerof the arena. For the OFT, animals were not previously exposed tothis arena to ensure novelty of the arena during the OFT task, withthe exception of the cohort of animals assessed at the recovery timepoint who were also subjected to OFT analysis at peak of infection.Animals were placed in the center of the arena and allowed toexplore the arena for 5 min. The behavior was recorded using a ceil-ing mounted video camera and analyzed using the automated track-ing software Ethovision for time spent in the center, number ofentries in the center, the total path length, and percent path lengthin the center.

2.11. Statistical analysis

Results were subjected to statistical analysis using Instat(GraphPad). Unpaired Student’s t test was performed for experi-ments where differences between 2 groups needed to be analyzed.Experiments with 3 groups were subjected to statistical analysesusing analysis of variance (ANOVA) followed by the Bonferronipost hoc test. Statistical significance was determined at p < 0.05.Multivariate analysis of serum and brain cytokine data were per-formed by principal component analysis (PCA) and orthogonal par-tial least square-discriminant analysis (OPLS-DA) (Sengupta et al.,2011), using Simca-P+ 12.0 (Umetrics, Sweden). Briefly, PCA is anunsupervised method whereas OPLS-DA is a supervised methodwhere the class entity toward the sample set is provided a priori,indicating group-specific distinctions. Q2(cum) is the diagnosticcross-validation parameter for the OPLS-DA model that representsthe separation between the groups.

3. Results

3.1. Murine mild malaria model

To study the effect of mild malarial infection on the brain, weused the P. chabaudi adami model. Infection was induced by ani.p. injection of 106 to 107 infected erythrocytes in 6–8 week oldC57BL/6 mice. Fig. 1A shows a schematic representation of the spe-cific time points of the study. In accordance with earlier reports,Giemsa stained counting of blood smears showed that P. chabaudiadami caused a self-limiting infection that peaked around day 9and the parasites became undetectable by day 11 (Fig. 1B) (Sanniet al., 2001). Analysis of spleens revealed a significant increase insplenic index, a measure of splenomegaly, at peak parasitemia,which returned to baseline 15 days post-parasite clearance(Fig. 1C). Flow cytometric analysis of splenocytes revealed almosta doubling of CD4+ and CD8+ T cell, and CD19+ B cell numbers atpeak, which returned to baseline 15 days post-parasite clearance(Fig. 1D). Such splenomegaly and increase in splenic T and B cellnumbers is a cardinal feature of malaria (Helmby et al., 2000).Based on the splenic index and flow cytometry data, we considered15 days post-parasite clearance as the recovery time point(Fig. 1A). Sequestration of parasites in the post-capillary venulesis one of the central features of cerebral malaria (Hansen, 2012;Grau and Craig, 2012; Gay et al., 2012). The intimate contact ofthe parasitized RBCs with the endothelium results in the disrup-tion of the neuroimmunological blood–brain barrier (BBB), a hall-mark of experimental cerebral malaria (de Souza et al., 2010;Nacer et al., 2012). Such pathophysiology is not observed inP. chaubaudi infections (Sanni et al., 2004). However, since we wereinterested in investigating the effect of a peripheral malarial infec-tion on the brain, it was imperative to ascertain the intactness ofthe BBB in our model. A decrease in endothelial integrity by themeasurement of Evans Blue leakage into the brain has been well-documented in murine models of malaria (Reis et al., 2012;Nacer et al., 2012; Hawkes et al., 2013; Pamplona et al., 2007).Evans Blue extravasation allows the observation of breaches ofmolecular size >60 kDa (Chen et al., 2006). It is used as a vascularintegrity marker because of its ability to bind to albumin, andplasma protein leakage across the BBB into the cerebral paren-chyma reflects the pathologically relevant mode of vascular injury(Nacer et al., 2012). Visual examination revealed no significant dif-ference in Evans Blue extravasation between control and infectedanimals (Fig. 1E). Quantitative colorimetric analysis of brain tissuefor the dye further confirmed the absence of Evans Blue in braintissue (Fig. 1F). Cresyl violet staining of brain sections revealed

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no gross pathology in the brain (Fig. 1G). Taken together, we con-cluded that we had successfully established a mild, self-limiting,malarial infection of P. chabaudi adami in C57BL/6 mice that peakedat around day 9, resulted in reversible changes in the spleen anddid not result in a BBB breach.

3.2. Mild malaria evoked serum and brain cytokine responses

We next investigated the nature of immune response in ourmodel by using cytokine array blots to ascertain relative protein lev-els of 40 pro- and anti-inflammatory cytokines, chemokines, andgrowth factors in serum. We observed that several cytokines, che-mokines, and growth factors were upregulated at peak parasitemia;most of these returned to baseline at recovery (Fig. S1A, C, Supple-mentary Table 1). Using Principal Component Analysis (PCA)(Fig. S2A) and Orthogonal Partial Least Square Discriminant Analysis(OPLS-DA) (Fig. S2C) of cytokines between control and infected ani-mals at peak parasitemia and recovery we observed that control,peak, and recovery cohorts all exhibited significantly different cyto-kine patterns. Using complete linkage multivariate hierarchicalclustering we grouped cytokines based on consistent patterns ofchanges in expression across control, peak, and recovery, andthereby identified 6 major clusters according to variation in theirexpression across time points (Fig. 2A). One-way ANOVA analysis

Fig. 2. Mild malaria induces proinflammatory cytokine responses in the serum and brashowing complete linkage hierarchical clustering of cytokine expression in (A) sera andrecovery point (Recovery) mice. Hierarchical clustering revealed six most prominent clbaseline (n = 9 per group). Cluster analysis of cytokines is color coded to indicate simila

allowed us to determine that cytokines/chemokines belonging tocluster 1 showed maximum increase in expression at the peak ofparasitemia and continued to be elevated at recovery (Fig. 2A,Supplementary Table 1). These included IP-10, IL-3, BLC, CCL17,CCL2, sICAM-1, TIMP-1, MIG, C5a, and IL-16. By contrast, levels ofmost cytokines in clusters 2, 3, 5 and 6 returned to baseline at recov-ery, whereas cluster 4 consisted of cytokines that did not change sig-nificantly across groups. In agreement with previous reports, theserum levels of the anti-inflammatory cytokine IL-10 increased sig-nificantly at peak (p = 0.0075, Student’s t test; Fig. S1C, Supplemen-tary Table 1) (Helmby, 2009). However, a large variation in IL-10levels at recovery resulted in it failing to show significant changeacross groups in ANOVA (Fig. 2A, Supplementary Table 1).

In order to investigate if the peripheral pro-inflammatoryresponse induced by parasite infection was associated with neuroin-flammatory changes, we analyzed the brain lysates of malaria-infected mice at peak and recovery time points. To the best of ourknowledge this is the first report of a detailed examination of braincytokine expression in P. chaubaudi infection. The pattern of cyto-kine/chemokine expression in the brain was quite distinct from thatobserved in the serum, suggesting a unique cytokine/chemokinepattern produced within the brain following malarial infection(Fig. S1B, D). PCA analysis revealed that the patterns of cytokinesat peak and recovery were significantly different from control

in. (A, B) Analysis of cytokine expression patterns in serum and brain. Heat maps(B) whole brain lysates across uninfected (Control), peak parasitemia (Peak), and

usters of cytokines varying across groups. Arrow indicates color representation ofrly regulated clusters across peak and recovery as compared to controls.

Fig. 3. Mild malaria induces expression of proinflammmatory cytokines in thehippocampus, increases microglial numbers and induces their activation. (A) Foldchange in hippocampal mRNA expression in infected mice at peak parasitemiapoint as compared to uninfected controls as measured by qPCR analysis (n = 4 pergroup). (B) Representative confocal micrographs of dentate gyrus (DG) sectionsfrom control and peak parasitemia mice indicating the presence of Iba1 stained(red) microglia (arrows) and cellular nuclei marked with Hoechst (green). Insetshave been magnified to indicate morphology associated with quiescent andactivated microglia. Scale bars represent 50 lm. (C, D) Quantitative analysisshowing quiescent, activated, and total microglia numbers and their redistributionwithin the DG subfields at peak parasitemia. (C) Bar graphs representing quiescent,activated and total microglial numbers at (C-upper panel) peak and (C-lower panel)recovery time points in uninfected (control, open bars) and infected mice (solidbars) mice. (D) Bar graphs representing microglia numbers in the hilus, subgranularzone (SGZ) and granule cell layer (GCL) regions of the DG subfield at (D-upperpanel) peak and (D-lower panel) recovery time points in uninfected (control, openbars) and infected (solid bars) mice. All experiments had individual age-matchedcontrols (n = 6 per group). Results are expressed as mean ± SEM. *p < 0.05;**p < 0.01, ***p < 0.001 (Student’s t test). (For interpretation of the references tocolor in this figure legend, the reader is referred to the web version of this article.)

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(R2 = 0.897, Fig. S2B). OPLS-DA transformation of the data showedthat cytokine pattern of infected mice, whether at peak or recovery,was significantly different from that of control mice (Fig. S2D).

Similar to serum cytokine analysis, complete linkage multivar-iate hierarchical clustering identified brain cytokines that showedaltered expression patterns across control, peak, and recoverygroups, as represented by the heat map (Fig. 2B). From the hierar-chical clusters we identified 6 most prominent clusters of cyto-kines according to variation in their expression across timepoints. By one-way ANOVA on these clusters, we concluded thatcytokines in clusters 1, 2 (except M-CSF which did not alter signif-icantly) and IFN-c, IL-1a in cluster 6 were upregulated at peak(Fig. 2B, Supplementary Table 2). Levels of majority of thesecytokines returned to baseline at recovery. The increased levelsof G-CSF, GM-CSF, and IL-5 (cluster 2) at peak parasitemia weresuggestive of microglial activation (Liva et al., 1999; Guo et al.,2013; Liva and de Vellis, 2001). Levels of cytokines in clusters 3and 4 remained unchanged at peak and were downregulated atrecovery—except IL-2 and CCL4, which remained unchanged. Lev-els of cytokines in clusters 5 and 6—except IFN-c and IL-1a—didnot vary significantly across the groups (Fig. 2B, SupplementaryTable 2). Brain pro-inflammatory cytokines/chemokines such asIL-1b (cluster 1), C5a, IL-6, TNF-a, and IP-10 (cluster 2), and IFN-c,IL-1a (cluster 6) were upregulated at peak and returned to baselineat recovery. Interestingly, pro-inflammatory cytokines/chemokinessuch as IL-27, SDF-1 (cluster 3) and IL-17, CXCL2, IL-23, and CCL5(cluster 4) were not elevated at peak but were downregulated atrecovery. Some growth factors and anti-inflammatory cytokinese.g., GM-CSF, G-CSF, and IL-4 (cluster 2) were also upregulated atpeak. Taken together these data indicate that levels of most pro-inflammatory cytokines returned to baseline at recovery, whilelevels of growth factors and anti-inflammatory cytokines did not.

We also investigated if mild malarial infection resulted inincreased influx of monocytes and CD8+ T cells in the brain by flowcytometry. Peripheral myeloid cells infiltrating the brain exhibit aCD45hi phenotype, while the resident cells exhibit a CD45med

(microglia) and CD45lo phenotype (Kettenmann et al., 2011). Therewas no difference between the CD45hi cells in control and infectedmice at peak of infection (7.52 ± 2.6 � 102 cells/brain in control micevs 4.84 ± 1.6 � 102 cells/brain at peak; p > 0.05, Student’s t test, n = 4per group). Similarly the numbers of CD8+ T cells were statisticallyindistinguishable between the two groups (2.91 ± 1.6 � 102 cells/brain in control mice vs 0.86 ± 0.3 � 102 cells/brain at peak;p > 0.05, Student’s t test, n = 3–4 per group).

Thus, a mild malarial infection induced a pro-inflammatorycytokine response in the periphery. It also resulted in expressionof neuroinflammatory cytokines in the brain, but did not resultin recruitment of peripheral macrophages or CD8+ T cells. The cyto-kine expression profile did not completely return to baseline atrecovery either in the serum or in the CNS, even though splenicimmune responses had reverted to baseline.

3.3. Mild malaria induces proinflammatory cytokine expression andalters hippocampal microglial numbers, activation, and distribution

We next investigated the immunological effects of mild malariaon the hippocampus addressing both changes in the cytokine andmicroglial profile. Mild malaria was induced when animals were6–8 weeks old and were followed across time for peak (day 9)and recovery (day 25), all groups had their individual age- and lit-ter-matched controls to avoid age-dependent variation on plastic-ity measures during this temporal window (Hamilton et al., 2013;Curlik et al., 2014; Ho et al., 2012). Volumetric analysis of the den-tate gyrus (DG) hippocampal subfield and the granule cell layer(GCL) within the DG revealed no significant change betweeninfected and uninfected age-matched controls (Fig. S3A, B).

Fig. 4. Mild malaria affects multiple stages of adult hippocampal neurogenesis. (A)Shown is the BrdU treatment regime for analysis of proliferating hippocampalprogenitors at peak and recovery time points. (B) Representative 20� photomicro-graphs of BrdU immunopositive proliferating progenitors in the subgranular zone(SGZ) (black arrowheads) of the dentate gyus (DG) subfield of control and infectedmice at peak and recovery. Scale bars represent 200 lm. (C) Bar graphs representingthe total number of BrdU+ cells within the SGZ region of the DG subfield in control(open bars) and infected (solid bars) mice at peak and recovery (n = 7 per group).(D) Representative 10� photomicrographs of DCX+ immature neurons within theSGZ of the DG subfield of control and infected mice at peak and recovery. Scale barsrepresent 200 lm. (E) Bar graphs representing the number of DCX+ cells per sectionwithin the DG subfield in control (open bars) and infected (solid bars) mice at peakand recovery (n = 7 per group). (F) Shown is the BrdU treatment regime for analysisof differentiation and survival of hippocampal progenitors. (G) Representativeconfocal micrographs with merged confocal z-stack images of BrdU+/NeuN+

neurons and BrdU+/GFAP+ astrocytes in the DG subfield. Large arrowheads inmerged images indicate colocalization of BrdU with either NeuN or GFAP. Smallarrowheads indicate GFAP immunostained cell processes. Scale bars represent10 lm. (H) Bar graphs representing percent colocalization of BrdU with NeuN+ orGFAP+ cells in control and infected mice (n = 5 per group). (I) Representative 20�photomicrographs of surviving BrdU+ cells within the DG subfield of infected andcontrol mice 21 days post-infection (see panel F for BrdU treatment regime). Scalebars represent 100 lm. (J) Bar graphs representing quantitative analysis ofsurviving BrdU+ cells in the SGZ/GCL from control mice and infected mice 21 dayspost-infection (n = 7 per group). Results are expressed as mean ± SEM. *p < 0.05;**p < 0.01, ***p < 0.001 (Student’s t test).

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Quantitative real-time PCR analysis of cytokine mRNA from thehippocampi of mice at peak of infection indicated a significantincrease in IL1-b and IL-6 mRNA and decreased fractalkine expres-sion levels, while TNF-a and IFN-c remained unchanged (Fig. 3A).These cytokine expression profiles are suggestive of an inflamma-tory profile in the hippocampus, similar to that exhibited in thewhole brain cytokine arrays. To evaluate the activation status ofhippocampal microglia we collected brains from infected mice atpeak and recovery time points and immunostained them for Ion-ized calcium binding adaptor molecule 1 (Iba1). Iba1 marks the cellbody and processes of microglia, and can be used to identify theactivation status of these cells as revealed by shift in morphology.Iba1 recognizes all cells of the monocyte/macrophage lineage, inthe brain. This includes predominantly microglia but would alsoinclude any peripherally recruited macrophages (Imai et al.,1996). Since we did not observe any change in CD45hi cells withinthe brain at peak, we believe that Iba1 staining is predominantlyreflective of microglial changes. Quiescent microglia have aramified morphology, whereas activated microglia exhibit anamoeboid morphology (Fig. 3B) (Kettenmann et al., 2011). Quanti-fication of Iba1+ microglial cells and their morphological analysisrevealed a significant increase in activated and total microglia inthe subgranular zone (SGZ) within the DG subfield at peak, as com-pared to age-matched controls (Fig. 3C, upper panel). At recovery,most of the microglia had returned to a quiescent state (Fig. 3C,lower panel). Strikingly, at the peak of infection microglia redis-tributed from the hilus and GCL to the SGZ (Fig. 3D, upper panel)but returned to normal at recovery (Fig. 3D, lower panel). Wenoted that the baseline microglial distribution within the hilus,SGZ, and GCL subregions of the DG varied in the control group atpeak (6–8 weeks), and control group at recovery (8–10 weeks).This age-dependent redistribution of microglia was confirmed ina separate cohort of mice sacrificed at postnatal (P) day P55 andP75 to represent the two ages in our study, which revealed a signif-icant decline in percentage of microglia in the hilus and an increasein SGZ at P75 as compared to P55 (Fig. S3C). Microglial activation atpeak was also observed within the subventricular zone (SVZ), theother major neurogenic niche of the mammalian brain, howeverno change was noted in total microglial numbers in the SVZ at peak(Fig. S4A).

3.4. Mild malaria inhibits multiple stages of adult hippocampalneurogenesis

We sought to elucidate the effect of mild malaria-associatedneuroinflammation on the proliferation of adult hippocampal pro-genitors. Neural stem cells in the adult hippocampus reside in theSGZ of the DG subfield. These progenitors express stage-specificmarkers that are used to identify their developmental progression(Seri et al., 2004). Infected and control mice were injected with themitotic marker 5-bromo-20-deoxyuridine (BrdU) 2 h prior to sacri-fice at peak and recovery (Fig. 4A). Quantitative analysis indicatedthat at peak of infection there was a significant decline in the num-ber of BrdU+ progenitors in the SGZ as compared to age-matched,uninfected controls (Fig. 4B, C). This was validated using an endoge-nous cell cycle marker, Proliferating Cell Nuclear Antigen (PCNA),with a significant decline observed in PCNA+ progenitors noted atpeak of infection (Fig. S3D). BrdU+ and PCNA+ progenitors revertedto baseline at recovery as compared to controls (Fig. 4B, C andFig. S3D). We also addressed ongoing progenitor turnover withinthe SVZ, where we observed a decline in progenitor proliferationas noted by a decrease in the number of BrdU+ cells lining the lateralventricles (Fig. S4B). Doublecortin (DCX), a microtubule-associatedprotein expressed in immature neurons serves as an endogenousmarker for adult hippocampal neurogenesis (Brown et al., 2003).Immunostaining and quantification of DCX+ cells in the SGZ

indicated a significant decrease in the number of DCX+ cells per sec-tion at peak time points of malarial infection as compared to theirage-matched controls, with a reversal to baseline at recovery

Fig. 5. Effect of mild malaria on the putative neural stem cell population. (A)Representative confocal micrographs including merged images from Nestin-GFPreporter mice indicating Nestin-GFP+/GFAP+ Type 1 putative neural stem cells,Nestin-GFP+ Type 2a progenitor cells and Nestin-GFP+/DCX+ Type 2b progenitorcells within the subgranular zone of the dentate gyrus (DG) subfield. Largearrowheads indicate immunostaining within the cell body and small arrowheadsindicate immunostained cell processes. Scale bars represent 20 lm. (B) Quantitativeanalysis of the percent distribution of Type 1, Type 2a, and Type 2b hippocampalprogenitors within the DG of uninfected (Control) and the infected animals at thepeak time point (n = 7 per group). Results are expressed as mean ± SEM. **p < 0.01(Student’s t test).

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(Fig. 4D, E). The baseline DCX+ cell number in the control groups atpeak (6–8 weeks age) and recovery (8–10 weeks age) was noted toshift, reflective of an age-dependent change. Quantitative analysisto examine the morphological maturation of DCX+ cells, as assessedby determining the fraction of DCX+ cells with complex tertiary den-drites revealed no significant alterations in the dendritic complexityof DCX+ cells between control and infected animals (Fig. S3E, F).

To address whether cell fate acquisition of neuronal progenitorsin the adult hippocampal niche is altered by infection, we labeled acohort of progenitor cells with BrdU in naïve animals followed byinduction of infection as illustrated in the schematic (Fig. 4F). Toassess the percentage of BrdU+ progenitors that acquire a neuronalversus glial identity, we measured the percentage colocalization ofBrdU with Neuronal Nuclei (NeuN), a neuronal marker, or GlialFibrillary Acidic Protein (GFAP), an astroglial marker (Fig. 4G).Confocal z-stack analysis, revealed a significant decline in the per-centage of BrdU+/NeuN+ cells, along with a commensurate increasein the percentage of BrdU+/GFAP+ cells (Fig. 4H), indicating a shiftin cell fate choice in infected animals. We also examined thepersistence of BrdU+ progenitors in animals subjected to malarialinfection using the same treatment cohort (Fig. 4F), which indi-cated a significant decline in the number of surviving BrdU+ cellsobserved within the SGZ/GCL of infected mice 3 weeks post-infec-tion as compared to their controls (Fig. 4I, J). Taken together theseresults indicate that mild malaria impairs adult hippocampal neu-rogenesis, influencing distinct stages of hippocampal progenitordevelopment, namely proliferation, survival, and differentiation.

3.5. Mild malaria decreases the number of Type I quiescent neuralprogenitors (QNPs)

Given we noted a significant decline in the number of prolifer-ating hippocampal progenitors, as indicated by a decline in BrdU+

and PCNA+ cell number, we used Nestin-GFP reporter mice to nextaddress the specific stages of proliferating hippocampal progeni-tors that were sensitive to malarial infection. Nestin, an intermedi-ate filament protein, marks neural progenitor/stem cells (Lendahlet al., 1990; McKay, 1997). Nestin-GFP+/GFAP+ progenitors areslowly dividing quiescent neural progenitors (QNPs, Type 1progenitors) which divide asymmetrically to give rise to Nestin-GFP+/GFAP� Type 2a transit amplifying neural progenitors (ANPs).The transit amplifying neural progenitors divide rapidly and giverise to Nestin-GFP+/DCX+ Type 2b progenitors (Kempermannet al., 2004). Confocal z-stack analysis to assess the distributionof Type 1, Type 2a, and Type 2b hippocampal progenitors revealeda significant decline in the percentage of Nestin-GFP+/GFAP+ QNPs(Fig. 5A, B). No significant alteration was noted for the Nestin-GFP+/GFAP� Type 2a and Nestin-GFP+/DCX+ Type 2b progenitors. Theseresults indicate that mild malarial infections results in a selectivedecline in the number of QNPs, the putative neural stem cells,within the hippocampal neurogenic niche.

3.6. Mild malaria evokes specific deficits in social interaction andanxiety-like behavior

Since mild malarial infection evoked neuroinflammatorychanges, with enhanced cytokine expression and activation ofmicroglia, accompanied by perturbations of adult hippocampalneurogenesis, we next addressed whether these molecular and cel-lular changes were associated with changes in behavioral perfor-mance. We examined locomotor and exploratory behavior,depression-related behavior on the tail suspension test (TST)(Castagné et al., 2011), object recognition memory on the novelobject recognition (NOR) task (Bevins and Besheer, 2006;Antunes and Biala, 2012), social behavior in the social interactiontest, and anxiety-like behavior in the open field test (OFT)

(Prut and Belzung, 2003) in distinct cohorts of infected and controlmice (Fig. 6A and Fig. S5A).

Control and infected mice were allowed to explore an arena, atseveral time points post-infection—encompassing peak and recov-ery—and were assessed for distance covered and velocity. Nochange was noted in total distance covered or in velocity betweeninfected and uninfected (control) mice (Fig. 6B, C). Control andinfected mice also did not differ in their performance on the TSTas assessed by the time spent immobile either at peak (Fig. 6D)or at recovery (Fig. S5B). Object recognition memory, as assessedby ascertaining the discrimination index, a measure of preferencefor the novel object over familiar in the NOR task, was not impairedby infection with both infected and uninfected controls exhibitinga similar discrimination index at peak of infection (Fig. 6E).

Mild malarial infection evoked a significant decline in socialinteraction as estimated by the number of active interactions andthe time spent in active interactions (Fig. 6F, G) as compared toage-matched controls. This significant decline in social interactionbehavior was reverted to baseline when social interactions wereassessed at the recovery time point (Fig. S5C, D). In addition tothe decline in social behavior noted at peak infection, we alsoobserved a specific increase in anxiety-like behavior on the OFT.Infected mice exhibited a significant decline in total path length

Fig. 6. Effect of mild malaria on locomotor, depression-like, cognitive, social and anxiety-like behavior. (A) Shown is the schematic for the treatment paradigm and behavioralanalysis performed. (B, C) Locomotor behavior as assessed by (B) distance traveled in cm and (C) velocity cm/s did not vary across days of infection in infected mice ascompared to age-matched controls when exposed daily to the same open-field arena. Arrowheads indicate peak time point. (D) Control and infected mice at peak time pointdid not differ in the time spent immobile in the tail suspension test (TST) which assesses depression-like behavior. (E) Novel Object Recognition (NOR) assessed one dayfollowing exposure to familiar objects did not differ between control and infected mice at peak time point as indicated by no change in the discrimination index [({time spentwith novel object – time spent with familiar object}/total object exploration time) x 100]. (F, G) Social behavior in the social interaction test was significantly reduced ininfected mice at peak as indicated by significant decline (F) in number of active interactions and (G) in time spent in active interaction. (H�L) Open Field Test (OFT) analysisindicated a significant increase in anxiety-like behavior in infected mice at peak as compared to their controls. (H) Shown are representative tracks from a control and infectedmouse in the OFT arena. Enhanced anxiety-like behavior was noted through a significant decline in (I) total path length traversed, (J) percent path length in the center, (K)percent time in center and (L) number of visits to the center in the OFT arena by infected mice at peak as compared to controls when exposed to a novel arena. Results areexpressed as mean ± SEM, n = 5–8 per group. White bars represent controls while solid bars represent infected cohort. *p < 0.05, **p < 0.01, ***p < 0.001 (Student’s t test).

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traversed within a novel arena (Fig. 6H, I) with a further specificdecline in percent path length (Fig. 6J), percent time spent(Fig. 6K), and number of visits (Fig. 6L) to the center of the arena.These measures of behavioral performance on the OFT task werereverted to normal at the recovery time point (Fig. S5 E�H) withno change observed from controls at this time point. Takentogether, these behavioral studies reveal that animals subjectedto mild malarial infections show a specific constellation of behav-ioral changes, involving a deficit in social behavior and enhancedanxiety responses, with no changes observed in locomotor/explor-atory behavior, cognition and depression-related behaviors.

4. Discussion

We provide novel evidence that mild murine malaria evokestransient neuroinflammatory, neurogenic and behavioral conse-quences. While previous reports have investigated the neurologicalsequelae of cerebral malaria on inflammation, neuronal architectureand behavior, to the best of our knowledge our findings are the firstevidence of the neuronal effects of mild malaria, that arise in theabsence of a BBB breach or gross neuropathology. We chose a self-limiting model of mild malaria in which parasite load is low(�10%) and systemic complications associated with severe malariaare not observed. Our results show that a single episode of self-limiting malaria induces neuroinflammation, influences distinctdevelopmental stages of adult hippocampal neurogenesis, evokesspecific changes in the microglial profile, and is accompanied by dis-crete behavioral alterations, all of which resolve by recovery. Mildmalarial infection at peak time point altered the serum and braincytokine profile, with the induction of several proinflammatorycytokines/chemokines. This inflammatory profile was recapitulatedin the hippocampus with enhanced microglial numbers, a shifttoward an activated microglial state, and increased expression ofproinflammatory cytokines. Particularly striking was the redistribu-tion of microglia into the SGZ in the DG. This was accompanied by asignificant decline in hippocampal progenitor turnover, reduction innumber of quiescent neural stem cells, impaired post-mitotic sur-vival and neuronal differentiation. Notably these cellular changesin both microglia and hippocampal progenitors reverted to baselineby recovery. However a distinct brain cytokine profile that differedfrom both that of peak infection and control, emerged at recovery.The neuroinflammatory and cellular changes ensuing from mildmalarial infection were coincident with selective deficits in socialand anxiety-like behaviors, with no change in locomotor, cognitive,and depression-related behavioral tasks.

While our study provides the first report of the neurological con-sequences of mild malaria, several previous studies have investi-gated CNS effects of the murine model of cerebral malaria.Cerebral malaria is thought to involve a CD4+ T cell-mediatedrecruitment of antigen-specific CD8+ T cells to the brain. The CD8+

T cells cause neuroimmunological BBB disruption (Hansen, 2012;Nacer et al., 2012; Suidan et al., 2008). Studies show dysfunctionof aversive and object recognition memory, enhanced anxiety, loco-motor deficits, concomitant with increased IL-1b and TNF-a levels,and histopathological changes in the hippocampus, brain stem,and cerebrum in cerebral malaria (Desruisseaux et al., 2008; Reiset al., 2010; de Miranda et al., 2011; Dai et al., 2010). Our findingsreveal that the patterns of inflammatory, cellular and behavioralchanges that arise with mild malaria are distinct in nature and mag-nitude from those observed with cerebral malaria. Although pro-inflammatory cytokine expression increases in the brain during mildmalaria, the degree and type of inflammatory response differs, withthe changes thought to be indicative of benign hypermetabolismrather than cytopathic effects (Rae et al., 2004; Hanum, 2003). Fur-ther, differences also include the absence of any histopathologicaland volumetric changes in the brain, in particular within the

hippocampus in the mild malaria model. In addition, there is norecruitment of peripheral macrophages or CD8+ T cells to the brainin mild malaria, and no BBB breach. While we observe definitive evi-dence of decreased neurogenesis following mild malaria, thus far noreports have addressed neurogenic changes in cerebral malaria.Finally, behavioral sequelae of mild malaria are restricted totransient alterations of social and anxiety-like behavior, with nocognitive, locomotor or depressive behavior related consequences.

Several infection models, including non-neurotropic and neuro-tropic infectious agents, have been examined for their effects onthe CNS (Jurgens et al., 2012; Evans et al., 2014; Monje et al., 2003;Ekdahl et al., 2003; Gareau et al., 2011; Hermes et al., 2008; Vyaset al., 2007; de Miranda et al., 2012; Jurgens and Johnson, 2012).Specific protozoan parasitic infections, such as those caused byToxoplasma gondii, are associated with parasite sequestration inthe brain, neuroimmunological activation, as well as deficits inexploration, sensorimotor gating, spatial and recognition memoryand social behavior, accompanied by reduced threat and odor per-ception increasing the probability of predation (Evans et al., 2014;Vyas, 2013; Vyas et al., 2007). Dengue virus, thought to be predom-inantly non-neurotropic, is associated with neurological manifesta-tions. In murine models it enhances hippocampal TNF-a and IL-6expression, hippocampal neuronal apoptosis, and enhanced anxi-ety-like behavior that precedes locomotor dysfunction (deMiranda et al., 2012). Infection with a non-neurotropic influenzastrain enhances microglial reactivity and increased hippocampalexpression of IL-1b, IL-6, TNF-a and IFN-a, and a decline in neurotro-phic and immunomodulatory factors. Further, influenza infection isassociated with reduced dendritic arborization of dentate granulecells, and results in cognitive impairment and locomotor dysfunc-tion (Jurgens et al., 2012; Jurgens and Johnson, 2012). The collectivepattern of neurological sequelae associated with diverse infectionshave been referred to as ‘sickness behavior’, and spans the spectrumfrom regulation of endocrine and autonomic functions, sleep andfeeding, circadian rhythms, locomotion, exploratory behavior, aswell as social and emotional behaviors (Dantzer et al., 2008). Proin-flammatory cytokines (Dantzer, 2004) and microglial activation(Henry et al., 2008), has been linked to the development of sicknessbehavior. Several studies have used peripheral lipopolysaccharide(LPS) injection as a primary stimulus to evoke sickness behavior,and to study both the influence of peripheral immune response onthe neuroimmune axis and the behavioral consequences of infection(Van Dam et al., 1992; Gatti and Bartfai, 1993; Layé et al., 1994;Breder and Saper, 1996; Quan et al., 1999; Dantzer et al., 1999;Bluthé et al., 1999, 2006). In a repeated LPS administration model,serum and brain cytokine profiles, in particular GCSF, IL-6, IL-10,and KC, showed substantial overlap with a similar temporal pattern(Erickson and Banks, 2011). By contrast, the cytokine profile for mildmalaria showed a distinct pattern across serum and brain, and thesepatterns were also different at peak and recovery time points. Takentogether, infection-induced neuroimmunological and cytoarchitec-tural outcomes, as well as behavioral changes appear to vary in theirnature, duration and magnitude, suggesting that a substantial diver-sity exists in the neurological manifestations of individual infectiousagents. Our findings with mild malaria infection indicate transientneurogenic and microglial changes, with discrete behavioralchanges noted in anxiety and social interaction, but no deficits incognition and locomotion, accompanied by a cytokine/chemokineprofile that dynamically shifts from that observed at peak parasite-mia to the pattern at recovery. The coincidence of neuroinflammato-ry and cytoarchitectural changes at peak with the alterations insocial and anxiety behavior suggest the possibility that these molec-ular and cellular changes may mechanistically contribute to the per-turbation of behavior. However, while our results indicate a strongcorrelation between the altered cytokine profile, microglial activa-tion, impairment of neurogenesis and onset of behavioral changes,

134 S.K. Guha et al. / Brain, Behavior, and Immunity 42 (2014) 123–137

a caveat to keep in mind is that we have not yet established a clearmechanistic relationship between these molecular/cellular changesand the behavioral outcomes of mild malaria. Further, while ourmanuscript has clearly focused on changes within the hippocampalregions, it would be interesting to assess the effects of mild malariaon other brain regions in future investigations.

Our results are in agreement with prior reports that self-limitingmalarial infection typically results in an early, predominantly proin-flammatory cytokine response, which then shifts to an anti-inflam-matory response in the later phases of infection (Stevenson andRiley, 2004). The pattern of expression of cytokines/chemokines inthe brain at peak infection was strikingly distinct from that notedin serum, likely as a consequence of an absence of BBB breach andthe fact that several cytokines/chemokines do not cross the barrier(Banks and Erickson, 2010). Peripheral proinflammatory cytokineslike TNF-a, IL-1b, and IL-1a have blood to brain saturable transportsystems (Banks et al., 1989; Gutierrez et al., 1993; Erickson et al.,2011), and can induce neurotoxicity and initiate a neuroinflamma-tory cascade (Qin et al., 2007). The proinflammatory cytokines dif-fuse into the brain (Hashimoto et al., 1991; Broadwell andSofroniew, 1993) to induce an innate immune response, mediatedprimarily by microglia (Lampron et al., 2013). Circulating IP-10and C5a have been shown to have a role in the recruitment of inflam-matory leukocytes to brain blood vessels in cerebral malaria (Nieet al., 2009; Patel et al., 2008). However, we failed to detect anyincreased influx of monocytes or CD8+ T cells, despite the increasedlevels of both these chemokines. It is possible that a particularthreshold of the chemokines is required for the influx and that thisthreshold is not reached in mild malaria. We also observed elevatedhippocampal IL-1b and IL-6, but not IFN-c and TNF-a mRNA expres-sion at the peak of infection, in contrast to prior reports from cere-bral malaria models where all these cytokines are enhanced. Theperipheral immune response induced by the parasite and/or therecognition of soluble malarial antigens by the immune cells oneither side of the BBB may play a role in triggering a proinflamma-tory response in the brain (Coban et al., 2007; Ishii et al., 2008).Our results show that the BBB was not breached based on the Evansblue extravasation test which allows the observation of breaches ofmolecular size >60 kDa (Chen et al., 2006). However, we cannot pre-clude the possibility that small parasite proteins may cross the BBBand trigger a local neuroinflammatory response.

Studies suggest a strong influence of the peripheral milieu ofcytokines/chemokines on the turnover of adult neural stem cells(Hattiangady and Shetty, 2008). Inflammatory cytokines such asIL-1b and IL-6 are reported to decrease neuronal progenitor turnover(Kohman and Rhodes, 2013), and SDF-1 is associated with maintain-ing proliferation and differentiation of adult hippocampal progeni-tors (Kolodziej et al., 2008), whereas fractalkine regulatesmicroglial activation and migration (Lyons et al., 2009; Paolicelliet al., 2014). It has been suggested that microglia may integrateinformation about the peripheral inflammatory status and serve asmediators of inflammation-induced changes in the neurogenicniche (Hanisch and Kettenmann, 2007; Sierra et al., 2010;Schwartz et al., 2013). Our study also highlights the fact that inresponse to such neuroinflammatory events microglia redistributeto the SGZ in the DG, where neural stem cells/progenitors reside.This observation is noteworthy as microglia not only exhibits phago-cytosis-dependent regulation of adult hippocampal neurogenesis(Sierra et al., 2010), but also share contact dependent and indepen-dent interaction with neurons within the CNS (Hoek et al., 2000;Wright et al., 2000; Cardona et al., 2006; Bessis et al., 2007). It istempting to speculate that, in addition to this putative phagocyticfunction, secretion of potential factors by the activated and redis-tributed microglia may also serve to influence progenitor turnoverand survival. The neurogenic effects of mild malaria resemble thepattern documented in response to peripheral inflammatory stimuli

induced by administration of LPS (Monje et al., 2003; Ekdahl et al.,2003). This suggests that malaria-induced perturbations in the hip-pocampus might be an outcome of the activation of similar inflam-matory and neuroimmune pathways. While other infectiousdisease models have not particularly assessed the stages of progen-itor development sensitive to infectious insults, we find that the qui-escent neural stem cell pool within the hippocampus may beparticularly sensitive to malarial infection with a decline noted atpeak. While we have primarily focused on effects on adult hippo-campal neurogenesis, we did observe both a decline in progenitorturnover within the SVZ as well as local microglial activation inthe linings of the lateral ventricles, suggesting that the neurogenicchanges evoked by mild malaria might also be applicable to otherneurogenic niches in addition to the hippocampus.

Our study shows that mild malaria induced behavioralalterations are restricted to the specific subset of social and anxietyassociated behaviors, but does not affect all emotional behaviors asexhibited by lack of behavioral despair on the depression relatedtask, TST. Hippocampal neurogenesis has been linked in particularto anxiety behavior, with a decline in hippocampal neurogenesisassociated with anxiogenic responses (Sah et al., 2012; Kheirbeket al., 2012; Revest et al., 2009). In contrast, while decreased neu-rogenesis is not sufficient to evoke depression-like behavior, block-ade of neurogenesis does prevent specific behavioral effects ofantidepressant treatment (Santarelli et al., 2003). It is temptingto speculate that the microglial and neurogenic changes that arecoincident with the behavioral alterations may contribute mecha-nistically to the perturbation of emotionality. It is interesting thatan incident of mild malarial infection only invokes such a specificsubset of behavioral alterations as other infectious insults, suchas cerebral malarial and dengue induce a wide ranging cohort ofbehavioral dysfunctions including changes in general locomotoractivity, depression-like behavior, cognition, as well as alteredsocial exploration and anxiety-like behavior (Desruisseaux et al.,2008; Reis et al., 2010, 2012; de Miranda et al., 2012). Particularlyintriguing is the fact that these behavioral alterations were tran-sient and had receded by recovery. It would be interesting toaddress whether a history of malarial infection alters the vulnera-bility of an individual to mood-associated behavior when facedwith an immune challenge or environmental stressor in the future.

While the cytoarchitectural and behavioral changes appear tobe transient in nature, it is indeed interesting that the cytokineprofile at recovery remains perturbed. This suggests the intriguingpossibility that a history of a parasitic infection of this nature canresult in localized immune pathway changes within brain circuitsthat persist even after parasite clearance. Given the significant glo-bal burden of malarial infection, our results provide impetus forfuture clinical and epidemiological studies of the neurologicaland behavioral sequelae of mild malaria.

In conclusion, in this study we report that a single episode ofmild malaria induces neuroinflammatory changes and results inanxiety-like symptoms and changes in social behavior at peak ofparasitemia. The infection affects hippocampal neurogenesis bydecreasing progenitor proliferation, survival, and altering neuronalfate specification. Using Nestin-GFP reporter mice, we furtherestablish that the turnover of quiescent neural stem cells wasaffected by mild malaria and was associated with a local redistri-bution of activated microglia to the proximity of the neurogenicniche. Taken together, our findings indicate that a single episodeof mild malarial infection evokes transient neuroinflammatory,neurogenic and social/anxiety behavior associated changes.

Acknowledgments

We thank Dr. S. Suryavanshi and Dr. S. Atole for their help withthe animal studies. We also thank Ms. S. Narayanan, Ms. A. Tiwari

S.K. Guha et al. / Brain, Behavior, and Immunity 42 (2014) 123–137 135

and Mr. S. Marathe for technical support. Thanks are also due toMs. S. Gupta, for help in writing the R scripts. We are grateful toProf. S.G. Kernie (Columbia University, USA), for providing the miceexpressing transgenic green fluorescent protein (GFP) under theNestin promoter and Prof. N. Kumar (Tulane University School ofPublic Health and Tropical Medicine), for the kind gift ofPlasmodium chabaudi adami. The authors gratefully acknowledgeDrs. U. Kolthur and S. Khanam for their critical inputs. This studywas funded by intramural funds from the Tata Institute of Funda-mental Research, Mumbai, India and external grants from The LadyTata Memorial Trust, Mumbai, India and The Indian Council ofMedical Research, Government of India.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.bbi.2014.06.009.

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