The hippocampal transcriptomic signature of stress resilience in mice with microglial fractalkine receptor (CX3CR1) deficiency

Accepted Manuscript The hippocampal transcriptomic signature of stress resilience in mice with microglial fractalkine receptor (CX3CR1) deficiency. Neta Rimmerman, Nofar Schottlender, Ronen Reshef, Nadav Dan-Goor, Raz Yirmiya PII: DOI: Reference:

S0889-1591(16)30525-6 http://dx.doi.org/10.1016/j.bbi.2016.11.023 YBRBI 3025

To appear in:

Brain, Behavior, and Immunity

Received Date: Revised Date: Accepted Date:

17 September 2016 16 November 2016 22 November 2016

Please cite this article as: Rimmerman, N., Schottlender, N., Reshef, R., Dan-Goor, N., Yirmiya, R., The hippocampal transcriptomic signature of stress resilience in mice with microglial fractalkine receptor (CX3CR1) deficiency., Brain, Behavior, and Immunity (2016), doi: http://dx.doi.org/10.1016/j.bbi.2016.11.023

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

The hippocampal transcriptomic signature of stress resilience in mice with microglial fractalkine receptor (CX3CR1) deficiency.

Neta Rimmerman, Nofar Schottlender, Ronen Reshef, Nadav Dan-Goor, and Raz Yirmiya Department of Psychology, the Hebrew University, Jerusalem, Israel

Corresponding author: Raz Yirmiya, Ph.D. Department of Psychology The Hebrew University of Jerusalem Jerusalem, 91905, Israel Tel: 972-2-5883695 FAX: 972-2-5882947 E-mail: [email protected]

1

Abstract Clinical studies suggest that key genetic factors involved in stress resilience are related to the innate immune system. In the brain, this system includes microglia cells, which play a major role in stress responsiveness. Consistently, mice with deletion of the CX3CR1 gene (CX3CR1 -/- mice), which in the brain is expressed exclusively by microglia, exhibit resilience to chronic stress. Here, we compared the emotional, cognitive, neurogenic and microglial responses to chronic unpredictable stress (CUS) between CX3CR1 -/- and wild type (WT) mice.

This was followed by hippocampal whole transcriptome (RNA-seq)

analysis. We found that following CUS exposure, WT mice displayed reduced sucrose preference, impaired novel object recognition memory, and reduced neurogenesis, whereas CX3CR1 -/- mice were completely resistant to these effects of CUS. CX3CR1-/mice were also resilient to the memory-suppressive effect of a short period of unpredictable stress. Microglial somas were larger in CX3CR1-/- than in WT, but in both genotypes CUS induced a similar decline in hippocampal microglial density and processes length. RNA sequencing and pathway analysis revealed basal strain differences, particularly reduced expression of interferon (IFN)-regulated and MHC class I gene transcripts in CX3CR1-/- mice. Furthermore, while CUS exposure similarly altered neuronal gene transcripts (e.g. Arc, Npas4) in both strains, transcripts downstream of hippocampal estrogen receptor signaling (particularly Igf2 and Igfbp2) were altered only in CX3CR1 -/- mice. These findings indicate that emotional and cognitive stress resilience involves CX3CR1-dependent basal and stress-induced alterations in hippocampal transcription, implicating inhibition of CX3CR1 signaling as a novel approach for promoting stress resilience.

2

Introduction

Humans and animals exhibit a wide variation in responsiveness to stressful and traumatic experiences. Some individuals exhibit extreme detrimental effects of stress and trauma, which can lead to development of depression or posttraumatic stress disorder (PTSD), while others present no effects or only mild detrimental symptoms that recover rapidly (stress resilience) (Fleshner et al, 2011; Russo et al, 2012; Southwick and Charney, 2012). This variability in stress responsiveness is influenced by a myriad of factors, including stressor type, duration and intensity, previous experiences, neuroendocrine responsiveness, and genetic predispositions of the individual (Feder et al, 2009; Fleshner et al, 2011; Russo et al, 2012; Southwick et al, 2012).

In humans, early findings on the molecular mechanisms of stress resilience identified an association between stress susceptibility and polymorphisms in the hypothalamic-pituitary axis, monoamine neurotransmission, growth factors, and neuropeptide modulation (Feder et al, 2009). In addition, robust novel findings identified genes related to the innate immune system, mainly interferon-related transcription and signaling, as key factors in stress susceptibility and resilience to PTSD and depression (Bahn and Chan, 2015; Breen et al, 2015; Garbett et al, 2015; Glatt et al, 2013; Mostafavi et al, 2014; Passos et al, 2015; Raison and Miller, 2013; Schlaak et al, 2012; Yehuda et al., 2009). In animals, the investigation of molecular mechanisms of stress susceptibility and resilience has progressed using three experimental approaches: 1) exploring strain differences in stress responsiveness (Andrus et al, 2012; Fuchsl et al, 2014; Golden et al, 2011; Jung et al, 2014; Mozhui et al, 2010), 2) assessing subsets of animals within a specific strain that exhibit differential stress responsiveness (Berton et al, 2007; Krishnan et al, 2007; Maier and Watkins, 2010), or 3) characterizing stress susceptibility in

3

transgenic models (Basso et al, 2009; Boucher et al, 2011; Espallergues et al, 2012; Goshen et al, 2008; Kreisel et al, 2014; Lotan et al, 2014). These studies uncovered active and passive molecular mechanisms of stress susceptibility and resilience in several brain regions (e.g. hippocampus, nucleus accumbens, ventral tegmental area, prefrontal cortex), involving stress-induced changes in gene expression, histone methylation, microRNA regulation, protein modifications and signaling, mostly pertaining to neuronal signaling (Berton et al, 2006; Covington et al, 2010; Dias et al, 2014; Elliott et al, 2010; Krishnan et al, 2007; Lisowski et al, 2013a; Lisowski et al, 2013b; Maier et al, 2010; Vialou et al, 2010; Wilkinson et al, 2011).

The recent findings in humans suggesting a major role for innate immunity in resilience to stress prompted us to explore this system in rodents, where transcriptional signatures can be explored in the brain. Previous studies indicate that the brain’s innate immune cells, microglia, are involved in regulating the responsiveness of organisms to stress (Yirmiya et al, 2015). Many types of acute (Frank et al, 2007; Kreisel et al, 2014; Sugama et al, 2007) and chronic (Hinwood et al, 2012; Tynan et al, 2010; Walker et al, 2014; Wohleb et al, 2011; Wohleb et al, 2013) stressors induce microglial activation, characterized by increases in microglial number, altered pattern of processes branching, increased expression of activation markers, stimulation of the microglial NLRP inflammasome, increased IL-1β production, and recruitment of peripheral macrophages into the brain. We have recently shown that the effects of chronic unpredictable stress (CUS) on microglia within the hippocampal dentate gyrus (DG) are dynamic, with an initial phase of microglial proliferation and activation followed by apoptosis and longterm decline and dystrophy (Kreisel et al, 2014). These stress-induced microglial alterations have important roles in mediating stress responses, evidenced by findings that chronic treatment with minocycline, which attenuates microglial activation, blocked the

4

development of depressive-like symptoms and impaired cognitive functioning following exposure to various regimens of chronic stress associated with microglial activation (Hinwood et al, 2012; Kreisel et al, 2014). Furthermore, in the CUS-induced microglial decline model we revealed that treatment of the CUS-exposed mice with compounds that stimulate microglial proliferation and activation reversed the stress-induced depressivelike symptoms and impaired hippocampal neurogenesis (Kreisel et al, 2014). The current study is the first to probe the hippocampal transcriptomic signature of mice with stress resiliency due to a genetic modification in an established stressresponsive mechanism. Specifically, we assessed the hippocampal transcriptome and its responsiveness to stress in WT mice and in CX3C receptor-1 (CX3CR1-deficient mice, which display a stress-resilient phenotype (Hellwig et al, 2015; Milior et al, 2015; Wohleb et al, 2013). We focused our transcriptomic analysis on the hippocampus because of the cardinal role of this area in cognitive and emotional stress responsiveness, and because microglial alterations within the hippocampus of CX3CR1-deficient (CX3CR1-/-) mice were previously shown to be involved in behavioral adaptations to environmental manipulations (Hellwig et al, 2015; Maggi et al, 2009; 2011; Milior et al, 2015; Reshef et al, 2014). In the brain, the CX3CR1 receptor is exclusively expressed by microglia and mediates the communication between these cells and neurons, which constantly regulate the

activation

status

of

microglia

via

the

secretion

of

CX3C

ligand-1

(CX3CL1/fractalkine) (Jung et al, 2000). CX3CR1-/- mice have been shown to display alterations in microglial density and morphology (Milior et al, 2015; Reshef et al, 2014), accompanied by parallel changes in dendritic spine density, synaptic maturation, electrophysiological properties, plasticity, and behavior (Maggi et al, 2011; Ragozzino et al, 2006; Rogers et al, 2011; Reshef et al., 2014). Using RNA-seq analysis we show here that the stress-resilient phenotype of CX3CR1 -/- mice is associated with basal differences

5

in the hippocampal transcriptome, including reduced expression of IFN-regulated and MHC-I gene transcripts, as well as active molecular adaptations to stress, such as increased transcription downstream of hippocampal estrogen receptors.

6

Materials and Methods Subjects Subjects were 3-4 months old male homozygous CX3CR1-GFP (CX3CR1 -/-) transgenic mice (on a C57BL/6 genetic background) and their congenic wild type (WT) controls. Animals were housed in air-conditioned rooms (23° C), with food and water ad libitum, and were kept in a reversed light/dark cycle, with lights off from 9 a.m. to 9 p.m. All experiments were approved by the Hebrew University Ethics Committee on Animal Care and use. Stress exposure paradigms Subjects were exposed to chronic unpredictable stress (CUS), consisting of daily exposure to two stressors in a random order over a 5-week period. The effect of a shorter period of unpredictable stress was examined by exposing mice to various combinations of 2–3 of the same stressors over a period of 2 days. The list of CUS stressors included: cage shaking for 1 h with loud music and lights on, lights on during the entire night (12 h), lights-off for 3 h during the daylight phase, flashing (stroboscopic) light for 6 h, placement in 4ºC cold room for 1 h, mild restraint (in small cages) for 2 h, 45º cage tilt for 14 h, wet cage for 14 h, exposure to fox, ferret, bobcat, or coyote smell for 2 h, noise in the room for 3 h, and water deprivation for 12 h during the dark period.

Behavioral measurements Sucrose preference: following baseline adaptation to sucrose for 3-4 days, mice were presented in the beginning of the dark circadian phase with two graduated drinking tubes, one

7

containing tap water and the other 1% sucrose solution for 4 h. Sucrose preference was calculated as the percentage of sucrose consumption out of the total drinking volume. The object recognition test: this test was carried out as previously described (Leger et al 2013). Briefly, mice were habituated to the test cages for three consecutive days (during 1 h, 5 min, and 5 min exposure periods, respectively). On the fourth day, mice were allowed to explore two identical objects until they reached a criterion (a total of 20 sec nose-directed exploration of the objects) or for 3 min. Mice who failed to explore the objects for at least 10 sec were discarded from the experiment. Twenty-four hours later, mice were allowed to explore one old and one new object. Nose-directed exploration time for each of these objects was recorded until the criterion was reached or for 3 min total. Immunohistochemistry Animals were perfused transcardially with cold phosphate-buffered saline (PBS) followed by 4% paraformaldehyde in 0.1 M PBS, and the brains were quickly removed and placed in 4% paraformaldehyde. After 24 h, the brains were placed in 30% sucrose solution in PBS for 48 h and then frozen in OCT. Coronal sections (8 µm) were serially cut along the rostro-caudal axis of the dorsal hippocampus using a cryostat (Leica, Wetzlar, Germany) and mounted on slides. Microglia were visualized using a primary antibody to the microglial marker ionized calcium-binding adapter molecule-1 (IBA1) (rabbit anti Iba-1 1:250, Wako, Japan), followed by a secondary antibody (goat anti rabbit, 1:200; Invitrogen, Carlsbad, CA, USA), as previously described (Kreisel et al, 2014). The rate of neurogenesis in the hippocampus was measured by staining for doublecortin (DCX), using goat anti-DCX (1:1000, Millipore, Chemicon, Tamecula, CA, USA) as the primary antibody, and biotin-SP-conjugated donkey anti-Guinea pig (1:200; Jackson Laboratories, West grove, PA, USA) as the secondary

8

antibody, with final visualization using a conjugated streptavidine Ab (Jackson Laboratories, West grove, PA, USA), as previously described (Kreisel et al, 2014). Image analysis Images were captured using a Nikon Eclipse microscope and camera. Cells were manually counted using 10X magnification in a defined area exclusively containing the dentate gyrus (DG) or CA3 region of the dorsal hippocampus for each slide, using Nikon Imaging Elements Software (NIS-Elements). Four sections of each brain were counted.

Microglia cell

processes length was measured by capturing images at 40X magnification and by manual tracing of the processes of all IBA+ cells in these sections (249-389 cells per condition) using the NIS Elements software. Confocal images were captured using an Olympus FV-1000 confocal microscope. Slices were imaged at 0.165– 0.2 µm/pixel in the XY dimension and at 0.5 µm steps in the Z dimension, using collapsed z-stacks. Real-time quantitative PCR Mice were sacrificed by decapitation. Each brain was quickly removed on an ice-cold glass plate, and the hippocampus was dissected out under a binocular, tissues were weighed, flash frozen in liquid nitrogen, and stored in -80° Celsius until RNA extraction. RNA was extracted using PerfectPure RNA extraction kit (5 PRIME, Darmstadt, Germany) and RNA samples (2 µg) were reverse transcribed using the QuantiTect Reverse Transcription Kit from Qiagen (Hilden, Germany) including DNase treatment of contaminating genomic DNA. Expression of mRNA was determined by qPCR, using glyceraldehyde-3-phosphate dehydrogenase (Gapdh) as a normalizing gene as previously described (Rimmerman et al, 2011). The following list of gene transcripts was validated: Ttr, Igf2, Gpr88, Arc, H2-q7, Ifit3, Ptgds, Igfbp2, and P2yr12. Primers were designed using PrimerQuest IDT (Integrated DNA Technologies, Inc, San Diego, CA, USA). The following primers were used, Gapdh,

9

Forward: TCT CCC TCA CAA TTT CC; Reverse: GGG TGC AGC GAA CTT TA. Ttr, Forward: GTT TAA AGG CAT CCT TTC CAT CTT; Reverse: CCA CTC TGC TTT CTG ACC TAT C. Igf2, forward: GGA GCA GTG ATC AGG TAA TGA A; Reverse: GCC AAG CGA TAG AGA GAG ATA AA. Gpr88 forward: GGA GAC ACG GGA AAC AGA TAT T; reverse: GAC ACA TGC ACA CAG TTT GG. Arc, forward: CCC AGC TCC AAT TAC CTT GT; Reverse: GGT CTC AGA ACA CCA ATA GAC C. H2-q7, Forward: AGC CAA ACA CTG GGT ACA TC; Reverse: CAA TCA ACC CTC AGC TCA AGA. Ifit3, Forward: TGG CAG TTG CAG GGA TAA A; Reverse: GTT GTC CTC AGG TTC ATG GT. Ptgds, Forward: CAA CTA TGA CGA GTA CGC TCT G; Reverse: AGA GTC TGG GTT CTG CTG TA. Igfbp2, Forward: CCC GAA CAC CAG CAG AAA; Reverse: GAG CTC AGT GTT GGT CTC TTT. P2yr12 Forward: CTG GGA CAA ACA AGA AGA AAG G; Reverse: CCT TGG AGC AGT CTG GAT ATT RNA Sequencing Sample preparation- the KAPA Stranded mRNA-Seq Kit (KAPA Biosytems) was used to build cDNA libraries for single-read sequencing on the Next-Seq machine (Illumina). Starting with 500 ng of total RNA, mRNA was first purified using polyA selection, then chemically fragmented. The mRNA fragments were converted into single-stranded cDNA using random hexamer priming of reverse transcription. Next, the second strand was generated to create double-stranded cDNA, followed by end repair and the addition of a single ‘A’ base at each end of the molecule. Adapters that enable attachment to the flow cell surface were then ligated to each end of the fragments. The adapters also contain unique index sequences, which allow the libraries to be pooled and then individually identified during downstream analysis (multiplexing). PCR was performed to amplify and enrich the ligated material to create the final cDNA Library. Libraries were run in the TapeStation (Agilent) to check the fragment size distribution.

10

Sequencing: The cDNA libraries were sequenced using sequencing-by-synthesis technology (illumina). Twelve samples were sequenced together in a NextSeq flow cell giving 30-40 million SE raw reads for each library with a length of 75bp. Trimming and filtering of raw reads- the NextSeq basecalls files were converted to fastq files using the bcl2fastq (v2.15.0.4) program with default parameters. The provided SampleSheet.csv file contained samples' names and barcodes only, so no trimming or filtering was done at this stage and a fastq file was created for each sample separately. Raw reads (fastq files) were inspected for quality issues with FastQC (v0.11.2, http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). According to the FastQC report, reads were quality-trimmed at both ends, using in-house Perl scripts, with a quality threshold of 32. In short, the scripts use a sliding window of 5 bases from the read's end and trim one base at a time until the average quality of the window passes the given threshold. Following quality-trimming, adapter sequences were removed with Trim Galore (version 0.3.7, http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/), using the command “trim_galore -a $adseq –length 15” where $adseq is the appropriate adapter sequence. The remaining reads were further filtered to remove very low quality reads, using the fastq_quality_filter program of the FASTX package (version 0.0.14, http://hannonlab.cshl.edu/fastx_toolkit/), with a quality threshold of 20 at 85 percent or more of the read's positions. Mapping and quantification -the processed fastq files were mapped to the mouse transcriptome and genome using TopHat (v2.0.13)(Kim et al., 2013). The genome version was GRCm38, mapping allowed up to 5 mismatches per read, a maximum gap of 5 bases, and a total edit distance of 10 (full command: tophat -G genes.gtf -N 5 --read-gap-length 5 -read-edit-dist 10 --segment-length 20 --read-realign-edit-dist 5 genome processed.fastq).

11

Quantification was done with the Cufflinks package (v2.2.1)(Trapnell et al, 2010; 2013), using the cuffquant program with Ensembl annotations (release 84), the genome bias correction (-b parameter), multi-mapped reads assignment algorithm (-u parameter) and masking for genes of types IG, TR, Mt, rRNA, tRNA, miRNA, misc_RNA, scRNA, snRNA, snoRNA, sRNA, scaRNA, piRNA, vaultRNA, ribozyme, artifact and LRG_gene (-M parameter). Raw counts were obtained by running cuffnorm on the cuffquant output. Normalization and differential expression analysis Two methods were used: I. normalization with cuffnorm, using output format of Cuffdiff (Trapnell et al, 2013), and II. normalization and differential expression analysis using the DESeq2 package (Love et al, 2014). I. For cuffnorm normalization with Cuffdiff output, results were visualized in R, using the cummeRbund package (version 2.8.2) and an in-house R scripts. Counts and FPKM distributions, as well as MDS analysis, were used for comparing global expression between samples, outliers evaluation and background expression level estimation. Sample CkoA (first replicate of knockout mice without stress treatment) was found to differ greatly from the rest of the samples, thus was filtered out of the analysis. The normalization process and the visualization of the normalized expression values were repeated without sample CkoA. Differential expression (without sample CkoA) was calculated with cuffdiff, using the default minimal count threshold (-c 10 parameter) for statistical significance testing. Samples were assigned a condition (-L parameter) and four comparisons were made: 1) Within control (non-stressed) mice, comparing CX3CR1-/- to WT mice; 2) Within CUS-exposed mice, comparing CX3CR1-/- to WT mice; 3) Within WT mice, comparing CUS-exposed to nonstressed mice; 4) Within CX3CR1 -/- mice, comparing CUS-exposed to non-stressed mice. Gene-level cuffdiff output was combined with gene details (such as symbol, Entrez accession, etc.) taken from the results of a BioMart query (Ensembl, release 78). Significantly

12

differentially expressed genes were defined as ones with at least 0.4 FPKM level of expression in at least one of the conditions and a q-value less than 0.05. Differential expression results were visualized in R with in-house scripts based on the cummeRbund code. The full dataset is available online (Supplemental dataset 1). II. For the DESeq2 method, normalization and differential expression were done using the DESeq2 package (version 1.10.1). Genes with a sum of counts less than 2 over all samples were filtered out prior to normalization, then dispersion and size factors were calculated. Differential expression was calculated using a design, which included the genotype factor, the stress-treatment factor and the interaction between them, compared with a reduced model that lacked the interaction term, and using the LRT test (all other parameters were kept at their defaults). Comparisons between specific conditions (such as stressed vs. control WT mice) were obtained with the Wald test (given as a parameter to the results function). The significance threshold for all comparisons was taken as padj<0.1. Several quality control assays, such as counts distributions and principal component analysis, as well as differential expression results, were calculated and visualized in R (version 3.2.1, with packages 'RColorBrewer_1.1-2', 'pheatmap_1.0.8' and 'ggplot2_2.1.0'). Results were then combined with gene details (such as symbol, Entrez accession, etc.) taken from the results of a BioMart query (Ensembl, release 84) to produce the final Excel file. The two methods, i.e., Cuffdiff and DESeq2, yielded mostly similar results. For clarity of presentation, we chose to present the data using the Cuffdiff method only. We also validated these findings using qPCR. Ingenuity pathway analysis Pathway and global functional analyses were performed using Ingenuity Pathway Analysis 6.0 (IPA; Ingenuity® Systems, www.ingenuity.com). A data set containing gene

13

identifiers and corresponding expression values was first uploaded into the application, and each gene identifier was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base (IPKB). The functional and canonical pathway analysis identified pathways from the IPA library that were most significant to the data set. Genes from the data set that met the p-value cutoff of 0.005 and were associated with biological functions or with a canonical pathway in the IPKB were considered for the analysis. Statistical analysis All data are presented as mean ±SEM. Statistical comparisons were computed using SPSS 19.0 software and consisted of t-tests, one-way and two-way analyses of variance (ANOVAs) (using Wilks’ Lambda), followed by the Fisher's least significant difference (LSD) post hoc analyses, when appropriate.

14

Results Our first step was to determine whether CX3CR1 -/- mice show resilience to the hedonic and cognitive effects of stress, using the sucrose preference and object recognition tests, respectively. CUS-exposed WT mice displayed reduced sucrose preference, compared with their own baseline and with all other groups during the testing period. In contrast, CX3CR1-/- mice showed no significant CUS-induced reduction in sucrose preference (Fig. 1a). CUS-exposed WT mice also showed a significant impairment in the novel object recognition memory test, whereas CX3CR1-/- displayed no memory decline following CUS exposure (Fig. 1b). To examine whether this differential effect is already observed following a shorter period of unpredictable stress (SUS) exposure, mice were also tested after a 2-day exposure to the SUS regimen. Similarly to the findings after CUS, novel object recognition memory was significantly impaired in WT mice but not in CX3CR1 -/- mice exposed to 2-days of unpredictable stress (Fig. 1c). Sucrose preference was not tested at that time because preliminary results demonstrated that the SUS regimen does not influence sucrose preference in either strain. Histological

analysis

of

IBA1-immunostained

(microglia)

cells

in

the

hippocampus revealed significant CUS-induced reductions in the number of microglia in the dentate gyrus (DG) of both WT and CX3CR1-/- mice, compared with their respective non-stressed genotype controls (Fig. 2a and Supplemental Fig. 1). In contrast, neither the genotype, nor the exposure to CUS, was associated with differences in the number of IBA1-stained cells in the CA3 region (data not shown). Morphometric analysis revealed a significant main effect of genotype on the soma size of IBA1-stained cells in the DG. Specifically, CX3CR1 -/- mice (either non-stressed or stressed) had significantly larger cell soma size compared to non-stressed or stressed WT controls (Fig. 2b and 2e). The average

15

(per cell) total length of microglial processes was significantly reduced by CUS exposure, in the DG of the dorsal hippocampus in WT and in CX3CR1-/- mice (Fig. 2c and 2e). Analysis of neurogenesis in the DG revealed that CX3CR1-/- mice displayed overall significantly lower numbers of newborn (doublecortin (DCX)-labeled) neurons. Furthermore, whereas the number of DCX-labeled neurons was significantly decreased in CUS-exposed WT mice, there was no CUS-induced reduction in the number of these cells in CX3CR1 -/- mice (Fig. 2d). In order to study transcriptional regulation in the stressed hippocampus and identify differentially regulated gene transcripts that may underlie the resilience to CUS in CX3CR1 -/- mice, we next performed RNA sequencing (RNA-Seq) analysis. Ingenuity Pathway Analysis (IPA) side-by-side comparisons were used to detect the effects of genotype and CUS exposure on transcriptional regulation. For all IPA analyses the cutoff p-value was set at p<0.005. Genes appearing in Tables 1 and 3 include a partial list of significant genes that had at least one comparison with a q-value (false discovery rate) <0.05. Using this comparison, we detected many significantly altered gene transcripts between the two genotypes. The list of gene transcripts that were especially different (down-regulated) in control (non-stressed) CX3CR1 -/- mice (with control WT mice as a reference) included transcripts related to interferon signaling and antigen presentation (e.g. Ifit1, Ifit3, Iigp1, Gbp4, Gbp5, Gbp7, H2-d1, H2-k1) as well as other genes (e.g. Cx3cr1, Abhd14a, Bst2, Col6a1, Mid1)(Table 1). Consistently, IPA canonical pathway analysis between the two genotypes (under control conditions) revealed significant alternations in pathways related to antigen presentation and interferon signaling (Supplemental Fig. 2). Top upstream regulators

16

identified as significantly affecting hippocampal gene transcription in control (nonstressed) CX3CR1-/- mice (with control WT as reference) included predicted activation downstream of IL-10RA, PTGER4, and STAT6 (Table 2a). In addition, a tendency towards activation was predicted downstream of 17β-estradiol. The top upstream regulator with predicted inhibition was NOS2. A strong tendency towards inhibition was predicted downstream of STAT1, IFNG, and IFNB1 (Table 2a). Top hits for diseases and biological functions pointed to transcriptional differences between the genotypes in immune regulation, cell growth, proliferation and migration (Table 2b). In order to focus on the genes that were differentially regulated by CUS in the two genotypes we performed an IPA side-by-side comparison of CUS-exposed WT mice (with control WT mice as reference) and CUS-exposed CX3CR1 -/- mice (with control CX3CR1 -/- mice as reference) (Table 3). We found two CUS-regulated sets of gene transcripts: 1) a small group of four gene transcripts that were similarly altered by the CUS paradigm in both genotypes, including the up-regulated genes clic6 and Ttr, and the down-regulated genes Arc and Npas4 (Table 3a), and 2) gene transcripts that were differentially altered by CUS exposure in the two genotypes (Table 3b). Importantly, the genes in this second set may account for the differential behavioral and neurogenic responsiveness to stress. The only gene that was differentially upregulated in the CUSexposed WT mice (with control WT as reference) was Slc19a7 (Table 3b). Downregulated genes included Gpr88, Lamc2, Myh11, Ptgds (lipocalin-type), Slc19a3, Wfs1, and Yam1 (Table 3b). The list of genes differentially up-regulated in the CUS-exposed CX3CR1 -/- mice included among others: Col8a1, Ecrg4, Enpp2, Folr1, Gpr88, Igf2, Igfbp2, KI, Mitf, and Prlr (Table 3b). Differentially down-regulated genes included Coq10b, Egr1, and Wt1 (Table 3b)

17

Following CUS exposure most of the top canonical pathway hits were different for CUS-exposed WT vs. CX3CR1-/- mice (Supplemental Fig 3a,b). Only two top canonical pathways were similarly altered by CUS in both genotypes: tight junction and acute phase response canonical signaling pathways. Analysis of top upstream regulators following CUS in WT mice revealed a predicted inhibitory effect downstream of the transcription regulator CREB1, and a tendency for predicted inhibition downstream of the growth factor BDNF and the estrogen receptor 2 (ESR2) (Table 4a). A similar analysis for CX3CR1 -/- mice revealed a predicted inhibitory effect downstream of CREB1, a tendency for predicted inhibition downstream of BDNF, and predicted activation downstream of ESR1 (Table 4a), pointing to similar CUS-induced transcriptional regulation downstream of CREB1 and BDNF in both strains, and differential transcription downstream of estrogen receptors (ESRs). We further performed IPA analysis of Diseases and Biological functions for the effect of CUS in each genotype (Table 4b). We found that all hits (excluding one) in the behavior and psychological disorders categories, including transcripts involved in anxiety, mood disorders, and depression, were associated with CUS-exposure in WT, but not CX3CR1 -/- mice (Table 4b). These findings are in line with the observed neuro-behavioral resilience to CUS exhibited by the CX3CR1 -/- mice. Finally, in order to validate the RNA-Seq data we conducted real time quantitative PCR experiments, assessing the expression of selected genes that were significantly changed by genotype or by CUS in the RNA-Seq analysis (Fig. 3). All gene transcripts were calibrated to the housekeeping gene Gapdh. Consistent with the results of the RNASeq analysis, P2yr12, H2-q7, Ifit3, Ttr, Arc, Ptgds, Igf2, and Igfbp2 transcripts showed a main effect of genotype and were significantly elevated or reduced in the hippocampus of CX3CR1 -/- mice (compared with WT mice), regardless of the CUS manipulation (Fig. 3).

18

P2yr12, the gene transcript for the microglial P2YR12 purinergic GPCR, which has been shown to be involved in microglia-dependent neuronal plasticity (Sipe et al., 2016), showed a main effect of genotype, with increased levels of transcript in CX3CR1 -/- mice (compared with WT mice), regardless of the CUS manipulation. Arc transcripts showed a significant main effect of CUS exposure, and were significantly down regulated by CUS in both genotypes (Fig. 3e). A significant interaction between CUS exposure and genotype was found for the gene transcripts Gpr88 and Ptgds (lipocalin-type) (Fig. 3g, 3f). Furthermore, although the Igf2 gene transcript did not show a significant interaction of genotype by CUS exposure, Igf2 gene expression was significantly increased only in the hippocampus of CUS-exposed CX3CR1-/- mice compared to control CX3CR1-/- mice (figure 3h); a similar trend was observed for Igfbp2 (Fig. 3i).

19

Discussion The emotional and cognitive stress resilience of CX3CR1 -/- mice is correlated with measures of hippocampal neurogenesis and microglial morphology. The behavioral analysis revealed that CX3CR1-deficiency mediates resilience to stress-induced anhedonia. This finding is consistent with previous reports, showing that CX3CR1 -/- mice display blunted stress-induced depressive-like (Hellwig et al, 2015; Milior et al, 2015) and anxiety-like behaviors (Hellwig et al, 2015; Wohleb et al, 2013). CX3CR1 -/- mice also demonstrated resilience to the detrimental effect of stress on memory functioning. Although this effect was obtained in two separate experiments, under either acute or chronic unpredictable stress conditions, these findings were less robust and the existence of such resiliency should be further examined, using additional learning and memory tasks. Interestingly, in contrast with our findings on stress resilience, CX3CR1 -/- mice were previously shown to display exacerbated sickness behavior when exposed to an inflammatory signal (acute LPS challenge)(Corona et al, 2010; Corona et al, 2013),. This inconsistency raises the intriguing hypothesis that the behavioral modulatory effects of CX3CR1 signaling depend on the nature of the microglial stimulator, with opposite effects in the context of stress versus inflammation. Hippocampal neurogenesis is considered an important mediator of emotional and cognitive processes, and its stress-induced suppression has been implicated in memory impairment and depression (Duman and Monteggia, 2006). Our finding that CX3CR1 -/- mice do not exhibit CUS-induced reduction in neurogenesis corroborates findings on their resiliency to the

20

suppressive effects of chronic stress on hippocampal long-term potentiation (Milior et al, 2015), a process known to contribute to neuroplasticity and memory functioning (Lynch, 2004). We have previously shown that stress-induced decline in neurogenesis depends on microglial status, since manipulations of microglial activation can produce parallel proneurogenic and anti-depressive effects in CUS-exposed mice (Kreisel et al, 2014). Here we found, consistent with others (Bachstetter et al, 2011; Maggi et al, 2011; Reshef et al, 2014; Rogers et al, 2011), that CX3CR1-/- mice exhibit lower basal levels of neurogenesis. Our findings support the idea that decline from basal neurogenesis levels rather than lower levels per se underlie stress susceptibility. Together, these findings suggest that deficient microglial CX3CR1 signaling confers resistance to stress-induced decline in neurogenesis, which may contribute to the behavioral stress resiliency of CX3CR1 -/- mice. Previous studies demonstrated that CX3CR1-/- exhibit impairments in synapse development (Paolicelli et al., 2011), resulting in altered brain functional connectivity (Zhan et al., 2014). The contribution of these developmental processes to stress resilience is intriguing, given ample evidence that depression is associated with alterations in functional brain connectivity (including both decreases and increases in the connectivity of specific circuits) (Zeng et al., 2012; Kaiser et al., 2016). The current study does not directly address these developmental changes and therefore their contribution to stress resilience cannot be deduced. Future studies, in which the CX3CR1 system is chronically blocked only in adult animals, should elucidate the role of altered CX3CR1-mediated neurodevelopmental processes in stress resilience. Over the past decade it became evident that exposure to various stressful conditions can induce structural and functional modifications in microglia, ranging from proliferation, hyper-ramification and inflammatory activation to processes retractions, decline in numbers and dystrophy (Yirmiya et al, 2015). Along with their stress resilience,

21

-/-

CX3CR1 mice were found to exhibit resistance to stress-induced microglial alterations (Hellwig et al, 2015; Milior et al, 2015). In the CUS model utilized here, microglia were shown to undergo dynamic changes, with an initial proliferation and activation, followed by apoptosis and decline several weeks following stress initiation (Kreisel et al, 2014; Yirmiya et al, 2015). In the current study we verified the CUS-induced reductions in microglia numbers and processes length within the DG. However, these effects were similarly induced by CUS in CX3CR1-/- mice, suggesting that these microglial changes are not the immediate cause of the neuro-behavioral responses. Rather, the CX3CR1-/resilience to stress involves other microglial changes or compensatory mechanisms. Hippocampal transcriptional signatures predict stress resilience in CX3CR1 -/- mice Analysis of the hippocampal transcriptional signature in CX3CR1 -/- vs. WT mice revealed many gene transcripts and molecular pathways that are involved in the basal and stressresponsive phenotypic differences between the strains. Altered gene transcripts can be divided into three main categories: 1) gene transcripts affected by stress, regardless of genotype, 2) gene transcripts affected only by genotype regardless of stress, and 3) gene transcripts showing a genotype by stress interaction. The notion that these molecular differences are involved in the differential responsiveness to stress is validated by the IPA analysis of Diseases and Biological Functions, identifying sets of CUS-affected transcripts that predict the presence of mood and depressive disorders, anxiety and learning impairments in WT, but not in CX3CR1-/- mice (Table 4b). Hippocampal transcription of genes related to neuronal activity are similarly influenced by stress in CX3CR1 -/- and WT mice. Among the gene transcripts that were significantly down regulated in both strains are the immediate early genes activity-regulated cytoskeleton-associated protein (Arc) and

22

Neuronal PAS domain 4 (Npas4). Arc is a neuronal immediate early gene, which is normally regulated by neuronal stimulation (Lyford et al, 1995). The down regulation of Arc transcripts following CUS in both genotypes is in line with the predicted inhibition of genes downstream of BDNF and CREB signaling (Ying et al, 2002). A related gene, Npas4, is an early-response transcription factor, which increases BDNF levels (Bloodgood et al, 2013), and neuronal plasticity (Ramamoorthi et al, 2011). Importantly, reduced levels of hippocampal Npas4 were found in mice exposed to social isolation, restraint stress, or corticosteroids (Furukawa-Hibi et al, 2012). The similar effects of CUS on Arc and Npas4 transcription in both strains suggest that the stress resiliency of CX3CR1-/- mice does not stem from effects on commonly investigated neuronal-related antidepressant pathways (e.g. CREB1 and BDNF). Rather, the stress resiliency probably arises from other passive or active mechanisms reflected in our transcriptomic analysis, such as IFN- and/or estrogen-induced genes. Basal differences in hippocampal gene expression may underlie the differential stress-responsiveness between CX3CR1-/- and WT mice. Analysis of hippocampal gene transcription under basal conditions revealed reduced transcription of IFN-regulated genes in CX3CR1-/- mice. This finding is consistent with the results of clinical studies demonstrating that lower vs. higher transcription of IFN-regulated genes is associated with resilience vs. susceptibility, respectively, to develop or suffer PTSD and depression (Breen et al, 2015; Glatt et al, 2013; Mostafavi et al, 2014; Passos et al, 2015; Schlaak et al, 2012). Moreover, administration of IFNs as immunotherapy for cancer or hepatitis-C was found to induce major depressive episodes (Capuron and Miller, 2004), a phenomenon that has been modeled in animals by chronic peripheral or central administration of IFNs (Felger et al, 2013; Hoyo-Becerra et al, 2015; Zheng et al, 2015). A role for microglia in mediating

23

these depressive effects may be suggested by the finding that treatment with the microglial inhibitor minocycline was shown to reverse IFN-induced depressive-like behaviors (Zheng et al, 2015). CX3CR1 -/- mice also exhibited a reduced basal level of MHC class I gene transcripts, including the B2m polypeptide chain, as well as classical MHC-I class Ia members (H2-d and H2-k), and the less studied group members (H2-q). Apart from regulating immune-cell recognition, MHC-I proteins play a role in activity-dependent establishment and function of cortical connections, as well as regulation of synaptic plasticity and pattern formation (Corvin and Morris, 2014; Huh et al, 2000). Consistently, it has been shown that mice deficient in MHC-I molecules display alterations in LTP, LTD and synapse elimination (Lee et al, 2014) and exhibit impaired social behavior (Ma et al, 2015), similar to CX3CR1 -/- mice (Maggi et al, 2011; Milior et al, 2015; Zhan et al, 2014). The involvement of MHC-I molecules in neuroplasticity, cognition and behavior, and IFNs in stress-responsiveness and depression suggest that their down-regulation in CX3CR1 -/- mice contributes to their stress resilient cognitive and emotional phenotype. Several additional molecular differences between CX3CR1 -/- and WT mice were identified using the IPA analysis of upstream regulators, including genes related to immune/microglial regulation, such as IL-10 receptor antagonist and nitric oxide synthase (NOS) (Table 2a). The predicted upstream regulator role of hippocampal 17β-estradiol in CX3CR1-/- mice may be particularly important, because apart from the circulating levels of 17β-estradiol affected by sex and circadian rhythms, this hormone can be synthesized locally within the hippocampus (in both sexes) in a neural activity-dependent manner (Hojo et al, 2004; 2008). 17β-estradiol also modulates synaptic plasticity including LTP, LTD, dendritic spine turnover, and neurogenesis, and has been shown to affect memory, mood, and neuroinflammation (Komatsuzaki et al, 2005; Mukai et al, 2006; Tanapat et al, 1999; Tsurugizawa et al, 2005).

24

Furthermore, consistent with our molecular findings, it has been reported that microglia cultures exposed to estrogen exhibit decreased numbers of MHC-I (H2-D and H2-K) positive cells, increased IL-10 and decreased IFNs (Dimayuga et al, 2005). Thus, altered basal transcription levels of genes downstream of 17β-estradiol in the hippocampus may underlie the stress resiliency of CX3CR1-/- mice. Differential hippocampal stress-responsive transcriptional programs in the two genotypes reveal active adaptations that may underlie stress-resilience. Following CUS exposure, IPA upstream regulator analysis revealed differential transcriptional regulation downstream of the estrogen receptors between CX3CR1 -/- and WT mice. Specifically, CUS exposure was accompanied by a predicted inhibition of transcription downstream of ESR2. This inhibition was observed only in WT mice. Furthermore, an activation of transcription downstream of ESR1 was detected only in CX3CR1 -/- mice. The differential predicted transcriptional regulation downstream of ESRs points to a possible novel active molecular process of stress resilience. Indeed, previous studies revealed that ESR stimulation by 17β estradiol or specific agonists is protective, reversing object recognition memory impairment, depressive-like behavior and reduced hippocampal 5-HT levels in overiectomized (OVX) rats (Bastos et al, 2015; Xu et al, 2015), whereas the lack of this receptor (in ESR1-deficient mice) was associated with deleterious cognitive and inflammatory responses to an immune challenge (Hwang et al, 2015). The possible differential estrogenic signaling between strains following CUS has been extrapolated by IPA based on the elevated transcript numbers of ESR1-regulated genes in the CUS-exposed CX3CR1-/- mice. These included insulin-like growth factor 2 (Igf2), and insulin-like growth factor binding protein 2 (Igfbp2), which have been previously studied in the context of stress susceptibility (Takeo, 2009). Specifically, reduced Igf2 gene expression was reported in the hippocampus of adult offspring exposed

25

to chronic maternal separation stress (Kohda et al, 2006), as well as in mice exposed to repetitive chronic restraint stress, which also suppressed Igfbp2 expression (Luo et al, 2015). Several lines of evidence suggest that the increased expression of Igf2 in CX3CR1/-

mice is particularly important for their stress resilience: 1) Hippocampal Igf2 gene

transcript overexpression and the resultant elevated protein levels were previously found to reverse the anhedonia and despair in the forced swim test induced by chronic mild stress (Luo et al, 2015); 2) Chronic treatment with the tricyclic antidepressant desipramine up-regulated hippocampal Igf2 levels in mice selected for low or high stress reactivity. Additionally, in mice selected for low stress reactivity the gene transcripts for Igfbp2, as well as other genes found here to be elevated in CX3CR1 -/- mice (Enpp2, Ttr, and Prlr) were also up-regulated (Lisowski et al, 2013a). In conclusion, our results provide strong support for the involvement of neuronalmicroglial interactions via the CX3CL1-CX3CR1 signaling system in the responsiveness to stress, and reveal novel hippocampal molecular pathways for stress resilience. Specifically, the emotional and cognitive stress resilience in CX3CR1 -/- mice is associated with basal alterations in microglial morphology along with reduced transcription of MHCI and downstream of IFNs, and altered transcription downstream of 17β-estradiol. Furthermore, following CUS, CX3CR1-/- mice exhibit no reduction in neurogenesis, as well as lack of some transcriptional changes induced by CUS in WT mice (e.g., no reductions in transcriptional regulation downstream of ESR2), reflecting passive resilience mechanisms. Importantly, in CX3CR1-/- mice CUS caused transcriptional changes in a relatively large number of genes that were not altered in WT mice (e.g. downstream of ESR1) demonstrating the cardinal importance of active transcriptional adaptations in stress resilience. These findings can be utilized for developing novel screening approaches in humans, evaluating the CX3CR1-dependent molecular changes

26

that were found here to be associated with stress resilience. Furthermore, new approaches to manipulate CX3CR1 signaling and related molecular systems could serve as targets to developing novel anti-depressive therapeutic procedures.

Funding and Disclosure This research was supported by the ISRAEL SCIENCE FOUNDATION grants No. 206/12 and 1379/16 and by the ISRAEL SCIENCE FOUNDATION – FIRST Program grant number 1357/13 (to R.Y.). The authors declare no financial disclosures

27

Acknowledgments We thank Shir Blondheim, Anat Sirkis, Amitai Marcus, Yaelle Halberstam, Idan Arad, Claudia Pienica and Einat Ashkenazi for help with running the experiments. We thank Ms. Zehava Cohen for help in preparation of the figures. We thank Dr. Sharona Elgavish and Yuval Nevo from the Info-CORE Bioinformatics Unit of the I-CORE Computation Center at the Hebrew University in Jerusalem for their help with RNA sequencing and analysis.

28

References Andrus BM, Blizinsky K, Vedell PT, Dennis K, Shukla PK, Schaffer DJ, et al (2012). Gene expression patterns in the hippocampus and amygdala of endogenous depression and chronic stress models. Mol Psychiatry 17(1): 49-61. Bachstetter AD, Morganti JM, Jernberg J, Schlunk A, Mitchell SH, Brewster KW, et al (2011). Fractalkine and CX 3 CR1 regulate hippocampal neurogenesis in adult and aged rats. Neurobiol Aging 32(11): 2030-2044. Bahn S, Chan MK (2015). What can we learn about depression from gene expression in peripheral tissues? Biol Psychiatry 77(3): 207-209. Basso AM, Bratcher NA, Harris RR, Jarvis MF, Decker MW, Rueter LE (2009). Behavioral profile of P2X7 receptor knockout mice in animal models of depression and anxiety: relevance for neuropsychiatric disorders. Behav Brain Res 198(1): 83-90. Bastos CP, Pereira LM, Ferreira-Vieira TH, Drumond LE, Massensini AR, Moraes MF, et al (2015). Object recognition memory deficit and depressive-like behavior caused by chronic ovariectomy can be transitorialy recovered by the acute activation of hippocampal estrogen receptors. Psychoneuroendocrinology 57: 14-25. Berton O, Covington HE, 3rd, Ebner K, Tsankova NM, Carle TL, Ulery P, et al (2007). Induction of deltaFosB in the periaqueductal gray by stress promotes active coping responses. Neuron 55(2): 289300. Berton O, McClung CA, Dileone RJ, Krishnan V, Renthal W, Russo SJ, et al (2006). Essential role of BDNF in the mesolimbic dopamine pathway in social defeat stress. Science 311(5762): 864-868. Bloodgood BL, Sharma N, Browne HA, Trepman AZ, Greenberg ME (2013). The activitydependent transcription factor NPAS4 regulates domain-specific inhibition. Nature 503(7474): 121125. Boucher AA, Arnold JC, Hunt GE, Spiro A, Spencer J, Brown C, et al (2011). Resilience and reduced c-Fos expression in P2X7 receptor knockout mice exposed to repeated forced swim test. Neuroscience 189: 170-177. Breen MS, Maihofer AX, Glatt SJ, Tylee DS, Chandler SD, Tsuang MT, et al (2015). Gene networks specific for innate immunity define post-traumatic stress disorder. Mol Psychiatry 20(12): 1538-1545. Capuron L, Miller AH (2004). Cytokines and psychopathology: lessons from interferon-alpha. Biol Psychiatry 56(11): 819-824.

Corona AW, Huang Y, O'Connor JC, Dantzer R, Kelley KW, Popovich PG, et al (2010). Fractalkine receptor (CX3CR1) deficiency sensitizes mice to the behavioral changes induced by lipopolysaccharide. J Neuroinflammation 7: 93. Corona AW, Norden DM, Skendelas JP, Huang Y, O'Connor JC, Lawson M, et al (2013). Indoleamine 2,3-dioxygenase inhibition attenuates lipopolysaccharide induced persistent microglial

29

activation and depressive-like complications in fractalkine receptor (CX(3)CR1)-deficient mice. Brain Behav Immun 31: 134-142. Corvin A, Morris DW (2014). Genome-wide association studies: findings at the major histocompatibility complex locus in psychosis. Biol Psychiatry 75(4): 276-283. Covington HE, 3rd, Lobo MK, Maze I, Vialou V, Hyman JM, Zaman S, et al (2010). Antidepressant effect of optogenetic stimulation of the medial prefrontal cortex. J Neurosci 30(48): 16082-16090. Dias C, Feng J, Sun H, Shao NY, Mazei-Robison MS, Damez-Werno D, et al (2014). beta-catenin mediates stress resilience through Dicer1/microRNA regulation. Nature 516(7529): 51-55. Dimayuga FO, Reed JL, Carnero GA, Wang C, Dimayuga ER, Dimayuga VM, et al (2005). Estrogen and brain inflammation: effects on microglial expression of MHC, costimulatory molecules and cytokines. J Neuroimmunol 161(1-2): 123-136. Duman RS, Monteggia LM (2006). A neurotrophic model for stress-related mood disorders. Biol Psychiatry 59(12): 1116-1127. Elliott E, Ezra-Nevo G, Regev L, Neufeld-Cohen A, Chen A (2010). Resilience to social stress coincides with functional DNA methylation of the Crf gene in adult mice. Nat Neurosci 13(11): 1351-1353. Espallergues J, Teegarden SL, Veerakumar A, Boulden J, Challis C, Jochems J, et al (2012). HDAC6 regulates glucocorticoid receptor signaling in serotonin pathways with critical impact on stress resilience. J Neurosci 32(13): 4400-4416. Feder A, Nestler EJ, Charney DS (2009). Psychobiology and molecular genetics of resilience. Nat Rev Neurosci 10(6): 446-457. Felger JC, Mun J, Kimmel HL, Nye JA, Drake DF, Hernandez CR, et al (2013). Chronic interferonalpha decreases dopamine 2 receptor binding and striatal dopamine release in association with anhedonia-like behavior in nonhuman primates. Neuropsychopharmacology 38(11): 2179-2187. Fleshner M, Maier SF, Lyons DM, Raskind MA (2011). The neurobiology of the stress-resistant brain. Stress 14(5): 498-502. Frank MG, Baratta MV, Sprunger DB, Watkins LR, Maier SF (2007). Microglia serve as a neuroimmune substrate for stress-induced potentiation of CNS pro-inflammatory cytokine responses. Brain Behav Immun 21(1): 47-59.

Fuchsl AM, Neumann ID, Reber SO (2014). Stress resilience: a low-anxiety genotype protects male mice from the consequences of chronic psychosocial stress. Endocrinology 155(1): 117-126. Furukawa-Hibi Y, Yun J, Nagai T, Yamada K (2012). Transcriptional suppression of the neuronal PAS domain 4 (Npas4) gene by stress via the binding of agonist-bound glucocorticoid receptor to its promoter. J Neurochem 123(5): 866-875. Garbett KA, Vereczkei A, Kalman S, Brown JA, Taylor WD, Faludi G, et al (2015a). Coordinated

30

messenger RNA/microRNA changes in fibroblasts of patients with major depression. Biol Psychiatry 77(3): 256-265. Garbett KA, Vereczkei A, Kalman S, Wang L, Korade Z, Shelton RC, et al (2015b). Fibroblasts from patients with major depressive disorder show distinct transcriptional response to metabolic stressors. Transl Psychiatry 5: e523. Glatt SJ, Tylee DS, Chandler SD, Pazol J, Nievergelt CM, Woelk CH, et al (2013). Blood-based gene-expression predictors of PTSD risk and resilience among deployed marines: a pilot study. Am J Med Genet B Neuropsychiatr Genet 162B(4): 313-326. Golden SA, Covington HE, 3rd, Berton O, Russo SJ (2011). A standardized protocol for repeated social defeat stress in mice. Nat Protoc 6(8): 1183-1191.

Goshen I, Kreisel T, Ben-Menachem-Zidon O, Licht T, Weidenfeld J, Ben-Hur T, et al (2008). Brain interleukin-1 mediates chronic stress-induced depression in mice via adrenocortical activation and hippocampal neurogenesis suppression. Mol Psychiatry 13(7): 717-728.

Hellwig S, Brioschi S, Dieni S, Frings L, Masuch A, Blank T, et al (2015). Altered microglia morphology and higher resilience to stress-induced depression-like behavior in CX3CR1-deficient mice. Brain Behav Immun.

Hinwood M, Morandini J, Day TA, Walker FR (2012). Evidence that microglia mediate the neurobiological effects of chronic psychological stress on the medial prefrontal cortex. Cereb Cortex 22(6): 1442-1454.

Hojo Y, Hattori TA, Enami T, Furukawa A, Suzuki K, Ishii HT, et al (2004). Adult male rat hippocampus synthesizes estradiol from pregnenolone by cytochromes P45017alpha and P450 aromatase localized in neurons. Proc Natl Acad Sci U S A 101(3): 865-870. Hojo Y, Murakami G, Mukai H, Higo S, Hatanaka Y, Ogiue-Ikeda M, et al (2008). Estrogen synthesis in the brain--role in synaptic plasticity and memory. Mol Cell Endocrinol 290(1-2): 31-43. Hoyo-Becerra C, Liu Z, Yao J, Kaltwasser B, Gerken G, Hermann DM, et al (2015). Rapid Regulation of Depression-Associated Genes in a New Mouse Model Mimicking Interferon-alphaRelated Depression in Hepatitis C Virus Infection. Mol Neurobiol 52(1): 318-329. Huh GS, Boulanger LM, Du H, Riquelme PA, Brotz TM, Shatz CJ (2000). Functional requirement for class I MHC in CNS development and plasticity. Science 290(5499): 2155-2159. Hwang CJ, Yun HM, Park KR, Song JK, Seo HO, Hyun BK, et al (2015). Memory Impairment in Estrogen Receptor alpha Knockout Mice Through Accumulation of Amyloid-beta Peptides. Mol Neurobiol 52(1): 176-186. Jung S, Aliberti J, Graemmel P, Sunshine MJ, Kreutzberg GW, Sher A, et al (2000). Analysis of fractalkine receptor CX(3)CR1 function by targeted deletion and green fluorescent protein reporter

31

gene insertion. Mol Cell Biol 20(11): 4106-4114. Jung YH, Hong SI, Ma SX, Hwang JY, Kim JS, Lee JH, et al (2014). Strain differences in the chronic mild stress animal model of depression and anxiety in mice. Biomol Ther (Seoul) 22(5): 453459. Kaiser RH, Whitfield-Gabrieli S, Dillon DG, Goer F, Beltzer M, Minkel J, et al (2016). Dynamic Resting-State Functional Connectivity in Major Depression. Neuropsychopharmacology 41(7): 1822-1830. Kohda K, Jinde S, Iwamoto K, Bundo M, Kato N, Kato T (2006). Maternal separation stress drastically decreases expression of transthyretin in the brains of adult rat offspring. Int J Neuropsychopharmacol 9(2): 201-208. Komatsuzaki Y, Murakami G, Tsurugizawa T, Mukai H, Tanabe N, Mitsuhashi K, et al (2005). Rapid spinogenesis of pyramidal neurons induced by activation of glucocorticoid receptors in adult male rat hippocampus. Biochem Biophys Res Commun 335(4): 1002-1007. Kreisel T, Frank MG, Licht T, Reshef R, Ben-Menachem-Zidon O, Baratta MV, et al (2014). Dynamic microglial alterations underlie stress-induced depressive-like behavior and suppressed neurogenesis. Mol Psychiatry 19(6): 699-709. Krishnan V, Han MH, Graham DL, Berton O, Renthal W, Russo SJ, et al (2007). Molecular adaptations underlying susceptibility and resistance to social defeat in brain reward regions. Cell 131(2): 391-404. Lee H, Lee EJ, Song YS, Kim E (2014). Long-term depression-inducing stimuli promote cleavage of the synaptic adhesion molecule NGL-3 through NMDA receptors, matrix metalloproteinases and presenilin/gamma-secretase. Philos Trans R Soc Lond B Biol Sci 369(1633): 20130158. Lisowski P, Juszczak GR, Goscik J, Stankiewicz AM, Wieczorek M, Zwierzchowski L, et al (2013). Stress susceptibility-specific phenotype associated with different hippocampal transcriptomic responses to chronic tricyclic antidepressant treatment in mice. BMC Neurosci 14: 144. Lisowski P, Wieczorek M, Goscik J, Juszczak GR, Stankiewicz AM, Zwierzchowski L, et al (2013). Effects of chronic stress on prefrontal cortex transcriptome in mice displaying different genetic backgrounds. J Mol Neurosci 50(1): 33-57. Lotan A, Lifschytz T, Slonimsky A, Broner EC, Greenbaum L, Abedat S, et al (2014). Neural mechanisms underlying stress resilience in Ahi1 knockout mice: relevance to neuropsychiatric disorders. Mol Psychiatry 19(2): 243-252. Love MI, Huber W, Anders S (2014). Moderated estimation of fold change and dispersion for RNAseq data with DESeq2. Genome Biol 15(12): 550. Luo YW, Xu Y, Cao WY, Zhong XL, Duan J, Wang XQ, et al (2015). Insulin-like growth factor 2 mitigates depressive behavior in a rat model of chronic stress. Neuropharmacology 89: 318-324. Lyford GL, Yamagata K, Kaufmann WE, Barnes CA, Sanders LK, Copeland NG, et al (1995). Arc, a growth factor and activity-regulated gene, encodes a novel cytoskeleton-associated protein that is

32

enriched in neuronal dendrites. Neuron 14(2): 433-445. Lynch MA (2004). Long-term potentiation and memory. Physiol Rev 84(1): 87-136. Ma L, Piirainen S, Kulesskaya N, Rauvala H, Tian L (2015). Association of brain immune genes with social behavior of inbred mouse strains. J Neuroinflammation 12: 75. Maggi L, Scianni M, Branchi I, D'Andrea I, Lauro C, Limatola C (2011). CX(3)CR1 deficiency alters hippocampal-dependent plasticity phenomena blunting the effects of enriched environment. Front Cell Neurosci 5: 22. Maier SF, Watkins LR (2010). Role of the medial prefrontal cortex in coping and resilience. Brain Res 1355: 52-60. Milior G, Lecours C, Samson L, Bisht K, Poggini S, Pagani F, et al (2015). Fractalkine receptor deficiency impairs microglial and neuronal responsiveness to chronic stress. Brain Behav Immun. Mostafavi S, Battle A, Zhu X, Potash JB, Weissman MM, Shi J, et al (2014). Type I interferon signaling genes in recurrent major depression: increased expression detected by whole-blood RNA sequencing. Mol Psychiatry 19(12): 1267-1274. Mozhui K, Karlsson RM, Kash TL, Ihne J, Norcross M, Patel S, et al (2010). Strain differences in stress responsivity are associated with divergent amygdala gene expression and glutamate-mediated neuronal excitability. J Neurosci 30(15): 5357-5367. Mukai H, Takata N, Ishii HT, Tanabe N, Hojo Y, Furukawa A, et al (2006). Hippocampal synthesis of estrogens and androgens which are paracrine modulators of synaptic plasticity: synaptocrinology. Neuroscience 138(3): 757-764. Paolicelli RC, Bolasco G, Pagani F, Maggi L, Scianni M, Panzanelli P, et al (2011). Synaptic pruning by microglia is necessary for normal brain development. Science 333(6048): 1456-1458. Passos IC, Vasconcelos-Moreno MP, Costa LG, Kunz M, Brietzke E, Quevedo J, et al (2015). Inflammatory markers in post-traumatic stress disorder: a systematic review, meta-analysis, and meta-regression. Lancet Psychiatry 2(11): 1002-1012. Raison CL, Miller AH (2013). The evolutionary significance of depression in Pathogen Host Defense (PATHOS-D). Mol Psychiatry 18(1): 15-37.

Ragozzino D, Di Angelantonio S, Trettel F, Bertollini C, Maggi L, Gross C, et al (2006). Chemokine fractalkine/CX3CL1 negatively modulates active glutamatergic synapses in rat hippocampal neurons. J Neurosci 26(41): 10488-10498. Ramamoorthi K, Fropf R, Belfort GM, Fitzmaurice HL, McKinney RM, Neve RL, et al (2011). Npas4 regulates a transcriptional program in CA3 required for contextual memory formation. Science 334(6063): 1669-1675. Reshef R, Kreisel T, Beroukhim Kay D, Yirmiya R (2014). Microglia and their CX3CR1 signaling are involved in hippocampal- but not olfactory bulb-related memory and neurogenesis. Brain Behav

33

Immun 41: 239-250. Rimmerman N, Juknat A, Kozela E, Levy R, Bradshaw HB, Vogel Z (2011). The non-psychoactive plant cannabinoid, cannabidiol affects cholesterol metabolism-related genes in microglial cells. Cell Mol Neurobiol 31(6): 921-930. Rogers JT, Morganti JM, Bachstetter AD, Hudson CE, Peters MM, Grimmig BA, et al (2011). CX3CR1 deficiency leads to impairment of hippocampal cognitive function and synaptic plasticity. J Neurosci 31(45): 16241-16250. Russo SJ, Murrough JW, Han MH, Charney DS, Nestler EJ (2012). Neurobiology of resilience. Nat Neurosci 15(11): 1475-1484. Schlaak JF, Trippler M, Hoyo-Becerra C, Erim Y, Kis B, Wang B, et al (2012). Selective hyperresponsiveness of the interferon system in major depressive disorders and depression induced by interferon therapy. PLoS One 7(6): e38668. Sipe GO, Lowery RL, Tremblay ME, Kelly EA, Lamantia CE, Majewska AK (2016). Microglial P2Y12 is necessary for synaptic plasticity in mouse visual cortex. Nat Commun 7: 10905. Southwick SM, Charney DS (2012). The science of resilience: implications for the prevention and treatment of depression. Science 338(6103): 79-82. Southwick SM, Vythilingam M, Charney DS (2005). The psychobiology of depression and resilience to stress: implications for prevention and treatment. Annu Rev Clin Psychol 1: 255-291.

Sugama S, Fujita M, Hashimoto M, Conti B (2007). Stress induced morphological microglial activation in the rodent brain: involvement of interleukin-18. Neuroscience 146(3): 1388-1399. Takeo C, Ikeda K, Horie-Inoue K, Inoue S (2009). Identification of Igf2, Igfbp2 and Enpp2 as estrogen-responsive genes in rat hippocampus. Endocr J 56(1): 113-120. Tanapat P, Hastings NB, Reeves AJ, Gould E (1999). Estrogen stimulates a transient increase in the number of new neurons in the dentate gyrus of the adult female rat. J Neurosci 19(14): 5792-5801.

Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L (2013). Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol 31(1): 46-53. Tsurugizawa T, Mukai H, Tanabe N, Murakami G, Hojo Y, Kominami S, et al (2005). Estrogen induces rapid decrease in dendritic thorns of CA3 pyramidal neurons in adult male rat hippocampus. Biochem Biophys Res Commun 337(4): 1345-1352. Tynan RJ, Naicker S, Hinwood M, Nalivaiko E, Buller KM, Pow DV, et al (2010). Chronic stress alters the density and morphology of microglia in a subset of stress-responsive brain regions. Brain Behav Immun 24(7): 1058-1068. Vialou V, Robison AJ, Laplant QC, Covington HE, 3rd, Dietz DM, Ohnishi YN, et al (2010). DeltaFosB in brain reward circuits mediates resilience to stress and antidepressant responses. Nat

34

Neurosci 13(6): 745-752. Walker FR, Beynon SB, Jones KA, Zhao Z, Kongsui R, Cairns M, et al (2014). Dynamic structural remodelling of microglia in health and disease: a review of the models, the signals and the mechanisms. Brain Behav Immun 37: 1-14. Wilkinson MB, Dias C, Magida J, Mazei-Robison M, Lobo M, Kennedy P, et al (2011). A novel role of the WNT-dishevelled-GSK3beta signaling cascade in the mouse nucleus accumbens in a social defeat model of depression. J Neurosci 31(25): 9084-9092. Wilkinson MB, Xiao G, Kumar A, LaPlant Q, Renthal W, Sikder D, et al (2009). Imipramine treatment and resiliency exhibit similar chromatin regulation in the mouse nucleus accumbens in depression models. J Neurosci 29(24): 7820-7832. Wohleb ES, Hanke ML, Corona AW, Powell ND, Stiner LM, Bailey MT, et al (2011). betaAdrenergic receptor antagonism prevents anxiety-like behavior and microglial reactivity induced by repeated social defeat. J Neurosci 31(17): 6277-6288. Wohleb ES, Powell ND, Godbout JP, Sheridan JF (2013). Stress-induced recruitment of bone marrow-derived monocytes to the brain promotes anxiety-like behavior. J Neurosci 33(34): 1382013833. Xu Y, Sheng H, Tang Z, Lu J, Ni X (2015). Inflammation and increased IDO in hippocampus contribute to depression-like behavior induced by estrogen deficiency. Behav Brain Res 288: 71-78. Yehuda R, Cai G, Golier JA, Sarapas C, Galea S, Ising M, et al (2009). Gene expression patterns associated with posttraumatic stress disorder following exposure to the World Trade Center attacks. Biol Psychiatry 66(7): 708-711. Ying SW, Futter M, Rosenblum K, Webber MJ, Hunt SP, Bliss TV, et al (2002). Brain-derived neurotrophic factor induces long-term potentiation in intact adult hippocampus: requirement for ERK activation coupled to CREB and upregulation of Arc synthesis. J Neurosci 22(5): 1532-1540. Yirmiya R, Rimmerman N, Reshef R (2015). Depression as a microglial disease. Trends Neurosci 38(10): 637-658. Zeng LL, Shen H, Liu L, Wang L, Li B, Fang P, et al (2012). Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain 135(Pt 5): 1498-1507. Zhan Y, Paolicelli RC, Sforazzini F, Weinhard L, Bolasco G, Pagani F, et al (2014). Deficient neuron-microglia signaling results in impaired functional brain connectivity and social behavior. Nat Neurosci 17(3): 400-406. Zheng LS, Kaneko N, Sawamoto K (2015). Minocycline treatment ameliorates interferon-alphainduced neurogenic defects and depression-like behaviors in mice. Front Cell Neurosci 9: 5.

35

Figure legends Figure. 1. Effects of chronic unpredictable stress (CUS) on sucrose preference and novel object recognition memory in CX3CR1 -/- and WT mice. (a) WT, but not CX3CR1 -/- mice, showed CUS-induced impairment in sucrose preference. This finding was reflected by a significant three-way interaction between time (before and after the CUS exposure) X stress (CUS vs. non-stressed control (C)) X genotype (CX3CR1 -/- vs. WT) (F1,

34=

4.43, P<0.043) (n=8-10/group). Post hoc analysis revealed that sucrose

preference in the CUS-exposed WT mice was significantly lower than the preference in all other groups as well as the preference of this group during baseline. (b) WT, but not CX3CR1 -/- mice, showed CUS-induced impairment in novel object recognition memory. Three-Way ANOVA with time as a repeated measure revealed a significant effect of time (representing the object recognition (OR) memory during the test) (F1, 35=43, p<0.001) (n=8-10/group). Although the 3-way interaction did to reach statistical significance, post hoc analysis revealed significant differences between novel OR and baseline for nonstressed WT and CX3CR1-/- mice, as well as for CUS-exposed CX3CR1-/- mice, but not for CUS-exposed WT mice. Furthermore, OR memory of CUS-exposed WT mice was significantly different from OR memory of non-stressed WT and CX3CR1-/- mice, and CUS-exposed CX3CR1 -/- mice. (c) Effects of short (2-days) unpredictable stress (SUS) exposure on novel object recognition memory. WT mice, but not CX3CR1-/- mice, displayed SUS-induced impairment in novel OR memory. Three-Way ANOVA with time as a repeated measure revealed a significant effect of time (F1, 35=33.48, p<0.001), as well as a significant interaction between time and stress (F1, 35=4.76, p<0.04) (n=8-10/group). Although the 3-way interaction failed to reach statistical significance, post hoc analysis revealed significant differences between novel OR and baseline in non-stressed WT and CX3CR1 -/- mice, as well as in CUS-exposed CX3CR1 -/- mice, but not for CUS-exposed

36

WT mice. Furthermore, OR memory in CUS-exposed WT mice was significantly different from OR memory in non-stressed WT and CX3CR1 -/- mice, as well as from CUS-exposed CX3CR1-/- mice. #P<0.05 compared with the corresponding group at baseline. *P<0.05 compared with the other groups during the post-stress test. Figure. 2. Effects of CUS exposure on microglia number and morphology, and on neurogenesis in CX3CR1 -/- and WT mice. (a) Effects of CUS on microglial number. CUS exposure significantly decreased the number of microglia in the hippocampal DG of both WT and CX3CR1 -/- mice. This was reflected by a significant effect of CUS (F 18=8.2,

2,

P<0.005). (b) Effects of CUS on microglial soma area. CX3CR1-/- mice (both non-

stressed and CUS-exposed) displayed a larger soma size than the corresponding groups of WT mice, reflected by a significant effect of genotype (F1,

910

=24.3, P<0.001). CUS

exposure did not produce a significant effect on soma size. (c) Effects of CUS on the average (per cell) total length of microglial processes. Both CUS-exposed WT and CX3CR1 -/- mice displayed reduced total processes length as compared with their corresponding non-stress control groups. This was reflected by a significant effect of CUS exposure (F1, 1235 =19.07, P<0.001). (d) Effects of CUS on neurogenesis. Analysis of the number of DCX-labeled cells revealed a significant effect of genotype (F1, p<0.05), CUS exposure (F1,

13

13

=5.5,

=5.8, p<0.05), and a significant genotype by stress

interaction (F1, 13 = 11.488, P<0.01), reflecting the differential suppressive effect of CUS on neurogenesis in WT but not in CX3CR1-/- mice. (e) Representative pictures of IBA1immunostained microglia from WT and CX3CR1-/- mice who were either non-stressed or subjected to CUS. The results are based on measurements in 249-389 cells, from 3-6 animals/group. *P<0.05 compared with the corresponding control (non-stressed) group. #P<0.05 compared with the corresponding WT group.

37

Figure 3. Validation of selected genes showing significant CUS or genotype effects in the RNA-seq analysis by qPCR. (a) P2ry12 gene expression validation revealed a main effect of genotype (F1, 32=28.55, p<0.001), with higher expression levels in CX3CR1-/than in WT mice. (b) H2-q7 gene expression validation showed a significant main effect of genotype (F1, 15 = 20.0, p<0.001), with lower expression levels in CX3CR1 -/- compared to WT mice (c) Ifit3 gene expression validation revealed a main effect of genotype (F1, 16 = 9.75, P<0.01), with lower expression levels in the CX3CR1 -/- than in WT mice. (d) Ttr gene expression validation revealed a main effect of genotype (F1, 30= 5.3 p<0.03), with higher expression levels in CX3CR1-/- than in WT mice. (e) Arc gene expression validation revealed a main effect of genotype (F1,16 = 8.45 p<0.01), and a main effect of CUS exposure (F1,16 = 9.89, p<0.006), with significantly reduced expression in both CUSexposed WT and CX3CR1 -/- mice compared with their respective non-stressed controls. (f) Ptgds gene expression validation revealed a main effect of genotype (F1, 34= 13.45, p<0.001) and a significant interaction between CUS exposure and genotype (F1, 34= 4.96, p<0.04), reflecting the significant CUS-induced expression reduction in WT but not in CX3CR1 -/- mice. g. Gpr88 gene expression validation revealed a significant interaction of CUS exposure by genotype (F1, 32=6.14, p<0.02), reflecting the significant CUS-induced expression reduction in WT but not in CX3CR1 -/- mice *, p<0.052. (h) Igf2 gene expression validation revealed a main effect of genotype (F1, 33 = 29.82, P<0.001), with higher expression levels in with higher expression levels in CX3CR1-/- than in WT mice, as well as a significant increase in Igf2 expression in CUS-exposed CX3CR1-/- compared to control CX3CR1 -/- mice. (i) Igfbp2 gene expression validation showed a significant main effect of genotype (F1, 34 = 6.56, p<0.02), with higher expression levels in CX3CR1/-

than in WT mice. All measurements are based on n=5-10/group. #P<0.05, compared

38

with the corresponding WT group. *p<0.05, compared with the corresponding control (non-stressed) group from the same genotype.

39

Table(s)

Table 1. Side by side comparison of selected gene transcripts significantly regulated by genotype. a b Log2(fold change) corresponding q-values Gene CWT vs. SWT vs. CWT vs. CKO vs. CWT SWT CWT CKO transcript CKO SKO SWT SKO vs. vs. vs. vs. Abhd14a B2m Bst2 Col6a1 Col6a4 Gbp2 Gbp3 Gbp4 Gbp5 Gbp7 H2-d1 H2-k1 H2-q4 H2-q6 H2-q7 Ifit1 Ifit3 Mid1 Xdh Iigp1 Oasl2 Wdfy1 a

CKO

SKO

SWT

SKO

-0.8

-0.5

-0.2

0.1

0.02

0.04

1.00

1.00

-0.4

-0.5

0.0

-0.1

0.21

0.02

0.99

0.99

-1.2

-0.8

0.0

0.5

0.02

0.08

1.00

1.00

-0.8

-0.7

0.1

0.2

0.02

0.02

1.00

1.00

-3.6

-0.8

-0.8

1.9

0.03

1.00

0.78

1.00

-0.8

-0.5

0.3

0.5

0.04

0.23

1.00

0.75

-1.0

-1.2

0.1

-0.1

0.16

0.02

1.00

1.00

-1.6

-1.8

0.8

0.6

0.02

0.02

0.14

1.00

-1.0

-0.9

0.1

0.2

0.02

0.02

1.00

1.00

-0.7

-0.8

0.2

0.1

0.02

0.02

1.00

1.00

-1.1

-1.1

0.1

0.1

0.02

0.02

1.00

1.00

-1.2

-0.9

0.0

0.3

0.02

0.02

1.00

1.00

-1.4

-1.4

0.1

0.0

0.02

0.02

1.00

1.00

-2.0

-1.8

0.0

0.2

0.02

0.02

1.00

1.00

-2.8

-2.9

0.3

0.2

0.02

0.02

1.00

1.00

-1.5

-1.5

0.1

0.1

0.02

0.02

1.00

1.00

-1.0

-1.3

0.2

-0.2

0.02

0.02

1.00

1.00

-1.6

-1.3

-0.2

0.1

0.02

0.02

1.00

1.00

-0.8

-0.7

-0.3

-0.1

0.02

0.02

0.78

1.00

-1.3

-0.9

0.2

0.6

0.02

0.02

1.00

0.85

-1.5

-1.4

0.1

0.2

0.02

0.02

1.00

1.00

-1.5

-1.3

0.0

0.2

0.02

0.02

1.00

1.00

Data is presented as log2 (fold change). Up-regulated genes appear in red and .b down-regulated genes in blue Corresponding q-values (false discovery rate) appear in green; genes that were included in the table had at least one significant comparison with q-value <0.05.

Table(s)

Table 2a. Top IPA hits for predicted upstream regulators in the hippocampus of control CX3CR1 -/- mice (vs. control WT as reference) Upstream a regulator

Predicted activation state

BiasActivatio corrected n z-scoreb z-score

IL10RA

Activated

2.27

PTGER4

Activated

2.153

STAT6 β-estradiol

Activated

2.304 3.165

NOS2

Inhibited

-1.862

STAT1

-1.946

IFNB1

-2.086

Target molecules in dataset

2.219 Gbp2,Gbp5,Iigp1,Ttr, HLA-A 2.621 Egr1,Gbp2,Gbp4,Herc6, Ifit1b,Rtp4, Slfn13, Usp18 1.964 Egr1,Erdr1,Gbp2,Ifit3, Isg15 1.802 Cd74,Cdkn1a,Dusp6,Egr1, Gbp2,Gpr88,Ifit1b,Igfbp6,Isg15, Nr4a1,Pglyrp1,Pou1f1,Ramp3,Rtp 4,Srp54, Trim25,Ttr, HLA-A -1.982 Cdkn1a,Erdr1,Ifit1b,Ifit3, Isg15 -3.366 B2m,Cdkn1a,Egr1,Gbp2,Gbp5, Herc6,Ifit1b,Ifit3,Ifitm3,Isg15, Slfn13,Usp18 -2.89 Bst2,Cdkn1a,Gbp2,Gbp4,Gbp5,Gb p7,Ifit1b,Ifit3,Isg15,Slfn13, Usp18

-3.223 Alox5ap,Angptl4,B2m,Bst2, Cd74,Cdkn1a,Chst7,Cx3cr11, Egr1,Gbp2,Gbp4,Gbp5,Gbp7, Herc6,,Ifit3,Ifitm3,Iigp1,Isg15, Lag3 ,Lamc2,Rtp4,Srp54, Usp18, HLA-A a Top IPA upstream predicted regulators in the hippocampus of CUS-exposed WT mice (vs. control WT as reference), and CUS-exposed CX3CR1-/- mice (vs. control CX3CR1/- as reference).b Activation Z scores > 1.95 or < (-1.95) were considered significant. When the calculated bias term was <0.25, the Z -scores were considered unbiased and prediction of activation/inhibition was noted in the table. For significant Z-scores in which the bias term was >0.25, the bias corrected Z-score was regarded significant for scores >1.95 or < (-1.95), but no activation prediction was made. The p-value for each IFNG

-1.84

Table(s)

Table 2b. Top Significant IPA hits for Diseases and Biological functions in the hippocampus of control CX3CR1-/- mice (vs. control WT mice as a reference). Diseases or functions categories

Annotation

Target molecules in dataset

Immunological Diseasesystemic autoimmune Alox5ap, B2m, C1qtnf6, Cd74, syndrome Cx3cr1, Cxcl14, Gbp2, Gbp4, Helz2, Herc6 , HLA-A, Ifit1b, Ifit3, Iigp1, Isg15, Itpr3, Mitf, Nr4a1, Oasl2, Pglyrp1, Qki, Ramp3, Rtp4, Trim25, Xdh Alox5ap, Angptl4, B2m, C10orf90, Cellular Growth and proliferation Cables1, Cckbr, Cd74, Cdkn1a, Proliferation Col6a1, Cx3cr1, Cxcl14, Dusp6, Egr1, Erdr1, Gbp2, Ifit3, Ifitm3, Igfbp6, Isg15, Itpr3, Lag3, Ltk, Mapk11, Mid1, Mitf, Nr4a1, Ovol2, Pglyrp1, Pou1f1, Rac3, Rims3, Rpl29 (includes others), Rps14, S1pr5, Trim25, Ttr, Usp18, Wt1, Xdh, Zbtb48 Cellular Movement

migration

Alox5ap, Angptl4, C1galt1, Cd74, Cdkn1a, Cx3cr1, Cxcl14, Egr1, Erdr1, Igfbp6, Lag3, Lamc2, Ltk, Mapk11, Nr4a1, Ovol2, Pglyrp1, Podxl, Rac3, Ramp3, Robo4, S1pr5, Xdh, HLA-A

Table(s)

Table 3a. Side by side comparison of selected gene transcripts significantly regulated by CUS exposure. a

Log2(fold change) Gene transcript CWT vs. SWT vs. CWT vs. 0.1

CKO vs. SKO -0.6

CWT vs. CKO 1.00

SWT vs. SKO 1.00

CWT vs. SWT 0.02

CKO vs. SKO 0.02

0.1

0.2

-0.7

-0.6

1.00

1.00

0.02

0.05

0.9

4.3

1.0

4.4

0.02

0.02

0.02

0.02

0.7

1.1

1.6

2.0

1.00

0.02

0.02

0.02

Npas4 Ttr Clic6 a

b

SWT -0.4

CKO 0.2

Arc

corresponding q-values

SKO

Data is presented as log2 (fold change). Up-regulated genes appear in red and down-regulated genes in blue. b Corresponding q-values (false discovery rate) appear in green; genes that were included in the table had at least one significant comparison with q-value <0.05.

Table(s)

Table 3b. Side by side comparison of selected gene transcripts significantly differentially regulated by genotype and CUS exposure. Log2(fold change) Gene transcript

CWT vs. SWT vs. CKO SKO

a

corresponding q-values

CWT vs. CKO CWT vs. SWT vs. CWT vs. SWT vs. SKO CKO SKO SWT

b

CKO vs. SKO

A2m

0.1

1.1

-0.3

0.8

1.00

0.02

1.00

Ace

0.2

1.2

0.1

1.1

1.00

0.02

1.00

0.05 0.02

Ak7

0.7

1.1

0.4

0.9

0.51

0.02

1.00

0.02

Aqp1

0.4

3.2

0.4

3.2

1.00

0.02

1.00

0.04

Calb2

0.0

-0.1

0.7

0.6

1.00

1.00

0.03

0.20

Col8a1

-0.9

2.6

-0.1

3.4

1.00

0.02

1.00

0.02

Col8a2

0.1

1.8

0.4

2.1

1.00

0.08

1.00

0.02

Drc7

0.1

1.1

0.5

1.6

1.00

0.02

1.00

0.02

Ecrg4

0.4

2.7

0.3

2.5

1.00

0.02

1.00

0.02

Egr1

0.4

0.1

-0.2

-0.5

0.29

1.00

1.00

0.02

Enpp2

0.2

1.3

0.0

1.0

1.00

0.02

1.00

0.02

Erdr1

-2.9

-1.8

-0.1

1.0

0.02

0.02

1.00

0.02

Gpr88

-1.1

0.9

-1.4

0.6

0.02

0.02

0.02

0.43

F5

0.8

3.1

0.7

2.9

1.00

0.02

1.00

0.02

Folr1

0.2

2.4

0.9

3.1

1.00

0.02

1.00

0.02

Igf2

-0.1

1.1

-0.2

1.1

1.00

0.02

1.00

0.02

Igfbp2

0.1

0.9

-0.1

0.8

1.00

0.02

1.00

0.02

Kcne2

0.5

2.7

1.2

3.4

1.00

0.02

1.00

0.02

Krt18

0.4

1.3

1.8

2.7

1.00

0.04

1.00

0.06

Lamc2

-2.0

-0.1

-1.5

0.4

0.02

1.00

0.02

1.00

Mitf

-1.8

0.9

-0.5

2.1

0.02

1.00

1.00

0.02

Prlr Prr32

-0.2 1.3

1.0 1.8

0.4 0.8

1.6 1.3

1.00 1.00

0.31 0.02

1.00 1.00

0.02 0.21

Ptgds

-0.2

0.6

-0.4

0.4

1.00

0.02

0.07

0.28

Six3

0.3

2.1

0.0

1.8

1.00

0.03

1.00

0.10

Slc13a4

0.1

1.7

0.0

1.6

1.00

0.02

1.00

0.02

Slc17a9

0.9

-1.6

1.9

-0.6

0.05

0.02

0.02

0.61

Slc38a8

-0.2

1.7

-0.5

1.4

1.00

0.02

1.00

0.04

Slc4a5

0.0

2.7

0.6

3.3

1.00

0.25

1.00

0.02

Sostdc1

0.0

1.5

0.2

1.8

1.00

0.02

1.00

0.02

Tmem212

-0.6

1.2

0.0

1.8

1.00

0.02

1.00

0.04

Wt1

0.7

-0.1

0.1

-0.8

0.06

1.00

1.00

0.02

Yam1

-1.3

-0.2

-0.5

0.6

0.02

1.00

0.02

0.02

a

Data is presented as log2 (fold change). Up-regulated genes appear in red and down-regulated genes in blue. b Corresponding q-values (false discovery rate) appear in green; genes that were included in the table had at least one significant comparison with q-value <0.05.

Table(s)

Table 4a. Top IPA predicted upstream regulators in the hippocampus of each of the strains ( CUS-exposed Vs. control as reference). Upstream Predicted BiasActivation z-Target molecules in dataset a Regulator Activation corrected scoreb State z-score CUS-WT (Vs. control WT) Arc,Avp,Calb2,Dusp1,Fos, Inhibited -2.347 -2.552 CREB1 Npas4, Nr4a1 -1.751 -1.972 Arc,Calb2,Dusp1,Fos,Nr4a1 BDNF -1.856 -1.944 Avp,Fos,Kcnt2,Ptgds ESR2 -/-

-/-

CUS-CX3CR1 (Vs. control CX3CR1 ) Inhibited -3.075 -2.545 CREB1 ESR1

a

Activated 2.278

2.719

RELA

-3.539

-2.4

NFKB1

-3.284

-2.216

EGF

-3.26

-2.018

BDNF

-2.541

-1.846

Arc,Calb2,Dio2,Egr1,Fos, Npas4, Nr4a1 Ace,Aqp1,Cldn1,Cldn2,Clic6, Cl8a1,Egr1,Enpp2,Folr1,Fos, Igf2,Igfbp2,Kcne2,Kl,Prlr, Prr32,Ramp3,Slc13a4, Slc39a4,Slca5,Sostdc1,Steap1, Sulf1, Ttr A2m,Dio2,Egr1,Fos,Igfbp2, Nr4a1, Pecam1,Ptgds, Wt1 A2m,Egr1,Enpp2,Fos,Nr4a1, Wt1 Arc,Aurkb,Cldn2,Dio2,Egr1, Fos,Igf2, Igfbp2,Nr4a1 Arc,Calb2,Egr1,Fos,Nr4a1, Sostdc1

Top IPA upstream predicted regulators in the hippocampus of CUS-exposed WT mice (vs. control WT as reference), and CUS-exposed CX3CR1-/- mice (vs. control CX3CR1-/- as reference).b Activation Z scores > 1.95 or < (-1.95) were considered significant. When the calculated bias term was <0.25, the Z -scores were considered unbiased and prediction of activation/inhibition was noted in the table. For significant Z-scores in which the bias term was >0.25, the bias corrected Z-score was regarded significant for scores >1.95 or < (-1.95), but no activation prediction

Table(s)

Table 4b. Top significant IPA hits for Diseases and Biological functions in the hippocampus of each of the strains (CUS-exposed Vs. control as reference). Diseases or Biological functions categories

Annotation

Target molecules in Dataset

CUS-WT (Vs. control WT) Behavior

anxiety

Avp,Cort,Dusp1,Fos

Behavior

learning

Arc,Avp,Fos,Neto1,Ttr,Unc13c

Psychological disorders

mood disorders

Avp,Gabrr2,Slamf9,Ttr,Wfs1

Psychological disorders

depressive disorder

Avp,Gabrr2,Ttr,Wfs1

CUS-CX3CR1-/- (Vs. control CX3CR1-/-) Behavior

long-term recognition memory

Arc,Egr1

Figure(s)

Figure(s)

Figure(s)

Highlights •

CX3CR1 deficiency confers resilience to the hedonic and cognitive effects of stress.

•

CX3CR1-/- mice exhibit resistance to stress-induced neurogenesis suppression

•

CX3CR1 -/- mice exhibit altered hippocampal microglial morphology

•

Reduced IFN-regulated transcription in CX3CR1-/- mice associates with passive resilience.

•

Increased estrogenic-regulated transcription in CX3CR1-/- mice associates with active resilience

40