Nighttime-specific gene expression changes in suprachiasmatic nucleus and habenula are associated with resilience to chronic social stress

doi:10.21203/rs.3.rs-3961297/v1

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Nighttime-specific gene expression changes in suprachiasmatic nucleus and habenula are associated with resilience to chronic social stress

https://doi.org/10.21203/rs.3.rs-3961297/v1

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published 02 Oct, 2024

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The molecular mechanisms that link stress and circadian rhythms still remain unclear. The habenula (Hb) is a key brain region involved in regulating diverse types of emotion-related behaviours while the suprachiasmatic nucleus (SCN) is the body's central clock. To investigate the effects of chronic social stress on transcription patterns, we performed gene expression analysis in the Hb and SCN of stress naive and stress exposed mice. Our analysis revealed a large number of differentially expressed genes and enrichment of synaptic and cell signalling pathways between resilient and stress-naïve mice at zeitgeber 16 (ZT16) in both the Hb and SCN. This transcriptomic signature was nighttime-specific and observed only in stress-resilient mice. In contrast, there were relatively few differences between the stress-susceptible and stress-naïve groups across time points. Our results reinforce the functional link between diurnal gene expression patterns and differential responses to stress, thereby highlighting the importance of temporal expression patterns in homeostatic stress responses.

Biological sciences

Biological sciences/Neuroscience

Mood disorders are complex heterogeneous disorders that affect cognition, emotional processing and basic physiological functioning ¹. There is a growing evidence of a close connection between circadian rhythms and mood regulation ². A majority of depressed patients show more severe symptoms in the morning than in the evening ³. Circadian processes including diurnal rhythms in secretion of growth hormones and melatonin, daily core body temperature, and sleep/wake cycles are disrupted in some depressed patients ^4,5. These internal rhythmic processes are maintained on a 24-hour cycle by multiple peripheral clocks throughout the body which are synchronised to a single central principal clock in the mammalian hypothalamus – the suprachiasmatic nucleus (SCN) ⁶. SCN itself has been shown to affect depressive and anxious-like responses in mice ^7,8. Furthermore, the SCN projects to several hypothalamic structures that regulate circadian processes disrupted in depression, including: the paraventricular nucleus, subparaventricular zone, and dorsomedial hypothalamus ^4,9. Overall, direct and indirect SCN projections to mood-regulating brain areas are likely responsible for the diurnal rhythmicity of symptoms observed in depressed patients.

One such mood-regulating brain region that is thought to receive inputs from the SCN is the habenular complex (Hb), which is composed predominantly of lateral (LHb) and medial habenula (MHb) subregions ¹⁰. The Hb relays information between the forebrain emotion-processing limbic system and the midbrain depression-associated aminergic centres ¹¹. The Hb is heavily involved in mediating stress responses ^12,13 and has also been associated with circadian modulation of depression ¹⁴. Elevated diurnal firing in LHb neurons projecting to the serotonergic dorsal raphae nucleus (DRN) is associated with social avoidance in chronically stressed mice ¹⁵, while passive coping, a behaviour mirroring apathy and resignation in humans, has been associated with increased activity in numerous circuits, including: LHb-DRN projections ¹⁶, medial prefrontal cortex projections to LHb ¹⁷, and LHb projections to the ventral tegmental area (VTA) ¹⁸. The pharmacological inhibition of the LHb rescues depressive-like behaviour ^19,20. In contrast, MHb lesion impairs voluntary exercise and induces despair-like and anhedonic behavioural phenotypes in mice, without affecting the circadian period and sleep architecture ^21–23. Despite its importance, the effects of stress on molecular pathways in the Hb remain underexplored.

Daily variation in the firing of both LHb and MHb neurons is driven by their functional molecular clock ^14,24,25. The LHb, but not MHb, exhibits an independent endogenous molecular clock that is synchronised by and in phase with the SCN clock ^10,24–26. LHb receives input from the SCN indirectly through the dorsomedial and lateral hypothalamic areas ^27–29. Some evidence suggests that SCN innervates the LHb directly ^14,26,30. Hence, the source of depression-associated circadian disruption might be due to perturbations of circadian rhythms at the level of the SCN, the Hb or both.

In order to investigate the relationship between mood regulation and circadian disruption, we exposed mice to chronic social defeat stress (CSDS). CSDS is a robust and widely used chronic stress model that induces depression-like and PTSD-like symptoms in a subset of exposed rodents ^31,32. Experimental mice exhibiting social avoidance are deemed “stress-susceptible”, while those that do not are “stress-resilient” ³³. These two subpopulations show different behavioural, physiological and molecular profiles, allowing for investigations of underlying mechanisms that resilience-promoting mechanisms ³¹. For example, CSDS in rodents disrupts body temperature, locomotor activity rhythms and sleep patterns where some processes are disrupted in all stress exposed mice and others only in the stress-susceptible group ^34–36. LHb cells projecting to the DRN in stress-susceptible mice exhibit blunted diurnal activity compared to stress-resilient and stress-naïve mice ¹⁵. Despite these findings, the temporal transcriptomic profiles of the Hb and SCN in the stress-resilient and stress-susceptible mice remain unexplored.

In this study, we investigated the effects of CSDS on the SCN and Hb transcriptome across the light/dark cycle. Mice exposed to CSDS were sampled at four time points: ZT4 (mid-inactive phase), ZT10 (transition to active phase), ZT16 (mid-active phase), and ZT22 (transition to inactive phase). We hypothesised that the greatest changes would be observed in the stress-susceptible mice in the Hb during the light phase. We expected relatively few differences between the resilient and stress-naïve mice, especially in the SCN. However, we found the largest transcriptomic changes at ZT16 in resilient relative to stress-naïve mice, in both SCN and Hb. Our observations also highlight the importance of measuring transcriptomic profiles across time points to determine the largest homeostatic change in response to stress.

2.1. Animals and housing

Male C57BL/6J mice (8–10 weeks old; Jackson Laboratory) All mice were housed with ad libitum access to food and water at 20 ± 1°C and 50 ± 10% humidity under a 12-hr light/dark cycle (7:00 am / ZT0: lights on; 7:00 pm / ZT12: lights off). All experimental procedures were conducted in accordance with the National Institute of Health Guide for Care and Use of Laboratory Animals and approved by the New York University Abu Dhabi Animal Care and Use Committee (IACUC Protocol: 19 − 0004).

2.2. Chronic social defeat stress

CSDS was performed as described previously ³³. Experimental C57 mice were exposed to 10 minutes of physical stress followed by 24 hours of sensory stress in the cage of an unfamiliar CD1 aggressor mouse (Fig. S1A). CSDS was conducted daily over 15 days between ZT8 and ZT9. The control mice were housed in pairs in a separate room and moved each day at the time of defeat. All the mice were single-housed following the end of CSDS until sacrifice.

Social avoidance towards a novel non-aggressive CD1 mouse was measured in a two-stage social interaction (SI) test, as previously reported ³³. The test was conducted between ZT7 and ZT10. TopScan video tracking system (CleverSys. Inc.) was used to automatically track mouse movement.

2.3. Tissue sampling and nuclei isolation

Mice were anaesthetized using isoflurane and sacrificed 48–72 hours after the SI test by cervical dislocation. Sampling was done at four time points (ZT4, ZT10, ZT16, ZT22) across the light-dark cycle (Fig. 1A-B). Extractions were carried out on a cold sterile surface, and the extracted brains were flash-frozen by immersion into − 60°C isopentane (Sigma Aldrich M3263) for 20 seconds. The brains were kept on dry ice at -80°C until further processing. The flash-frozen brain samples were then sliced on a Leica CM1950 cryostat (250 µm thickness) (Fig. S1B). The regions were isolated with reference to the Mouse Brain Atlas ³⁷. For both the SCN and Hb, bilateral punches from within each biological replicate were combined (Fig. S1B).

2.4. RNA isolation, library preparation and RNA-sequencing

RNA was isolated from the punches using the Qiagen RNeasy Micro Kit, as per the manufacturer's instructions. Isolated RNA was quantified and checked for quality on an Agilent Bioanalyzer instrument (Agilent Technologies) (Fig. S1C). Samples with RIN values above 7 were used for the library preparation. All the libraries were prepared together using the NEBNext® Ultra™ II RNA Library Prep Kit (New England BioLabs Inc.) as per the manufacturer’s protocol within one sitting. Sequencing was done on NovaSeq S1 flow cell (200 cycles) following Illumina's recommendations.

2.5. Differential expression and pathway enrichment analysis

Count preprocessing

Raw FASTQ sequenced reads were first assessed for quality using FastQC (v0.11.5) ³⁸. The reads were then passed through Trimmomatic (v0.36) for quality trimming ³⁹ and processed with Fastp to remove poly-G tails ad Novaseq-specific artefacts ⁴⁰. The reads were aligned to the mouse reference genome GRCm38.82 using HISAT2 with the default parameters ⁴¹. The resulting SAM alignments were then converted to BAM format and coordinate-sorted using SAMtools (v1.3.1) ⁴². Raw counts were generated by passing the sorted alignment files through HTSeq-count (v0.6.1p1) ⁴³. Concurrently, the sorted alignments were processed in Stringtie (v1.3.0) for transcriptome quantification ⁴⁴. Finally, Qualimap (v2.2.2) was used to generate RNAseq-specific QC metrics per sample ⁴⁵. All of the methods were executed using predefined YAML workflows using the BioSAILs workflow management system ⁴⁶.

Differential expression

Differential expression testing was performed in R package DESeq2 (v1.40.2) using multiple Wald tests ⁴⁷. The p values were adjusted using the Benjamini–Hochberg procedure. To obtain a better estimate of expression changes in low count genes, we performed log₂-fold-change (LFC) shrinkage using the apeglm method ⁴⁸. We used an adjusted p value of 0.05 (5% FDR) and a log₂-fold-change (LFC) of 0.5 to identify differentially expressed genes (DEGs). Samples within the same behavioural phenotype from ZT4 and ZT10 time points were computationally pooled together to provide an estimate of the 'daytime' expression, while ZT16 and ZT22 time point samples were pooled to provide an estimate of the 'nighttime' expression for each phenotype (Fig. 1B). Pooling all the samples within the same behavioural phenotype provided an estimate of time-invariant 'global' expression corresponding to average expression throughout the day. Differential expression between the behavioural phenotypes was carried out at each of these levels.

SCN and Hb samples were assessed for presence of respective canonical markers from published scRNA-seq studies ^49,50. The sequenced SCN samples specifically expressed known SCN markers, while Hb strongly expressed both the LHb and MHb markers, suggesting sampling accuracy (Fig. S2A). In order to assess whether technical biases are driving the differential expression patterns, the raw counts were normalized and stabilized for variance using DESeq2 with a design formula including region, time point and phenotype as covariates. The output was then used to compute sample similarity in all the genes (Fig. S2B), as well as the top 2000 most variable genes (Fig. S2C), which was then clustered based on their Eucledian distance. Beyond the sample region, these heatmaps do not suggest any other specific grouping that would indicate a bias. Furthermore, we performed principle component analysis on all the samples (data not shown). These results were replicated using surrogate variable analysis (SVA) (data not shown) ⁵¹. First principal component represented the regional identity of the samples and explained 82% of data variability (Fig.S2D).

Pathway analysis: Enrichment analysis was conducted in the Ingenuity Pathway Analysis (IPA, Qiagen) on all genes that had passed the 5% FDR significance criterion and was conducted only in those comparisons where the number of differentially expressed genes (DEGs) was higher than 20. Pathways were considered significantly enriched if –log(B-H p-value) was greater than 1.3. Based on the known roles of different genes and their relationship in the functionally-predefined pathways, IPA calculates the z score which represents the predicted direction of change of the pathway (activation: z > 0; inhibition: z < 0). The analysis included only the pathways associated with the mouse nervous system, neurons, astrocytes, other tissues and unspecified cell types.

Gene ontology

We visualised the associated DEGs and the top 5 IPA pathways from the resilient-to-control comparisons at ZT16 for both nuclei using gene-concept networks. Gene-concept networks represent the relative coordinates of the enriched pathways based on the number and differential expression of the associated DEGs. Therefore, it is possible to identify overlapping DEGs that have the largest contribution to the enrichment of the top 5 activated pathways.

In silico TF Motif Enrichment Analysis

To explore which transcription factors (TF) might be driving the differential expression at specific time points, we used FIMO ⁵² (fimo --oc {gene.fa} --verbosity 1 --bgfile --nrdb– --thresh 1.0E-4 H12CORE_meme_format.meme {gene}) with the Mouse TF motif list from HOCOMOCO ⁵³ to provide TF binding motif enrichment within 20 kb upstream of DEG promoters. The FASTA sequences were retrieved using BED2FASTA ⁵². FIMO results were then filtered for hits corresponding to q ≤ 0.05 and for TF that were expressed above the median of normalized and transformed expression value. TF hits were then sum-aggregated by TF families. Given the unbalanced amount of DE genes by conditions, aggregated TF hits were normalized by the amount of hit genes per condition. Finally, the delta between upregulated and downregulated hits was computed for each TF family. Positive delta values correspond to enriched binding of the TF family in the promoter of upregulated, and conversely negative values to binding in the promoter of downregulated DEGs.

Detection of rhythmic genes

Log-normalised count values were used to determine the rhythmic expression profile of core clock genes across all three behavioural phenotypes using the non-parametric JTK method for identification of oscillations in genome-scale datasets ⁵⁴. JTK method was run in the R-implemented version of the ECHO (v.4.0) tool ⁵⁵.

3.1. CSDS induces social avoidance in a subset of stress-exposed mice

To investigate the effects of chronic stress on SCN and Hb transcriptome, we exposed mice to 15 days of CSDS. Following CSDS, mice were classified as stress-resilient or stress-susceptible using SI test (Fig. 1A, S3A-C). Stress-naïve and stress-resilient mice exhibited significantly larger social interaction values than the stress-susceptible mice (Fig. S3A-C). Resilient and susceptible mice exhibited decreased movement and velocity relative to the stress-naïve mice (Fig. S3D-E).

3.2. Stress-resiliency is associated with large gene upregulation in SCN and Hb at ZT16

In order to investigate differential expression between the behavioural phenotypes at different time points, we sampled SCN and Hb at ZT4, ZT10, ZT16 and ZT22 time points 48–72 hours following the SI test (Fig. 1A-B). Computationally merging ZT4 and ZT10 provided a daytime expression estimate, while merging ZT16 and ZT22 provided nighttime expression estimate (Fig. 1B). Combining all time points together provided computational estimate of time-independent expression patterns (Fig. 1B). Comparing the phenotypes revealed that, in both the SCN and Hb, the highest number of significant genes was observed between resilient and stress-naïve mice at all time comparison levels (Table 1–2).

Table 1

Numbers of significant genes according to various cutoff values of p and FDR-adjusted p values at the investigated comparison levels in the SCN.
Comparison		Cutoff Value
Comparison		p value				q value (FDR-adjusted)
Timepoint	Phenotype	0.1	0.05	0.01	0.001	0.1	0.05	0.01	0.001
Global	R vs C	3765	2155	612	130	75	25	11	8
Global	R vs S	3172	1743	475	83	14	14	8	4
Global	S vs C	1723	872	228	52	12	11	3	1
Day	R vs C	2936	1588	409	82	24	19	9	6
Day	R vs S	3044	1557	348	45	5	5	2	1
Day	S vs C	1582	757	154	26	9	8	7	5
Night	R vs C	6226	4208	1604	471	1161	570	137	21
Night	R vs S	4465	2585	592	85	29	20	15	10
Night	S vs C	2162	1142	295	68	18	17	8	2
ZT4	R vs C	3184	1597	362	61	3	3	1	1
ZT4	R vs S	3713	2025	519	87	13	12	5	1
ZT4	S vs C	2043	1033	240	50	11	9	8	5
ZT10	R vs C	2316	1149	273	49	8	7	5	4
ZT10	R vs S	1955	952	230	31	6	5	4	1
ZT10	S vs C	1546	716	135	16	3	1	1	0
ZT16	R vs C	6213	4244	1820	669	1766	1059	338	80
ZT16	R vs S	6194	3866	1055	143	73	27	12	4
ZT16	S vs C	2875	1629	464	106	60	28	8	1
ZT22	R vs C	2605	1331	309	31	1	1	1	1
ZT22	R vs S	2920	1829	700	205	214	45	12	3
ZT22	S vs C	1618	814	189	22	2	2	2	1

Table 2

Numbers of significant genes according to various cutoff values of p and FDR-adjusted p values at the investigated comparison levels in the Hb.
Comparison		Cutoff Value
Comparison		p value				q value (FDR-adjusted)
Timepoint	Phenotype	0.1	0.05	0.01	0.001	0.1	0.05	0.01	0.001
Global	R vs C	2570	1482	494	169	152	110	47	8
Global	R vs S	1225	557	114	24	9	7	5	4
Global	S vs C	2483	1349	316	66	6	2	2	1
Day	R vs C	2276	1034	170	24	0	0	0	0
Day	R vs S	6691	4193	1230	159	5	3	1	1
Day	S vs C	2332	1071	203	29	0	0	0	0
Night	R vs C	3075	1788	568	147	110	77	12	1
Night	R vs S	1689	758	166	21	7	5	3	1
Night	S vs C	1585	767	141	17	0	0	0	0
ZT4	R vs C	1949	915	175	27	1	1	0	0
ZT4	R vs S	3347	1738	323	31	0	0	0	0
ZT4	S vs C	1251	500	84	10	1	1	1	1
ZT10	R vs C	1567	686	123	16	1	1	1	1
ZT10	R vs S	3262	1466	219	11	2	2	1	1
ZT10	S vs C	1361	607	73	9	1	1	0	0
ZT16	R vs C	3719	2415	988	252	493	205	35	2
ZT16	R vs S	3584	1963	565	166	157	85	23	3
ZT16	S vs C	3582	2054	535	75	6	6	3	2
ZT22	R vs C	864	362	57	10	3	2	2	1
ZT22	R vs S	621	247	48	14	5	1	1	0
ZT22	S vs C	1032	426	67	6	0	0	0	0

In the SCN (Fig. 2A-B), at global (average gene expression across all 4 time points) and daytime (average gene expression at ZT4 and ZT10) comparisons only a few genes were differentially expressed between any of the groups. Comparison of individual gene expression patterns at ZT4 and ZT10 time points did not reveal substantial differences between the phenotypes. However, at nighttime (average gene expression at ZT16 and ZT22) comparisons, we found 338 DEGs in resilient relative to stress-naïve mice, out of which large majority were upregulated (Fig. 2A, C). The nighttime differences in the SCN predominantly arise from the differential expression observed at ZT16 where we found 758 DEGs in resilient relative to stress-naïve mice, out of which ~ 60% were upregulated (Fig. 2B, E). At ZT22, we observed 34 upregulated DEGs in resilient relative to susceptible mice. However, other phenotypic comparisons at ZT22 did not reveal any major differential expression patterns. These results suggest that the largest changes in stress-induced SCN gene expression occur in resilient mice during nighttime at ZT16.

In the Hb (Fig. 3A-B), differential expression patterns between the phenotypes in terms of number and direction of DEGs were similar to those observed in the SCN. Global comparisons in the Hb revealed 68 upregulated DEGs in the resilient relative to stress-naïve mice (Fig. 3A, C). Gene expression patterns did not differ much between any of the three behavioural phenotypes during the daytime. Specifically, ZT4 and ZT10 time point comparisons did not yield any discernible differences in gene expression patterns (Fig. 3B). However, nighttime comparison between the behavioural phenotypes revealed 55 upregulated DEGs in resilient relative to stress-naïve mice (Fig. 3A, D). We found that the large differences in global and nighttime gene expression profiles are again mainly due to the large numbers of upregulated genes at ZT16 in stress-resilient relative to control and susceptible mice (Fig. 3A-B). Specifically, 143 DEGs were identified in the resilient relative to stress-naïve mice at ZT16, with almost all being upregulated (Fig. 3B, E). We observed 78 upregulated DEGs in resilient mice compared to stress-susceptible mice, but again little-to-no differences between the susceptible and control mice (Fig. 3B). No robust differences were observed at ZT22 between any of the behavioural phenotypes (Fig. 3B). Hence, stress-induced differences in Hb gene expression patterns in the resilient mice, both at global and nighttime analysis, are driven by changes at the ZT16 time point, similar to the SCN.

Given the strong differential expression at ZT16 between resilient and stress-naïve mice, we examined possible TF candidates driving these differences in silico (Fig. S4). We found a greater number of possible TFs associated with downregulated than upregulated DEGs in both SCN and Hb. The two brain regions showed enrichment of 35 common TF families. Differential expression in the SCN and Hb was associated with 8 and 39 unique TF families, respectively.

Overall, these results show robust transcriptomic differences in resilient relative to stress-naïve mice in both the SCN and Hb. The largest observed differences occur at ZT16 in both regions, with SCN showing larger transcriptional response than the Hb in terms of DEG numbers and associated LFC. Susceptible mice did not show significant differential expression patterns relative to either the resilient or control mice. Taken together, the results suggest resiliency-associated transcriptional signature to be temporally-restricted to the middle of the active phase.

3.3. Stress resilience is associated with enrichment of signalling and plasticity pathways in SCN at ZT16

To better understand the molecular pathways and functions of DEGs between the resilient and stress-naïve mice, we performed pathway enrichment analysis in the SCN at nighttime and at ZT16 and found a number of enriched pathways (Table 3, Fig. 4). At ZT16, the enriched pathways included an expanded list of those observed from the nighttime comparison (Table 3, Fig. 4A-B). All the enriched pathways in the resilient mice can be divided into two broad functional groups: signalling and plasticity-related pathways (Fig. 4A). Furthermore, at ZT16, circadian rhythm signalling pathway was significantly enriched in the resilient mice. Both signalling and plasticity-related pathways in the resilient mice were enriched by common DEGs (subset shown in Table 4). Many upregulated DEGs were associated with relatively few pathways (Fig. 4C). These results suggest that stress resilience is strongly associated with upregulation of relatively few genes in SCN at ZT16 which subsequently have broad impact on signalling and plasticity regulation.

Table 3

IPA-predicted z score of enriched pathways between the stress-resilient and stress-naive mice in the SCN. Only relevant pathways with |z| > 2.0 in any of the comparisons are shown.
Enriched Pathway	Comparison
Enriched Pathway	Nighttime R vs C	ZT16 R vs C
CREB signaling in neurons	5	6.3
Phagosome formation	4.7	6
S100 family signaling	4.6	5.2
G-protein coupled receptor signaling	3.9	5.2
Synaptogenesis signaling	3.8	5.1
Serotonin receptor signaling		3.9
cAMP-mediated signaling	3.5	3.8
Neurovascular coupling signaling	3.0	3.8
SNARE signaling	2.8	3.5
Chemokine signaling		3.5
Reelin signaling in neurons		3.4
Oxytocin in brain signaling		3.3
Calcium signaling	2.1	3.1
Ephrin receptor signaling		3.1
Cholecystokinin/gastrin-mediated signaling		2.7
ERBB signaling		2.7
Opioid signaling		2.6
α-adrenergic signaling		2.5
Synaptic long-term depression		2.4
Estrogen receptor signaling		2.4
Neuregulin signaling		2.3

Table 4

DEGs associated with 4 or more enriched IPA pathways in resilient relative to stress-naive mice at ZT16. All presented genes have |LFC| > 1.0. Genes colored red are upregulated, while genes colored blue are downregulated.
Gene Functional Group	SCN	Hb
Ion channels	Cacng3, Gabra1, Grin2a, Grin2c, Kcnj4	Cacna1h, Grin2a
Receptors	Adora2a, Adra2c, Chrm1-5, Cnr1, Drd2, Fzd10, Gnal, Grm5, Hctr1-2, Hrh2, Htr1d, Htr2a, Htr4, Ngfr, Ntsr1, Oprd1, Ptger3, Rxfp3, Sstr 2–4, Tacr3, Trhr
Enzymes	Camk4, Ngef, Nos1, Prkcg	Camk1d, Gad1, Ppp3ca
Other	Creb3l1, Rasd2, Rgs2, Rgs7	Adcy1, Adcy9

3.4. Stress resilience is associated with enrichment of synaptogenesis and calcium signalling pathways in Hb at ZT16

In the Hb, we explored pathway enrichment at global, nighttime and ZT16 comparisons between resilient and stress-naïve mice (Fig. 5). Fewer pathways were as highly enriched and activated in the Hb of the resilient mice at ZT16 compared to the SCN (Table 5, Fig. 5A-B). The most enriched pathways in this comparison are associated with mechanisms of synaptic plasticity (Table 5, Fig. 5B). Similar to the SCN, circadian rhythm signalling pathway was enriched in the resilient mice at global, nighttime and ZT16 comparisons. Relatively few DEGs were associated with a high number of enriched pathways in the Hb of resilient mice (Table 4). Furthermore, the most enriched pathways were associated with a handful of genes (Fig. 5C). These results suggest that stress resilience is associated with upregulation of plasticity-associated genes in the Hb at ZT16. Although both the SCN and Hb exhibited robust changes specifically at ZT16, the SCN shows larger and functionally more diverse transcriptomic response to stress than the Hb.

Table 5

IPA-predicted z score of enriched pathways between the stress-resilient and stress-naive mice in the Hb. Only relevant pathways with |z| > 2.0 in any of the comparisons are shown.
Enriched Pathway	Comparison
Enriched Pathway	Global R vs C	Nighttime R vs C	ZT16 R vs C
Opioid signaling	2.6	2.2	2.5
RAC signaling	2.2	2
Calcium signaling	2.1	2.4	3.3
Synaptogenesis signaling			3.3
cAMP-mediated signaling			2.8
Dopamine-DARPP32 Feedback in cAMP signaling			2.6
Synaptic long-term potentiation			2.4
Granzyme A signaling			2.2
Oxidative phosphorylation			-2.4

3.5. Stress induces arrhythmicity of Per1 and Arntl in the SCN of stress exposed mice

Given the enrichment of circadian rhythmicity signalling pathway, we explored whether chronic stress affected the rhythmicity of core clock genes in the SCN and Hb. To this end, we performed JTK analysis and found rhythmic expression of the core clock genes (Per1, Per2, Per3, Cry1, and Arntl) in the SCN (p_adj <0.05) of stress-naïve mice (Fig. 6A-B). Resilient and susceptible mice exhibited similar rhythmic expression profiles of the core clock genes in the SCN as stress-naïve mice, with the exception of Arntl and Per1. Arntl was arrhythmic in both stress-resilient and stress-susceptible mice, while Per1 was arrhythmic in the SCN of stress-susceptible mice. In the Hb the expression of core clock genes was rhythmic in all three behavioural phenotypes, with the exception of Per1, Per3, and Cry1 which were arrhythmic in the Hb of stress-naïve mice (Fig. 6A-B).

Stress resilience and homeostatic response

The close relationship between circadian rhythms and mood disorders warranted a systemic investigation of transcriptomic changes in the principal clock (SCN) and brain regions that modulate mood. We focused on the Hb because it is a semi-autonomous oscillator with a strong role in stress regulation and mood disorders ¹⁴. We found that chronic stress elicited a robust change in the gene expression in the SCN and to a lesser degree in the Hb (Fig. 2–3). This transcriptomic signature was specific to resilient mice and was strongest at ZT16. In contrast, comparing susceptible and stress-naïve mice did not reveal strong differential expression patterns. Large differential upregulation in resilient mice was associated with enrichment and predicted activation of signalling and plasticity-related pathways in both nuclei (Fig. 4–5).

Resilience is an active homeostatic process and involves transcriptional changes in a diffuse network of interconnected brain regions ⁵⁶. The current understanding is that chronic stress imposes an adaptive challenge that requires behavioural flexibility and physiological homeostatic buffering ⁵⁷. The inability to elicit such a response leads to failure in stress-coping and strongly contributes to associated stress-induced pathologies ^57,58. Resilient mice do not show behavioural deficits likely because of large transcriptomic changes such as those observed in our study. Early studies found that resilient mice exhibited large and unique upregulation of neurotrophic factors and stress-responsive genes in the prefrontal cortex ³¹. Others found strong gene upregulation in resilient mice relative to control and susceptible mice in midbrain regions such as the nucleus accumbens and VTA ^59,60. The physiological consequences of large transcriptomic changes in resilient mice are also likely responsible for observed differences in circuit-specific neural activity between resilient and susceptible mice. For example, VTA dopaminergic (DA) cells in susceptible, but not resilient, mice exhibit elevated firing ⁶¹. The pathophysiologically elevated firing is counteracted in resilient mice by compensatory mechanisms that are absent in susceptible mice ⁶². Furthermore, our findings that large transcriptomic changes occur in resilient mice at a specific night time point (ZT16) add a significant temporal dimension to the current understanding of the molecular mechanisms that drive stress-resilience. Our data, along with previously published physiological and circuit studies, suggest that temporal patterns of regional activity may play a crucial role in driving a particular behavioural phenotype ¹⁵. Our data provides further evidence of a tight association between large transcriptional changes and successful stress-coping mechanisms. Moreover, future studies with sampling performed over several day/night cycles following chronic stress would allow for stability estimation of this transcriptomic signature.

SCN transcriptomic profile

We observed significant upregulation of Adora2a in the SCN of resilient mice at ZT16. Adenosine is known to regulate Per1 and Per2 expression via A1/A2A receptor activation which is hypothesised to integrate light and sleep signalling to track homeostatic sleep pressure and regulate sleep architecture - both of which were previously found to be differentially impacted in resilient and susceptible mice ^34,36,63. Upregulation of Adora2a in the SCN of resilient mice at ZT16 may be a subcomponent of adaptive homeostatic response that maintains consolidated sleep ^34,36. Interestingly, the upregulation of A2A receptors in the forebrain is associated with depressive-like behaviour and decreased synaptic plasticity following unpredictable chronic mild stress (CMS) ⁶⁴. Resilient mice displayed strong upregulation of Ntrk1 and Ngfr in the SCN at ZT16. NGF binds to Ntrk1 and Ngfr ^65,66. NGF levels are significantly lower in depressed patients ⁶⁷ while increasing its levels has antidepressant properties ^68,69. Furthermore, BDNF, which has functional roles in synaptic plasticity, stress response, and antidepressant action in several regions, also binds to Ngfr ^70,71. Ngfr is specifically synthesised in cholinergic neurons which strongly modulate SCN neuronal excitability and circadian rhythmicity at night ^72–74.

We also observed high upregulation of cholinergic muscarinic receptors Chrm 1–5. These receptors have a functional role in fear and emotional responsiveness ^75,76. Cholinergic signalling in the SCN can induce phase shifts in locomotor rhythms ⁷⁷. However, at present it is unclear how increased muscarinic receptor expression at ZT16 in the SCN drives resilience. A recent study demonstrated that the stress-associated neuropeptide CRH increases cholinergic interneuron firing that causes potentiation of dopamine transmission ^78,79. Besides cholinergic receptors, Drd2 and Crh were upregulated in the SCN of resilient mice at ZT16. Dopaminergic input from VTA to SCN modulates circadian entrainment through activation of excitatory Drd1 receptors ⁸⁰. The putative homeostatic role of inhibitory Drd2 in circadian regulation in resilient mice is currently unknown. Our observation that Crh is upregulated in resilient mice adds to the recent findings that CRH-expressing neurons are also present in the SCN ⁸¹. CRH increases gating of BDNF signalling ⁸². Thus, increased CRH expression at ZT16 may be responsible for putative BDNF-induced antidepressant-like effects in stress-resilient mice. The resilient phenotype has also been associated with increased cAMP and GPCR-mediated signalling ^59,83. These are a subset of enriched plasticity-related pathways in our dataset that are predicted to be activated in the SCN of resilient mice. However, the functional consequences of these changes are currently unclear since plasticity-related transcription factor Creb3l1 was downregulated, while many others, such as Gbx1, were strongly upregulated in the resilient mice. Previously, Creb downregulation has been associated with stress resilience ^84,85. Overall, few studies have examined stress effects on SCN physiology and those predominantly focused on the expression of clock genes ^7,86–88. Our observations highlight potentially novel molecular mechanisms in the SCN that promote resilience to stress that warrant further investigation.

Hb transcriptomic profile

Adcy1 and Adcy9 were upregulated in the Hb of resilient mice relative to controls at ZT16. Adenylyl cyclases play a pivotal role in both cAMP-mediated and the ERK½-CREB signalling cascade which are known to modulate neuronal physiology ^89–91. Overexpression of Adcy1 in the forebrain prevents BDNF downregulation in stressed mice, thereby promoting stress resiliency ⁹². Adcy1 forebrain overexpression induces greater long-term potentiation (LTP) ⁹¹. Moreover, we observed ZT16-specific upregulation of other plasticity-modulating genes, such as Grin2a, Grin2b, Cacna1h, Camk2b, and Ppp3ca which correlate with large predicted activation of synaptogenesis and LTP signalling pathways. Resilient mice exhibit greater hippocampal LTP relative to susceptible mice because of higher expression of Grin2b and Camk2 ⁹³. Upregulation of plasticity-related genes may be a part of a larger homeostatic response associated with stress resiliency ^94,95. We also observed upregulation of Gad1, Gabra5, Kcnq3 and Npy1r genes in stress-resilient mice. Sub-chronic mild stress decreases long-term depression (LTD) of GABAergic neurons in the LHb ⁹⁶. Furthermore, male mice with Kcnq3 mutation that inhibits GABA binding exhibit reduced self-grooming, behaviour indicative of apathy ^32,97. Neuropeptide Y action through its Y1 receptor has been associated with stress resilience and anxiolysis in several brain regions, including the Hb ^98,99. In the LHb, Npy1r-expressing neurons play a critical role in chronic stress adaptation by driving palatable food intake and reducing anxiety-like behaviour ¹⁰⁰. Thus, it is possible that elevated Npy1r expression at ZT16 in the Hb drives increased food intake in resilient mice to compensate for lower levels of adipose tissue and serum leptin ¹⁰¹.

Clock genes

Since our analysis revealed a strong effect of stress on the SCN and Hb at a specific time point, we examined the rhythmic expression of clock genes in both of these nuclei. Chronic stress induces arrhythmicity of Arntl in the SCN of both resilient and susceptible mice which might imply that putative homeostatic adaptations in resilient mice do not protect against stress-induced disruption in rhythmic expression of this clock gene. Despite previous observation that knockdown of Arntl leads to behavioural despair, anxiety and helplessness following CMS, our results suggest that Arntl arrhythmicity in the SCN does not directly impact mood regulation ⁷. Rhythmic Cry expression in susceptible, but not resilient, mice is unexpected as it suggests a compensatory mechanism that maintains rhythmic expression of some genes. Another study found that CMS in rats induces perturbation of Per2 in the LHb, without affecting the SCN rhythms ¹⁰².

Previous work has shown that exposure to CSDS in the day or night induces differences in locomotor activity and tissue specific Per2 expression profile^103,104. In contrast no difference in locomotor rhythms or Per2 expression were observed in mice kept in constant dim red light where clock rhythms are likely free running^86,105. These observations imply that exposure during different light cycles modulate rhythmic behavioural and gene expression profiles. Future studies will investigate whether the transcriptomic profile of mice exposed to chronic stress at night induces similar transcriptomic profiles observed in resilient mice at ZT16.

In our study, Per1, Per3, and Cry1 were arrhythmic in the Hb of stress-naïve mice, but not resilient and susceptible mice. Although unexpected, these results can be attributed to the subdivisional differences at the level of MHb and LHb ^10,24–26. Clock gene expression in the MHb has yet to be systematically investigated in mice, though it has been reported previously in hamsters and rats ^106,107. Despite both the LHb and MHb showing diurnal variation in firing ^24,25, the LHb displays more evident rhythms of higher amplitude than the MHb ¹⁰. In the present study we did not differentiate between the two subregions and the reduced spatial resolution might have contributed to the observed arrhythmicity of clock genes in stress-naïve mice. Moreover, different stressors have been previously shown to differentially affect neural dynamics ³². Thus, the differential effect of CSDS and CMS on the transcriptomic landscape may be driven by the stressor type. Higher sampling resolution would, however, be required to accurately describe effects on the phase of core clock gene expression, thus warranting future studies.

The SCN and Hb are heterogeneous nuclei with many functionally and molecularly distinct subregions. Previous transcriptomic studies have only examined specific subpopulations of the Hb for purposes of circuit manipulations ^18,108,109. Likewise, studies linking SCN and stress were limited to investigating a limited predetermined list of genes ⁷. To the best of our knowledge, no study has systematically examined the SCN or Hb bulk transcriptome in the context of murine chronic stress models across multiple time points throughout the circadian cycle. Our study is the first to show that the largest transcriptomic changes in the SCN and Hb of resilient mice occur at night with a peak at specific time point. This highlights the importance of sampling over a 24-hour cycle to accurately determine dynamic changes in gene expression following stress exposure. Most studies fail to consider diurnal changes in expression profiles. Our time-dependent transcriptomic analysis suggests novel potential molecular mechanisms for driving resilience to stress that would not be evident within a constrained daytime sampling period. Such findings support time-dependent treatment administration that might enhance its efficacy. Crucially, our results highlight the link between temporal expression patterns and homeostatic stress responses.

Acknowledgements

We thank Justin Blau and Dan Ohtan Wang for critical reading of the manuscript.

The authors have received funding from the following sources: NYUAD Start-Up Fund (DC), NYUAD Annual Research Fund (DC), NYUAD Research Enhancement Fund (DC), University Research Challenge Fund (DC), Al Jalila Research Foundation (AJF201638; DC), NYUAD Center for Brain and Health (NYUAD Research Institute CG012 Tamkeen grant; DC), PN was supported by Tamkeen under the NYU Abu Dhabi Research Institute Award to the NYUAD Center for Genomics and Systems Biology (ADHPG-CGSB).

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Contributions (CRediT Statement)

Conceptualization: Priyam Narain, Aleksa Petković, Marko Šušić, Dipesh Chaudhury

Data curation: Aleksa Petković, Marko Šušić, Priyam Narain

Formal analysis: Marko Šušić, Aleksa Petković, Priyam Narain, Giuseppe Antonio-Saldi,

Nizar Drou

Visualisation: Marko Šušić, Aleksa Petković, Priyam Narain, Giuseppe Antonio-Saldi

Validation: Aleksa Petković, Priyam Narain, Marko Šušić, Giuseppe Antonio-Saldi

Funding acquisition: Dipesh Chaudhury

Investigation: Aleksa Petković, Marko Šušić, Priyam Narain, Salma Haniffa, Marc Arnoux, Mariam Anwar

Methodology: Priyam Narain, Aleksa Petković, Marko Šušić, Dipesh Chaudhury

Project administration: Priyam Narain, Dipesh Chaudhury

Resources: Dipesh Chaudhury

Software: Marko Šušić, Giuseppe Antonio-Saldi , Aleksa Petković, Nizar Drou, , Priyam Narain,

Writing-original draft: Aleksa Petković, Priyam Narain, Dipesh Chaudhury

Writing-review-editing: Aleksa Petković, Marko Šušić, Priyam Narain, Dipesh Chaudhury

Supervision: Dipesh Chaudhury

Conflict of Interest

The authors report no biomedical financial interests or potential conflicts of interests.

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The authors have declared there is NO conflict of interest to disclose

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Nighttime-specific gene expression changes in suprachiasmatic nucleus and habenula are associated with resilience to chronic social stress

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Abstract

Figures

1. Introduction

2. Material and Methods

2.1. Animals and housing

2.2. Chronic social defeat stress

2.3. Tissue sampling and nuclei isolation

2.4. RNA isolation, library preparation and RNA-sequencing

2.5. Differential expression and pathway enrichment analysis

3. Results

3.1. CSDS induces social avoidance in a subset of stress-exposed mice

3.2. Stress-resiliency is associated with large gene upregulation in SCN and Hb at ZT16

3.3. Stress resilience is associated with enrichment of signalling and plasticity pathways in SCN at ZT16

3.5. Stress induces arrhythmicity of Per1 and Arntl in the SCN of stress exposed mice

4. Discussion

Concluding Remarks

Declarations

Acknowledgements

References

Additional Declarations

Supplementary Files

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