Immune transcriptomic profile in adult female pigs: dominance status has more influence than environmental enrichment

doi:10.21203/rs.3.rs-4016210/v1

Background

Assessing animals’ affective states is crucial for evaluating the welfare of farm animals, yet practical tools are lacking. In this study, we tested whether the peripheral blood mononuclear cell (PBMC) transcriptome can be used to show variations in sows’ affective state. To do this, we compared animals whose emotional states were assumed to differ due to their lives in more or less enriched environments and to their different dominance statuses. Sows (n = 71) were housed in conventional (C) or enriched (E) environments from gestation day 0 (G0) until three weeks before farrowing (G105), after which they were transferred to conventional maternity rooms. On G98, saliva and hair were collected for cortisol measurement. From G99 to G103, behavioral analyses were conducted, and dominance hierarchy was evaluated. Blood samples were collected for PBMC transcriptome analysis on G98 and on lactation day 12 (L12).

Results

Both environmental enrichment (EE) and dominance status influenced cortisol and variables related to social and exploratory behavior, indicating an influence on sows’ affective states. While EE affected only a few genes (31 differentially expressed genes (DEGs) in G98, dominance status significantly impacted the PBMC transcriptome (449 DEGs in G98). Compared with subordinate sows (SUB), dominant (DOM) sows exhibited upregulated inflammatory genes, downregulated B-cell activation, and upregulated plasma cell function. In groups of sows, dominance status is partly related to sow parity, and we compared the effect of dominance with that of parity. Some similar DEGs emerged when comparing different parity animals (high-parity (HP) vs. low-parity (LP); 542 DEGs on G98, 180 in common with dominance-impacted genes), indicating that some effects of dominance on the transcriptome were in fact more due to age or reproductive cycles than to dominance itself. EE and dominance effects appeared relatively short-term, as DEG numbers decreased on L12 (four DEGs for E vs. C, 25 for DOM vs. SUB).

Conclusions

Both EE and dominance status influenced sows’ affective states; however, dominance status exerted a more pronounced influence on sows’ PBMC transcriptome than did environmental enrichment. In particular, dominance status modulated genes associated with B cells and plasma cell functions.

Peripheral blood mononuclear cell

pig

gestation

lactation

social environment

environmental enrichment

gene expression

parity

immune function

Affective states represent the subjectivity of feelings or experiences, which include emotions (brief, intense and triggered by a specific stimulus) and moods (stable and less intense; 1). Decades of research in psychoneuroimmunology have demonstrated that immune system activity is modulated by affective states (2, 3). In nonhuman animals, acute stressful events influence various immune features, such as antibody production, lymphocyte proliferation, cell trafficking, and the release of inflammatory mediators (4–7). Chronic stress modulates the inflammatory response (8–10). In humans, chronic states of malaise, such as depression, are associated with dysregulated inflammatory responses (11). Prolonged exposure to adverse conditions results in a repeatable pattern in the blood immune cell transcriptome, characterized by an increase in proinflammatory gene expression and a decrease in the expression of genes involved in type I interferon responses and antibody synthesis (12–15). This pattern is known as the “Conserved Transcriptional Response to Adversity” (CTRA; 16). Interestingly, while the CTRA phenotype characterizes stressed and depressed individuals, the reversed phenotype could identify those with a better mood than the average population (17). However, it remains unclear whether the transcriptomic patterns of psychological well-being observed in primates also exist in other species.

In the field of animal production, public concern regarding farm animal welfare is growing. On farms, production results, health and body condition indicators can be used to assess poor welfare, but available tools lack the sensitivity to assess animal’s affective state, which represents the animal’s current welfare state (18). Recently, a definition of animal welfare has been proposed as a balance or accumulation of pleasant and unpleasant experiences over time (18). While many physiological and behavioral tools are now available to identify responses to acute emotions, it is more difficult to assess long-term affective states (moods). Researchers have estimated stable affective states using behavioral tests that are not directly applicable within farm settings (19). Therefore, finding new physiological markers capable of reflecting affective states to complete the existing welfare indicator panel is a significant challenge for applied research.

Environmental enrichment (EE) is a way to improve the affective states of captive animals (20). In pigs, EE can be achieved by the provision of rooting substrates, materials for manipulation, larger living spaces, and access to social partners or outdoor environments (21). Changes in emotional sensitivity to reward loss and attention bias demonstrate the positive influence of EE on pigs' affective states (22, 23). Furthermore, studies have demonstrated the impact of EE on the immune system of pigs (24–27). Beyond the physical environment, the social milieu is another crucial element shaping affective states. For instance, individuals’ hierarchical position within a stable social group modulates anxiety and stress responses (28). In mice and pigs, an inverse relationship is observed between dominance and anxiety-like behaviors (29, 30), and related neural mechanisms have been described (29, 31). In European breeding systems, sows are often housed in mixed parity groups during gestation, which contributes to the development of stable social structures that significantly influence their daily social and nonsocial behaviors (32) and stress responsiveness (33).

In this study, we aimed to determine whether the peripheral blood mononuclear cell (PBMC) transcriptome could serve as a tool to investigate stable affective states in pigs. For this purpose, we investigated the effects of two factors that influence affective states in sows: the level of enrichment and their social position in their living environment.

2.1. Experimental design and animals

The experiment was carried out at the experimental farm of the Chambre Régionale d'Agriculture de Bretagne (Saint-Nicolas-du-Pélem, France). On this demonstration farm, animals are raised and marketed for meat in the same way as on a conventional commercial farm. Young nulliparous sows (Landrace x Large White) are randomly assigned upon purchase to one of two independent reproductive units, which feature distinct housing environments. In the conventional (C) unit, groups of 19–24 sows are housed during gestation in pens with concrete slatted floors, a minimum space of 2.4 m² per sow and two hanging metal chains as a minimal enrichment material. In the enriched (E) unit, groups of 19–24 sows are housed on deep straw bedding with greater space per sow (3.5 m²). The sows remain in the same housing environment throughout their entire reproductive life, and both groups include animals of mixed parities (0 to 7 gestations). Sows are individually fed through an automatic dispensing system, and management and feeding are similar for all units.

The experiment was carried out in two replicates: the first (R1) from November 2021 to February 2022 and the second (R2) from March to July 2022. Throughout the trial, we used the breeding sows on the farm at the time of the study, who were raised in the C environment (a group of 19 sows in R1 and a group of 17 sows in R2, n = 36) and E environment (a group of 18 sows in R1 and a group of 17 sows in R2, n = 35) since their first reproductive cycle. As nulliparous sows presented limited familiarity with the environment and due to their physiological differences compared to multiparous sows, they were excluded from further analysis, totaling 60 multiparous sows (C, n = 31 and E, n = 29). Among those, we selected 28 multiparous sows (seven sows per environment and replicate) on gestation day 98 (G98) for biological sampling. The selected sows were involved at least in their second gestation and thus possessed significant experience with their respective C or E environments before the start of the experiment. On G105 (approximately ten days before the expected farrowing date), sows from both units were transferred to individual farrowing pens on a slatted floor, where they farrowed and then remained with their litter for four weeks until weaning. The experimental timeline is summarized in Fig. 1. After the experiment, sows and piglets remained in the farm’s production cycle and were marketed for meat once reaching standard weight (for pigs) or culling age (for sows).

2.2 Biological analyses

2.2.1. Saliva and hair cortisol

Saliva samples were collected from gestating sows on days G35 and G98 at 09:00 h using cotton buds (Salivette®, Sarstedt 51588 Nümbrecht, Germany). The animals chewed the material voluntarily until it was moistened. The samples were immediately placed on ice until centrifugation at 2500 × g for ten minutes at 4°C and frozen at -20°C until cortisol analysis. On G98, hair samples were obtained from the neck area by shaving close to the skin with electric hair clippers, avoiding the inclusion of hair follicles. The hair samples were stored at room temperature in hermetically sealed bags and immediately frozen at -20°C upon arrival at the laboratory for subsequent analysis. Cortisol was extracted from hair samples according to the method outlined by Heimbürge and colleagues (34). Both salivary and hair cortisol concentrations were analyzed in duplicate with a luminescence immunoassay kit (LIA, IBL, Hamburg, Germany) following the manufacturer’s instructions.

2.2.2. Blood collection, cell count and PBMC isolation

An experienced veterinarian collected blood samples (three EDTA tubes of 10 mL per sow) from the anterior vena cava on G98 and the mammary vein on the 12th day of lactation (L12). On G98, animal keepers guided the sows individually to a separate room away from the group and maintained them with a nose sling while the sampler collected blood from the anterior vena cava. On L12, blood was collected from the mammary vein directly in the sows’ lactating crates, requiring no additional containment. Blood samples were transported in a thermobox with refrigerated rolls and arrived at the laboratory within three hours after the sampling procedure.

The total numbers of lymphocytes, monocytes, neutrophils, eosinophils, and basophils were measured with an MS-9® hematology automatic cell counter (Melet Schloesing laboratories, Osny, France). To isolate the PBMC fraction, 9 ml of EDTA-treated blood was half diluted with PBS buffer and centrifuged for 30 minutes at 400 × g over a Ficoll density gradient (Histopaque 1077, Sigma‒Aldrich, Saint-Quentin Fallavier, France). Cells at the interphase were collected and washed, and the remaining red blood cells were lysed through an 8-minute incubation in a hypotonic solution. After two washes with PBS, the cells were counted, pellets of 2 million cells were prepared, and lysis buffer solution (Macherey Nagel, Düren, Germany) was added to the PBMC pellets, which were stored at -75°C until RNA extraction.

2.2.3. RNA isolation, cDNA library preparation, and sequencing

RNA was isolated using the NucleoSpin RNA Kit according to the manufacturer’s instructions (Macherey‑Nagel, Düren, Germany). The RNA concentration was measured with a DS-11 nanospectrophotometer (Denovix, Wilmington, USA), and the RNA quality was checked with an Agilent 2100 bioanalyzer (Agilent Technologies, Santa Clara, USA). The RIN values ranged from 7 to 9.4. The RNA samples were then sent to the PGTB facility (Bordeaux, France) for library preparation and sequencing. Libraries were prepared from 500 ng of total RNA following the QIAseq Stranded mRNA Library Kit (Qiagen, Hilden, Germany) protocol and using 12 cycles for amplification. The final libraries were checked on a TapeStation 4200 (Agilent Technologies) using an HS D1000 kit and quantified by fluorescence with a Qubit 1X dsDNA High Sensitivity (Thermo Fisher Scientific, Waltham, USA) on a BioTek Synergy H1 (Agilent Technologies) before equimolar pooling. Sequencing was performed on a NextSeq 2000 (Illumina, San Diego, USA) using a P3 2x150 bp kit. Dragen RNA v3.8.4 software (Illumina) was used for mapping and annotation against the Sus scrofa reference genome GCF_000003025.6.

2.3. Behavioral observations

On G98, all sows were individually marked with a number sprayed on their back, and video footage was continuously recorded from G99 to G105. All the behavioral observations were conducted on the video footage by a single observer using The Observer XT 14.1 software (Noldus Information Technology B.V., The Netherlands).

On G99, G101, and G103, sows’ behaviors and postures were scored using 10-minute instantaneous scan sampling (35). Scan sampling was performed during one morning (10:00 h − 13:00 h) and one afternoon (13:00 h − 16:00 h) session of three hours per day, resulting in 108 samples per sow. During each scan, the posture (standing, lying, sitting) and activity of each sow were scored. Seven categories of activities were identified: inactivity, pen exploration, locomotion, social interactions (aggression, nosing interactions, and oral manipulation of pen mates), self-oriented stereotypies (vacuum chewing, tongue rolling, and head waving), chewing the metal chains, and others (eating, drinking and maintenance behaviors). Postures and behavioral activities recorded over the six observation sessions were expressed as proportions of total scans.

From G99 to G103, social interactions were scored using continuous all-occurrence behavioral sampling (35) during one session of three hours (10:00 h -13:00 h) per day, resulting in 15 hours of observation per sow. Four categories of social interactions were identified: aggression (bites, threats, chases, displacements, and head knocks), nosing snout, body exploration (nosing head, nosing body, and allogrooming), and other social interactions (mounts, nudges, oral manipulations, and head-over-body). Social interactions were averaged over the five observation days and were expressed as frequencies (occurrences per hour).

For each agonistic interaction, we identified the initiator, the receiver, the outcome (tie or clear winner/loser), and the winner. The sow was considered the interaction winner if it induced a behavior of avoidance or flight from the other sow. Agonistic behaviors were used to determine dominance status by calculating the individual Elo score using the EloRating R package, as described in Lanthony et al. (34). Briefly, dominance matrixes were built based on the outcome (winner/loser) of the agonistic interactions with a clear outcome (interactions with a tie outcome were not used). The Elo-score was calculated iteratively from a unique start score (default = 1000) and was modified according to the outcomes of the chronological sequence of the agonistic interactions over five days.

2.4. Statistical analyses

2.4.1. Categorization of the animals

To evaluate the influence of dominance status on the investigated variables, sows were ranked based on their Elo score and classified as dominant (DOM, n = 24), intermediate (INT, n = 21), or subordinate (SUB, n = 15), ensuring an equal distribution, with approximately one-third of the group members in each dominance category. We also categorized sows based on their current gestation number: nulliparous sows were excluded from the investigation, the low-parity (LP, n = 29) group included sows in their second or third gestations (554 ± 73 days of age upon entering the maternity room), and the high-parity (HP, n = 31) group included sows involved in their fourth or subsequent gestations (867 ± 163 days of age upon entering the maternity room).

Notably, 83% of the DOM sows belonged to the HP group (20 of 24 sows), while 80% of SUB sows were in the LP group (12 of 15 sows). Among the INT sows, 62% were LP, and 38% were HP. Consequently, we investigated the influence of dominance status and parity on the transcriptome of the animals with two different statistical models and compared the results to discern the genuine dominance status effects from those potentially associated with parity/aging.

2.4.2. Statistical analysis of cortisol concentrations, blood cell numbers, and behavioral variables

All the statistical analyses were performed using R version v. 4.1.2 (36). Behavioral analyses were carried out for all the multiparous sows (n = 60), and the cortisol concentration and blood cell count were analyzed for the 28 multiparous sows used for biological sampling.

Linear mixed-effect models were employed for statistical analyses using the lmer function of the lme4 package. The effects reported in the results section for EE and dominance status were derived from the statistical model containing the environment (E vs. C), dominance status (DOM, INT, and SUB), their two-way interactions as fixed effects, and the replicate as a random effect. For salivary cortisol and white blood cell counts, the models additionally included the sampling day and sow as random effects. The normality of the distribution and the equality of residual variance were checked visually, and adequate transformations were performed when necessary (logarithmic or square root transformations and arcsine square root for percentages). Factor effects were considered significant if the p value was < 0.05. After identifying significant factors using ANOVA, paired comparisons were carried out by post hoc Tukey tests using the emmeans package.

2.4.3. Analysis of differential gene expression

Gene count tables were imported into R, and gene symbol annotation was performed using the ‘sscrofa_gene_ensembl' database extracted via the R package “biomaRt”. Hemoglobin A and B expression represented more than 5% of the total reads in four samples collected on G98 and one on L12, indicating red blood cell contamination in the PBMC preparation. To avoid possible confounding effects, these samples were excluded from downstream analysis. Normalization and differential gene expression analysis were conducted using the DESeq2 package following the standard procedure (37). Genes with low expression (i.e., those with fewer than ten counts and absent in at least half of the samples) were excluded from the dataset. An outlier sample on L12 was excluded from the dataset, resulting in a final count of 24 animals on G98 and 26 on L12.

The data from G98 and L12 were analyzed separately. Due to the configuration of the DESeq2 package, the matrix models consisted only of fixed variables. Two statistical models were constructed by time point. Model 1 included the environment (E and C), dominance status (DOM, INT, and SUB), and experimental replicate (R1 and R2) effects. Model 2 included the environment (E and C), parity (HP and LP), and experimental replicate (R1 and R2) effects. To obtain the desired comparisons (E vs. C or DOM vs. SUB from Model 1 or HP vs. LP from Model 2), the contrast argument of the results function was employed. Genes considered significantly differentially expressed (DEGs) presented a fold change (FC) greater than 1.2 or less than 0.83 (approximately 20% of differences between groups) and a BH-adjusted p value < 0.1 (38). The DEG lists resulting from the DOM vs. SUB and HP vs. LP contrasts were subjected to enrichment analysis with the DAVID bioinformatic database v2022q2 (39), using the Biological Processes (BP) Gene Ontology as the data source and the Homo sapiens background. Pathways with at least 3 genes and p values < 0.05 were considered enriched.

A complete description of the data and raw results (blood cell count, RNA-Seq count tables, and results of the analyses with R and DAVID software) are presented in Mescouto et al. (preprint; (40)), and the datasets are available on Data INRAE’s institutional collection website (https://doi.org/10.57745/NHE0MP).

3.1. Environmental enrichment improved sows’ affective state

Upon arrival at the farrowing unit, no significant differences in body weight were detected between the E sows (265 ± 8.8 kg, 711 ± 212 days, n = 29) and the C sows (279 ± 6.8 kg, 720 ± 196 days, n = 31; P > 0.05). E sows exhibited lower salivary cortisol concentrations than those in the C environment (P = 0.006; Fig. 2A). The sampling day did not influence salivary cortisol concentrations (P > 0.05). The hair cortisol concentration was lower in the E sows than in their C counterparts (P = 0.008; 7.95 ± 2.12 ng/g in the E group and 21.78 ± 14.5 ng/g in the C group). The blood numbers and percentages of lymphocytes, monocytes, and neutrophils were unaltered by EE on G98 and L12 (P > 0.05 for all). Significant differences were observed between sampling days for nearly all blood cell types (P < 0.05), excluding the lymphocyte number (P > 0.05).

Regarding social behaviors, compared with C sows, E sows tended to exhibit a reduced frequency of agonistic behaviors (P = 0.06). Surprisingly, they also exhibited fewer snout-to-snout contacts (P = 0.01; Fig. 2B). No significant differences between housing environments were observed for body exploration or other social behaviors (P > 0.05). Regarding the sows’ activity, the E sows spent more time exploring the pen (P < 0.001) and less time chewing the chain (P < 0.001). They also spent less time engaged in stereotypic behaviors (P = 0.04) than did the C sows (Fig. 2C). No significant differences were detected between environments for time spent inactive, engaged in locomotor or social activities, or engaging in other behaviors (eating, drinking, and maintenance; P > 0.05; data not shown).

3.2. Dominance status also influenced variables linked to affective states

DOM sows were heavier and older (304 ± 8.3 kg, 854 ± 201 days, n = 24) than INT (275 ± 8.1 kg, 664 ± 150 days, n = 21; P = 0.001) and SUB (237 ± 11.7 kg, 565 ± 113 days, n = 15; P < 0.001) sows at the maternity room entrance. There was an interaction effect between dominance status and sampling day on salivary cortisol (Fig. 3A; P = 0.002). On G35, DOM sows presented higher cortisol concentrations than INT sows (P = 0.006), and this difference disappeared on G98 (P > 0.05). For hair cortisol, no differences between the dominance groups were detected (P > 0.05). The numbers and percentages of lymphocytes, monocytes, and neutrophils in the sows' blood were unaltered by dominance status (P > 0.05 for all).

Dominance significantly influenced the body exploration of congeners (P = 0.01), nosing-snout (P = 0.02) and other social behaviors (P = 0.02). DOM sows (n = 24) expressed fewer of these behaviors than did SUB sows (n = 15; P = 0.01, 0.04 and 0.02; Fig. 3B). Dominance did not have a significant impact on the frequency of agonistic behaviors (P > 0.05). With respect to the time budget, dominance status affected locomotion (P = 0.02), the expression of other behaviors (eating, drinking and maintenance behaviors; P = 0.04), and social activities (P = 0.06). DOM and INT sows presented a lower frequency of locomotion than did SUB sows (P = 0.08 and P = 0.02, respectively). Additionally, INT sows tended to perform fewer social activities than did SUB sows (P = 0.07; Fig. 3C). No differences were detected between the dominance groups for pen exploration, chewing the chain, stereotypies, or inactivity (P > 0.05; data not shown).

3.3. Environmental enrichment had a limited influence on the PBMC transcriptome

On G98, among the 12,260 genes tested (E, n = 10 vs. C, n = 14), 31 were differentially expressed. Ten genes were downregulated (FC within the range [0.37:0.80]), while 21 were upregulated (FC within the range [1.21:8.2]; Fig. 4). Based on UniProtKB keyword annotation, two upregulated genes (LDHB and ATP5ME) and one downregulated gene (PXMP4) in E sows were implicated in energy metabolism. Additionally, genes associated with chromatin organization exhibited varied expression levels (C2orf15, TET2, C17orf49 and H2AZ1). Several upregulated DEGs were linked to protein synthesis, processing, and transport (SELENOS, RPLP1, PFDN6, and SEC61B), rRNA and mRNA processing (EXOSC3 and BCAS2), transcriptional regulation (AHRR, HINT1, and HMGN5), mitosis (BORA) and DNA damage repair (PDRG1). Furthermore, some genes related to innate immunity (TLR6 and CD55) were downregulated.

On L12, among the 12,268 tested genes (E, n = 14 vs. C, n = 12), only four were differentially expressed: three downregulated (OTUB2, PIK3R6, RNF39; FC in [0.5:0.74]) and one upregulated (ENSSSCG00000033790; FC = 9.21). OTUB2 is associated with protein modification, PIK3R6 is associated with the PI3K-AKT signaling pathway, and RNF39 is part of major histocompatibility complex class I. The expression of the uncharacterized gene (ENSSSCG00000033790) was upregulated on both G98 and L12.

3.4. Dominance status and parity rank strongly influenced the PBMC transcriptome

Among the sows sampled for transcriptome analysis, most dominant sows were from the high-parity group (8 of 9 sows), and most subordinates were from the low-parity group (7 of 8). However, the reverse was not true: among the high-parity animals, there were animals in all 3 social classes (8 DOM, 4 INT, 1 SUB), and this was also true for the low-parity sows (1 DOM, 7 INT, 7 SUB). Given the considerable overlap, which makes it impossible to include both factors within the same statistical model, we adopted separate statistical models (model 1 for dominance, model 2 for parity). This comparison enabled us to dissociate, among the DEGs and functions obtained from Model 1, those strictly linked to dominance from those more related to parity or age.

Dominance status influenced 449 DEGs in G98 (DOM, n = 6 vs. SUB, n = 8) and 25 DEGs in L12 (DOM, n = 10 vs. SUB, n = 7). Parity influenced 542 DEGs in G98 (HP, n = 11 vs. LP, n = 13) and 91 DEGs in L12 (HP, n = 13 vs. LP, n = 13; Table 1). The complete gene lists can be found in the preprint of Mescouto Lopes et al. (40). The dominance status and parity DEG lists on G98 had 180 genes in common (approximately 40% of the dominance status DEGs), while 17 genes were shared on L12 (approximately 68% of the dominance status DEGs). Additionally, four DEGs for dominance were consistent across both dates and independent of parity influence (up: CDH23, ENSSSCG00000033104; down: CR1, C4BPB, CEACAM1).

Table 1

Numbers of differentially expressed genes (DEGs) when comparing dominant vs. subordinate and high- vs. low-parity sows on gestation day 98 (G98) and lactation day 12 (L12).
	G98		L12		Shared DEGs
	Downregulated	Upregulated	Downregulated	Upregulated	G98 and L12
Dominance status	233 FC [0.1:0.83]	216 FC [1.2:3.21]	18 FC [0.29:0.73]	7 FC [1.34:2.58]	18
Parity	313 FC [0.2:0.82]	229 FC [1.2:3.6]	63 FC [0.32:0.82]	28 FC [1.22:2.99]	69
Shared DEGs	101	79	15	2

3.5. Dominance status and parity rank influenced different immune-related functions

Of the 449 DEGs identified in the dominance list, 244 were implicated in 106 enriched pathways. Among the 542 DEGs identified in the parity list, 217 were associated with 63 enriched pathways. We found 21 pathways affected both by dominance status and parity (Table 2), 85 enriched pathways exclusive to dominance status (Table 3), and 42 exclusive to parity (not shown). Pathways influenced by both dominance and parity included: adaptive immunity, innate immunity, cytokine response, cellular process, signaling, developmental process, and protein modification (Table 2). For both factors, DEGs associated with T-cell differentiation and activation, macrophage differentiation and cellular responses to IL-4 and IL-5 were affected, which demonstrated that these functions might be altered by the parity/age of the animals. Pathways critical for correctly addressing immune responses, such as cell‒cell adhesion, cell migration, and lamellipodium assembly, were also similarly impacted.

Table 2

Pathways affected in the same way by sow dominance and parity on gestation day 98.
Pathway¹	Dominance status		Parity
	Downregulated	Upregulated	Downregulated	Upregulated
Immune system (general)
Immune response	CCR6, CCR7, CCR9, CD22, CD79B, CRHR, CTSG, CXCR5, ENPP	CCL14, CD1D, CD4, CD8A, CTSS, FCGR2B, FCGR3A, IL1RAP, IL1RL1, TENM1, XBP1	BPI, CCR7/9, CRHR1, CTSG, ENPP1, GPR183, IL18R1, PAMR1, TNFSF11, ZAP70	BMPR1A, CCL14/23, CCR8, CD1D, CD4/86/96/244, CD8A, CXCR3, FCGR3A, IL1RAP, IL1RL1, MR1, MS4A2, TNFRSF4
Inflammatory response	ALOX15, APOL3, CCR7, CD40, CHST1, CHST2, GATA3, NFKB2, TBXA2R	AIF1, CAMK4, CCL14, F2RL1, FCGR2B, IL1RAP, IL1RL1, IL1RL	APOL3, CHST1/2, CCR7, GATA3, GGT5, IL18R1, PLA2G4B	CCL14/23, CD96, CXCR3, F2RL1, IL1RAP, IL1RL1/2, MS4A2, PTGS1, TAC4, TNFRSF4, TSPAN2, XCL1
T-cell differentiation	GATA3, SOX4, ZAP70	CD4, LIPA, RUNX2	CHD7, GATA3, SOX4, ZAP70	CD4, IL7R, RUNX2
T-cell activation	TREML2, CD48, LY9	CD4, CD8A	CD48, SLAMF7, TREML2, ZAP70	CD4, CD8A, CD86, DPP4
Macrophage differentiation	GATA3, ZBTB46	CD4, IL1RL1	GATA3, ZBTB46	CD4, IL1RL1
Cytokine response
Cellular response to IL-4	CORO1A, GATA3	HSPA5, XBP1	GATA3, LEF1	MRC1, XCL1
Reg. of IL-5 production	GATA3	IL1RAP, IL1RL1	CRLF2, GATA3	IL1RAP, IL1RL1
Cellular processes
Macromolecular complex assembly	CD40, CD247, RIC3, TFAP4	GCH1, HFE, IL1RAP, PICALM	CD247, DLG4, HES1, NHERF2, TEAD2, RIC3	CAPZA2, DMD, HFE, IL1RAP, SKAP2
Lamellipodium assembly	ABLIM3, AJUBA, VAV2	CCDC88A, FGD4, ITGB1	ABLIM3, ARHGEF4	CCDC88A, FGD4, ITGB1
Cellular response to BMP stimulus	ACVR2A, ADAMTS7, GATA3	BMPR2, RUNX2	ACVR2A, GATA3	BMPR1A, RUNX2
Cell migration	BRAT1, CASS4, CEACAM1, CORO1A, FMNL3, FSCN2, GPC1, JUP, L1CAM, PIK3C2B, PRAG1, VAV2	ADAM9, CCDC88A, DOCK1, ITGB1, MEGF9, MET, PAK1, SDC2	CASS4, FMNL3, FSCN2, HES1, JUP, L1CAM, PIK3C2B, RHOV	CD63, CCDC88A, ITGB1, MEGF9, PAK1
Cell‒cell adhesion	ACTN2, CEACAM1, CASS4, JUP, L1CAM, LY9, MCAM, MPZL3, SIGLEC10, THY1	ADAM9, ADGRE1, CD4, CD22, DST, EMB, ITGA4, ITGB1, NRP2, PDZD2, VCAN	CDH24, DLG4, DSP, ITGA9, JUP	ADGRV1, ANXA2, CD58, S100A11, TJP2, TMEM47
Signaling
Pos. reg. of PKB (Akt1) signaling	CASS4, CCR7, ERBB3, GATA3, TNFSF11, TSPYL5	F2RL1, FLT3, HIP1, ITGB1, MET, MFHAS1, PDGFB, TGFBR1, XBP1	CASS4, CCR7, ERBB3, GATA3, PIK3AP1, TNFSF11	EGF, FLT3, F2RL1, ITGB1, PDGFB
Cell surface receptor signaling pathway	CD247, CD27, CRHR1, EPHA1, FCRL1, FCRL3, FCRL4, TNFRSF13B	ADGRE1, ADGRV1, CD4, CD8A, FCGR2B, FCGR3A, IL13RA1, MET, MS4A10, TSPAN12	ADGRL1, CD247, CD27, CRHR1, FCRL4, TNFRSF25, TNFSF11	ADGRE1, ADGRV1, CD4, CD86, CD8A, CLEC2D, CXCR3, FCGR3A, IL13RA1, IL7R, MS4A2, SCTR
Calcium-mediated signaling	CCR6, CCR7, CCR9, CXCR5, TNFSF11, TRPM8, ZAP70	CD4, FCGR3A, MCTP1, RCAN2	CCR7, CCR9, PLA2G4B, TNFSF11, TRPM8, ZAP70	CALM2, CCR8, CD4, CXCR3, FCGR3A, MCTP1, RCAN2
¹Only four pathways, related to protein metabolism, ion homeostasis and developmental process, are not presented in this table.

For pathways specific to dominance, a significant number of downregulated DEGs were implicated in the adaptive humoral immune response and B-cell activation, suggesting a global reduction in those functions in DOM sows (Table 3). Other pathways associated with B-cell differentiation, proliferation and immunoglobulin production were affected. We found some upregulated genes associated with endoplasmic reticulum functions, which might indicate increased activity of this organelle. The analysis further revealed pathways linked to the inflammatory response, with the proinflammatory cytokine TNF-α and IL-1 pathways being associated with a greater number of upregulated genes. Notably, signaling pathways associated with inflammation, such as I-kappaB kinase/NF-kappaB signaling, mitogen-activated protein kinase cascade, and the c-Jun N-terminal kinases, were also exclusively affected. Finally, other signaling pathways (PKC and PI3K-AKT) and functions related to protein metabolism (protein processing, peptidyl-serine phosphorylation, regulation of protein stability, and ubiquitination) were also altered by dominance status.

Table 3

Presentation of a subset of pathways affected on gestation day 98 by sows’ dominance exclusively.
Pathway	Downregulated DEGs	Upregulated DEGs	p value
Adaptive immunity
Humoral immune response	CCR6, GATA3, GPR183, PAX5, POU2AF1	-	0.04
B-cell activation	CD22, CD40, CD79A, CXCR5, FCRL1, ZAP70	-	< 0.001
Neg. reg. of B-cell receptor signaling pathway	FCRL3, CD22	FCGR2B	0.01
Neg. reg. of B-cell proliferation	BLK, MNDA, TNFRSF13B	FCGR2B	0.006
B-cell differentiation	CD79A/B, LYL1, PAX5	ITGA4, ITGB1, FLT3	0.01
Neg. reg. of immunoglobulin production	CD22, CR1, FCRL3	FCGR2B	< 0.001
IgG-mediated phagocytosis	PLA2G6, VAV2	FCGR2B, PAK1	0.008
T-cell mediated immunity	CR1	CD8A, JAG1	0.04
Positive regulation of T-cell differentiation	CD27, GATA3, ZAP70	IL1RL2, XBP1	0.006
Innate immunity
Dendritic cell chemotaxis	CCR6, CCR7, GPR183	-	0.04
Cytokine response
Cellular response to TNF	ADAMTS7, CD40, GATA3	CD58, CCL14, GBP1, NFE2L2, NR1D1, RORA	0.007
Cellular response to IL-1	ADAMTS7, CD40	CCL14, GBP1, NR1D1, RORA	0.02
Endoplasmic reticulum
ER unfolded protein response	DERL3	ATF6, CREB3L2, HSPA5, NFE2L2, XBP1	0.003
ERAD pathway	BRSK2	ATF6, SEL1L, XBP1	0.007
ER stress-induced upregulation of transcription	-	ATF6, HSPA5, XBP1	0.02
Signaling
Reg. small GTPase mediated signal transduction	ARHGEF9, PLEKHG4B, SWAP70, VAV2	AMOT, ARHGAP32, FGD4, PLEKHG3, RALGAPA2	0.003
Pos. reg. of SMAD protein signal transduction	ACVR2A, BMP8A	BMPR2, HFE, TGFBR1, TGFBR2	0.003
Pos. reg. of PI3K activity	CCR7, CD19, VAV2	FLT3, PDGFB	0.003
Pos. reg. of JNK cascade	CCR7, CD27, SH3RF3, TNFSF11	CRACR2A, F2RL1, FCGR2B, MFHAS1	0.003
Pos. reg. of MAP kinase activity	AJUBA, CD40, TNFSF11	ADAM9, FLT3, PDGFB, TENM1	0.004
Pos. reg. of I-kappaB kinase/NF-kappaB	AJUBA, APOL3, CCR7, CD40, TNFSF11	CD4, F2RL1, NDFIP2, S100A4, TAB3	0.02

Due to the limited number of DEGs found in L12 for dominance and parity, we did not submit the genes to pathway analysis. The 17 genes related to both factors included genes related to cytokines (IL9R, TNFSF11), T-cell receptor (TRDC), neutrophil granules (CTSG), transcription factors (GATA3), transport (SLC4A4), signaling (RRAGD, PDZD2), cell cycle control (LZTS1), adhesion (CASS4, L1CAM), proliferation and differentiation (BLK, IGF2BP3), erythrocytes (RHEX), coiled-coil domain-containing (CCDC158) and uncharacterized (ENSSSCG00000023790) genes. By investigating the functions of the eight genes exclusive to dominance, we identified one gene associated with a macromolecular complex that binds to both microtubules and cytoplasmic dynein (ACTR1B), one associated with phospholipid transport (PITPNM3), one associated with an immune cell receptor (CD8A), two associated with cell-adhesion genes (CEACAM1 and CDH23), two associated with a complement family gene (C4BPB and CR1) and one associated with the nonannotated gene ENSSSCG00000033104. These last five genes were consistently affected by dominance across both dates (G98 and L12).

4.1. Environmental enrichment benefited sows’ affective states

The present study investigated the effects of environmental enrichment and dominance status on the peripheral blood mononuclear cell transcriptome in gestating and lactating sows. Our findings highlight the impact of environmental enrichment on sows’ welfare and are in accordance with previous research (41–43). The lower cortisol concentrations in saliva and hair, coupled with decreased stereotypic behaviors among the enriched group, were likely a result of reduced frustration and boredom attributed to the provision of a foraging substrate (44). Moreover, sows are rationed on feed to prevent excessive weight gain during gestation, and the ingestion of straw potentially helps sows alleviate hunger (43). Consistent with other studies in pigs, environmental enrichment (straw) stimulated the exploratory behavior of the pen while reducing the usage of chains, which are suboptimal enrichment materials (22, 45). Similarly, the decreased social exploration (nosing-snout) could be because the sows could largely satisfy their exploration needs on the straw and therefore turn away from exploring conspecifics. Additionally, sows in the enriched environment experienced fewer negative social interactions, possibly attributed to increased space allowance, thereby decreasing aggressive encounters and facilitating avoidance behaviors (22, 46). The farm where the study took place did not allow for the use of tools to study mental states according to the rules of the art (cognitive bias tests, for example). However, we assume that the positive impact of environmental enrichment on cortisol, stereotypies and exploratory behavior reveals an improved affective state.

4.2. Environmental enrichment modulated a few genes associated with anxiety and inflammation

Although environmental enrichment significantly impacted sows’ cortisol and behavior, it had a modest effect on the PBMC transcriptome in G98, influencing only 31 DEGs. This seems to be in agreement with investigations in other species (47–49). Two genes directly involved in the immune response were downregulated in sows in the enriched environment: TLR6 and CD55. In addition to their immune roles, these genes can be linked to central nervous system functions. Toll-like receptors (TLRs), which are expressed on antigen-presenting cells, recognize pathogen-associated molecular patterns (50), and their expression is associated with major depressive disorders (51, 52). CD55 recognizes C4b and C3b complement fragments, reducing complement system activation (53). Alterations in complement proteins and activation have been reported in the blood of individuals with various psychiatric disorders in humans (54–56), learned fear responses in mice (57) and in pigs with distinct coping styles (27). Two other genes associated with neuropsychiatric disorders, HINT1 and MCTP1, were upregulated and downregulated, respectively, in the enriched group. In mice, HINT1 mutations have been linked to increased anxiety (58), and its’ expression in the brain changes under stressful conditions, such as social isolation or altered light exposure (59, 60). MCTP1 overexpression has been proposed as a candidate gene for anxiety in mice and bipolar disorder in humans (61). Evaluating the expression of these genes and their network in other contexts will be crucial in investigating their potential role in revealing positive or negative affective states.

Other genes not directly linked to immune function but associated with proinflammatory conditions were upregulated in sows in the enriched environment: PIGF (62), AHRR (63), and ZCCHC9 (64). The upregulation of LDHB, which is involved in converting pyruvate to lactate during glycolysis, and the downregulation of PXMP4, which is associated with lipid metabolism (65), also suggest a metabolic profile of the proinflammatory response (66).

4.3. Dominance status influenced genes related to inflammation and the fate of B cells

Dominant sows displayed greater cortisol levels on G35 than other sows, which has already been observed by others, as has the disappearance of these effects later in gestation (67). The impact of dominance on cortisol is highly dependent on the social context. For example, in primates, social instability of the group and social history, rather than dominance itself, could be responsible for greater perceived stress and higher cortisol concentrations (68). Higher cortisol levels in dominant sows might be related to the cost of maintaining their social position. In addition to cortisol, the dominant sows were behaviorally less active (less social exploration, less nose‒nose interaction, less locomotory behavior), which might reflect the fact that they were also the oldest. Both cortisol and behavioral differences between dominants and subordinate sows indicated that dominance has a wide influence on the way an individual behaves and perceives its environment.

We also identified a strong global signature of dominance rank in the gene expression data. In particular, the dominant sows displayed increased expression of genes and pathways linked to inflammatory cytokines (IL-1 and TNF-α) and inflammatory signaling pathways (JNK and MAP kinases), suggesting a greater stress level in the dominant sows, which is also in agreement with their greater cortisol concentration. The effect of dominance on blood mononuclear cell transcriptomic profiles has also been reported in primates, but contrary to our findings, proinflammatory gene expression has been associated with lower dominance status (69, 70). The same has been reported in mice (71) and in humans (13, 72). The discrepancies between species might reflect the different implications of being dominant or subordinate depending on social groups and species.

Our findings showed that other enriched pathways were exclusively associated with dominance status. Indeed, many DEGs related to B lymphocyte activity were downregulated in the dominant sows. It is possible that some variations in the expression of genes linked to the B-cell lineage were simply due to lower B-cell proportions in the PBMCs of dominant sows than subordinate sows. We did not assess lymphocyte subset proportions in this study, and deconvolution tools for porcine blood cells are not currently available. However, the global downregulation of some genes specific to B cells, such as Fc receptor-like molecules (FCRL1, FCRL3 and FCRL4), suggests a potential decrease in the number of circulating B cells. Prior studies have indicated that dominance status influences blood B-cell numbers in pregnant sows (67) and male mice (71) but in opposing directions (e.g., higher cell counts in middle-ranking or dominant animals). In contrast to B lymphocyte activation and proliferation functions, the process of B-cell differentiation into plasma cells seemed to be enhanced in dominant sows. The distinct transcriptomes of B cells and plasma cells are regulated by opposing sets of transcription factors: those that maintain the B-cell program, including PAX5, BACH2, and BCL6, and those that support plasma cell differentiation, such as IRF4, BLIMP1, and XBP1 (73). In the dominant sows, PAX5 was downregulated, while PRDM1 (or BLIMP1) and XBP1 were upregulated. Furthermore, highly secretory cell types, such as antibody-secreting cells, exhibit increased endoplasmic reticulum (ER) stress and activation of the unfolded protein response (UPR) pathway. Accordingly, we identified enriched ER-related pathways exclusively in the dominance comparison. The effect of dominance on immunoglobulin production in sows has already been reported several times in the literature, although the direction of this effect varies from one study to another (74, 75). Nonetheless, a shift in B lymphocyte functions was not observed when comparing low- to high-parity sows, suggesting an effect specific to dominance. Interestingly, both the inflammatory response and immunoglobulin production are functions that are affected in the CTRA in humans (16).

4.4. Some effects apparently associated with dominance were more likely due to sows’ parity

For both factors, DEGs associated with T-cell differentiation and activation, macrophage differentiation and cellular responses to IL-4 and IL-5 were affected, which demonstrated that these functions might be altered by the parity or age of the animals. Indeed, the dominant sows in this study were also the oldest and heaviest and had higher parity ranks. These characteristics have already been reported before (76). However, it is important to note that all females involved in the study were relatively young: low-parity sows were very young females concluding their growth phase, while the oldest high-parity sows were in their 7th gestation and were approximately three years old (considering that a pigs’ lifespan can reach 15 years). Conversely, the transcriptomic responses noted in high-parity sows seemed more connected to the accumulation of pregnancies rather than aging. Indeed, during pregnancy and lactation, the concentrations of hormones such as estrogen, progesterone, prolactin and leptin vary sharply and influence immune cells (77). In addition to short-term effects, successive gestations have irreversible effects on female physiology. In women, high multiparity has been linked to adverse health effects (78, 79) and may lead to accelerated epigenetic aging (80). Hormonal variations due to pregnancy have also been shown to interfere with cytokine production patterns during aging, resulting in increased IL-4, IL-6, and IFN-γ production in mice (81). This parity-related effect could explain our observations in high-parity sows, where functions such as “cellular response to IL-4” and “positive regulation of IFN-γ production” were affected.

4.5. The imprint of social and environmental factors fades during lactation

The effects of environmental enrichment and dominance status were relatively short-term. Three weeks after the transfer to the farrowing unit, the number of genes affected by both factors decreased from 31 and 449 in G98 to four and 25 in L12, respectively. In the lactation rooms, both treatment groups were placed in individual crates with their piglets. Consequently, their previous dominance status was probably no longer relevant, and the effects of dominance on gene expression also disappeared. The disappearance of the dominance effects aligns with observations in rhesus macaques, which indicated that the gene expression profile associated with dominance was not fixed and could change if the dominance rank changed (70). Moreover, farrowing crates restrict sows’ movement and natural behavior, potentially contributing to the disappearance of environmental enrichment effects. However, a similar reduction in parity-related DEGs (from 542 on G98 to 91 DEGs on L12) suggested that these fading effects might also be due to the substantial metabolic and endocrine reprogramming occurring between pregnancy and lactation. Although not discussed in this study, this transition resulted in a significant transcriptional shift, as observed in cows (82).

In conclusion, this study showed that EE, despite having positive effects on the affective state of sows, had a limited impact on their blood transcriptome. On the other hand, we were able to demonstrate the major effects of the dominant status of sows within their social group. Some effects, notably on T-cell responses, were likely tied to parity rank, while others, such as the impact on B lymphocyte differentiation and some inflammatory clues, seemed specific to dominance. This study is a reminder that, for social animals kept in captivity, social relationships remain extremely powerful factors influencing brain and cognitive processes and, by ricochet, immune function.

Ethics approval: The experiment was carried out at the experimental farm of the Chambre Régionale d'Agriculture de Bretagne (CRAB) at Crécom (Saint - Nicolas du Pélem, France) and complied with the EU Directive 2010/63/EU for animal experiments. This study was approved by the French Regional Ethics Committee in Animal Experiments no. 007 and by the French Ministry of Higher Education and Research (APAFIS#27038-2020090315423669 v2).

Consent for publication: Not applicable

Availability of data and materials: Sequencing data is available in the NCBI Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo) repository under accession number GSE234561. The blood assays, RNA-seq count tables, and results of the analyses with R and DAVID software are available in Data in Brief (preprint; doi: http://dx.doi.org/10.2139/ssrn.4737724). The corresponding tables have been deposited at Recherche Data Gouv repository (https://entrepot.recherche.data.gouv.fr/dataverse/Thesis_Mescouto_Lopes) under accession number https://doi.org/10.57745/NHE0MP.

Competing interests: The authors declare that they have no competing interests.

Funding: This work was supported by INRAE (SANBA Health and Welfare of Farmed Livestock program) and the Brittany Region.

Author contributions: M Mescouto Lopes: conceived and designed the analysis, collected the data, contributed data or analysis tools, performed the analysis, and wrote the paper. C Clouard: conceived and designed the analysis, collected the data, contributed data or analysis tools, and performed the analysis. A Vincent: collected the data, contributed data, or analysis tools. F Thomas: collected the data. F Hérault, I Louveau and R Resmond: contributed data or analysis tools. H Jammes: conceived and designed the analysis. E Merlot: conceived and designed the analysis, collected the data, performed the analysis, wrote the paper, and acquired funding. All the authors have read and approved the final manuscript.

Acknowledgments: The authors are grateful to C Gerard and N Villain, engineers of Brittany’s Regional Agricultural Chamber (CRAB), and C Connan and P Lirzin, staff of the experimental farm of CRAB, for their help in following up the experiment, sampling, and data collection. We also thank C Trombani, veterinarian (Breizh Pig, France), for his help during saliva and blood samplings. We acknowledge as well R Comte, C Guerin, L Le Normand, F Mayeur-Nickel, and P Touanel for their technical assistance and C Diot for his help in analyzing RNA-Seq data (PEGASE, INRAE, Saint-Gilles, France). We are also grateful to the sows who took part in this study.

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No competing interests reported.

Immune transcriptomic profile in adult female pigs: dominance status has more influence than environmental enrichment

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