Abstract

   Metabolic risk factors are among the most common causes of
   noncommunicable diseases, and stress critically contributes to
   metabolic risk. In particular, social isolation during pregnancy may
   represent a salient stressor that affects offspring metabolic health,
   with potentially adverse consequences for future generations. Here, we
   used proton nuclear magnetic resonance (^1H NMR) spectroscopy to
   analyze the blood plasma metabolomes of the third filial (F3)
   generation of rats born to lineages that experienced either
   transgenerational or multigenerational maternal social isolation
   stress. We show that maternal social isolation induces distinct and
   robust metabolic profiles in the blood plasma of adult F3 offspring,
   which are characterized by critical switches in energy metabolism, such
   as upregulated formate and creatine phosphate metabolisms and
   downregulated glucose metabolism. Both trans- and multigenerational
   stress altered plasma metabolomic profiles in adult offspring when
   compared to controls. Social isolation stress increasingly affected
   pathways involved in energy metabolism and protein biosynthesis,
   particularly in branched-chain amino acid synthesis, the tricarboxylic
   acid cycle (lactate, citrate), muscle performance (alanine, creatine
   phosphate), and immunoregulation (serine, threonine). Levels of
   creatine phosphate, leucine, and isoleucine were associated with
   changes in anxiety-like behaviours in open field exploration. The
   findings reveal the metabolic underpinnings of epigenetically heritable
   diseases and suggest that even remote maternal social stress may become
   a risk factor for metabolic diseases, such as diabetes, and adverse
   mental health outcomes. Metabolomic signatures of transgenerational
   stress may aid in the risk prediction and early diagnosis of
   non-communicable diseases in precision medicine approaches.

   Keywords: prenatal maternal stress (PNMS), social isolation stress,
   metabolomics, transgenerational stress, multigenerational stress, ^1H
   NMR spectroscopy, biomarkers, risk prediction, developmental origins of
   health and disease (DOHaD), diabetes

1. Introduction

   The global rates of metabolic diseases have increased sharply over the
   past few decades [[34]1]. Metabolic diseases in the mother influence
   the risk of adverse health outcomes in her children, including abnormal
   brain development, contributing to the high prevalence of impaired
   emotional and behavioural function and the risk of mental illness
   [[35]2,[36]3]. Mental illnesses, such as anxiety disorders or major
   depressive disorders, are the leading causes of disability worldwide
   [[37]4]. The prevalence of neurodevelopmental disorders and mental
   illnesses is rapidly growing, increasing in parallel to the rise in
   metabolic diseases. Currently, the causal relationship between
   metabolic state in mother, child, and comorbid mental health issues
   remains unclear [[38]5,[39]6]; however, both conditions are linked to
   maternal stress and an adverse prenatal environment [[40]7].

   Maternal stress affects offspring physiology and neurodevelopment by
   programming the fetal hypothalamic–pituitary–adrenal (HPA) axis
   [[41]7,[42]8,[43]9,[44]10]. Fetal HPA axis programming can raise the
   lifetime risk for altered glucose and insulin metabolism, adiposity
   [[45]11,[46]12], anxiety disorders, and depression [[47]13]. In
   addition, maternal anxiety and prenatal stress have been linked to low
   birth weight [[48]14,[49]15] and the development of metabolic syndromes
   such as type 2 diabetes, cardiovascular disease, and obesity later in
   life [[50]16]. Notably, a lack of social support and social isolation
   represent significant stressors for pregnant women [[51]17]. During the
   COVID-19 pandemic, perceived social isolation had the largest effects
   on the substantially elevated symptoms of anxiety (59%) and depression
   (37%) experienced by pregnant women in Canada [[52]18].

   Prenatal stress has been linked to altered DNA methylation marks
   [[53]19] and microRNA signatures [[54]20,[55]21,[56]22] in both blood
   and brain tissues. These epigenetic alterations enable the inheritance
   of metabolic and behavioural phenotypes to subsequent generations
   [[57]23,[58]24]. Thus, epigenetic markers linked to
   experience-dependent insulin resistance and diabetes [[59]25,[60]26]
   and altered affective states [[61]27,[62]28,[63]29] may propagate
   across generations independent of changes in DNA sequence and affect
   the aging phenotype [[64]30,[65]31,[66]32]. In fact, the
   transgenerational phenotype of high anxiety frequently coincides with
   altered glucose metabolism and other metabolic variations
   [[67]28,[68]29,[69]30,[70]33,[71]34,[72]35]. Furthermore, work in human
   cohorts of prenatal maternal stress caused by a natural disaster has
   shown metabolic alterations that indicate a higher risk of metabolic
   and cardiovascular disease [[73]36]. Moreover, metabolic risk factors
   are among the most common causes of noncommunicable diseases [[74]37],
   and stress critically contributes to metabolic risk [[75]38,[76]39].
   Nevertheless, it has been difficult to disentangle the link between
   stress and metabolic disease risk.

   Because upstream epigenetic regulation driven by prenatal or remote
   ancestral stress is reflected in downstream cellular metabolic
   functions, the metabolome, i.e., the sum of all metabolites in an
   organism, arguably represents a direct result of transgenerational
   programming [[77]34,[78]40,[79]41] and provides insight into the
   metabolic underpinnings of epigenetically heritable diseases. Here, we
   used high-resolution proton nuclear magnetic resonance (^1H NMR)
   spectroscopy and supervised and unsupervised machine learning
   approaches to probe robust metabolic signatures in rat blood plasma
   generated by transgenerational and multigenerational maternal stress in
   offspring from the third filial generation (F3). The models allow
   differentiation between the metabolomic impact of transgenerational
   epigenetic inheritance versus cumulative generational prenatal stress
   on adverse health outcomes. Both stress models were previously linked
   to preterm birth and metabolic disease [[80]24], anxiety-like and
   depression-like symptoms [[81]29,[82]32,[83]35], impaired
   neurodevelopmental trajectories [[84]42,[85]43], and accelerated
   biological aging [[86]30,[87]31]. This study reveals clinically
   accessible peripheral markers that may provide insight into metabolic
   pathways linked to the programming of adverse health outcomes and
   altered behaviour based on trans- and multigenerational maternal stress
   in rats.

2. Results

2.1. Trans- and Multigenerational Stress Generate Unique Metabolic Profiles

   PCA was utilized to identify patterns in a subset of metabolites that
   were deemed significant using either a Mann–Whitney U test (p < 0.05)
   or VIAVC analysis. Metabolite profiles of the transgenerational
   prenatal stress (TPS) and multigenerational prenatal stress (MPS)
   groups were clearly separated from CONT ([88]Figure 1A,B). Despite some
   overlap between the TPS and MPS profiles compared to CONT, data in
   [89]Figure 1C demonstrate significant separation between the TPS and
   MPS groups and minimal confidence interval overlap.

Figure 1.

   [90]Figure 1
   [91]Open in a new tab

   Principle component analysis (PCA) plots showing unsupervised
   separation between the groups: (A) transgenerationally stressed rats
   (TPS) vs. non-stressed controls (CONT); (B) multigenerationally
   stressed rats (MPS) vs. non-stressed controls (CONT); and (C)
   transgenerationally stressed rats (TPS) vs. multigenerationally
   stressed rats (MPS). Each PCA was carried out using only the bins that
   were determined to be significantly altered. Each point (triangle,
   cross, circle, or square) represents one individual based on the list
   of blood plasma metabolites found to be statistically significant via a
   Mann–Whitney U test and VIAVC analysis. Coloured ellipses represent 95%
   confidence intervals. x and y axes show principal components 1 and 2,
   respectively, with brackets indicating the variance explained by each
   principal component. Note that the metabolomes of both TPS and MPS
   differed substantially from CONT and from each other.

   There were 144 total bins created. Of these bins, TPS vs. CONT, MPS vs.
   CONT, and TPS vs. MPS revealed eight, eight, and thirteen significantly
   altered bins, respectively. Compared to CONT, both the TPS and MPS
   groups exhibited upregulated formate and creatine phosphate, as well as
   downregulated glucose ([92]Table 1). The TPS comparison with the CONT
   group showed that leucine, isoleucine, alanine, 2-oxoisocaproate, and
   2-hydroxyisovalerate were downregulated. Unique to the MPS group,
   3-methylxanthine, threonine, and tyramine were upregulated compared to
   CONT, whereas betaine was downregulated ([93]Table 1). Lastly, a
   comparison of TPS to MPS showed upregulation of succinate, creatine,
   and tyramine and downregulation of citrate, lactate, choline, alanine,
   and serine ([94]Table 1).

Table 1.

   p-values of blood plasma metabolites found to be significant in male
   Long-Evans rats in either a Mann–Whitney U test, the variable
   importance analysis based on random variable combination (VIAVC), or
   both. Rats were either non-stressed (CONT),
   transgenerationally-stressed (TPS), or multigenerationally-stressed
   (MPS). Up- or downregulation of the metabolites is also indicated.
   Metabolites with multiple peaks are represented as metabolite.1,
   metabolite.2… metabolite.n.
   Group Metabolite NMR Chemical Shift Range of Bin (ppm) Mann-Whitney U
   Test VIAVC VIP Score Regulation
   F3-TPS vs. F3-CONT Creatine phosphate 3.035641–3.028 1.62 × 10^−2 4.40
   × 10^−9 1.51 Up
   Formate 8.5343705–8.442 2.83 × 10^−2 2.09 × 10^−6 1.51 Up
   Glucose.1 3.715–3.704 1.09 × 10^−1 7.02 × 10^−6 0.87 Down
   Leucine.1, isoleucine.1, 2-hydroxyisovalerate.1 0.9586–0.9476 7.27 ×
   10^−2 1.12 × 10^−47 0.84 Down
   Alanine 1.498–1.4878 4.85 × 10^−2 2.35 × 10^−20 0.81 Down
   Glucose.2 3.526–3.514123 3.68 × 10^−1 1.20 × 10^−7 0.80 Down
   Leucine.2, isoleucine.2 0.9812075–0.9682 4.85 × 10^−2 1.85 × 10^−30
   0.72 Down
   2-Hydroxyisovalerate.2, 2-oxoisocaproate 0.9476–0.9133615 5.70 × 10^−1
   6.83 × 10^−22 0.40 Down
   F3-MPS vs. F3-CONT Singlet at 8.38 ppm 8.442–8.388584 1.09 × 10^−1 3.46
   × 10^−37 1.54 Up
   Formate 8.5343705–8.442 7.27 × 10^−2 3.03 × 10^−54 1.14 Up
   Creatine phosphate 3.035641–3.028 2.83 × 10^−2 2.39 × 10^−2 1.09 Up
   3-Methylxanthine 8.388584–8.0675875 3.68 × 10^−1 4.02 × 10^−31 1.02 Up
   Threonine 3.5998105–3.59 4.85 × 10^−2 1.66 × 10^−20 0.94 Up
   Tyramine.1 7.0614405–6.05 6.83 × 10^−1 1.25 × 10^−20 0.82 Up
   Glucose, betaine 3.28–3.2684065 7.27 × 10^−2 2.81 × 10^−31 0.64 Down
   Tyramine.2 7.2946365–7.0614405 9.33 × 10^−1 3.90 × 10^−27 0.35 Up
   F3-TPS vs. F3-MPS Citrate.1 2.68–2.6728 6.50 × 10^−2 5.94 × 10^−11 1.64
   Down
   Citrate.2 2.543–2.5269 8.30 × 10^−2 3.34 × 10^−12 1.40 Down
   Citrate.3 2.5269135–2.511 1.05 × 10^−1 7.66 × 10^−11 1.31 Down
   Citrate.4 2.6727785–2.62297 1.05 × 10^−1 2.25 × 10^−8 1.17 Down
   Choline 3.22–3.203 2.07 × 10^−2 1.75 × 10^−13 0.91 Down
   Lactate 4.137413–4.12681 3.79 × 10^−2 - 0.91 Down
   Succinate 2.412–2.402 1.95 × 10^−1 1.01 × 10^−10 0.89 Up
   Alanine.1 1.487783–1.476 8.30 × 10^−2 9.29 × 10^−16 0.89 Down
   Alanine.2 1.498–1.4878 2.34 × 10^−1 3.88 × 10^−10 0.83 Down
   Serine.1, creatine 3.971–3.937 2.34 × 10^−1 4.95 × 10^−8 0.77 Up
   Serine.2 3.8517095–3.849 4.99 × 10^−2 5.3 × 10^−3 0.65 Down
   Alanine.3 3.8066745–3.798575 1.61 × 10^−1 1.27 × 10^−8 0.45 Down
   Tyramine 7.2946365–7.0614405 2.34 × 10^−1 2.99 × 10^−8 0.36 Up
   [95]Open in a new tab

2.2. Trans- and Multigenerational Stress Differentially Program Amino Acid
Metabolism Pathways

   Metabolite set enrichment analysis (MSEA) of the TPS compared to the
   CONT group ([96]Figure 2A) indicated significant differences in valine,
   leucine, and isoleucine (branch chain amino acids) biosynthesis (p <
   0.01) and degradation (p < 0.01). In addition, the glucose-alanine
   cycle (p < 0.01) and alanine metabolism were altered in the MSEA.
   Pathway topology supported the effects on branched-chain amino acid
   synthesis (p < 0.01) and degradation (p < 0.01) pathways but did not
   reveal significant pathway hits in the glucose-alanine cycle or alanine
   metabolic pathways ([97]Figure 2B). The aminoacyl tRNA pathway (p <
   0.01), however, was also shown to be affected by TPS.

Figure 2.

   [98]Figure 2
   [99]Open in a new tab

   Pathway-associated metabolite set enrichment analysis (MSEA) for TPS
   vs. CONT. (A) Plot showing affected biological processes in
   transgenerationally stressed (TPS) compared to non-stressed (CONT) rats
   based on metabolites identified as significantly altered between these
   groups. The p-value for each pathway is shown using the heatmap on the
   right of the figure and the fold enrichment shows how many times
   greater than chance the process is involved. (B) Metabolomic pathway
   analysis showing all matched pathways according to p-values from
   pathway enrichment analysis and pathway impact values from pathway
   topology analysis in TPS and non-stressed CONT rats. The y-axis shows
   the negative natural log of p, such that a higher value on the y-axis
   gives a lower p-value. The x-axis gives the pathway impact, which
   correlates to the number of metabolite hits in a particular pathway.
   Only metabolic pathways with p < 0.05 are labeled. TPS had the highest
   impact on valine, leucin, and isoleucine biosynthesis.

   MSEA revealed a significant impact on glycine, serine, and threonine
   metabolism (p = 0.013) in the MPS group when compared to CONT
   ([100]Figure 3A). The pathway topology analysis reflected this
   significance, showing changes to the glycine, serine, and threonine
   metabolic pathway (p < 0.01; [101]Figure 3B). No other pathways were
   found to be altered in this group, although betaine metabolism was
   somewhat significant in the metabolite set enrichment (p = 0.07).

Figure 3.

   [102]Figure 3
   [103]Open in a new tab

   Pathway-associated metabolite set enrichment analysis (MSEA) for MPS
   vs. CONT. (A) Plot displaying affected biological processes in
   multigenerationally stressed (MPS) compared to non-stressed control
   (CONT) rats based on metabolites identified as significantly different
   between these groups. The p-value for each pathway is indicated in the
   gradient on the right side. The fold enrichment shows how many times
   greater than chance the pathway is involved. (B) Metabolomic pathway
   analysis showing all matched pathways according to p-values from
   pathway enrichment analysis and pathway impact values from pathway
   topology analysis in MPS and non-stressed CONT rats. The y-axis shows
   the negative natural log of p, such that a higher value on the y-axis
   gives a lower p-value. The x-axis indicates the pathway impact, which
   correlates to the number of metabolite hits in a particular pathway.
   Only metabolic pathways with p < 0.05 are labeled. MPS had the highest
   impact on the glycine, serine, and threonine metabolism.

   When the impacts of trans- versus multigenerational stress were
   compared ([104]Figure 4), the metabolite set enrichment analysis found
   the tricarboxylic acid (TCA) cycle (p < 0.01), methionine metabolism (p
   < 0.01), glycine, serine, threonine metabolism (p < 0.01), and alanine
   metabolism (p = 0.05) to be significantly affected. The TPS versus MPS
   topology analysis ([105]Figure 4B) showed differential activation of
   the glycine, serine, and threonine metabolic pathway (p < 0.01) to be
   most significantly affected, followed by the TCA cycle (p < 0.01), the
   alanine, aspartate, and glutamate metabolic pathway (p < 0.01), the
   cyanoamino acid pathway (p = 0.03), the methane metabolic pathway (p =
   0.05), and the aminoacyl tRNA pathway (p = 0.05).

Figure 4.

   [106]Figure 4
   [107]Open in a new tab

   Pathway-associated metabolite set enrichment analysis (MSEA) for TPS
   vs. MPS. (A) Plot showing affected biological processes in
   transgenerationally stressed (TPS) compared to multigenerationally
   stressed (MPS) rats, based on metabolites identified as significantly
   altered between these groups. The p-value for each pathway is shown
   using the heatmap on the right of the figure and the fold enrichment
   shows how many times greater than chance the process is involved. (B)
   Metabolomic pathway analysis showing all matched pathways according to
   p-values from pathway enrichment analysis and pathway impact values
   from pathway topology analysis in TPS vs. MPS rats. The y-axis shows
   the negative natural log of p, such that a higher value on the y-axis
   gives a lower p-value. The x-axis indicates the pathway impact, which
   correlates to the number of metabolite hits in a particular pathway.
   Only metabolic pathways with p < 0.05 are labeled. The largest
   difference in pathway activity between TPS and MPS was observed in the
   glycine, serine, and threonine metabolism.

2.3. Trans- and Multigenerational Stress-Induced Shifts in Energy Metabolism
Are Associated with Altered Exploratory Behaviours

   [108]Table 2 presents the Spearman’s rank correlations of creatine
   phosphate and leucine/isoleucine to open field task metrics. Open field
   task scores can be found in the [109]Supplementary Materials
   ([110]Table S1). These data revealed a significant relationship between
   vertical exploratory time in the open field task and the concentration
   of creatine phosphate in the blood plasma metabolome in both TPS (Rho =
   −0.730, p = 0.007) and MPS rats (Rho = −0.691, p = 0.013). In the MPS
   group, creatine phosphate was also significantly correlated with the
   total distance traveled (Rho = −0.712, p = 0.009), the number of
   vertical moves such as rears (Rho = −0.636, p = 0.03), and the distance
   traveled in the center (Rho = −0.635, p = 0.003) of the open field
   arena ([111]Table 2). In general, less exploratory behaviour was linked
   to higher creatine phosphate levels.

Table 2.

   Spearman’s rank correlations of blood plasma metabolite concentrations
   of creatine phosphate and leucine/isoleucine in relation to open field
   (OF) behavioural measurements. Correlation coefficients were obtained
   utilizing metabolite concentrations from comparisons of non-stressed
   (CONT) versus multigenerationally-stressed (MPS) Long-Evans rats and
   non-stressed (CONT) versus transgenerationally-stressed (TPS)
   Long-Evans rats. *—indicates a correlation with a p-value less than
   0.05; **—indicates a correlation with a p-value less than 0.01.
   Creatine Phosphate Leucine/Isoleucine
   Test TPS vs. CONT MPS vs. CONT TPS vs. CONT MPS vs. CONT
   Rho p-value Rho p-value Rho p-value Rho p-value
   Total Distance −0.712 ** 0.009 0.646 * 0.023
   Number of Vertical Moves −0.636 * 0.026
   Vertical Time −0.730 ** 0.007 −0.691 * 0.013 0.660 * 0.02
   Central Distance −0.635 * 0.026 0.674 * 0.016 0.656 * 0.02
   [112]Open in a new tab

   TPS, when compared to CONT, also displayed significant correlations
   between leucine/isoleucine and total distance (Rho = 0.646, p = 0.02),
   vertical time (Rho = 0.66, p = 0.02), and central distance (Rho =
   0.596, p = 0.02) in the open field task ([113]Table 2). MPS, when
   compared to CONT, showed significant correlations between
   leucine/isoleucine in the blood plasma and the distance traveled in the
   center of the open field task (Rho = 0.656, p = 0.02; [114]Table 2). In
   general, lower exploratory activity was linked to reduced
   leucine/isoleucine levels.

3. Discussion

   Metabolic risk factors are among the most common causes of
   noncommunicable diseases, and stress critically contributes to
   metabolic risk. This study shows that ancestral maternal social
   isolation stress induces distinct and robust metabolic profiles in
   adulthood, which are characterized by significant changes in energy
   metabolism, such as upregulated formate and creatine phosphate, and
   downregulated glucose. The plasma metabolome induced by
   transgenerational stress differs from recurrent maternal stress in
   terms of energy and amino acid metabolisms, cellular signaling, and
   immunoregulation. The findings indicate that maternal stress may lead
   to long-term metabolic adjustments and risks across generations of
   offspring, leading to altered tissue function and potentially
   contributing to an altered behavioural phenotype. The findings suggest
   that ancestral maternal stress may be a risk factor in idiopathic
   insulin resistance, diabetes, obesity, and other chronic and
   non-communicable diseases.

   Substantial metabolic changes may explain the significant consequences
   of maternal stress on neuroendocrine and cardiometabolic functions,
   brain development, and emotional and cognitive impairments observed in
   the exposed offspring [[115]10,[116]22,[117]44,[118]45]. Moreover, it
   was shown that paternal stress can result in transgenerational
   programming of a metabolic phenotype in the F4 generation of the male
   lineage [[119]34,[120]41]. Here, we also show that trans- and
   multigenerational maternal stress causes distinct metabolic profiles in
   blood plasma, providing potentially predictive signatures of adverse
   health outcomes in the offspring.

   Both TPS and MPS altered 21 metabolites known to be present in blood
   plasma. Among them, upregulation of creatine phosphate and formate
   represented a primary marker of ancestral stress. Creatine phosphate
   plays a vital role in ATP regeneration in skeletal muscle and is a
   rate-limiting pathway in muscle performance enabling brief, high-power
   activity [[121]46] or meeting higher energy demand in response to
   stress [[122]47]. On the other hand, elevated formate may trigger a
   metabolic switch from low to high adenine nucleotide levels, increasing
   the rate of glycolysis and intracellular lactate levels [[123]48]. The
   involvement of these metabolites in both TPS and MPS suggests that
   ancestral stress may program a permanently upregulated energy
   metabolism, which supports a link between programmed stress
   vulnerability and documented low birth weights, lifetime diabetes and
   obesity risk, and other metabolic abnormalities in offspring exposed to
   stress in utero [[124]10,[125]49,[126]50].

   Metabolic abnormalities at birth are also considered a risk factor for
   mental disorders such as schizophrenia [[127]51], depression [[128]52],
   and attention deficit hyperactivity disorder [[129]14,[130]53]. Here we
   show that ancestral stress alters critical energy metabolism pathways,
   such as branched-chain amino acid (BCAA) biosynthesis and the
   tricarboxylic acid cycle, which mediate neurological and behavioural
   outcomes [[131]54]. The proteinogenic BCCAs leucine, isoleucine, and
   valine are essential amino acids involved in glucose metabolism,
   neurotransmitter synthesis, and neuronal function. Thus, disruption in
   BCAA metabolism has been linked to abnormal brain development [[132]54]
   and lifetime risk of neurodegenerative disorders, such as Alzheimer’s
   disease [[133]55]. These mechanisms also implicate BCAAs in the
   regulation of immune functions, as nutritional interventions of both
   supplemented and reduced BCAA intake have been shown to affect
   metabolic and inflammatory functions [[134]56,[135]57].

   When interfering and non-significant metabolites were excluded, the
   unsupervised PCA showed clear class separation between TPS and MPS,
   indicating metabolic profiles unique to each stress lineage.
   Interestingly, citrate and lactate were found to be significantly
   downregulated in the TPS group as opposed to MPS, but neither had a
   significantly altered citrate level compared to non-stressed controls.
   Both citrate and lactate are involved in energy generation,
   particularly in the production of ATP in the tricarboxylic acid (TCA)
   cycle, and in muscular ATP regeneration. If lactate levels are low in
   TPS animals, then metabolic acidosis and muscular fatigue may occur
   more quickly [[136]58]. Similarly, low citrate levels in MPS animals
   may indicate energy deficiency [[137]59]. By contrast, a relative
   increase in lactate in MPS animals, indicative of stress
   hyperlactataemia, is also secondary to anaerobic glycolysis induced by
   tissue hypoperfusion or hypoxia [[138]60]. While here it may serve
   adaptive functions, in disease states stress hyperlactataemia is a
   reliable predictor of mortality [[139]61]. This reflects the phenotype
   of MPS animals, whose metabolic and mental health impairments
   [[140]24,[141]31] and higher morbidity [[142]30] exceed those of TPS
   animals.

   TPS rats also revealed significantly downregulated choline and serine
   levels compared to the MPS group. Choline and its metabolites are
   required for several physiological functions, such as cell signaling
   and cholinergic transmission [[143]62], and may be involved in blood
   lipid regulation and the development of cardiovascular disease and
   cognitive decline in aging [[144]63]. Serine plays a central role in
   cellular proliferation and in nervous system development and
   functioning [[145]64]. Altered levels of serine in patients with
   psychiatric disorders and neurological abnormalities underscore its
   importance in brain development and function [[146]65]. Abnormal levels
   of these metabolites may help to explain anxiety-like behaviours in
   ancestrally stressed offspring, but it remains unclear why TPS is
   linked to lower levels than MPS. It has been hypothesized that
   cumulative stress across generations is mediated by an adaptive
   response to the constant presence of a stressor [[147]66], which at
   times may facilitate partial physiological adaptation and resilience to
   recurrent stress in MPS animals [[148]43,[149]67].

   Overall, ancestral stress had the largest impact on protein
   biosynthesis, energy generation, and immunity. Thus, the formation and
   breakdown of amino acids, as well as energy metabolism, are likely
   involved in the physiological and behavioural response to both TPS and
   MPS. These results are reinforced by the correlational analysis
   demonstrating associations between particular metabolites with
   anxiety-like behaviours. Previous studies have associated reduced
   exploratory behaviour, such as vertical rearing, with higher levels of
   anxiety [[150]68]. Furthermore, trans- and multigenerationally stressed
   rats showed higher stress sensitivity and precocious onset of motor
   hyperactivity and risk assessment behaviours [[151]69]. Here,
   anxiety-like behaviours, including reduction in the amount of vertical
   rearing and total and central distance traveled in the open field task,
   were associated with higher circulating creatine phosphate levels in
   the MPS group. Notably, the association between increased creatine
   phosphate and reduced rearing activity (vertical time), which is a
   behaviour linked to increased anxiety and psychomotor inhibition
   [[152]70], was observed in both the MPS and TPS groups. However, many
   of the psychophysiological links to anxiety-like behaviours in the open
   field task are tenuous [[153]71]. Nevertheless, it remains to be
   further determined if creatine phosphate concentrations may serve as a
   marker of anxiety risk in populations directly exposed to or with a
   family history of prenatal stress.

   BCAAs such as leucine and isoleucine provided further insight into the
   metabolic underpinning of anxiety-like behaviours. More time spent
   rearing was linked to greater leucine and isoleucine concentrations in
   rats from the TPS group. Greater levels of leucine and isoleucine were
   associated with more distance travelled in the center of the open field
   in the MPS group. This change was mirrored in the TPS lineage with
   greater levels of leucine and isoleucine translating to greater total
   and central distance travelled in the open field task, linking remote
   ancestral stress to altered cellular energy metabolism and behavioural
   change. Thus, higher levels of leucine and isoleucine may be linked to
   higher stress resilience, whereas lower levels of these BCAAs may serve
   as markers of anxiety-like behaviours. Lower levels of BCAAs have also
   been linked to adverse metabolic and inflammatory functions
   [[154]56,[155]57] in addition to poorer stroke recovery, increased risk
   of renal disease, and type 2 diabetes
   [[156]72,[157]73,[158]74,[159]75]. Causal inferences of BCAA metabolism
   in brain function and behaviour require further investigation.

   The present downstream cellular metabolic changes may reflect
   differential underlying epigenetic changes that are potentially
   heritable. The TPS generation of the maternal lineage in particular
   excludes direct exposure effects and unambiguously suggests heritable
   epigenetic transgenerational mechanisms for offspring three generations
   removed from maternal stress [[160]76,[161]77] to regulate cellular
   metabolism. Epigenetics may mediate the relationship between genotype
   and internal and external environments [[162]33] and cellular metabolic
   changes may directly reflect changes in these upstream regulatory
   pathways. Furthermore, gestational stress may impact the developing
   child through the placenta or through direct or indirect effects on the
   fetal brain, potentially contributing to the differences observed
   between TPS and MPS cohorts. However, the current findings must be
   interpreted carefully, as this study only involved males and only
   included a small number of control animals.

   The present study used a novel approach to quantify outcomes of
   ancestral stress and HPA axis dysregulation by taking a metabolomic
   perspective and providing possible mechanisms for health outcomes in an
   animal model. The metabolomic biomarkers identified in this study may
   provide potential biomarkers for the risk prediction and early
   diagnosis of non-genetically heritable stress-related diseases. The
   identification of predictive and diagnostic metabolic biomarkers that
   identify adverse health trajectories linked to maternal stress is
   critical for precision medicine approaches, enabling the identification
   of the most vulnerable individuals who are at the highest risk. Because
   the orchestrated regulation of metabolic pathways is essential for
   stress regulation, development, and successful aging, its failure may
   lead to cell and tissue dysfunction and result in chronic and
   non-communicable diseases. On the other hand, metabolic profiles are
   often cell-type specific [[163]78], thus providing an opportunity for
   the discovery of causal pathogenic mechanisms and early diagnosis of a
   disease. Due to the potentially significant health impact of social
   isolation, understanding the metabolic consequences of ancestral stress
   may provide new personalized therapeutic strategies to mitigate
   stress-associated adverse health outcomes.

4. Materials and Methods

4.1. Experimental Design

4.1.1. Animal Model

   The study involved 24 adult male Long-Evans rats obtained from the F3
   generation of three lineages: yoked controls (CONT, n = 8),
   transgenerational prenatal stress (TPS; n = 8), and multigenerational
   prenatal stress (MPS; n = 8). Four samples from the control group did
   not yield enough plasma for metabolomics analysis, resulting in a
   reduced control group size (CONT, n = 4). TPS rats were the third
   filial (F3) generation of a lineage in which only the first filial (F1)
   generation was stressed prenatally; subsequent generations remained
   unstressed ([164]Figure 5). MPS rats were the F3 generation of a
   lineage in which each consecutive generation (F1, F2 and F3) was
   prenatally stressed.

Figure 5.

   [165]Figure 5
   [166]Open in a new tab

   Illustration summarizing the experimental design generating lineages of
   transgenerational prenatal stress (TPS) and multigenerational prenatal
   stress (MPS) through filial generation zero (F0) to F3.

4.1.2. Gestational Stress

   Pregnant dams were stressed by social isolation, which has been shown
   to result in mild psychosocial stress in rats [[167]79] and profound
   epigenetic and behavioural phenotypes [[168]69,[169]80]. Rats were
   housed in pairs from weaning until postnatal (P) day 90. Then, dams
   were separated and housed alone, one per cage, throughout pregnancy
   until weaning of their offspring [[170]69,[171]80]. Pairing for mating
   began at P90 and the maximum time spent in social isolation prior to
   conception was 30 days. Control rats remained housed in pairs, except
   for the period from gestational day 21 to lactational day 21 (time of
   weaning), to allow for undisturbed rearing of their offspring.

4.1.3. Breeding Colony

   Animals were bred in-house for at least five generations prior to the
   beginning of the experiment. Each generation F0-F4 was outcrossed to
   avoid inbreeding. Distinct lineages were monitored through the JAX
   Colony Management System (JCMS; Jackson Laboratory, Bar Harbour, ME,
   USA). One male offspring per litter was randomly selected for this
   metabolomics study. Bystander effects of stress were avoided by using
   designated testing and housing spaces. All housing, handling, testing,
   and tissue sampling conditions were harmonized and carefully controlled
   across generations.

   The rats were housed in polycarbonate shoebox cages on corn cob bedding
   under a 12 h light/dark cycle with lights on at 7:30 AM. The room
   temperature was maintained at 20 °C with relative humidity at 30%. All
   procedures were performed in accordance with the guidelines of the
   Canadian Council on Animal Care and approved by the University of
   Lethbridge Animal Welfare Committee.

4.2. Behavioural Testing

   Anxiety-like behaviours towards a novel environment and risk taking
   were assessed in an open field test at P90. Rats were individually
   placed into an Accuscan activity monitoring box (Accuscan Instruments
   Inc., Columbus, OH, USA; 42 × 42 × 30 cm) and recorded for 10 min.
   VersaMax™ software (Accuscan Instruments Inc., OH, USA) monitored the
   animals’ horizontal and vertical activity to be analyzed using
   VersaDat™ software (Accuscan Instruments Inc., Columbus, OH, USA;
   [[172]29]). Vertical (rearing activity) and horizontal (distance
   travelled) exploration were considered.

4.3. Sample Collection and Preparation

   At the age of 140 days (P140), 600 μL of blood was collected from the
   lateral tail vein using a 23-gauge butterfly needle coated with lithium
   heparin, while rats were anaesthetized using 4% isoflurane. All blood
   samples were collected at the same time of day, between 8:00 and 9:00
   AM in the morning. EDTA and citrate collection tubes were avoided, as
   they give additional spectral signals in ^1H NMR spectroscopy
   [[173]81]. Blood was transferred to centrifuge tubes and plasma was
   obtained by centrifugation at 1600× g for 15 min at 4 °C. Samples were
   stored at −80 °C until further processing.

   Water soluble metabolites were extracted from plasma using a methanol
   precipitation protocol [[174]82]. Methanol was added in a 2:1 ratio
   (550 μL buffered plasma + 1.10 mL methanol) in 2.0 mL Eppendorf tubes.
   These were vortexed for 5 s, incubated at −20 °C for 20 min, and
   centrifuged at 12,000× g for 30 min at 4 °C. Supernatant was decanted
   to fresh tubes and allowed to dry under a gentle stream of nitrogen gas
   until all liquid had evaporated. Dried pellets were then re-suspended
   in 600 μL of phosphate buffer calibrated to pH 7.4 and vortexed for 10
   s, or until the pellets were completely dissolved. The phosphate buffer
   was prepared as a 4:1 ratio of KH2PO4:K2HPO4 in a 4:1 H2O:D2O solution
   to obtain a final concentration of 0.5 M. The D2O contained 0.03% w/v
   trimethylsilyl propanoic acid (TSP) as a chemical shift reference for
   ^1H NMR spectroscopy. To maintain metabolite integrity, 0.03% w/v of
   sodium azide (NaN3) was also added to the buffer as an antimicrobial
   agent. Finally, samples were centrifuged again at 12,000× g for 5 min
   at 4 °C and 550 μL of re-suspended supernatant was added to 5 mm NMR
   tubes for data acquisition.

4.4. NMR Data Acquisition and Processing

   NMR spectra were collected on a 700 MHz Bruker Avance III HD
   spectrometer (Bruker, Milton, ON, Canada; [175]Figure S1). The 1-D
   NOESY gradient water suppression pulse sequence ‘noesygpr1d’ was used.
   Each sample was run for 512 scans to a total acquisition size of 256 k.
   The spectra were zero filled to 512 k, automatically phased and
   baseline corrected, and line-broadened by 0.3 Hz. The processed spectra
   were then exported to MATLAB (The MathWorks, Natick, MA, USA) for
   statistical analysis. Spectra were binned using Dynamic Adaptive
   Binning [[176]83]. Each spectrum was normalized to the total unit area
   of all spectral bins and the spectral region corresponding to water was
   removed from data normalization. The data set was then Pareto scaled to
   reduce the influence of intense peaks while emphasizing weaker ones and
   log transformed [[177]84]. All peaks were referenced to TSP (0.00δ).

4.5. Statistical Analysis

   The overall structure of scaled spectra was visualized and compared
   across control and experimental groups using principal component
   analysis (PCA). A Shapiro–Wilk test for data normality was applied and
   all data were found to be non-parametric. A Mann–Whitney U test was
   subsequently used to determine which spectral bins were significantly
   altered in comparison groups [[178]85]. Bonferroni–Holm correction was
   applied to all univariate statistical tests to correct for multiple
   comparisons. Bins with p-values less than or equal to 0.05 were deemed
   significant and considered as potential markers defining substantial
   alterations in metabolite concentration between groups.

   PCA and Mann–Whitney U tests were carried out using the web-based
   Metaboanalyst software [[179]86] and MATLAB^® (MathWorks, Natick, MA,
   USA), respectively. The variable importance analysis based on random
   variable combination (VIAVC) algorithm [[180]87] was utilized to
   determine which set of metabolite bins led to the best discrimination
   between the comparison groups (referred to as the best subset). This
   machine learning, multivariate statistical algorithm makes use of
   random data combinations and iteratively tests each combination using
   both PLS-DA and the area under the curve of the receiver operator
   characteristic curve (ROC) to determine the best subset of metabolites
   leading to class separation [[181]88]. PCA score plots ([182]Figure 1)
   were generated using only those bins identified as significant by
   Mann–Whitey U and VIAVC testing ([183]Table 1). Pathway topology
   analysis and metabolite set enrichment analysis were carried out using
   Metaboanalyst [[184]89,[185]90]. The Rattus Norvegicus pathway library
   was utilized for both analyses, the hypergeometric test was selected
   for the over-representation analysis, and relative-betweenness
   centrality was chosen for the pathway topology analysis.

   Spearman’s rank correlations were carried out using SPSS 26 for Windows
   (IBM Corporation, Armonk, NY, USA) to determine the relationship
   between normalized metabolite concentrations and behavioural data. This
   analysis concerned only metabolites determined to be significantly
   altered between groups, based on the multivariate and univariate
   statistical tests outlined above. Samples with p-values equal to or
   less than 0.05 were considered significant.

4.6. Metabolite Identification

   A spectral database of pure metabolites was generated and used to
   identify most metabolites found in the NMR spectra. The Human
   Metabolome Database [[186]91,[187]92,[188]93] was used to supplement
   and aid the creation of our spectral library, and to identify some
   substances that were not obtained for the creation of this database.
   Furthermore, tools of the Chenomx 8.2 NMR Suite (Chenomx Inc.,
   Edmonton, AB, Canada) were used for identifying and quantifying NMR
   metabolites which allowed for the spectral deconvolution of biofluid
   samples into individual components.

Acknowledgments