Allostasis and Resilience of the Human Individual Metabolic

Article pubs.acs.org/jpr

Allostasis and Resilience of the Human Individual Metabolic Phenotype Veronica Ghini,†,# Edoardo Saccenti,‡ Leonardo Tenori,§ Michael Assfalg,⊥ and Claudio Luchinat*,†,¶ †

Magnetic Resonance Center (CERM), University of Florence, Via L. Sacconi 6, 50019 Sesto Fiorentino, Italy Consorzio Interuniversitario Risonanze Magnetiche di Metallo Proteine (CIRMMP), Via L. Sacconi 6, 50019 Sesto Fiorentino, Italy ‡ Laboratory of Systems and Synthetic Biology, Wageningen University and Research Center, Dreijenplein 10, 6703 HB Wageningen, The Netherlands § FiorGen Foundation, Via L. Sacconi 6, 50019 Sesto Fiorentino, Italy ⊥ Department of Biotechnology, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy ¶ Department of Chemistry, University of Florence, Via della Lastruccia 3, 50019 Sesto Fiorentino, Italy #

ABSTRACT: The urine metabotype of 12 individuals was followed over a period of 8−10 years, which provided the longest longitudinal study of metabolic phenotypes to date. More than 2000 NMR metabolic profiles were analyzed. The majority of subjects have a stable metabotype. Subjects who were exposed to important pathophysiological stressful conditions had a significant metabotype drift. When the stress conditions ceased, the original metabotypes were regained, while an irreversible stressful condition resulted in a permanent metabotype change. These results suggest that each individual occupies a well-defined region in the broad metabolic space, within which a limited degree of allostasis is permitted. The insurgence of significant stressful conditions causes a shift of the metabotype to another distinct region. The spontaneous return to the original metabolic region when the stressful conditions are removed suggests that the original metabotype has some degree of resilience. In this picture, precision medicine should aim at reinforcing the patient’s metabolic resilience, that is, his or her ability to revert to his or her specific metabotype rather than to a generic healthy one. KEYWORDS: metabolomics, NMR spectroscopy, individual metabolic phenotype, personalized medicine, allostasis, resilience, metabotype, precision medicine, principal component analysis

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INTRODUCTION The oldest model of physiological regulation was based on the concept that the human body is constantly adapting itself toward stable and constant biochemical and pathophysiological conditions: each biochemical/physiological parameter should be clamped at a “set point” value using a negative feedback to correct errors. This model is globally referred to as homeostasis (i.e., “stability through constancy”).1 In the modern vision, the regulation of physiological processes of a healthy organism is dominated by the allostasis model, that is, the process of achieving “stability through change”.1 Allostasis is the extension of the concept of homeostasis, and it takes virtually the opposite view: biochemical/physiological parameters fluctuate to represent the adaptation process of a complex physiological system to physical, physiological, and environmental challenges or stress conditions,2−4 taking into account normal variations in dynamic biological systems.5 This constrains regulation to be efficient, which implies preventing errors (by predicting the parameter levels and overriding local feedback) and minimizing costs.1 Allostatic responses are those physiological changes that occur in response to external perturbations or stimuli. © XXXX American Chemical Society

In this framework, an organism can be defined healthy when it is able to respond or adapt effectively to a change (allostasis); when the system is no longer able to respond or adapt, a disease state or illness develops. The adaptation has a price, and the cost that an organism may have to pay for being forced to adapt to a pathophysiological adverse situation is referred as “allostatic load”.6 The concept of allostatic load may be used for explaining how conditions of stress predispose the organism to illness and disease.4,6 As suggested by a reviewer of this paper, high allostatic load can be associated with great dysregulation of the organism; the dysregulation may be reversible, but the cumulative price for adapting could be sufficiently high that irreversible damage occurs. When confronted with physiological stress, a healthy organism is able to mount a protective response, reduce the potential for harm, and restore an (adapted) equilibrium7 through what is called resilience. There are enormous individual differences in how people respond to potentially Received: March 30, 2015

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DOI: 10.1021/acs.jproteome.5b00275 J. Proteome Res. XXXX, XXX, XXX−XXX

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Journal of Proteome Research

multivariate statistical approaches,12 toward its characterization13 and the understanding of its constituent building blocks via cross-species comparative investigation.17 In the present study, we used well established 1H NMR based metabolomics methods coupled with multivariate statistical analysis to ascertain and investigate the long-term stability of human metabolic phenotypes. To this aim, we monitored the individual metabolic phenotype of 12 individuals based on analysis of the urine metabolic profiles over a period of ten years, which provided the longest longitudinal study of the metabolic phenotype to date. Previous studies monitoring the stability of human metabolic phenotype covered relatively short time spans, of the order of months12,18−20 or 2 or 3 years,13 with the sole exception of the study by Yousri et al.21 in which the stability and the heritability of selected serum metabolites was considered but not the individual metabolic phenotype as a whole. Until now, the stability and conservation of urinary metabolic phenotypes over long time periods have remained unexplored.

stressful situations: some people become sick, that is, they are vulnerable as a result of a small adaptive capacity, while others are able to cope with stress, that is, they are resilient, showing larger adaptive capacity. The concept of resilience is explained by Logan and Barksdale4 using the example of a stretched rubber band that, after being released, returns to its original size. Sometimes, if it has been stretched many times, or used with relatively excessive strength, it will eventually lose its elasticity and will not return to its original size or will break. In this metaphor, the elastic band is the living organism, the stress the process of stretching, and the resilience its ability of regaining the original shape. A person who has more resilience against stress, that is, has a larger adaptive capacity, would have more resistant allostasis to external stress and stimuli than a person who has a relatively small adaptive capacity. Although the concepts of allostasis and resilience form an interesting theoretical framework to arrive to an operative definition of health, measuring health is a more complex task. For instance, nutrition impacts health every day by inducing subtle and pleiotropic effects that are not readily detectable using static homeostatic measures.8 It has been suggested that comprehensive omics analysis, performed under normal conditions or under physiological stress, may identify key parameters that are more adequate to describe healthy and compromised conditions when compared to current biomarkers, which are typically assessed during steady state and regarded as markers of disease.9 The metabolic phenotype, or metabotype, defined as a “multiparametric description of an organism in a given physiological state based on metabolomic data”,10 may prove an invaluable tool not only to describe and to characterize the health status of an individual, but also its adaptation to external stimuli. As a consequence, a person’s phenotype should be considered dynamic, and resilience becomes a key parameter. Moreover, phenotype dynamics will differ between individuals, and concepts such as homeostasis and allostasis need to be considered in view of a global description. Life, indeed, is maintained by numerous biochemical cycles, molecular and cellular mechanisms that operate in steady states: the individuals are modeled by these states, and the deviations from the optimal conditions (defined operationally as deviations from a previous stable and “healthy” set of states) can be directly linked to a pathological status. The idea that the individual metabolic phenotype is a dynamic entity, characteristic of each individual at any given time point, is very important to develop a personalized health care and drug therapy, but even more important is the possibility to monitor each subject through the conservation (homeostasis) over time of the stable core of his or her metabolic phenotype. The human metabolic phenotype can be a very accurate representation of the healthy (or pathological) state of a human organism, and therefore it can be considered an ideal tool to depict the current metabolic status of an individual and, eventually, his or her “phenotypic flexibility”. This flexibility can be considered as a representation of the organism’s resilience and of the way resilience increases the capacity to withstand physiological, pathological, and environmental stimuli that contribute to the development of diseases.11 In this case, a pathological status could be directly linked to individual deviations from his or her own “metabolic space”.12−16 Our groups have made early contributions regarding the definition of the individual metabolic phenotype through

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MATERIALS AND METHODS

Sample Collection

Human urine samples were collected from 12 healthy volunteers (eight males and four females). Each participant provided 20 samples collected on distinct days after an overnight fast, which resulted in a total collection of 240 urine samples. Urine samples were collected into prelabeled sterile collection cups, and they were kept refrigerated at 2−8 °C for a maximum of 2 h. Before storage in the repository of the Da Vinci European Biobank (daVEB, DOI: 10.5334/ojb.af, https://www.davincieuropeanbiobank.org, Italy)22 at −80 °C, the samples were centrifuged (2500g for 5 min at 4 °C) and filtered through 0.20 μm pore membranes to remove particulate matter and cells.23 Because of the absolute noninvasiveness of the sample collection and the fact that participation was on a voluntary basis, ethical approval was not needed. At the time of the collection, informed written consent was obtained from all participants.12,13 Data were anonymized and anonymously analyzed. We also made use of data collected during previous studies in which urine samples were collected from healthy volunteers using the same experimental design. 12,13 The studies, conducted in 2005, 2007, and 2008 involved 22, 20, and 4 subjects, respectively. The 12 individuals enrolled in the present study have participated at least in one of the past studies: four of them participated in all of the four collections. A graphical summary of the four studies is given in Figure 1. Sample Preparation

Frozen urine samples were thawed at room temperature and shaken before use. A 630 μL aliquot of each urine sample was centrifuged at 14 000g for 5 min, and 540 μL of the supernatant was added to 60 μL of potassium phosphate buffer (1.5 M K2HPO4, 100% (v/v) 2H2O, 10 mM sodium trimethylsilyl [2,2,3,3−2H4]propionate (TMSP) pH 7.4). A total of 450 μL of each mixture was transferred into 4.25 mm NMR tubes (Bruker BioSpin srl) for analysis.23 NMR Experiments 1

H NMR spectra were acquired using a Bruker 600 MHz metabolic profiler (Bruker BioSpin) operating at 600.13 MHz B


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for coping with the majority of these chemical shift variations. In the univariate analysis, each metabolite has been integrated after the alignment to a reference value of chemical shift, always obtaining a perfect alignment of the metabolite among all the spectra of all the individuals. Univariate Analysis

For each subject of the present study (2014 collection), we compared the averaged levels of 28 quantified metabolites with the average levels of the corresponding signals in the most recent available past collection. In particular: AR, AS, AU, and AW were compared with their corresponding 2008 collections; BH, BI, BU, and BV with the 2007 collection; and AG, AT, BE, and BD with the 2005 collection. For this analysis, the metabolite signals were normalized to the NMR signal intensity of the creatinine as the paper by Assfalg et al.12 Kruskal−Wallis test25 (nonparametric ANOVA test analogue) was chosen to infer differences on metabolite concentrations among different collections; false discovery rate correction was applied using the Benjamini and Hochberg method:26 an adjusted p-value of 0.05 was deemed significant.

Figure 1. Collection scheme. 2005 collection: 22 healthy individuals, (11 male and 11 female). 2007 collection: 20 healthy individuals (nine male and 11 female), of these 11 subjects (BH, BI, AR, AS, AU, AW, AI, AO, BC, BF, BG) participated also in the previous study. 2008 collection: four male healthy individuals. These four individuals participated also in the 2007 and 2005 collections. 2014 collection: 12 healthy individuals (eight male and four female); of these, AG, AT, DB, and BE participated in the first collection in 2005; BU and BV participated in the second collection in 2007; and BH and BI participated in both first and second collections in 2005 and 2007. Finally, AR, AS, AU, and AW participated in all the previous collections. 2015 collection: two healthy individuals (one male and one female); of these, AG participated in both first and fourth collections in 2005 and 2014, and AW participated in all the previous collections.

Individual Recognition

Principal component analysis (PCA) was used as dimension reduction technique and as a preparation of the data table before further statistical analyses. Data reduction was carried out by means of projection into a PCA subspace explaining 99.99% of the variance in the data. A test set validation (TSV) approach, which requires that models are constructed without any test set data, was applied to define the multivariate predictive analysis. Data were initially split in a test and training data sets. The training set consisted of a random selection of the 90% of data available for all individuals. The test set consisted of the remaining 10% of the data. The training data sets were subjected to canonical analysis (MANOVA) to define a further reduced subspace with optimum group separation (CA space). Dimensionality was chosen according to the MANOVA estimation of the dimensionality of the respective group means. Then, the test set data were mean centered and variance scaled using the mean and variance of the training set before being projected into the PCA/CA subspace defined by the training model. Finally, k-NN classification (with k = 7) was applied to each test set for each individual. The procedure was repeated 1000 times to derive average recognition accuracy for each subject. Detailed information on the overall procedure is given in the original publications.12,13

proton Larmor frequency and equipped with a 5 mm CPTCI 1 H−13C−31P and 2H-decoupling cryoprobe including a z axis gradient coil, an automatic tuning-matching (ATMA), and an automatic sample changer. A BTO 2000 thermocouple served for temperature stabilization at the level of approximately 0.1 K at the sample. Before measurement, samples were kept for at least 3 min inside the NMR probehead for temperature equilibration (300 K). For each urine sample, a monodimensional 1H NMR spectrum24 was acquired with a NOESY-presaturation pulse sequence (Bruker noesygppr1d.comp) with irradiation at the water frequency during the recycle and mixing time delays and a spoil gradient. A total of 64 scans with 64 K data points were collected using a spectral width of 12019 Hz, an acquisition time of 2.7 s, a relaxation delay of 4 s, and a mixing time of 100 ms. NMR Spectra Processing

Free induction decays were multiplied by an exponential function equivalent to a 1.0 Hz line-broadening factor before Fourier transform was applied. Transformed spectra were automatically corrected for phase and baseline distortions and calibrated (TMSP singlet at 0.00 ppm) using TopSpin 3.2 (Bruker Biospin srl). Bucketing was applied to the data after the spectral regions were discarded: δ > 9.5 ppm, 4.5 < δ = 6.0 ppm, and δ < 0.5 ppm, containing water and urea signals. The remainder of each spectrum was divided into sequential segments (“bins”) of 0.02 ppm width, which were integrated using AMIX software (Bruker BioSpin). Finally, total area normalization was carried out on all the spectra. The pH value of the sample is a major source of variation in peak positions; although the samples are buffered according to standard procedures, small pH differences will be detectable as a shift of some peaks (e.g., histidine peaks). In the multivariate analysis, binning methods (bin width of 0.02 ppm) were used

Individual Prediction

Individual prediction was performed using a similar approach. A training set was built by using 37 spectra randomly selected for each subject from the 2005, 2007, and 2008 collections. A PCA/CA subspace was defined as previously described. Individual prediction of the spectra of the 12 subjects participating in the 2014 collection was performed by centering and scaling using the mean and the variance of the training set and then projecting the autoscaled data into the PCA/CA subspace. k-NN classification (with k = 7) was applied to each test set for each individual.

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RESULTS AND DISCUSSION In previous studies, we found that the metabolic phenotype based on the NMR spectral profiles of multiple urine samples is C


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Figure 2. Heatmaps of the confusion matrix for the individual recognition for each subject in the four collections. (A) 2014, 12 subjects; (B) 2005, 22 subjects; (C) 2007, 20 subjects; (D) 2008, 4 subjects.

The collection of multiple urine samples is fundamental to describe the day-to-day variability of the metabolic phenotype of the subjects and to define their “metabolic spaces”. A very high individual discrimination accuracy can be reached both using multiple urine samples collected in (non) consecutive days (one urine per day, for a total of 20−40 days)13 and using multiple urine samples collected in the same day (4−5 urines per day, for 10 days).27 Indeed, we recently demonstrated that the individual metabolic phenotype can be subdivided into a static and a dynamic component, describing the average metabolite concentrations and their dynamic variability, respectively, and that the dynamic component accounts for 75% of the variation observed in the data.17 The metabolic space of each individual represents the concept of “flexible metabolic phenotype” as a metabolic consequence of the adaptation of the subjects to the daily external stimuli. In this framework, it is possible to describe this property with the term “allostasis”.2−4 The metabolic space can be conveniently represented in the (three-dimensional) PCA/ CA subspace for the different subjects shown in Figure 3, where subject-specific metabolic regions are defined. The samplespecific multiparametric spectral profiles of an individual are expected to fluctuate within its corresponding subject-specific metabolic space according to day-by-day changes. Every day our metabolism is able to adapt to different external stimuli, and our capacity of allostasis allows for the restoration of the

specific to each individual. The preliminary task of the present investigation was to confirm the validity of this finding on the basis of a repeated collection after 8−10 years from the first. The experimental collection scheme, including both the previous and the current collections, is outlined in Figure 1. In this work, we applied the same statistical analysis (PCA/ CA/k-NN) that was used in our previous studies on the human individual metabolic phenotype12,13 and in cross-species comparative analysis.17 For the 12 subjects in the present study, we obtained an average individual discrimination accuracy of 98.8% (Figure 2A), which is only slightly lower than what was attained in the previous studies. For the 22 individuals participating in the 2005 collection, the average classification accuracy was 99.6% (Figure 2B). Also the 20 subjects of the 2007 and the four subjects of the 2008 collections were well discriminated with an average accuracy of 99.3% and 99.9%, respectively (Figure 2C, D). The slightly lower accuracy obtained in the present study is attributed to the fact that 20 samples from each volunteer were collected with respect to the 40 of the previous collections. Nonetheless, the results show that, unquestionably, the subjects could be very well recognized. It was already shown that a very modest decrease of accuracy is to be expected by reducing the number of samples per individual from 40 to 20 (see Figure 4 in Assfalg et al.12). D


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Figure 3. Projection of the 1H NMR spectral buckets into PCA/CA subspace in the three most significant dimensions for each subject in the four collections. (A) 2014, 12 subjects; (B) 2005, 22 subjects; (C) 2007, 20 subjects; (D) 2008, 4 subjects; each subject is coded with a different color: each sphere represents a different NMR spectrum of a urine sample collected on different days.

Long-Term Recognition

equilibrium conditions. It appears that day-by-day intraindividual allostatic variations are by far smaller than the interindividual separation: even if overlap seems to exist between subjects, especially for the 2005 and 2007 collection, it should be remembered that the individual metabolic phenotype is a multivariate entity and that separation between subjects (confirmed by the high classification accuracy attained) is obtained in a n-dimensional space (a three-dimensional space was obviously used for visualization).

Because for each individual the metabolic phenotype was stable over the period 2005−2008, we fused this data by randomly choosing 37 spectra for each individual participating in the previous collections. This fused data set was built to take into account that some individuals provided fewer samples than others because they did not participate in all three collections. In this way, the bias due to the different sample size was avoided. However, it should be noted that the results were consistent even using each single collection (2005−2007−2008) as training set. The fused data set was employed to build a classification model for predicting the spectra of the 2014 collection applying the same statistical procedure (PCA/CA/k-NN). The results, shown as a heatmap in Figure 4, panel A, and in numerical form in Figure 4, panel B, are somehow surprising. For seven out of the 12 subjects, the prediction accuracy is very high, above 90% and even 100% in most cases, indicating that their metabolic phenotype is stable over a period of 8−10 years. For two subjects (BU and BV, the oldest and the youngest in the study), the accuracy was suboptimal, while for other three (AG, AW and BD), the classification accuracy was very low, as low as 5% for AW. The results were the same independently of which data collection was used as training set: subjects BH, BI, AR, AS, and

Two- and Three-Year Recognition

The present results clearly corroborated the finding that the urinary metabolic phenotype is unique and able to discriminate different individuals at any given time point in their life: this does not imply that each individual is also metabolically stable in time. What we want to verify here is whether subjects can be uniquely identified irrespectively of the data collection used for training the classification model. In our previous study, we showed that all subjects could be correctly classified when the 2005 collection was used for predicting the 2007/2008 collection and vice versa, indicating that the metabolic phenotype was largely unperturbed over a time frame of 2−3 years.12,13 E


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Figure 4. Individual prediction. (A) Heatmap of the confusion matrix for the individual prediction for each subject of the present collection (testing data set) using as a training data set 27 spectra randomly selected for each subject from the 2005, 2007, and 2008 collections. (B) Value of the individual prediction accuracy for each subject of the present collection. (C−E) Projection of the 1H NMR spectral buckets into PCA/CA subspace in the three most significant dimensions for each subject in all the past collection that he or she participated. Each subject is coded with a different scale of colors: each color represents a different collection, indicated also with a numeric label, 1 for 2005 collection, 2 for 2007 collection, 3 for 2008 collection, and 4 for 2014 collection. Moreover, each ball of the same color represents a different NMR spectrum of a urine sample collected on different days. (C) AR, AS, AU, and AW subjects. (D) AG, AT, BD, and BE subjects. (E) BH, BI, BU, and BV subjects.

AU (in the 2014 collection) were always well recognized,

This can be also seen in the (three-dimensional) PCA/CA subspace, Figure 4, panels C−E: for the subjects with very high recognition accuracy, the spheres representing urine samples from the 2014 collection are very close or even mixed to those of the previous collections. The “new” metabolic spaces of

whereas subject AW was always poorly recognized (for AG, AT, BD, and BE only the 2005 collection is available, and for BU and BV, only the 2007 collection). F


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Figure 5. Barplots of the values of the fold changes (FC) for each of the 28 quantified metabolites for AG, BD, and AW. Metabolites with FC negative (positive) values were at significantly lower (higher) concentration in the 2014 collection compared to those of the most recent past collection: 2005 collection for AG and DB and 2008 collection for AW. The metabolites that displayed significant concentration changes (p-value < 0.05) are shown with a green bar, whereas concentrations of metabolites that have not changed in a significant way (p-value > 0.05) are shown with a red bar.

these subjects are overlaid on the original ones in the PCA/CA subspace. On the other hand, it can be clearly seen that for the three individuals with very low accuracy, the 2014 samples are displaced away from the samples of the precedent collections, indicating a drift from the original individual metabolic space. Finally, for the two individuals with suboptimal recognition accuracy, we can observe a minor drift from their original metabolic space. For most of the individuals, the allostatic capacity to respond to stressful situations and stimuli remained unchanged over time with the final result that the original situations were maintained: these subjects maintained their original metabolic space. On the other hand, few individuals seemed to lose their capability to restore the original conditions; they drifted from the original metabolic space and reached a new equilibrium status. Following these results, all volunteers were asked for detailed information about any health problems and lifestyle changes occurred during the period 2005−2014. The individuals that had an optimal (AR, AS, AT, AU, BE, BH, BI) and a suboptimal (BU and BV) prediction accuracy did not report any relevant pathologies, illnesses, or life-style changes worth of note. By inspecting the life history profiles of these individuals, we realized that in the time span of almost 10 years, they did not experience any drastic change in their working environment

(except BE, the others have been working in the same place since the first study), living place (except AS and BV that relocated within the same city, and BE that relocated to a different city, apparently without any noticeable effects on the profiles), nutritional habits (no relevant changes in diets, except for BV and AG that diminished carbohydrate intake), or physical activity (none of them started or ceased agonistic activity). Finally, no major illnesses/diseases have been reported by these subjects. The volunteers that reported significant changes in their life during this time frame were AW, BD, and AG, the three subjects characterized by very low prediction accuracy. In the previous study,12,13 AW (male, age 37 at the time of the 2014 collection) could always be perfectly recognized (see, for instance, Table 2 in Bernini et al.13); thus, his metabolic profile at that time was stable. From the data fact sheets, we could infer different events that could have induced such a large displacement of the metabolic phenotype. One is relocation from southern to northern Europe. In the 2007 collection, we had a similar case, and this was found not to have consequences on the stability of the individual metabolic phenotype (see subjects BC, Table 2 in Bernini et al.). Moreover, we have demonstrated that diets and dietary habits play a minor role in the shaping of the metabolic phenotype.17,27 Although subject AW did not report any severe pathophysiological condition, in G


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derivatives are commonly characteristic of various types of cancer: interestingly, the aberrant choline-metabolite profile typical of cancer is characterized by an increase rather than a decrease of phosphocholine and of choline-containing metabolites, as demonstrated by numerous studies in cancer cells and solid tumors.37 However, choline was found to be one of the metabolites that contributed to the discrimination between thyroid lesions and the healthy thyroid tissues;38 decreased levels of this metabolite and its derivatives have been reported in the thyroid malignant nodules,39,40 in agreement with the present observation. We can conclude that these three subjects were poorly recognized because during the 2014 collection they were subjected to an intense or prolonged “stressful situation” that caused a physiological stress response leading the subjects to drift from their original metabolic spaces. In the case of AG and AW, these situations were transient, whereas in the case of BD, this is likely to be a permanent condition.

the spring of 2014, following a prolonged antibiotic treatment (azithromycin), AW reported the onset of a seemingly secondary lactose intolerance that resulted in the complete withdrawal of fresh dairy products from the diet at the time of sample collection. Although probably correlated, it is complicated to establish a causative relationship between the two events. It is known that secondary lactase deficiency can result from injury to the small bowel mucosal brush border secondary to viral or nonviral intestinal infection.28 Moreover, antibiotics can trigger temporary lactose intolerance by interfering with the intestine’s ability to produce the lactase enzyme. More importantly, it is well-known that the assumption of antibiotics leads to the remodeling of gut microflora,29,30 which is, in turn, a pivotal shaper of individual urinary metabolic phenotype.30−32 Interestingly, subject AW showed significant changes in hippurate, trigonelline, and hydroxyisobutyrate that are known to be metabolites associated with gut microflora activity32,33 (Figure 5). Moreover, subject AW showed a striking decrement of ascorbate levels. This could be a consequence of a vitamin C deficiency34 and could be related both to a long-term exposition to a stressful condition and to lactose intolerance. For the extremely low recognition of AG and BD (both females, age 38 at the time of the 2014 collection), more straightforward explanations can be provided. In the period 2005−2014, AG had two pregnancies, and in particular during the 2014 collection she was breastfeeding. Peaks attributable to lactose signals were clearly visible as a consequence of the process of lactation, which were not visible in the 2005 spectra. In addition, the peak of glycine is increased in this new collection with respect to the old one (Figure 5). This is not surprising as the urine metabolic phenotype is altered during pregnancy and lactation: the excretion of lactose and the increased excretion of glycine in urine during pregnancy and lactation have been both reported,35,36 and lactose has been proposed as a biomarker of lactation. Only trace amounts of lactose are found in the blood and urine of nonpregnant, nonlactating women because lactose is minimally absorbed from the diet in its intact form.36 Besides lactose and glycine, AG showed changes in several other metabolites: aspartate and pyruvate showed the greatest concentration changes between the two collections; to our knowledge, decreases in ascorbate and increases in pyruvate have not been associated with pregnant or breastfeeding states (Figure 5). In the period 2005−2014 also subject BD had two pregnancies, but at the moment of the collection she was not breastfeeding. However, and more importantly, in June 2014, she had a total surgical excision of the thyroid (total thyroidectomy) due to a cancer. Thyroid is a fundamental gland regulating the human energetic metabolism; therefore, patients who underwent total thyroidectomy must assume thyroidal hormones as a substitutive lifelong treatment. To the best of our knowledge, there are no metabolomics studies in the literature regarding the metabolic changes introduces by a surgical thyroid removal, but it is not hazardous to hypothesize that they must be important. Of the 28 metabolites analyzed for this subject, 17 metabolites have significantly altered concentration with respect to the 2005 collection (Figure 5). Among these, choline, tyrosine, and isoleucine were those that showed the greatest concentration changes. In particular, choline decreased from normal levels in 2005 collection to strikingly low levels in the 2014 collection, that is, few months after thyroidectomy. Concentration changes of choline and its

Resilience of Human Metabolic Phenotype

Whether the conditions reported by subjects AW and AG displaced their metabolic phenotypes to a stable or to a metastable condition was an interesting point to investigate. For this reason, AW was asked to provide additional 16 urine samples in January 2015, long after the antibiotic therapy was finished and after dairy products were reintroduced in the diet. Likewise, AG provided additional urine samples (only seven) in January 2015, a month after stopping breastfeeding. By using these new urine collections as test sets, the prediction accuracy for both subjects increased up to 90% independently of the data collection used as training set (Figure 6). These results clearly indicate another distinctive property of the human metabolic phenotype: even after being subjected to a great amount of stress, or to a prolonged stress over time, it is able to change to restore a new equilibrium; when the stress

Figure 6. (A) Value of the individual prediction accuracy for AG and AW subjects of the collection in 2015. (B) Values of the individual prediction accuracy for each of the 12 individuals (2014) in all the urine collections. H


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Journal of Proteome Research stops, it is also able to restore its original equilibrium. This flexibility can be seen as a manifestation of “resilience”.4 By looking at the metabolites changes, AW showed a striking decrease of ascorbic acid in the 2014 collection, probably as a consequence of a vitamin C deficiency; in the 2015 collection, the concentration level of this metabolite increased, returning toward the reference level; although it was still significantly lower than its level in the 2008 collection. In the urine spectra of subject AG belonging to 2015 collection, the signals of lactose (characteristic of the breastfeeding state) disappeared; on the contrary, the level of ascorbate and glycine remained unaltered, and that of pyruvate even increased in comparison with the 2014 collection. To further investigate the resilience of the metabolic phenotype and to demonstrate its ability to revert to the previous equilibrium once the stress conditions are ceased, we built (for both subjects, separately) a new PCA/CA/k-NN predictive two-group model using as training set the spectra collected in 2014 (one group) and the spectra collected in the previous collection, 2005 for AG and 2008 for AW, (another group). This model resulted in 100% discrimination between the two groups, confirming that a strong remodeling of the individual metabolic phenotype occurred for both individuals. Subsequently, we used this model for predicting the latest samples collected in 2015. From the score plots reported in Figure 7, it can be clearly observed that, for both subjects, the metabolic phenotypes described by the 2015 samples are different from those of 2014 and have reverted to the metabolic space of 2005/2008. This capability of resilience of the human metabolic phenotype and the possibility to monitor its actual state may prove to be very important for developing personalized health medicine and drug therapy. A convenient approach to measure to which extent the metabolic functions are perturbed by a stressor in the context of a global system, in terms of multiple changes in relative concentrations, is the so-called R-potential, introduced by Veselkov et al.41 The R-potential enables the characterization of disrupted functional activity by the sum of the absolute changes in concentrations of multiple solution components, in this case, urine metabolites, with reference to an unperturbed status defined to be in homeostatic equilibrium. An increase of the Rpotential is indicative of perturbed functional activity. The Rpotential has also a thermodynamic interpretation. Indeed Veselkov et al. suggested an entropy-based representation of both the diffusiveness of metabolic phenotypes and their collective divergence from homeostasis in unperturbed and perturbed system states, an approach of great interest for the study of the individual metabolic phenotype. As an example, Figure 8 shows the R-potential for subject AW: an increase, reflecting a metabolic perturbation, is evident between 2008 and 2014 collections as well a decrease between 2014 and 2015 collections, indicating a probable normalization or recovery of the normal metabolic function. Our data confirm the possibility of monitoring a subject during a long period of time: healthy subjects are characterized by a stable metabolic space over time where their metabolic phenotypes are able to vary according to daily stressors and external stimuli (metabotype propriety of allostasis) (Figures 2−4). In the absence of major pathophysiological events, the phenotype is stable also over a time scale of almost 10 years (Figure 4). When an individual is subjected to a prolonged or intense stressful situation, he or she might not be able to maintain the original equilibrium and could drift toward a new

Figure 7. Predictive clustering of NOESY urine spectra of (A) AW after the antibiotic therapy was finished and dietary products were reintroduced in the diet; and (B) AG after the breastfeeding was stopped. Clustering is obtained by projection of the January 2015 collection urines (green crosses) on a model built on the 2014 collection urine spectra (red points) plus the precedent collection urine spectra (blue points).

Figure 8. R-potential curve for subject AW calculated as proposed by Veselk et al. Data from the 2008 collection are considered as the unperturbed status. The perturbation of AW’s was supposed to happen in early 2014 (see text for more details).

(possibly stable) equilibrium: this is what happened to subjects AG, AW, and BD (Figure 4). We show here for the first time the resilience of the human metabolic phenotype: when a stressful situation ends, the metabolic phenotype is able to revert toward the is original metabolic space, and the original condition is restored (Figures 6 and 7). I


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Journal of Proteome Research Next Steps

clearly visible in the collection of nonconsecutive urine samples from the same individual. In the absence of major pathophysiological events, the phenotype is stable also over a time scale of almost 10 years. When individuals are exposed to significant pathophysiological stress conditions, their allostatic responses are so strong that phenotypes drift toward a new metabolic space. Drifts from individual metabolic space could be associated with pathological states or illnesses. Herein, we also demonstrated the effective resilience of the metabolic phenotype: once the stressful situation is removed, the metabolic phenotype reverts toward its original metabolic space, as a consequence of the restoration of the original equilibrium. According to the new medical model of precision medicine,46 the “metabotype” could be of enormous value to monitor patients during drug treatment, therapy, diet, or disease progression, thus providing a very efficient tool to follow individual response over time. In this paradigm, precision medicine should aim at reinforcing the patient’s metabolic resilience, focusing more on reverting the metabotype to its original status rather than to force it to a (maybe utopic) generic (true) healthy one.

Stimulated by the comments of one of the reviewers, we would like to speculate about possible perspectives and next steps strategies arising from the present work. Our results support the idea that the only way a stressful condition can affect the organism is by inducing biological changes. Therefore, the physiological response is critical for mediating the effects of the stress on human health. Here, we also demonstrated that the individual metabotype may be used as a dynamic mirror of the organism’s functioning, and it can be also useful to point out biomarkers (or a metabolic profile) of a stressful response of the organism, characterizing the deviation of the metabolic phenotype from its stable core after a challenge. The efficiency with which an individual returns to its equilibrium conditions after a stress is related with his or her capability of resilience, which has been defined as the reserve capacity of pathophysiological systems to recover from challenging conditions.42 Under this light, the metabotype can represent an operative tool to evaluate the human responses to a diversity of stimuli with the aim of estimating human pathophysiological resilience. Many in vitro and in vivo studies have been performed where a range of stimuli was induced and controlled;43,44 nonetheless, these studies are difficult to perform, and results are often difficult to interpret due the significant individual differences in stress perception, processing, and coping.42 Characterizing stress’s responses that have common interpretation across many individuals could provide an important opportunity for future research, and the urine metabolic phenotype (together with appropriate statistical analysis) could represent an excellent strategy to solve the problems related to high interindividual variability. Moreover, it is important to consider that, although several studies have demonstrated the adverse effects of long-term stressful situation on human health,6 a pathophysiological stress response is one of the natural mechanisms for human adaptation and survival. Therefore, the metabotype approach could be also very useful to characterize the metabolic signatures that distinguish the positive features of stress responses, usually associated with an acute stress condition, from the negative ones, usually associated with chronic stress.39 Finally, our results, together with the dynamic description of a living organism through the notions of allostasis and resilience, further support the need of modifying the classical 1948 World Health Organization definition of health: “a state of complete physical, mental, and social well-being and not merely the absence of disease and infirmity” by considering “the ability to adapt and self-manage in the face of social, physical, and emotional challenges.45 Of course, the challenge of operationalizing in this way the notion of health will require a considerable effort and future social, biological, and clinical research.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]fi.it. Phone: +39 055 4574296. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was partly supported by the European Commissionfunded FP7 projects COSMOS (Contract No. 312941) and INFECT (Contract No. 305340).

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ABBREVIATIONS ANOVA, analysis of variance; CA, canonical analysis; FC, fold change; k-NN, k-nearest neighbors; MANOVA, multivariate analysis of variance; NMR, nuclear magnetic resonance; NOESY, nuclear Overhauser effect; PCA, principal component analysis; TMSP, trimethylsilyl propionate; TSV, test set validation

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REFERENCES

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CONCLUSIONS In this study, we followed the individual urine metabolic phenotypes of 12 individuals over a period of 8−10 years providing the longest longitudinal study of metabolic phenotypes to date. We demonstrate that healthy subjects are characterized by a stable metabolic space over time where their metabolic phenotypes are able to vary according to daily stressors and external stimuli. The process used for achieving stability in case of environmental perturbations, allostasis, is J


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