Contribution of ROS and metabolic status to neonatal and adult CD8+ T cell activation.

In neonatal T cells, a low response to infection contributes to a high incidence of morbidity and mortality of neonates. Here we have evaluated the impact of the cytoplasmic and mitochondrial levels of Reactive Oxygen Species of adult and neonatal CD8+ T cells on their activation potential. We have also constructed a logical model connecting metabolism and ROS with T cell signaling. Our model indicates the interplay between antigen recognition, ROS and metabolic status in T cell responses. This model displays alternative stable states corresponding to different cell fates, i.e. quiescent, activated and anergic states, depending on ROS levels. Stochastic simulations with this model further indicate that differences in ROS status at the cell population level contribute to the lower activation rate of neonatal, compared to adult, CD8+ T cells upon TCR engagement. These results are relevant for neonatal health care. Our model can serve to analyze the impact of metabolic shift during cancer in which, similar to neonatal cells, a high glycolytic rate and low concentrations of glutamine and arginine promote tumor tolerance.

Introduction

Infections in children under six months cause approximately four million deaths per year [1]. Neonatal T cells have low, tolerant or skewed responses, and a relatively high threshold of activation, potentially involving epigenetic mechanisms [2-5]. Compounds present in neonatal serum contribute to the low response of neonatal cells, among them adenosine [6, 7], arginase [8], and other proteins [9, 10]. Arginase and adenosine are metabolic inhibitors associated with a lack of T cell responsiveness in cancer [11-14]. Indeed, the pathways of arginine and glutamine metabolism are implicated in tolerance to tumors and are considered as therapeutic targets [15-17].

CD8+ T cells from newborns have distinct transcriptional and epigenetic profiles biased towards innate immunity and, albeit in homeostatic proliferation, their clonal expansion and effector functions are diminished [18]. Lower mitochondrial mass and membrane potential, as well as differences in calcium fluxes and in calcium and potassium channels have been reported [19-21]. Naïve, memory and effector functions depend on different metabolic programs, adapted to cellular requirements. Naïve T cells are metabolically dormant (quiescent). After an encounter with an antigen, they may either tolerate the antigen, become activated, or turn anergic. Despite important advances in the field of immunometabolism, a comprehensive view of the interplay between antigen recognition, metabolic status and ROS is missing. Our study combines experimental measurements with computational modeling to assess the role of metabolism, in particular of ROS, on T cell signaling.

We evaluated cytosolic (c) and mitochondrial (m) ROS levels on naïve CD8+ T cells from human newborns and adults, at basal level and after TCR stimulation. Our results suggest fundamental differences in the ROS signaling and redox status between CD8+ T cells from newborns and adults. Alterations of redox and metabolic nodes in the model result in low neonatal CD8+ T cells response, establishing the involvement of the metabolic status of the neonatal cells in their impaired response. The effect of ROS in cell function has changed from a waste product to an important signaling messenger [22]. Furthermore, the manipulation of ROS is being considered for cancer immunotherapy [23].

Using a logical formalism implemented in the software GINsim ([24] http://ginsim.org), we defined a comprehensive dynamical model integrating the most relevant signaling, metabolic and transcriptional regulatory components controlling the activation of CD8+ T lymphocytes.

This model displays alternative stable states, which corresponds to different cell fates, i.e. quiescence, activation and anergy, depending on ROS status. Stochastic simulations further suggest that the lower activation rate of neonatal compared to adult CD8+ T cells upon TCR engagement is attributable to differences in ROS status or glutaminolysis at the cell population level.

Materials and methods

Ethics statement

The collection of cord blood samples, with informed mothers’ consent, was granted for this work by “Comité de Etica en Investigación Hospital General de Cuernavaca Dr. José G. Parres. CONBIOÉTICA-17-CEI-001-20160329”. The collection of adult blood was granted by the ethics committee of “Secretaría de Salud. Dirección de Atención Médica Departamento Centro Estatal de laTransfusión Sanguínea Oficio No: SH/CETS/438/2016”.

Blood collection and cell purification

Cord blood was collected at Hospital General de Cuernavaca Dr. José G. Parres, with informed mothers’ consent. Adult samples were obtained from leukocyte concentrates at the Centro Estatal de Transfusión Sanguínea. For each comparison, the minimum number of samples included in the neonate vs adult groups was three, for some cases the number of samples was up to nine. The total number of samples used for this study was 30 samples for neonatal CD8+ T cells and 30 for adult cells. No eligibility criteria were defined regarding the donors’ gender. This parameter was distributed randomly across the samples and equivalent numbers of male and female donors were used for neonatal and adult cells. No differences related to gender were detected. For adult samples, the ages of the donors were between 26 and 40 years. All cord blood samples were obtained from vaginal deliveries and no drugs known to affect T cells responses were administered to the mothers during labour. All the samples were immediately processed after collection. CD8+ T cells were purified as previously described [25]. Briefly, the blood was centrifuged on LymphoprepTM (Axis-Shield, UK) and Mononuclear cells were incubated with 1 mL of erythrocytes and the RossetteSepTM CD8+ T cell enrichment cocktail to obtain Total CD8+ T cells (15063; StemCell Technologies, Canada). The memory cells were eliminated using magnetic beads (8803; Pierce; Thermo Fisher Scientific, Bremen, Germany) loaded with a CD45RO-specific mAb (eBiosciences, San Diego, CA). We obtained ≥94% naïve CD8+ T cells. The total elapsed time from blood collection to CD8+ T cells purification was similar for adults and neonatal samples, 46 to 48 hours.

Cell stimulation

Naïve CD8+ T cells were cultured in RPMI containing 1% L‐glutamine, antibiotics (100 U/mL penicillin and 100 μg/mL streptomycin) and 5% fetal calf serum. For stimulation 1 μg/mL or each, anti-CD3/anti-CD28 mAbs was used (OKT3, 70-0037-U100, CD28.2, 70-0289-U100, Tonbo Biosciences), cross-linked with anti-mouse mAb (405301, BioLegend). Cells were incubated for 6 h under 5% CO2 at 37°C.

Flow cytometry

For activation assessment, the anti-CD69 FITC (Genetex GTX43516) was used. Cells were stained as previously described [15].

For redox measurements, cells were incubated for 15 minutes at 37°C in staining buffer with either: 2.5 μM of MitoSOX Red Mitochondrial Superoxide Indicator (M36008 ThermoFisher Scientific); or 2 μM of Dihydroethidium (DHE) (D11347 ThermoFisher Scientific); or 5 μM of Carboxy-H2DCFDA (C400 ThermoFisher Scientific). Cells were washed twice with staining buffer.

Flow cytometry was assessed on a FACScalibur cytometer using the CellQuest software. The software FlowJo (Tree Star, CA) was used for analysis.

Statistical analysis

Three to nine biological replicates were used for all experiments presented. Data are presented as means and standard deviations. An unpaired Mann-Whitney test was used for comparison between samples, and the Wilcoxon test was used for comparison between paired samples. The p-value is presented for each comparison, statistically significant differences are indicated.

Logical modeling

To model the T cell network, we first defined a regulatory graph, where each node represents a component of the signaling network. Most nodes are associated with Boolean variables, but some are allowed to take three levels of activity (0, 1 and 2) when biologically justified. Nodes are connected through arcs, which represent the regulatory influences between them, positive or negative. Next, we defined logical rules to determine the level of activity of each target node as a function of the levels of its regulators (S1 Table).

We used the GINsim software (v3.0.0b, [24] http://ginsim.org) to build the TCR-REDOX-Metabolism signaling model and to identify its stable states.

We used the MaBoSS software (v2.0,[26, 27]) to perform stochastic simulations of our logical model. These simulations use default parameters: 50’000 runs with identical up and down rates for all components.

All our model analyses are reproducible using an interactive Python notebook and virtual software environment, available together with the model file on GINsim repository (http://ginsim.org/node/229) [28].

Results

Evaluation of ROS in neonatal and adult CD8+ T cells

Glycolytic enzymes are over-expressed in the neonatal CD8+ T cells, correlating with higher ROS production [18, 29, 30]. We first considered additional naïve CD8+ T cell samples, obtained as previously described [25] and a second ROS sensitive probe to corroborate the higher cROS levels in the neonatal cells (Fig 1A and 1B). We then measured mROS levels and identified two cell populations based on these (high vs low) (Fig 1C). We quantified the percentage of cells in the high or low mROS gates from neonatal or adult CD8+ T cells. We found that neonatal cells have a higher percentage of cells with high mROS, whereas adult cells are more frequent in the low mROS gate (Fig 1D).

Fig 1

Neonatal CD8+ T cells have higher basal cROS and mROS levels than their adult counterparts.

Purified human CD8+ T lymphocytes from neonate (N) or adult (A) donors were incubated with 2 μM of DHE (A), or 10 μM of Carboxy-H2DCFDA (B) or 2.5 μM of the MitoSOX Red (C, and D) fluorescent dyes for 15 minutes. The fluorescence of the cells was assessed using a FACScalibur Flow Cytometer. The graphs show the fluorescence of the cells on the FL-2H channel (DHE, MitoSOX Red) or FL-1H channel (CH2DCFDA). Five to nine samples per group are shown. An unpaired Mann-Whitney test was used for comparisons between samples from N and A. The p-value is presented for each comparison.

Altogether these results demonstrate that a higher proportion of neonatal CD8+ T cells display high ROS levels, both in mitochondria and cytoplasm.

T cell activation and mROS

T cell activation induces glycolysis, which could affect the production of ROS [31]. We thus evaluated the changes in ROS after TCR/CD28 cross-linking. Cell activation did not significantly change the levels of cROS (Fig 2A). In mitochondria, however, activation of adult cells led to a reduction in the proportion of low mROS cells, which could be due to the Warburg effect [32]. On the contrary, in the neonatal cells, stimulation increased the proportion of cells with high mROS (Fig 2A).

Fig 2

The initial redox stress impairs the activation of neonatal human CD8+ T cells upon TCR/CD28 stimulation.

Purified human CD8+ T lymphocytes from neonate (N) or adult (A) donors were stimulated with anti-CD3/anti-CD28 antibodies. After 6 hours, the cells were collected for measurements. For the ROS evaluation, cells were incubated with 2 μM of DHE or 2.5 μM of the MitoSOX Red (A) fluorescent dyes for 15 minutes. The fluorescence of the cells was assessed using a FACScalibur Flow Cytometer. The graphs show the fluorescence or frequency of the cells after normalization to the basal levels of a total of 5 and 6 neonatal and adult cells’ samples, respectively. (B) Total RNA was extracted from untreated or TCR/CD28 stimulated cells, using the TRIzol reagent. The Hv1 proton channel mRNA levels were evaluated using specific primers on a qPCR using the GAPDH mRNA levels as a reference gene. During our measurements, (C) The cells were washed and incubated with an anti-CD69 FITC fluorescent antibody for 30 mins the fluorescence or frequency of the cells was analyzed using a FACScalibur Flow Cytometer. (D) A MitoSOX Red and anti-CD69 FITC double staining was performed on CD8+ T cells from neonates. The fluorescence or the frequencies of CD69+mROS- and CD69+mROS+ populations is represented on the bar graphs. An unpaired Mann-Whitney test was used for comparisons between samples from neonatal (N) and adult (A) cells, considering four samples per group. The p-values between the bars denote the significance between adult and neonatal cells. On top of each bar, we show the significance between paired samples of stimulated N or A groups as compared with the non-stimulated cells. A Wilcoxon test was used for these comparisons.

The proton channel Hv1 exports protons generated by NADPH-oxidase, which leads to the production of H2O2, contributing to a high level of cROS [33]. We measured the expression of Hv1 in neonatal and adult CD8+ T cells, before and after stimulation. In adult cells, Hv1 expression diminished after activation, while in those of neonates, activation induced Hv1 expression (Fig 2B).

To measure CD8+ T cell activation, we evaluated CD69 expression. Stimulation induced a higher expression of CD69+ cells in adult as compared to neonatal cells. Additionally, in the high mROS gate of the neonatal cells, expression of CD69 was lower in comparison to the cells not producing mROS (Fig 2C and 2D).

Modeling the interplay between T cell activation and mROS

To further understand the interplay between metabolism, ROS and T cell activation, we integrated our own data and literature information on interactions between metabolic pathways and cell signaling in our previous TCR logical model [34]. The resulting model shown in Fig 3 integrates 111 components and 244 interactions or arcs, of which 64 components and 168 interactions are new. This graph includes two inputs corresponding to TCR and CD28 signals. Second messengers, as well as mROS and cROS were also considered, together with key components of glycolysis, the citric acid cycle, the pathways of fatty acid synthesis and breakdown, of the oxidation of glutamine and pentoses, as well as the electron transport chain. Selected transcription factors link these pathways to output nodes representing cell responses, including Activation, Quiescence (dormant cells), Anergy (incomplete activation of transcription factors) and Metabolic Anergy (aerobic glycolysis without oxidative mitochondrial metabolism). We also included IL-2 and CD69 as early activation markers. Logical rules specify how each component responds to incoming interactions. The model, including extensive annotations, is provided at the url: http://ginsim.org/node/229 (see also S1 Table).

Fig 3

Logical regulatory graph of the TCR-REDOX metabolism model.

The model was designed to study the interaction between the redox state and cellular metabolism and its influence on the activation of human CD8+ T lymphocytes. The nodes can be divided into three main categories: mitochondrial nodes (upper left), TCR signaling nodes (upper right), cytosol component nodes (center right and bottom right). A color code further denotes the type of node: green (input), gray (metabolic pathway), pink (ROS or ROS source), yellow (redox sensitive), magenta (output) and purple (phenotype). Elliptical shapes denote Boolean nodes (taking the values 0 or 1), while rectangular shapes denote multilevel (ternary) nodes (taking the values 0, 1 or 2). The arcs connecting the nodes represent positive (green) or negative (red) regulatory influences. In the case of multilevel regulatory nodes, the thresholds required for the activation of the different target nodes can be found in the model available online (http://ginsim.org/node/229).

To evaluate the predictive role of the model, we computed its stable states (Fig 4A, upper panel). In the absence of TCR or CD28 signal, cells remain quiescent or are kept in metabolic anergy. In the presence of both TCR and CD28 signals, cells could either turn anergic, with high ROS, or undergo activation.

Fig 4

The analysis of the logical model recapitulates the impact of metabolism on T cell activation.

(A) Computation of the stable states on selected nodes of the model in three different scenarios (No perturbation; mROS: 2 or cROS: 2 and GLUTAMINOLYSIS: 0). Each row represents a single stable state. For each node, the white cells (0) denote the lowest functional levels, while the gray cells denote intermediate (1) or maximum (2) levels of activity. We further used the MaBoSS software to evaluate the reachability of the different phenotype nodes in the absence of stimulation or upon TCR/CD28 stimulation (B). The pie charts represent the frequency of each phenotype under each condition, while the time plots represent the evolution of each phenotype node over time.

Next, we enforced a fixed high ROS perturbation in order to compute the impact of a high ROS levels in neonatal CD8+ T cell activation. We introduced the perturbation either with mROS or cROS at the maximum functional levels and evaluated the outcome of T cell activation. The stable states under high ROS levels lead to two anergy states in which TCR or CD28 were engaged separately. When the full CD3/CD28 signals were engaged, the cells underwent an incomplete activation state, in which NFAT was activated but not NFκB, AP1 or IL-2 (Fig 4A). In this condition, anergy, metabolic anergy, or a combination of them are predicted (Fig 4A, middle panel).

High arginase levels have been reported in the serum of neonates, which could lead to lower glutamine levels. We thus computed the effect of a blockade of glutamine breakdown (Fig 4A, lower panel), which resulted in anergy, metabolic anergy and incomplete activation.

Finally, using the MaBoSS tool, ([26, 27] https://maboss.curie.fr/), we performed stochastic simulations to assess the reachability of these stable states in unstimulated (Fig 4B left) or TCR/CD28 stimulated cells (Fig 4B right). In the non-stimulated (NS) scenario, the majority of the cells (98%) remained quiescent, and only under 2% undergo metabolic anergy. After activation, 23% of cells become active. With fixed activation of either mROS or cROS, 31% or 25% of cells undergo metabolic anergy, respectively. Under these conditions, only 15% of cells reach an active state.

In conclusion, our model thus predicts a dramatic effect of ROS status on T cell activation, reproducing our own data, as well as previous observations reviewed in [35].

Discussion

Energy metabolism has a dramatic effect on immune cell homeostasis and response and is determinant for health and disease. Neonatal T cells have a unique metabolic and signaling profile, which results in a low response to stimulation.

Neonatal CD8+ T cells encompass a higher percentage of cells with a very high mROS levels, while low mROS cells are reduced in comparison with adults. Low levels of ROS promote T cell activation, particularly due to the inhibition of phosphatases and the potentiation of NFκB signaling. High levels of ROS are in contrast detrimental for cell activation [35].

One of the most important signaling hubs is intracellular Ca2+. This second messenger is altered in neonatal lymphocytes. In mouse cells, particularly in CD4+ T cells, it was reported that calcium fluxes are increased in neonatal cells [20]. However, in human cells, it was reported that neonatal and adult CD8+ T cells have equivalent calcium fluxes [19]. Our model predicts that high ROS will increase cytosolic calcium, leading to the activation of NFAT and thus a preferential expression of IL-4 over that of pro-inflammatory cytokines [36]. This is in agreement with high IL-4 expression in neonatal cells and other conditions in which high ROS induces the expression of IL-4 [37, 38].

The high ROS levels of neonatal cells could be attributed to active glycolysis already in unstimulated cells [18] and/or to a low capacity of their mitochondria to control electrons along the Respiratory Chain, producing mROS [39]. Interestingly, cell activation induces Hv1 channel in neonatal but not adult cells, in which its expression is reduced. This channel expels the excess protons generated by the action of NADPH-oxidase. Recent evidence suggests that this channel is also located in the internal mitochondrial membrane, contributing to the production and modulation of mROS [40]. This suggest that neonatal cells cannot use the high levels of NADH produced by glycolysis. A low mitochondrial function of the neonatal cells has been reported [19]. If, as predicted by the model, high ROS induces a high level of calcium waves in neonatal cells, this might convert the mitochondria into calcium storage organelles, which could reduce their metabolic function [41].

Based on our model, we predict that high levels of ROS prevent the activation of the pentose phosphate pathway, fatty acid synthesis and glutamine breakdown, which are necessary for proper T cell activation. The microenvironment of the neonatal lymphocytes shows high levels of the enzyme arginase [42], which would limit Arginine, which in turn would affect the glutamine available for synthesis de novo of glutathione [31]. The correct balance of these metabolic activities is crucial for T cell function, as exemplified by infiltrating lymphocytes in a tumor microenvironment in which ROS, glutaminase and arginase contribute to lower the activation potential of immune cells [15–17, 22, 23].

The dynamic analysis of our model indicates that under high ROS levels, over 25% of T cells would be in metabolic anergy, thereby lowering their activation potential, which would tentatively protect newborns from excessive activation at birth, when confronted with many novel antigens.

Some limitations of this study need to be declared, however, in order to consider our findings under the proper light. First, the number of samples was limited although we obtained statistical significance for our results. Second, the T cell pool from cord blood samples has a considerable amount of recent thymic emigrants, with reduced activation potential and tolerant features. Identifying these populations have been a challenge for CD8+ T cells, because of the lack of a bona-fide phenotypic marker. The reliable marker of recent thymus emigrants only identifies those of CD4+ Tcells [43]. Third, these experiments were performed in vitro, hence, the influence of other relevant cell types (e.g. dendritic cells and macrophages) in the surrounding microenvironment around CD8+ T cells in the redox signals could not be assessed.

In conclusion, the metabolic and redox profile of neonatal lymphocytes tentatively impairs their activation potential. This should be addressed in studies aiming at boosting neonatal immunity. In addition, our model could be useful in other situations, e.g. to identify the nodes that could be targeted in order boost T cell effector function in tumors.

Annotations for the TCR-REDOX-Metabolism model and specification of the logical rules.

This table has been generated using an export function of the software GINsim and lists the following information for each node of the model (first column):

a series of database entry identifiers documenting the sources of information used to build the model (second column);

the Boolean rules defined for each node; note that in the case of multilevel (ternary) nodes, two rules are specified, for values 2, and 1, respectively (third column, upper part of the cells); these rules combine literals (node names) with the standard Boolean operators NOT (denoted by the symbol !), AND (denoted by &), OR (denoted by |), and parentheses whenever required;

textual annotations explicating the underlying modeling assumptions.

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29 Oct 2019

PONE-D-19-24723

Contribution of ROS and metabolic status to  neonatal and adult CD8+T cell activation

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Reviewer #1: While interesting major elements are missing from the paper and need to be added.

1. Important details are missing in the methods, particularly the number of cord blood and adult samples obtained. Were these from particular sexes? What was the time from the obtaining of the cord blood samples to their processing? Was this variable and did this affect the result. What were the age and sex of the adult donors? Was there a similar interval between taking of blood from adult donors and its processing? Was the cord blood from natural births or caesarians. Had the mothers received any drugs during labour that might have affected the T cell responses. Was any attempt made to obtain parallel blood from the mothers to compare to the cord blood as this would exclude any drug or hormonal confounders that might be affecting the cord blood T cells.

2. Fig 2 seems to suggest there were only 3 cord blood and 3 adult samples obtained? How do the authors know these responses are representative? As cord blood may not be representative of blood of young children due to prevailing maternal and birth stress factors, was any attempt made to obtain blood from young babies to show that these exhibited the same pattern as the cord blood samples?

3. In view of the above the authors need to add a paragraph at the end of the study describing shortcomings in the study design and cautions in interpretation of the data.

Reviewer #2: The manuscript by Sanchez-Villanueva et al sought to determine the correlation of reactive oxygen species (cytosol and mitochondria) against the known lower T cell activation profile of neonates. Specifically, they obtained cord blood or adult blood and stimulated with a polyclonal activator and then determined mROS and cROS in CD8 T cells. They found neonates consistently had high levels of mROS while adults had a mix of high and low mROS. They then built a logic model to depict the effects of activation on neonatal CD8 T cells.

The overall results of the study are quite interesting with respect to the mROS differences. There are a few issues that should be addressed to improve the manuscript:

1. A through reading of the manuscript to improve the wording would help the reader. There are few word choices and sentence mechanics all throughout the manuscript that make it a bit difficult to read.

2. In figure 2, the authors found a large difference in HV-1 expression between infants and adults. However, it appears that GADPH was used as the normalization control which is no longer appropriate. GADPH is so widely impacted by immunological processes (i.e. TNFa or IFNg mRNA transcripts bind to it) that it is not an appropriate housekeeping control. Their data may still be relevant, but they should ensure the rigor of their findings with another calibrator, especially when comparing neonates to adults where transcripts such as IFNg are widely different during activation

3. Comparisons of neonates to adults naïve T cells is not an apples to apples comparison. Neonates are loaded with recent thymic emigrants in blood that express lower T cell function that may enter the competitive pool for activation. Since the authors did not gate on these or remove them, they should at least bring up the topic of RTEs in the discussion.

4. The authors should at least discuss the high mROS they observed in the context of NFAT and IL-2. Both of these are lower in neonates, affect the activation profile even at 6 hours, and are inhibited by high mROS. Contrary, IL-4 which is supposedly higher in neonates is also inhibited by high mROS and thus this should all be in the discussion section.

5. Calcium influx increases mROS in T cells as determined in a number of prior studies. The authors cite reference 20 to indicate that influx may be high in infants. There are a number of studies that indicate that the influx may also be lower in neonates and that the increase is only in CD4 T cells (and non-Th1 cells at that). They authors should at least discuss this.

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8 Nov 2019

Responses to Editor and Reviewers:

We thank the editor and the two reviewers for their constructive criticism, which enabled us to greatly improve our manuscript.

Please find enclosed below a point by point answer to all concerns:

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We have reviewed the manuscript, labeled the former and new modified file as asked by PLOS ONE

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We have changed the current ethics statement to:

The collection of cord blood samples, with informed mothers’ consent was granted for this work by Comité de Etica en Investigación Hospital General de Cuernavaca Dr. José G. Parres. CONBIOÉTICA-17-CEI-001-20160329

The collection of adult blood was granted by the ethics committee of Secretaría de Salud. Dirección de Atención Médica Departamento Centro Estatal de laTransfusión Sanguínea Oficio No: SH/CETS/438/2016.

This is shown in lines 131-136

2. (b) Once you have amended this/these statement(s) in the Methods section of the manuscript, please add the same text to the “Ethics Statement” field of the submission form (via “Edit Submission”).

We have done that.

3. We suggest you thoroughly copyedit your manuscript for language usage, spelling, and grammar. If you do not know anyone who can help you do this, you may wish to consider employing a professional scientific editing service.

The revised manuscript has been reviewed by Prof. Christopher Ian Pogson, English born scientist, graduated in Cambridge, UK. Former head of the Biochemistry Department of Manchester University Scientist and Editor of The Biochemical Journal, now retired.

Reviewers Comments to the Author:

Reviewer # 1: While interesting major elements are missing from the paper and need to be added.

1. Important details are missing in the methods, particularly the number of cord blood and adult samples obtained. Were these from particular sexes? What was the time from the obtaining of the cord blood samples to their processing? Was this variable and did this affect the result. What were the age and sex of the adult donors? Was there a similar interval between taking of blood from adult donors and its processing? Was the cord blood from natural births or caesarians. Had the mothers received any drugs during labour that might have affected the T cell responses. Was any attempt made to obtain parallel blood from the mothers to compare to the cord blood as this would exclude any drug or hormonal confounders that might be affecting the cord blood T cells.

For each comparison the minimum number of samples included in the neonate vs adult groups was three. For most cases the number of samples was five. The total number of samples used for this study was 30 samples for neonatal CD8+T cells and 30 samples of adult cells. Because we were using purified naïve cells, only 3 to 6 million CD8+ T cells were obtained from cord blood, and 5 to 10 million from adult samples. No eligibility criteria were defined regarding the donors’ gender, this parameter was distributed randomly across the samples, and an equivalent number of male and female donors was used for neonatal and adult cells. No differences related to gender were detected. For adult samples, the ages of the donors were in the range between 26 and 40 years. All cord blood samples were obtained from vaginal deliveries, no drugs known to affect T cells responses were administered to the mothers during labor, mostly because of budget limitations in the hospital. All the samples were immediately processed after collection. This information has been included in the Materials and Methods section of the revised manuscript, lines: 130-150. We did not collect blood from mothers because we only obtained permission for noninvasive procedures.

2. Fig 2 seems to suggest there were only 3 cord blood and 3 adult samples obtained? How do the authors know these responses are representative? As cord blood may not be representative of blood of young children due to prevailing maternal and birth stress factors, was any attempt made to obtain blood from young babies to show that these exhibited the same pattern as the cord blood samples?

In Fig 2A, we obtained 5 adult cells samples and 5 neonatal cell samples, in Figs 2B and 2C we obtained only 3 adult samples and 3 neonatal samples. For Fig 2D, we captured 4 neonatal blood samples. We made more evaluations of CD69 protein with the same results. Other members in the lab have also measured this protein with similar results, but together with ROS measurements, we made 3 evaluations with statistically significant results. The data shown in Fig 2 are consistent with a high ROS levels in the neonatal cells and come from independent biological samples (because of the limitations on the number of cells), thus strengthening our conclusions. Mothers in Hospital Parres do not receive anesthetics, hormones or drugs during delivery, unless they go into caesarian-section. All samples were captured from vaginal deliveries, with no treatment whatsoever. The number of CD8+ T cells for about 40 ml of cord blood is about 3 and 5 million cells. It is almost impossible to obtain CD8+T cells from infants later in life. In addition, the amount of blood that could be authorized for young infants’ samples is 1 mL. Care was taken to obtain the blood immediately after delivery and before placenta expulsion, to have a sample more representative of the infant blood. Cells were put in culture medium during the purification period of two days, which arrests the cells in resting conditions. We have compared cord blood from vaginal deliveries versus programmed caesarian sections and these proved to be largely similar. Although published reports claim that cord blood is not representative of the infant blood, these do not state how they obtained the samples (Olin et al. (2018). Cell 174 (5): 1277-92). Note that most labs are only allowed to get cord blood samples from arterial blood of expelled placentas, in which the blood started the coagulation process and both serum and cell types are altered because they get trapped in the clot. In contrast, in our study, cord blood was sampled immediately after delivery and before placenta expulsion.

3. In view of the above the authors need to add a paragraph at the end of the study describing shortcomings in the study design and cautions in interpretation of the data.

This has been included in the revised manuscript, lines 510-561.

Reviewer # 2: The overall results of the study are quite interesting with respect to the mROS differences. There are a few issues that should be addressed to improve the manuscript:

1. A through reading of the manuscript to improve the wording would help the reader. There are few word choices and sentence mechanics all throughout the manuscript that make it a bit difficult to read.

The revised manuscript has been reviewed by Prof. Christopher Ian Pogson, English born scientist, graduated in Cambridge, UK. Former head of the Biochemistry Department of Manchester University and Editor of The Biochemical Journal now retired.

2. In figure 2, the authors found a large difference in HV-1 expression between infants and adults. However, it appears that GADPH was used as the normalization control which is no longer appropriate. GADPH is so widely impacted by immunological processes (i.e. TNFa or IFNg mRNA transcripts bind to it) that it is not an appropriate housekeeping control. Their data may still be relevant, but they should ensure the rigor of their findings with another calibrator, especially when comparing neonates to adults where transcripts such as IFNg are widely different during activation.

A set of experiments were performed with the aim of selecting the proper reference gene for qPCR comparisons. We are enclosing below a table with information regarding the quality of the primers.

The relative expression of these genes was evaluated in Peripheral Blood Mononuclear Cells (PBMC) and Cord Blood Mononuclear Cells (CBMC) samples at 0, 3 and 6 hours after anti-CD3/anti-CD28 in vitro stimulation. The gene GAPDH was the one selected due to the stability of its expression levels across the stimulation period (Fig 1A).

In Fig 1B, we present the relative expression of GAPDH mRNA upon anti-CD3/anti-CD28 in vitro stimulation on purified CD8+T cells from neonates or adults. No differences were detected due to the effect of the TCR stimulus.

Fig 1. Evaluation and selection of the internal reference gene for qPCR comparisons. Total RNA was extracted from untreated or TCR/CD28 stimulated cells, using the TRIzol reagent. The GAPDH, B2M and ACTB mRNA levels were evaluated using specific primers on a qPCR. (A) PBMC or CBMC were evaluated. (B) Purified CD8+ T cells from neonates (N) or adults (A) were evaluated. A paired two-tailed t test was used for comparison between samples.

3. Comparisons of neonates to adults naïve T cells is not an apples to apples comparison. Neonates are loaded with recent thymic emigrants in blood that express lower T cell function that may enter the competitive pool for activation. Since the authors did not gate on these or remove them, they should at least bring up the topic of RTEs in the discussion.

The T cell pool from cord blood samples has a considerable amount of recent thymic emigrants, with reduced activation potential and tolerance features. Identifying these populations have been a challenge for CD8+T cells, because of the lack of a bona-fide phenotypic marker. The reliable marker of recent thymus emigrants only identifies CD4+ T cells. We discuss this point in the revised manuscript, lines 512-516.

4. The authors should at least discuss the high mROS they observed in the context of NFAT and IL-2. Both of these are lower in neonates, affect the activation profile even at 6 hours, and are inhibited by high mROS. Contrary, IL-4 which is supposedly higher in neonates is also inhibited by high mROS and thus this should all be in the discussion section.

The cases of NFAT and IL2 are now further commented in the revised manuscript, lines: 410-417, 467-470. For IL-4, as suggested by the reviewer, we included the impact of mROS on IL-4 expression in the discussion 567-470.

5. Calcium influx increases mROS in T cells as determined in a number of prior studies. The authors cite reference 20 to indicate that influx may be high in infants. There are a number of studies that indicate that the influx may also be lower in neonates and that the increase is only in CD4 T cells (and non-Th1 cells at that). They authors should at least discuss this.

We added a paragraph addressing this point in the revised manuscript, lines: 463-470.

Submitted filename: Response to Reviewers.pdf

Click here for additional data file.

26 Nov 2019

Contribution of ROS and metabolic status to neonatal and adult CD8+ T cell activation

PONE-D-19-24723R1

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4 Dec 2019

PONE-D-19-24723R1

Contribution of ROS and metabolic status to neonatal and adult CD8+ T cell activation

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