1. Immunology and Inflammation
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Bystander hyperactivation of preimmune CD8+ T cells in chronic HCV patients

  1. Cécile Alanio
  2. Francesco Nicoli
  3. Philippe Sultanik
  4. Tobias Flecken
  5. Brieuc Perot
  6. Darragh Duffy
  7. Elisabetta Bianchi
  8. Annick Lim
  9. Emmanuel Clave
  10. Marit M van Buuren
  11. Aurélie Schnuriger
  12. Kerstin Johnsson
  13. Jeremy Boussier
  14. Antoine Garbarg-Chenon
  15. Laurence Bousquet
  16. Estelle Mottez
  17. Ton N Schumacher
  18. Antoine Toubert
  19. Victor Appay
  20. Farhad Heshmati
  21. Robert Thimme
  22. Stanislas Pol
  23. Vincent Mallet
  24. Matthew L Albert  Is a corresponding author
  1. Institut Pasteur, France
  2. Centre d'Immunologie et des Maladies Infectieuses (CIMI-Paris), France
  3. Emory, United States
  4. Albert-Ludwigs-Universität, Germany
  5. Assistance publique - hôpitaux de Paris, France
  6. The Netherlands Cancer Institute, The Netherlands
  7. Lunds University, Sweden
  8. Université Paris Descartes, France
  9. Hôpital Cochin, France
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Cite this article as: eLife 2015;4:e07916 doi: 10.7554/eLife.07916

Abstract

Chronic infection perturbs immune homeostasis. While prior studies have reported dysregulation of effector and memory cells, little is known about the effects on naïve T cell populations. We performed a cross-sectional study of chronic hepatitis C (cHCV) patients using tetramer-associated magnetic enrichment to study antigen-specific inexperienced CD8+ T cells (i.e., tumor or unrelated virus-specific populations in tumor-free and sero-negative individuals). cHCV showed normal precursor frequencies, but increased proportions of memory-phenotype inexperienced cells, as compared to healthy donors or cured HCV patients. These observations could be explained by low surface expression of CD5, a negative regulator of TCR signaling. Accordingly, we demonstrated TCR hyperactivation and generation of potent CD8+ T cell responses from the altered T cell repertoire of cHCV patients. In sum, we provide the first evidence that naïve CD8+ T cells are dysregulated during cHCV infection, and establish a new mechanism of immune perturbation secondary to chronic infection.

https://doi.org/10.7554/eLife.07916.001

eLife digest

Long-lasting or “chronic” infections massively perturb the immune system as a way to favor their own growth. In particular, they can stop T cells – a subtype of immune cells that help to destroy viruses – from working well. For example, HIV and hepatitis C viruses can overwork T cells and cause them to die. This can make individuals vulnerable to other infections.

In healthy people, T cells that have participated in the fight against particular infections continue to live to provide a memory of those past infections. Groups of “naïve” T cells that have not yet encountered an infected cell also patrol the body, ready to respond to infections by a new virus. There are relatively few virus-specific naïve T cells in the body, so until recently it has been hard to study them. As a result, researchers know little about how these cells are affected by long-lasting infections, and whether chronic infection affects our capacity to fight unrelated infections.

Alanio et al. have now used a highly sensitive technique to compare naïve T cells found in the blood of three groups of people: those with chronic hepatitis C infections, those who have been cured of a chronic hepatitis C infection, and healthy people. This revealed that the naïve T cells are negatively affected by chronic hepatitis C infections, and become hypersensitive: they get easily overexcited, which can lead to their death. This compromises the immune defenses at the moment they are most needed.

Closer inspection showed that the naïve T cells of patients with hepatitis C are hypersensitive because they have less of a protein called CD5 on their surface. This protein acts as a natural brake for the T cells, and thus having less results in the T cells mounting stronger immune responses. Although this might be beneficial when fighting certain infections, this may also account for conditions where T cells attack healthy tissues.

Finally, Alanio et al. found evidence that people who have been cured of a chronic hepatitis C infection recover a healthy set of naïve T cells within two years. Treating patients as soon as an infection is diagnosed therefore has several benefits: as well as clearing the virus, this will reset the immune system balance and reduce the damage that hyperactive immune cells cause to the body.

The results also have implications for vaccinations, which work by pushing naïve T cells to arm themselves against a particular virus. The discovery that naïve T cells are hypersensitive in patients with hepatitis C suggests that we may need a distinct strategy for efficiently vaccinating these patients. It is indeed possible that standard vaccines – tested in groups of healthy people – may result in unexpected and unwanted immune responses in individuals with hepatitis C.

These open questions will be addressed in further studies. It will also be of interest to know if other chronic viruses have the same ability to alter the activity of naïve T cells.

https://doi.org/10.7554/eLife.07916.002

Introduction

Functional impairments of CD8+ T cells have been characterized in several persistent viral infections, including human immunodeficiency virus (HIV) and hepatitis C virus (HCV) infection in humans, simian immunodeficiency virus (SIV) infection in macaques, and lymphocytic choriomeningitis virus (LCMV) infection in mice (Ahmed and Rouse, 2006). In particular, it has been shown that chronic infection skews memory/effector CD8+ T cell differentiation (Stelekati and Wherry, 2012), and drives virus-specific CD8+ T cells towards an « exhausted » phenotypic state, as marked by high expression of the programmed cell death-1 (PD-1) molecule (Kim and Ahmed, 2010). Chronic infections have also been reported to impair immune responses to unrelated infectious microbes in mouse models (Stelekati and Wherry, 2012; Richer et al., 2013), as well as in humans infected with HCV (Park and Rehermann, 2014). This phenomenon correlates with a interferon (IFN) stimulated gene (ISG) transcriptional signature, suggesting an indirect effect of systemic type I IFN secondary to innate immune activation (Stelekati et al., 2014). Following from these observations, we hypothesized that chronic infection may alter the T cell preimmune repertoire, which plays an important role in shaping the adaptive immune responses (Jenkins and Moon, 2012). Employing a newly validated approach for the study of low-frequency (< 10–5) antigen-specific T cells (Alanio et al., 2010), we evaluated this prediction in patients with chronic viral infection of the liver.

The α/β T cell preimmune repertoire is defined as the set of mature but antigen inexperienced lymphocytes that circulate in blood and secondary lymphoid organs, ready to be activated by cognate high-affinity peptide-class I MHC (pMHC) complexes (Jenkins et al., 2010). They are maintained in the periphery by survival factors such as IL-7, as well as transient contacts with low affinity non-cognate pMHC complexes (Sprent and Surh, 2011). Over the last decade, studies using newly-developed tetramer-enrichment assays - sensitive enough to detect and track antigen-specific populations prior to immunization - have provided new insights into the impact of preimmune repertoire heterogeneity (Jenkins et al., 2010). First, the number of antigen-specific T cells (i.e. precursor frequency) is not equivalent across inexperienced populations, with the absolute number positively correlating with the magnitude of responses that are induced upon priming (Obar et al., 2008; Moon et al., 2007; Kwok et al., 2012; Schmidt et al., 2011; Kotturi et al., 2008; Tan et al., 2011). Second, antigen-inexperienced CD4+ and CD8+ T cell populations contain variable proportions of memory-phenotype (MP) cells (Legoux et al., 2010; Su et al., 2013). These cells have been explained in the literature as a result of cross-reactivity or homeostatic proliferation (Sprent and Surh, 2011). Cross-reactivity is now recognized as an essential feature of the T-cell receptor (TCR) / MHC interaction (Mason et al., 1998), and a major determinant of virus-specific MP cells in the CD4+ T cell repertoire of unexposed healthy donors (Su et al., 2013). Alternatively, homeostatic proliferation may occur in settings of lymphopenia (Jones et al., 2013). Finally, differential CD5 expression by antigen-specific T cell populations has been shown to dictate clonal recruitment and expansion (Fulton et al., 2015; Tabbekh et al., 2013). To date, the impact of non-heritable influences such as human chronic viral infection on the quantitative and qualitative aspects of the preimmune repertoire remains unknown.

In our study, we focused on patients with chronic hepatitis C virus infection (cHCV), which show CD8+ T cell dysfunction (Park and Rehermann, 2014; Rehermann and Nascimbeni, 2005). In particular, HCV-specific responses are typically (i) weak – both in term of numbers and function, (ii) of low avidity, and (iii) blocked in their differentiation into central memory cells, despite the availability of cognate pMHC complexes (Park and Rehermann, 2014). cHCV is to date the only chronic viral infection that can be cured, offering the unique possibility to interrogate the reversibility of immune perturbations post-viral clearance (Pol et al., 2013). Herein, we applied the highly sensitive tetramer-associated magnetic enrichment (TAME) technique for investigating at the antigen-specific level the impact of chronic viral hepatitis infection on the CD8 T cell preimmune repertoire (Alanio et al., 2010). Although precursor frequencies were similar to healthy controls, we observed significant impairments of the preimmune repertoire in cHCV patients. Inexperienced T cell populations contained increased proportions of MP cells. This correlated with naïve-phenotype CD8+ T cells having lower surface expression of CD5, which accounted for a lower threshold for TCR signaling and the generation of potent immune responses from cHCV patients. Importantly, the positive effect of chronic infection on naïve T cell recruitment into immune responses is transient, as cHCV patients who clear their virus following successful therapy (referred to as Sustained Virologic Responders or SVR) can experience a reversion towards a healthy naïve T cell repertoire within 2 years. These data provide the first evidence for chronic infection resulting in the bystander dysregulation of the antigen-specific preimmune repertoire in humans, and highlight the added benefit of early viral clearance in patients with chronic HCV infection.

Results

Perturbed naïve CD8+ T cell repertoire during chronic infection

To test the hypothesis that chronic viral infection perturbs preimmune repertoire homeostasis, we evaluated the influence of cHCV infection on the phenotype of circulating CD8+ T cells. 29 cHCV and 37 Sustained Virologic Responders (SVR, i.e. patients achieving clearance of the virus after therapy) patients were included in the study (Table 1). 62% of the chronic and 100% of the SVR patients received at least one anti-HCV treatment (of those treated, 69% received conventional IFN-ribavirin bitherapy, 31% IFN + direct antiviral agent (DAA), and IFN-free DAA combination therapy alone in the case of a single SVR patient). Healthy donors from the blood bank were included as controls. Total lymphocyte numbers were within the normal range for all tested patients (median 2.2 +/- 0.6 G/l). Within the CD3+ lymphocyte population, we observed similar percentages of circulating CD8+ T cells (Figure 1—figure supplement 1). However, absolute numbers of CD3+ were significantly increased in our cohort of cHCV (KW p<0.0001), translating into increased absolute numbers of CD8+ T cells in cHCV patients (KW p=0.0002) (Figure 1—figure supplement 2). We further subsetted the CD8+ T cells according to their surface expression of CD45RA and CD27. Based on prior studies (Alanio et al., 2010; De Rosa et al., 2001) and our confirmatory experiments using 5 phenotypic markers for naïve or memory T cells, we determined that co-expression of high levels of CD45RA and CD27 were sufficient to classify naïve T cells in both HD and cHCV patients (Figure 1—figure supplement 3). Decreased percentages of naïve CD8+T cells have previously been reported in cHCV (Shen et al., 2010). Here, we confirmed these findings in age- and CMV- matched chronically infected patients (KW p=0.0007, Figure 1A,B). Interestingly, we found that after correcting for the higher CD8+ T cell numbers in cHCV patients, the absolute numbers of naïve CD8+ T cells were within the normal range as determined by the study of healthy donors (Figure 1C). We therefore interpreted the lower proportion of naïve T cells to simply be a result of an expansion of the memory cell compartment.

Figure 1 with 4 supplements see all
Perturbed naïve CD8+ T cell repertoire during chronic HCV infection.

Percentages and absolute numbers of CD3+ and CD3+CD8+ cells in Healthy Donors (HD), Sustained Virologic Responder (SVR), and chronic HCV (cHCV) patients are provided in Figure 1—figure supplement 1 and 2. (A) Representative examples of CD45RA+CD27+ naïve CD8+ T cell compartment in the three donor subsets. FACS plots are gated on Live CD3+CD8+ cells. Validation of CD45RA/CD27 gating strategy for identifying naïve CD8+ T cells in cHCV patients is provided in Figure 1—figure supplement 3. (B) Percentages of naïve CD8+ T cells in the three donor subsets. (C) Absolute numbers (G/L) of naïve CD8+ T cells in HD, SVR, and cHCV patients. ns (not significant, p>0.05), *(p≤0.05), **(p≤0.01), and ***(p≤0.001) refer to Dunn’s multiple comparison test of each subset toward HD. (D) Normalized numbers of sjTRECs per 150,000 naïve CD8+ in HD and cHCV samples. Normalized numbers of sjTRECs per total CD8+ T cells are provided in Figure 1—figure supplement 4. (E) Representative example of the distribution of 24 FACS-screened Vβ families in naïve CD8+ T cells from one HD and one cHCV sample. Families are ordered by increasing size in both individuals. (F) Lorenz curves representing the cumulative distribution of % of usage of 24 FACS-screened Vβ families from 7 HD and 7 cHCV patients. Mean Gini coefficients and standard deviations are indicated. Red line indicates an extreme example of an unequal distribution, observed in the case of a T-cell lymphoma where >90% of the TCR repertoire is explained by one particular Vβ chain. (G) Individual Gini coefficients of all tested samples are represented for HD and cHCV subgroups.

https://doi.org/10.7554/eLife.07916.003
Table 1

Donors included in the study.

https://doi.org/10.7554/eLife.07916.008
All donorscHCVSVRHD
n=29n=37n=25
Male, n (%)16 (55)21 (57)12 (48)
Age, years, median (IQR1-3)48 (42-55)48 (44-58)38 (31-46)
IgG anti-CMV positive, n (%)14 (48)24 (51)11 (44)
Cirrhosis, n (%)5 (17)8 (22)na
Treatment experienced, n (%)18 (62)37 (100)na
Treatment (n per type: 0/1/2/3)11/12/6/00/26/10/1na
Delay post-treatment, years, median (IQR1-3)3.8 (3.4-4.2)1.7 (0.9-3.2)na

To directly test this prediction, we isolated CD8+ T cells and measured the frequency of signal joint TCR excision circles (sjTREC), by-products of TCR rearrangement, and previously validated as a measure of thymic production (Rehermann and Nascimbeni, 2005; Clave et al., 2009). Confirming previous studies, we found a significant decrease in sjTREC content of CD8+ T cells (MW p=0.01, Figure 1—figure supplement 4). To address the bias due to differential naïve T cell number, we isolated CD45RA+/CD27+ naïve CD8+ T cells and assessed sjTREC frequencies. Surprisingly, we also observed within the naïve compartment a significantly lower sjTREC content in cHCV patients as compared to HD (MW p=0.03, Figure 1D). To further characterize this phenotype, we assessed the Vβ distribution within the naïve repertoire of cHCV patients. cHCV patients showed a biased repertoire with increased representation of selected Vβ families. A representative example of Vβ usage plotted as percentage accross the 24 tested families, and ordered by increasing size from one cHCV patient and one HD is shown (Figure 1E). To compare distributions, Lorenz curves were constructed as a graphical representation of the diversity of the repertoire (Figure 1F). Inequality measurements in the Vβ distribution, comparing cHCV patients to HD, indicated proportions of naïve T cells being altered in their Vβ usage. In brief, for a given percentage (x) of the 24 Vβ chains, Lorenz curves indicate the proportion of the T cell population that have Vβ chains among the 24 * x% least abundant ones. An equal distribution is represented as the dotted line. By contrast, an extreme, unequal distribution is shown in red, as in the case of a T-cell lymphoma where >90% of the TCR repertoire is explained by one particular Vβ chain (red line). We included Gini coefficient as a numeric measure of Lorenz curve’s based observations. It corresponds to the ratio of the area between the line representing equal use of all Vβ chains (dotted line) and the observed Lorenz curve to the total area below the line representing equal use. The higher the coefficient, the more unequal is the distribution. In line with our observation, we found Gini coefficients increased in cHCV patients (M-W p=0.03, see Material and Methods for details of calculation) (Figure 1G). These data support an overall perturbed naïve CD8+ T cell repertoire in cHCV patients, with increased peripheral expansion of selected populations.

MP Mart1-specific CD8+ T cells during chronic infection may be reversed by viral clearance

To evaluate more precisely the impact of these perturbations on antigen-specific populations, we applied recently developed strategies to detect, quantify and phenotype rare inexperienced antigen-specific CD8+ T cells (Klenerman and Thimme, 2012; Alanio et al., 2013). Specifically, we utilized TAME to enumerate and subdivide Mart1-specific T cell populations. While similar absolute numbers of Mart1-specific CD8+ T cells were observed in our respective study groups (Figure 2A,B), SVR and cHCV patients showed a more differentiated phenotype (Figure 2C), defined by fewer CD45RA+/CD27+ and increased proportions of memory-phenotype (MP) cells (KW p<0.0001, Figure 2D). Of note, these MP cells were mostly of central-memory (CD45RA-CD27+) phenotype (Figure 2—figure supplement 1). Also, when considering only naïve-phenotype Mart1-specific cells, precursor frequencies were still comparable across the different study groups (Figure 2—figure supplement 2). We were able to purify sufficient numbers of Mart1-specific naïve- and memory- phenotype CD8+ T cells from one HCV patient to perform an immunoscope analysis on the Vβ chain usage (Figure 2E). In line with our data in bulk T cells populations (Figure 1), we observed a restricted repertoire of Mart1-specific naïve T cells, with evidence of an expanded Vβ clonotype in memory cells. These data argue in favor of MP cells being the progeny of a perturbed naïve T cell repertoire. Although they could be expanded in response to either specific or non-specific signals, we favor the latter hypothesis based on prior knowledge of Mart1 antigen pattern of expression (Pittet et al., 1999).

Figure 2 with 2 supplements see all
Peripheral differentiation of Mart1-specific CD8+ T cells during cHCV infection.

(A) Representative examples of Mart1-specificCD8+ T cellpopulations in HD, SVR, cHCV patients. FACS plots are gated on TAME-enriched LiveFSCloSSCloCD3+CD8+ PBMCs. (B) Precursor frequency of Mart1-specific cellsin the three donor subsets. Precursor frequency of naïve-phenotype Mart1-specific cellsis provided in Figure 2—figure supplement 1. (C) Representative examples of the CD45RA/CD27 phenotype of TAME-enriched Mart1-specificpopulations in patients subsets as in A. D/ Percentages of memory-phenotype (MP) cells in Mart1-specific populations in the three donor subsets. Further subsetting of MP inexperienced T cells into CD45/CD27-based T cell differentiation phenotype is provided in Figure 2—figure supplement 1. E/ Immunoscope profile of naïve and memory Mart1-specific populations FACS-sorted from one cHCV patient.

https://doi.org/10.7554/eLife.07916.009

We next extended our observations to other antigen specificities by using four additional multimers (hTERT1572-580, human CMV pp65495-503, Ebola NP202-210 (Sundar et al., 2007), HIV-1 Gag p1777-85) that are expected to detect inexperienced self- and virus-specific CD8+ T cell populations in tumor-free, CMV-, Ebola- and HIV- seronegative individuals. Here again, we found high proportions of T cells with a memory-phenotype in both self (Mart1- and hTERT- specific) and viral (CMV-, Ebola- and HIV- specific) antigen-inexperienced populations of cHCV patients as compared to healthy donors (representative plots are shown in Figure 3A and B; and combined results from 2–6 individuals per group in Figure 3C; self-specific: KW p<0.001; non-self-specific: KW p=0.009). When subsetted using CD45RA and CD27 phenotypic markers, the MP cells found in cHCV patients were preferentially of CD45RA-CD27+ central memory phenotype (Figure 3—figure supplement 1).

Figure 3 with 1 supplement see all
Memory-phenotype cells within self and non-self antigen-inexperienced populations.

(A) Representative examples of Mart1-, hTERT-, CMV-, Ebola- and HIV- specificpopulations from HD, SVR, and cHCV patients. Enriched tetramer-specific populations are overlaid on total CD8+ T cells. (B) CD45RA/CD27 phenotype of tetramer-specific populations gated in A. (C) Percentages of memory-phenotype cells in Mart1- and hTERT- (self); CMV-, Ebola- and HIV- (non-self) specific populations from HD, SVR and cHCV patients. Further subsetting of MP inexperienced T cells into CD45/CD27-based T cell differentiation phenotype is provided in Figure 3—figure supplement 1.

https://doi.org/10.7554/eLife.07916.012

We next compared cHCV patients to those who achieved viral clearance. Sequential samples (available from five patients who achieved cure) suggested that immune restoration of the naïve compartment is possible (positive time dependency p-value p=0.03, Figure 4A and B). These patients were all treated by IFN-RBV biotherapy (n = 3), or triple therapy that included an NS3 inhibitor (n = 2, patients S2 and S12). Testing our observation in our cross-sectional cohort, we replicated our findings, showing a statistically significant recovery of naïve antigen-specific CD8+ T cells as a function of time (MW p=0.04; Figure 4C). These results indicate that the differentiated cells within the perturbed repertoire of cHCV patients are a reflection of active HCV infection, and likely not a result of cross-reactivity or true memory T cell differentiation. Together the results in Figures 14 highlight an overall perturbation of the preimmune CD8+ T cell compartment during active cHCV infection.

Memory phenotype of Mart1-specific CD8+ T cells during chronic infection may be reversed by viral clearance.

(A) Example of CD45RA/CD27 phenotype of Mart1-specific cells during chronic phase, and over time after viral clearance in one HLA-A0201 SVR patients (patient S7). (B) Percentages of Mart1 memory-phenotype cells over time after viral clearance on 5 SVR patients with longitudinal sampling – including S7 presented in E. (C) Percentages of memory-phenotype cells in Mart1-specific populations vs. time elapsed since clearance of the virus in SVR patients (time-stratified, in years). These data include all HLA A0201 SVR patients; first available data is incorporated for follow-up patients presented in F.

https://doi.org/10.7554/eLife.07916.014

Decreased expression of CD5 on naïve CD8+ T cells associates with TCR hypersensitivity in cHCV patients

To establish a mechanistic understanding of our findings, we considered the key homeostatic factors governing maintenance of the naïve CD8+ T cell compartment (Jenkins et al., 2010; Ho and Hsue, 2009; Hazenberg et al., 2000). We hypothesized that an altered threshold for TCR activation could explain the differentiation phenotype of inexperienced T cells. CD5 expression has been shown to correlate with the threshold of activation in mice (Grossman and Paul, 2015). It is typically high on naïve T cells, showing diminished levels as a function of T cell differentiation (Figure 5—figure supplement 1). We observed phenotypic changes (i.e. low CD5 expression) that were significant for the comparison between CD45RA+/CD27+ naïve CD8+ T cells in cHCV vs HD (KW p=0.02, Figure 5A and B). Based on its role in regulating TCR signaling, we predicted that lower CD5 expression on naïve T cells would result in their hyperactivation upon stimulation. This was tested functionally by evaluating TCR signaling in naïve CD8+ T cells, stimulating cells with low doses of plate-bound anti-CD3 and anti-CD28 Abs. While only weak induction of phosphorylated ERK (p-ERK) could be observed in HD during the first hour of stimulation, TCR stimulation induced a strong p-ERK signal in naïve cells from seven of sixteen cHCV patients tested (histograms from one responding cHCV and one HD are shown in Figure 5C; MW p=0.03, Figure 5D). Using the same stimulation protocol, we investigated expression of activation markers (i.e., CD25, CD69) measured after 24 hr stimulation. Consistent with p-ERK data, we observed higher percentages of cells expressing CD25 on naïve CD8+ T cells from cHCV as compared to HD (representative example from one cHCV and one HD are shown in Figure 5E; MW p=0.02, Figure 5F). Similar results were obtained for CD69 analysis (data not shown). We also observed increased percentages of naïve CD8 T cells undergoing activation-induced cell death – as assessed by active caspase 3 staining after 24 hr - in cHCV patients as compared to HD (MW p=0.002; Figure 5—figure supplement 2). These findings are all consistent with strong TCR engagement despite the use of low doses of cross-linking antibodies in cHCV patients.

Figure 5 with 5 supplements see all
Decreased cell surface expression of CD5 on cHCV naïve CD8+ T cells correlates with hypersensitivity to TCR activation.

(A) Representative histograms of CD5 on naïve CD8+ T cells from one HD and one cHCV patient. (B) MFI of CD5 on the surface of naïve CD8+ T cells from HD, SVR, and cHCV patients. Representative histograms and MFI of CD5 on the other T cell differentiation subsets are provided in Figure 5—figure supplement 1. (C) Representative overlay of histograms of phospho-ERK (p-ERK) signal at different time points following TCR stimulation from one HD and one cHCV patient. Plots are gated on naïve CD8+ T cell populations. (D) Percentages of p-ERK positive cells in naïve CD8+ T cells from HD and cHCV patients 5 min after CD3/CD28 stimulation. (E) Representative overlay of histograms of CD25 expression, detected at 24 hr after TCR stimulation from one HD, and one cHCV patient. Plots are gated on naïve CD8+ T cell populations. (F) Percentages of CD25+ cells in naïve CD8+ T cells from HD and cHCV patients 24 hr after CD3/CD28 stimulation. Representative examples and percentages of active-caspase 3-expressing cells after similar stimulation are provided in Figure 5—figure supplement 2. (G and H) Percentages of p-ERK (5mins), and CD25 (24 hr) after TCR stimulation in naïve CD8+ T cells from HD, with or without prior CD5 blockade with α-CD5 antibodies. Percentages of active-caspase 3-expressing cells under similar conditions are provided in Figure 5—figure supplement 3. Impact of CD5 blockade on TCR activation in cHCV patients is provided in Figure 5—figure supplement 4. Similar evaluation of naïve CD8+ T cell repertoire during chronic HBV infection is provided in Figure 5—figure supplement 5.

https://doi.org/10.7554/eLife.07916.015

To test the mechanistic link between CD5 expression and hyperactivation of naïve T cells, we evaluated the effect of blocking CD5 signaling. When PBMCs from HD were exposed to blocking anti-CD5 Abs (αCD5) prior to TCR stimulation, we observed (i) increased levels of p-ERK after 5 min (Wilcoxon p=0.007, Figure 5G), (ii) increased CD25 expression after 24 hr (Wilcoxon p=0.03, Figure 5H), and (iii) increased percentages of dying naïve CD8 T cells as assessed by active caspase 3 staining after 24 hr (Wilcoxon p=0.003) (Figure 5—figure supplement 3). When compared to cHCV patients, αCD5 partially reproduced the hyperactivation phenotype of naïve T cells from cHCV patients (Figure 5—figure supplement 4A,B). By contrast, when αCD5 was applied to the cHCV patients, we observed no further increase in TCR-induced activation (Figure 5—figure supplement 4 A–D). These data provide direct evidence for a negative role of CD5 on TCR-induced activation and activation-induced cell death, and support the concept that CD5 molecule is responsible, in part, for the hyperactivation phenotype observed in naive T cells of cHCV patients. Together, these data support a model where low expression of CD5 on naïve T cells in cHCV patients results in dysregulation of the homeostatic TCR threshold.

Memory phenotype cells can be expanded to generate robust CD8+ T cell responses

We next evaluated the consequences of a low threshold for TCR activation on the ability of inexperienced T cells to expand and differentiate after stimulation with cognate peptide. After 8–11 days of in vitro priming, we observed increased percentages of Mart1-specific CD8+ T cells when expanded from PBMCs of cHCV patients as compared to those from HD (cHCV vs. HD, Day 8, M-W p=0.02, cHCV vs HD, Day 11, M-W p=0.009, Figure 6A and B; individual FACS plots for all donors are provided in Figure 6—figure supplement 1). The positive impact of chronic infection on naïve T cell expansion was titratable, with more striking differences in the proportion of MP cells after expansion observed when cells were primed with high doses of peptide (Sprent and Surh, 2011) (Day 8, M-W p=0.03; Figure 6—figure supplement 2). Finally, we found that the Mart1-specific CD8+ T cells generated from cHCV patients express slightly higher amounts of granzyme B (representative example from three cHCV and three HD is shown in Figure 6C; MW p=0.02, Figure 6D). Interestingly, a tendancy for similar differences in Granzyme B expression could be seen in freshly isolated Mart1-specific CD8+ T cell populations in cHCV patients (M-W p=0.09 as compared to HD, Figure 6—figure supplement 3). Hyperreactive preimmune repertoire was further supported by our observation of increased secretion of IFNγ by freshly isolated and antigen-restimulated cells – shown for Mart1, hTERT and CMV peptides in tumor-free, CMV-seronegative cHCV donors (2-way Anova p=0.0002; Figure 6E and F).

Figure 6 with 3 supplements see all
Memory phenotype cells can be expanded to generate robust CD8+ T cell responses.

(A) Examples of Mart1-specific populations expanded from HD and cHCV patients after 8 days of in vitro priming (IVP) with low (10–8, upper line) and high (10–6, bottom line) doses of Mart1 peptide. FACS plots from all donor tested are provided in Figure 6—figure supplement 1. (B) Percentages of Mart1-specific cells expanded after 8 and 11 days of IVP with low and high doses of Mart1 peptide as in A. Proportions of MP cells within those expanded populations are indicated in Figure 6—figure supplement 2. (C) Representative histograms of intracellular granzyme-B expression by Mart1-specific T cells expanded from 3 HD and 3 cHCV after 8 days of IVP with high doses of peptide as in A. (D) Percentages of granzyme-B-expressing Mart1-specific T cells expanded from HD and cHCV patients after 8 days of IVP with low and high doses of Mart1 peptide. Baseline percentages are indicated in Figure 6—figure supplement 3. (E) Representative examples of IFNγ detection intracellularly after in vitro restimulation with CMV or Mart1 peptides in CMV seronegative, tumor-free HD and HCV patients. IFNγ-positive populations are overlaid on total CD8+ T cells. (F) Percentages of cells with IFNγ-positive staining after Mart1-, hTERT-, and CMV- in vitrorestimulation in HD and cHCV patients. sn, seronegative; sp, seropositive.

https://doi.org/10.7554/eLife.07916.021

Together, our results favor a model where low levels of CD5 on naïve-phenotype cells from cHCV donors allow low-affinity interactions with non-cognate antigens to result in T cell differentiation, thereby providing an explanation for the increased frequency of MP cells in cHCV patients. Additionally, our data indicate that qualitative alterations of the CD8+ T cell preimmune repertoire in cHCV patients may result in a boosted response to cognate immune stimulation.

Distinct preimmune repertoire perturbations during chronic HBV infection

Testing our ability to identify preimmune repertoire perturbations in other clinical conditions, we collected 18 cHBV patients using standard sampling procedures. We found normal percentages of CD3+CD8+ T cells (data not shown), and decreased percentages of bulk naïve CD8+ T cells (MW p=0.009, Figure 5—figure supplement 5A). With the limited amount of cells available, we focused our analysis to (i) absolute count and phenotype of Mart1-specific T cells, (ii) CD5 expression on bulk naïve T cells, and (iii) response to TCR cross-linking. We demonstrated lower absolute numbers of Mart1-specific CD8+ T cells in HBV patients (MW p=0.003, Figure 5—figure supplement 5B) and increased frequencies of MP Mart1-specific cells (MW p=0.03, Figure 5—figure supplement 5C) as compared to HD, but (ii) similar levels of CD5 expression (MW p=ns, Figure 5—figure supplement 5D), and (iii) a similar a activation profile of bulk naïve T cells as compared to HD (MW p=ns, Figure 5—figure supplement 5E). These results indicate that different persistent viral infections of the liver can trigger distinct preimmune repertoire perturbations. Additional studies will be required to fully evaluate the heterogeneous disease pathogenesis of HBV infections as reflected by the observed immune phenotypes.

Discussion

Our study provides novel evidence for chronic viral infection as a cause of CD8+ T cell preimmune repertoire dysregulation. Specifically, we demonstrated that naïve CD8+ T cells are dysregulated in the context of cHCV, marked by (i) decreased sjTRECs levels, (ii) a restricted Vβ repertoire, and (iii) a lower threshold for TCR engagement.

Prior examples suggestive of preimmune repertoire perturbations have been documented in humans. An increased threshold for TCR activation in naïve CD4+ T cells in elderly persons has been proposed as participating in the diminished response to vaccination that occurs with increasing age (Li et al., 2012). Conversely, a decreased threshold for TCR activation, secondary to sustained cytokine production, leads to diverse autoimmune manifestations in rheumatoid arthritis patients (Singh et al., 2009; Deshpande et al., 2013). With respect to chronic infection, functional defects in the naïve T cell compartment have also been documented in HIV-infected individuals, with non-cognate activation of T cells correlating with disease progression (Favre et al., 2011). One major caveat for these studies is that their analysis was limited to global dysregulation of the bulk naïve T cell repertoire.

The challenge of studying perturbations of antigen-specific populations is their low precursor frequency. Taking advantage of the possibility to study viremic vs. cured patients, we chose to investigate this question in cHCV patients. Analyzing rare (i.e., frequency = 10–7 - 10–5) antigen-specific inexperienced CD8+ T cells populations, we show increased proportions of memory-phenotype cells in cHCV patients, and demonstrate that this correlates with naïve T cells being hyperreactive to TCR signaling in the context of the chronic infection. Despite these altered phenotypes, the absolute number of antigen-specific cells was comparable to healthy donors. Of note, cHCV patients are not thought to experience altered thymic output. As such, our findings provide direct evidence that MP antigen-specific T cells can arise in non-lymphopenic humans.

It has been suggested that a high degree of cross-reactivity with environmental antigens is the trigger for differentiation and MP conversion (Sprent and Surh, 2011). This finding has been reported for human viral peptide / MHC restricted CD4+ T cells in unexposed donors (Su et al., 2013). While cross-reactivity is a possible explanation for our findings, we demonstrate in cured patients that the antigen-specific inexperienced T cell populations are restored to a naïve phenotype. This result will need to be confirmed in a larger longitudinal cohort study. It favors an alternative model, where homeostatic proliferation accounts for the perturbed naïve T cell repertoire in cHCV patients. Supporting this conclusion, we note the evidence for rapid reversibility to a healthy preimmune repertoire after transient lymphopenia (Jones et al., 2013). Consistent with our findings, Jones et al. studied multiple sclerosis patients and showed an anti-CD52 (also known by alemtuzumab) treatment-induced narrowing of the Vβ repertoire and the dilution of sjTREC after treatment, with a complete restoration of normal levels two years post-therapy (Jones et al., 2013).

Infection and inflammation is known to lower the threshold of TCR signaling in memory T cells, making them more sensitive to activation (Richer et al., 2013). This effect is mediated by inflammatory cytokines (Raué et al., 2013). Our results extend this concept to naïve T cells and introduce CD5 downregulation as a mechanism for hyperreactivity. CD5 tunes the TCR signaling threshold in peripheral T cells, with naïve cells expressing higher levels than central memory or effector T cells (Tabbekh et al., 2013). In mice, Hawiger et al demonstrated that anti-CD5 blocking antibodies, or the use of CD5-/- transgenic MOG-specific T cells, resulted in higher sensitivity to experimental autoimmune encephalitis (Hawiger et al., 2004). In B cells, CD5 has also been shown to regulate activation and low CD5 expression correlates with high sensitivity to activation induced cell death (Tabbekh et al., 2013). In line with these findings, we demonstrate an increased sensitivity of CD5lo naïve CD8+ T cells to TCR ligation in cHCV patients. We further provide direct evidence that this hypersensitivity phenotype can be partially reproduced in HD by blocking CD5. While not evaluated in our patient cohort, we propose that elevated levels of inflammatory cytokines may be responsible for the altered CD5 expression on naïve cells (Park and Rehermann, 2014). Finally, we applied our strategy for evaluating preimmune repertoire perturbations to other clinical conditions, and demonstrate in cHBV patients that a distinct persistent infection of the liver triggers a different preimmune signature. This observation may be related to the differing innate inflammation induced as a result of infection (Duffy et al., 2014).

The combination of low levels of CD5 and increased proportions of MP in inexperienced antigen-specific populations may provide a compounded effect, resulting in a highly reactive CD8+ T cell compartment. We provide evidence here that chronic HCV infection facilitates the generation of robust self-specific responses from the pool of preimmune cells. Given the important role for cellular immunity in the pathogenesis of autoimmune manifestations (Palermo et al., 2001), we speculate that circulating self-reactive effector CD8+ T cells may contribute to the systemic immune activation observed during chronic HCV infection, and account for some of the extra-hepatic autoimmune-like manifestations (Lee et al., 2012). If our prediction is correct, the ability to restore a physiologically normal preimmune repertoire in cured patients may thus justify early treatment as a means to limit immune-mediated manifestations of the disease. Further investigation in longitudinal cohorts is warrented to confirm these hypotheses, as well as assess the impact on the generation of non-self-specific responses (e.g., in the context of vaccination).

In summary, our study demonstrates that naïve CD8+ T cells are dysregulated during cHCV, with marked perturbations of the preimmune repertoire. Specifically, low levels of CD5 at the surface of naïve T cells, and high proportions of memory-phenotype cells represent two mechanisms by which antigen-inexperienced CD8+ T cells are susceptible to stimulation and antigen-induced expansion. These findings should be considered when designing future immunotherapeutic strategies.

Materials and methods

Human subjects, blood samples processing and HLA typing

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29 cHCV, 37 SVR, and 18 cHBV patients were included (Table 1). All subjects were followed in the Liver Unit of Hôpital Cochin (Paris, France) or the Department of Internal Medicine II (Freiburg, Germany). French samples were obtained as part of study protocol C11-33 approved by the INSERM clinical investigation department with ethical approval from the CPP Ile-de-France II, Paris (ClinicalTrials.gov identifier: n° NCT01534728). German samples were obtained in the University Hospital Freiburg according to regulations of local ethic committee. Both study protocols conformed to the ethical guidelines of the Declaration of Helsinki, and patients provided informed consent. Patient peripheral blood mononuclear cells (PBMCs) were obtained from leukapheresis, or whole blood collections. Healthy donor PBMCs were obtained from buffy coat preparations or whole blood collections (Etablissement Français du Sang, France). PBMCs were processed within 5 hr of their collection. They were used either fresh, or frozen and thawed when needed – and in both cases, cells were rested overnight in serum-free RPMI at 37° before performing functional studies. Absolute lymphocyte counts were determined on the day of collection at the hospital laboratories for HCV and SVR patients, and on fresh samples using AccuCheck Counting Beads (Life Technologies, France) for healthy donors. For all samples, PBMCs were isolated by Ficoll-Paque gradient separation (GE Healthcare, France) after 1:4 dilution in RPMI1640 (Gibco, Life Technologies, France) and controlled for viability (>90%). Molecular HLA-A and –B loci typing were determined using extracted genomic DNA according to standard clinical laboratory procedures (Hôpital St Louis, Paris, France).

MHC class I multimers

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Photocleavable-HLA-A*02:01 multimers were constructed using peptide exchange technology as previously described (Jenkins et al., 2010; Toebes et al., 2006; Altman and Davis, 2003; Hadrup et al., 2009). Briefly, heavy chain of HLA-A0201 and β2m were produced separately in E. coli. Refolding was achieved by diluting each subunit in buffer containing the A0201 UV photocleavable peptide (KILGFVFJV, 95% purity, PolyPeptide, France) (Toebes et al., 2006; Blattman et al., 2002). After biotinylation with recombinant BirA enzyme (Avidity, Denver, USA), monomers were selected by size exclusion chromatography (Akta Purifier, GE Healthcare, France) and stored at -80°C until use. For specific peptides, synthetic 9mer were purchased (75% purity, BioMatik, Toronto, Canada): MART126-35(Leu27) (ELAGIGILTV), hCMV pp65495-503 (NLVPMVATV), hTERT1572-580 (RLFFYRKSV), Ebola NP202-210 (RLMRTNFLI)(Sundar et al., 2007), and HIV-1 Gag p1777-85 (SLYNTVATL). 200 μM peptides were exchanged on calculated amounts of monomers (2 μM final concentration) for 1h under UV-lamp (366nm, 2*8W, Chromacim, France). Titrated amounts of PE or APC-streptavidin (Invitrogen, France) were added. After incubation with D-biotin (25 μM final, Sigma, France), fluorescently labeled multimers were kept in the dark at 4°C until use. Mart1 PE pentamers were purchased (ProImmune, UK) as quality control for our in-house production.

Tetramer associated magnetic enrichment (TAME) of antigen-specific CD8+ T cells

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TAME was performed as previously described (Alanio et al., 2010; Alanio et al., 2013; Kyewski and Klein, 2006). Briefly, purified PBMCs (2x107 to 4x108) were incubated with FcR blocking reagent (Miltenyi, France), then stained with PE and/or APC pMHC-multimers at 20nM final concentration for 30 min. Samples were incubated with anti-PE-microbeads and positive selection was performed using MS MACS separation columns (Miltenyi, France). Unbound cells (“Depleted” fraction) were collected. Bound cells (“Enriched” fraction) were eluted. As previously published(Alanio et al., 2010), tetramer-positive populations were gated as LiveDump-CD8+Tetramer+ cells. To approximate the number of the epitope-specific T cells within each sample, we used a calculation previously described by Moon et al (Arstila et al., 1999; Moon et al., 2009). Precursor frequency is defined as the number of tetramer-positive events in the “Enriched” fraction divided by the number of total CD8+ in the sample.

Ab staining, flow cytometry and cell sorting

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PBMCs were stained with titrated amounts of monoclonal Ab (mAbs) obtained from BD Biosciences, Biolegend, or eBiosciences (Supplementary file 1). Live/Dead Fixable Aqua reagent (Life Technologies, France) was included at the same incubation step (dilution 1/200) in order to exclude dead cells. For PhosFlow experiments, cells were stained with surface Abs for 20 mins, then fixed with PFA 3.2% for 10 min at 37°C, and permeabilized by addition of 90% methanol on ice. Intracellular staining of granzyme B was performed using Transcription Factor Buffer Set (BD Biosciences). Samples were acquired using an LSR Fortessa cell analyzer (BD Biosciences, France). Data were analyzed using FACS DIVA 6.0 (BD) and FlowJo 8.8.7 (Tree Star) softwares. Where indicated, stained cells were sorted using a FACS AriaII (BD) in a P2+ facility.

Intracellular cytokine staining

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Human PBMCs were rested overnight in RPMI 1640 GlutaMAX-10% pooled human serum. Cells were plated at 5x106/mL in 24-well plates, and restimulated in vitro with MART126-35(Leu27), hCMV pp65495-503, or hTERT1572-580 peptides (10 μM final). After 1 hr of stimulation, GolgiPlug (5 μg/mL final, BD) was added. After 7 hr, cells were stained for surface Abs, then intracellularly using standard procedures (Cytofix/Cytoperm; BD).

sjTRECs quantification

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One million FACS-sorted T cells were lysed in TRIzol Reagent (Life Technologies, France). Genomic DNA was extracted following manufacturer’s instructions. Quantification of thymic sjTREC was performed by RT-PCR (ABI PRISM7700; Applied, France) (Obar et al., 2008; Moon et al., 2007; Moon et al., 2009; Talvensaari et al., 2002). Data were expressed per 150 000 cells, after normalization for the albumin genomic copy number.

Immunoscope

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After TAME, 1500 naïve and memory Mart1-specific CD8+ T cells were sorted into RLT Buffer (Qiagen, France). Total RNA was extracted (Qiagen Microkit). cDNA were generated using the Supercript II enzyme (Invitrogen, France). RT-PCR reactions, thermal cycling conditions, calculations for relative usage of each Vβ family, and immunoscope profiles were performed as previously described (Alanio et al., 2013; Bouvier et al., 2011) (Supplementary file 2).

Determination of naïve Vβ families by flow cytometry

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One million PBMCs were stained for T cell surface markers and a set of three Abs directed against TCR-Vβ families (Supplementary file 3; IOTest Beta Kit, Beckman/Coulter, France). TCR-Vβ families were classified in increasing order of percentage usage. The Lorenz curve was constructed as a graphical representation of the diversity of the repertoire (Alanio et al., 2010; De Maio, 2007). After ordering Vβ chains by abundance, from lowest to highest, the Lorenz curve shows the cumulative distribution : for a given percentage (x) of the 24 Vβ chains, it indicates the proportion of the T cell population which have Vβ chains that are among the 24 * x% least abundant ones. Gini coefficient was calculated as the ratio of « area between the line representing equal use of all Vβ chains (dotted line) and the observed Lorenz curve » to « total area below the line representing equal use ». As such, the higher the Gini coefficient, the more unequal the distribution is.

In vitro TCR activation assays

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96-well plates were coated overnight with biotin anti-human CD3 and anti-human CD28 (1 μg/mL and 0.5 μg/mL final concentration, respectively). Unstimulated and PMA/Ionomycin conditions (50 ng/mL and 1 μg/mL respectively) were used as negative and positive controls. Measurements for T cell activation included: PhosFlow, as described above; and phenotypic activation, as measured by expression of CD25 and CD69 following a 24–48 hr culture. For experiments with blocking CD5, cells were preincubated with 5μg/mL anti-human CD5 for 1 hr before being plated for TCR stimulation.

In vitro priming of antigen-specific CD8+ T-cell precursors

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PBMCs from HLA-A*0201-positive donors were primed in vitro using the ELAGIGILTV (ELA) peptide derived from Melan-A/MART-1 antigen (residues 26–35), using previously published method with minor adaptations (Martinuzzi et al., 2011). Briefly, thawed PBMCs were resuspended in AIM medium (Invitrogen), plated at 5x106 cells/well in a 24-well tissue culture plate, and stimulated with 10nM (low dose, 10–8) or 1 µM (high dose, 10–6) of Mart1 peptide ELAGIGILTV in the presence of GM-CSF (0.2 μg/ml, R&D Systems). After 24 hr, dendritic cells maturation was induced by the addition of a cytokine cocktail comprising TNF-α (1000 U/mL), IL-1β (10 ng/mL), IL-7 (0.5 ng/mL) and PGE2 (1 μM) (R&D Systems). On day 2, fetal calf serum (FCS; Gibco) was added to reach 10% by volume per well. Fresh RPMI-1640 (Gibco) enriched with 10% FCS was used to replace the medium every 3 days. Frequency and phenotype of ELA-specific CD8+ T-cells were determined on day 8–11.

CMV serology

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CMV serology was determined on plasma samples from HD and HCV patients by ELISA for CMV-specific IgG Abs (Liaison XL, Diasorin). Donors were defined as seropositive for CMV if specific IgG>13 U/mL, and seronegative if IgG<13 U/mL.

Statistical analysis

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Statistics were performed using Prism 5, GraphPad software (San Diego, USA). Single continuous variable data were analyzed by Mann-Whitney (MW), or Kruskal-Wallis (KW) followed by Dunn’s Multiple Comparison Test. Multi-feature continuous variable data sets were analyzed by Anova and Bonferroni post-test. Paired non-parametric datasets were analysed using Wilcoxon’s statistical test. Correlation were analysed using Spearman linear regression. For all these tests, a cut-off value of p≤0.05 was chosen (*p≤0.05; **p≤0.01; ***p≤0.001). For longitudinal data on SVR patients, after linearisation of the data by squaring, a mixed model was fitted with a fixed time effect and random patient effects for both the slope and the intercept. p-value gives significance for the fixed slope effect. The R function lme (package nlme) was used.

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Decision letter

  1. Rafi Ahmed
    Reviewing Editor; Emory, United States

eLife posts the editorial decision letter and author response on a selection of the published articles (subject to the approval of the authors). An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent (see review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.

[Editors’ note: this article was originally rejected after discussions between the reviewers, but the authors were invited to resubmit after an appeal against the decision.]

Thank you for choosing to send your work entitled "Bystander hyperactivation of preimmune CD8+ T cells in chronic HCV patients can be reversed by viral clearance" for consideration at eLife. Your full submission has been evaluated by Tadatsugu Taniguchi (Senior editor), a guest Reviewing editor, and two peer reviewers, and the decision was reached after discussions between the reviewers. Based on our discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife.

The reviewers found the story interesting but they also found that the data was very preliminary. Particular concerns were raised regarding the lack of clear information on the treatment the patients had received and which patients were included in the different experiments figures and the speculative nature of the Discussion with little offered in terms of clinical observations that would match the authors' findings.

A better explanation of the patient cohort and what exactly was done in several of the experiments would improve the manuscript, especially as testing the physiological significance of these changes in the composition of foreign-antigen specific T cells will be difficult to determine in humans.

Testing the physiological significance of these changes in the composition of foreign-antigen specific T cells will be difficult to determine in humans, but we would be forgiving of this limitation if the manuscript was more clear-cut. Given the varied major concerns listed by the reviewers, this report would need to be radically improved in order for the authors to make definitive conclusions.

Reviewer #1:

Alanio et al. have analyzed tumor – and virus-specific cells in tumor – and virus negative subjects and found differences for these populations between healthy subjects, people with chronic and treated HCV infection and those with chronic HBV infection. They found specific T cells with different phenotypes in these populations, notably significant numbers of memory T cells in addition to naïve cells in chronic HCV. They suggest this is due to lower CD5 expression and thus more cell death in naïve cells in chronic HCV.

Overall I think there are quite a few intriguing observations in this study, but at the same time I do have a few questions for the authors and also a few concerns about some of the data. Currently it is not easy to follow the interpretations of the study and to see the importance of the observations. Specifically:

1) It is not fully clear to me that the authors have a strong case that the distinct phenotype they observe has also actual clinical consequences. They discuss autoimmunity and also that antigen-experienced cells in HCV infection might be more prone to stimulation and cell death. But this is all rather vague and speculative. One could equally argue that the number of truly naïve cells remained actually stable and that just some additional memory-like cells were observed, with unknown consequences.

2) The paper does not detail what kind of therapy the patients were receiving (and almost all of them were already treatment experienced in both the chronic and the SVR group). Assuming they had received IFN before, could that not have had significant impact on the phenotype of the T cells? And what was the latest therapy in the SVR group? IFN versus DAA would make an important difference. Also, cells from the healthy subjects were at least partially obtained from buffy coats. Depending on how those are obtained (leftovers from the blood bank?) this could also influence both cell numbers and phenotype. Furthermore, nothing is known about age and other clinical parameters for HD, yet those could have significant impact on results (often HD tend to be younger).

3) The authors also observed decreased number of naïve cells in the case of HBV infection, but none of the other findings extended to this patient group. This is curious and warrants at least some discussion.

4) I am somewhat concerned about the approach to define naïve cells by CD45RA and CD27 alone, based on the data in Figure 1–figure supplement 1. I would argue that this figure shows a significant proportion of this population to be CD127 negative, CD45RO positive and CCR7 negative. The latter two markers are also surprisingly hard to evaluate, since the staining of the total CD8 population indicates inadequate separation between positive and negative cells.

5) The data in Figures 3E and F needs more detail. In Figure 3E, was the treatment IFN? And on how many specific cells are these plots based? They look not like the best flow data. In Figure 2F, which patients were studied at which time-point? The range for naïve cells in chronic HCV ranges from 25% to 90%, so the selection of patients in the different post treatment timeframes could greatly alter the results, given the small numbers of data points. Did all subjects show an increase in naïve cells from chronic to post treatment?

6) For some of the analyses it would be important to know how many specific cells were actually included. For example the ICS data (Figure 4D) and the immunoscope study (Figure 4E) could be greatly influenced/skewed by low cell numbers in the analysis. For the ICS experiment, flow plots should be provided as well, and the results not be reported as a multiple of unstimulated versus unstimulated cells as this not allowing a meaningful interpretation of the data.

7) I think the data in Figure 6 needs to be analyzed differently. The focus on fold change is too dependent on the unstimulated background, which seems higher for the HD sample. The different timpanist also seems to be quite heterogeneous, yet only one time-point is used. I find these data not fully convincing. Were buffy coat cells used for HD? And if so, how long after blood draw were the cells collected?

8) Can the data in Figure 7 really be interpreted? The variation in that assay seems to be extremely high. At least it would be good to show the results as individual data points instead of bar graphs.

Reviewer #2:

The manuscript by Alanio et al. examines memory phenotype CD8 T cells in the preimmune repertoire from chronically infected HCV patients compared to HCV-cured patients and healthy donors. The authors found an increased proportion of pre-immune CD8+ T cells exhibited a memory phenotype (rather than the expected naïve phenotype) in chronically infected HCV patients. This correlated with decreased CD5 expression, increased stimulation following TCR stimulation and increased caspase 3 cleavage following activation, suggesting increased sensitivity to TCR stimulation and activation-induced cell death. In vitro mechanistic studies showed that blocking CD5 on CD8 T cells from healthy donors recapitulated aspects of the increased sensitivity to TCR stimulation phenotype seen for CD8 T cells from chronic HCV patients. Furthermore, the authors showed that naïve CD8 T cells from chronic HCV patients had decreased TREC levels and a more restricted Vβ repertoire, indicative of a perturbed naïve CD8 T cell repertoire in chronic HCV patients. Overall, the study has been well designed and the findings add important information to our knowledge of memory phenotype CD8 T cells in humans in the context of chronic viral infection. However, there are some issues with the manuscript that are detailed below.

1) Of the three antigen-specific populations studied, two were specific for self antigens – MART1 and hTERT. Thus, although the authors state that these T cells are antigen "inexperienced", the antigen is present in normal tissues and it seems feasible that chronic HCV infection leads to enhanced presentation of peptides from these self antigens, and subsequent antigen-specific T cell stimulation. In which case the acquisition of non-naïve phenotype is not a surprise. These concerns are partly offset by additional analysis of hCMV specific CD8+ T cells (Figure 4), since the frequency of this population is the lowest of all the specificities studied (Figure 4A), making it more difficult to be confident about apparent phenotypic differences. It would be useful if the authors could extend their observations to another non-self antigen (ideally, one with a higher precursor frequency), but at the very least the fact that most of the study is focused on self-specific T cells should be discussed in more depth.

2) The data on TCR sensitivity and CD5 expression is interesting, and would be in line with the authors' suggestion that proliferation of naïve phenotype CD8+ T cells (as evidenced by loss of TREC – Figure 2) might lead to decreased CD5 expression levels. However, the data shown in Figure 7 do not make a strong case that CD5 expression levels are the root cause of the increased TCR sensitivity of the naïve cells from cHCV patients. The experiments presented show that blockade of CD5 leads to an enhanced response by CD8+ T cells – in line with many previous studies. What is needed is to test whether this blockade would normalize the differences in sensitivity between T cells from HD and cHCV donors. To be specific, the authors need to apply anti-CD5 blockade to both HD and cHCV T cells and determine whether TCR hypersensitivity is now equivalent between the groups (supporting the authors' idea that CD5 expression levels are the key determinant). Alternatively, they may find that T cells from cHCV patients still show the same degree of enhanced reactivity following TCR engagement – a result that would imply that CD5 blockade increases sensitivity by both populations to the same extent, and that other features of the cells from cHCV patients are responsible for their enhanced sensitivity.

3) The authors show that the proportion of antigen-specific CD45RA+CD27+ naïve CD8 T cells is reduced in chronic HCV patients compared to healthy donors and HCV-cured patients. The stated conclusion is that chronic HCV infection results in increased memory phenotype CD8 T cells; however, it would be useful for the authors to define the phenotypic traits of the non-naïve populations and show whether there is a consistent increase in either CD45RA-CD27+ or CD45RA-CD27- antigen specific CD8+ T cells in chronic HCV patients compared to healthy donors and HCV-cured patients. There appears to be substantial differences between groups or experiments in the flow cytometry plots – e.g. comparing the CD27 expression by non-naïve MART-specific cells in Figure 3C and Figure 3E – and it would be good to know whether this averages out with compiling samples from multiple individuals.

[Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]

Thank you for resubmitting your work entitled "Bystander hyperactivation of preimmune CD8+ T cells in chronic HCV patients" for further consideration at eLife. Your revised article has been favorably evaluated by Tadatsugu Taniguchi (Senior editor), a guest Reviewing editor, and two reviewers. The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:

In general, both reviewers are satisfied with the revised version. Reviewer #1 has raised some issues that still need to be addressed before your manuscript can be accepted. However, no additional experiments are required. You just need to address these concerns by making changes in the text. Regarding Author response image 5, please include these data in the manuscript as a supplemental figure.

Reviewer #1:

Alanio et al. have significantly revised their manuscript on bystander activation of unprimed CD8 T cells in chronic HCV infection. I appreciate the amount of work, especially given the challenges and limitations when working on human samples. This has definitely improved the manuscript, but, at the same time, I still have a few questions for the authors:

1) The observation that there are changes in the composition of the CD8 T cell pool based on memory markers remains intriguing. I am still not sure, however, whether this is a relative or an absolute change. The reasons are that the gates on the naïve populations are different for each subject (as seen in Figure 1A), making the quantification of the relative number of naïve T cells seem a little random. At the same time (if I understood the new information about absolute T cell counts correctly), absolute T cell counts were performed using different methods in HD vs SVR and cHCV patients, raising the possibility that the differences in absolute CD3 numbers as shown in Figure 1–figure supplement 2 could be a result of different methodology. These issues should be reconciled.

2) I find it very difficult to interpret the TCR data. The cross sectional results on bulk naïve cells in Figure 1 show somewhat subtle differences that could be caused by a multitude of factors. Data on specific cells like from the one subject in Figure 2E should be more revealing. In this case, it seems clear that just one clonotype (already dominant in the naïve cells) is responsible for all memory cells. Would that not imply that one clonotype might have some cross-reactivity and thus have expanded?

3) The analysis of specific cells for naïve versus memory population has been significantly strengthened by the new Figure 3, justifying the overall conclusion that more T cells display a memory phenotype. I have still some concerns, especially regarding the longitudinal and cross-sectional analysis of MART responses (Figure 2 and Figure 4), given that the gates on naïve cells seem to be different in every plot. How was the "right" gate determined in each case? Where the different time-points for a patient not stained in a single experiment or why do the staining patterns/signal strength look so different? Regarding the cross sectional analysis in Figure 4C, my concern remains that the differences seen in the few patients more than 2 years after treatment (already barely significant) might be driven by patient selection (exemplified by patients S11 and S12 with low memory percentages already pre and post treatment, potentially skewing the results for the 6 subjects included at more than 2 years post treatment). The case that treatment normalizes the phenotype remains not very strong in my opinion (did the bulk CD8 T cells show changes? more data points might be available).

4) The functional experiments using CD5 blockade remain inconclusive, given that an effect is only seen in HD. As this effect is modest and does not lead to the same level of reactivity as seen in the cHCV patients (R6) it seems unclear whether CD5 is the main mechanism here.

5) The in vitro expansion and ICS experiments in Figure 6 are a bit problematic. First, for each of the subjects the naïve MART precursor frequency should be given, since some of the cHCV subjects had relatively high frequencies as seen in Figure 2–figure supplement 2. In any case, assuming specific cells in cHCV expand better, could this not be explained by them being memory and not naïve cells? For the functional data, the difference in granzyme B is modest and no difference is observed directly ex-vivo (Figure 6–figure supplement 3). As for the IFN assay, are the authors really suggesting that populations requiring TAME for visualization can be detected via ICS directly ex-vivo? Detecting such small populations via ex-vivo ICS from 5 million PBMC seems unlikely.

In summary, to me this revision has significantly strengthened the basic observations supporting the notion that HCV infection indeed impacts other unrelated T cell populations. I remain less convinced about the suggested mechanisms behind these findings and also whether HCV treatment really restores the T cell compartment.

Reviewer #2:

The authors have made numerous revisions to the manuscript, which address the major concerns I raised. The only request would be to include the data shown in Author response image 5 in the manuscript. These data make, in my opinion, an important point about the impact and selectivity of the anti-CD5 blockade, and offers material support to the authors' hypothesis. This figure could be included in the supplementary material.

https://doi.org/10.7554/eLife.07916.036

Author response

[Editors’ note: the author responses to the first round of peer review follow.]

We have addressed all of the points raised – including the clarification of patient treatment information and the coding of which patients were used in the different studies. It is also much appreciated that the editor and referees acknowledge the inherent limitations for human investigations; accordingly, we have modified the Discussion so that it is more clear what aspects of our predictions are speculative in nature. In light of our new results and the reorganization of the report, we believe that the revised manuscript is considerably improved.

Reviewer #1:

1) It is not fully clear to me that the authors have a strong case that the distinct phenotype they observe has also actual clinical consequences. They discuss autoimmunity and also that antigen-experienced cells in HCV infection might be more prone to stimulation and cell death. But this is all rather vague and speculative. One could equally argue that the number of truly naïve cells remained actually stable and that just some additional memory-like cells were observed, with unknown consequences.

We thank the reviewer for pushing us to clarify our message. We have modified the Discussion in order to make the point that the proposed link to the extra-hepatic manifestations observed in cHCV patients is speculative and will need to be evaluated in longitudinal cohort studies. If our prediction is correct, the ability to restore a physiologically normal preimmune repertoire in cHCV patients may justify early treatment as a means to limit immune-mediated manifestations of the disease. We have also performed new experiments that reinforce our prior observations. With respect to antigen-specific inexperienced T cells being more prone to activation, this has been directly tested using ex vivo expansion as readout. We found MART-1 specific T cells from cHCV patients more abundant and possessing greater effector function (i.e., Granzyme B expression) after cognate peptide stimulation, comparing with healthy controls (see revised Figure 6 A-D). These findings indicate that qualitative alterations of the CD8 T cell preimmune repertoire in cHCV patients do indeed impact critical aspects of induced immune responses. It also reinforces our conclusion that lower threshold for TCR activation on naïve T cells might trigger antigen-independent T cell differentiation in cHCV patients. As discussed below, our initial results were confirmed when testing virus-specific T cells, reactive to viral antigens for which the donors had not been exposed (e.g., HIV, Ebola).

Regarding the query about absolute cell numbers, the reviewer is correct in their interpretation, and we now have clarified this point in the revised manuscript – the precursor frequency of naïve-phenotype Mart1-specific T cells is unaffected (see Figure 2–figure supplement 2). This result, however, does not alter our conclusions regarding memory-phenotype cells.

2) The paper does not detail what kind of therapy the patients were receiving (and almost all of them were already treatment experienced in both the chronic and the SVR group). Assuming they had received IFN before, could that not have had significant impact on the phenotype of the T cells? And what was the latest therapy in the SVR group? IFN versus DAA would make an important difference.

We apologize for the lack of clarity. 62% of our chronic HCV patients were treatment-experienced, of which 2/3 received bitherapy with pegylated interferon (IFN) and ribavirin (RBV), and 1/3 were treated with a combination of a direct antiviral agent (DAA) and interferon. To assess the potential interference, we stratified our patient groups, and as shown in Author response image 1, the observed effect of higher percentages of memory-phenotype Mart1-specific CD8+ T cells can be seen in both non-treated and previously treated cHCV patients. We have also provided an update to Table 1, indicating that the median delay post-treatment of included cHCV patients was 3.8 years (IQR1-3 3.4-4.2). Given the timing of our collections, we do not believe there to be a risk of prior therapy confounding our results.

Author response image 1
In both cHCV (left) and SVR (right), proportions of memory-phenotype (MP) cells within Mart1-specific populations are not a function of previous drug regimen.
https://doi.org/10.7554/eLife.07916.028

With respect to SVR patients, all patients received prior treatment (by definition): 70% received an IFN/RBV therapy, and 30% were treated with a DAA-based therapy. While the stratification of patients did not indicate a significant difference between persons receiving the different drug regimen, larger cohorts with precise post-treatment collection times would be required to establish the optimal regimen to maximize immunological restoration. We have amended the manuscript, including details about prior treatment regimens.

With the aim of increasing clarity of our manuscript, we also provide a new table (Supplementary file 1) that is stratified for HLA-A0201 positive donors, the subset of patients used for studying antigenspecific T cell phenotypes.

Also, cells from the healthy subjects were at least partially obtained from buffy coats. Depending on how those are obtained (leftovers from the blood bank?) this could also influence both cell numbers and phenotype. Furthermore, nothing is known about age and other clinical parameters for HD, yet those could have significant impact on results (often HD tend to be younger).

In our study, buffy coats from HD were collected by the French blood bank, according to blood transfusion procedures. PBMCs were processed within 5 hours of their collection. They were used fresh, frozen and thawed when needed. In both cases, cells were rested overnight in serum-free RPMI at 37° before performing functional studies. We systematically controlled sample quality and excluded any samples containing >10% of dying cells. For cytometric studies, we further gated residual dead cell using live/dead probes. Importantly, allowing appropriate comparison, leukapheresis collection from cHCV and SVR patients followed the exact same procedure (e.g., location, transport time, quality control). Additional information is now added to the revised Methods section. Finally, the age of HD is now indicated. Of note, age-matched controls were selected for our study (see Table 1).

3) The authors also observed decreased number of naïve cells in the case of HBV infection, but none of the other findings extended to this patient group. This is curious and warrants at least some discussion.

We apologize for the lack of clarity. To address the question of the pre-immune repertoire in HCV patients, we designed a specific protocol for leukapheresis collection, permitting recovery of up to 1010 PBMCs from each donor. This allowed assay development and optimization, and multiple assays to be performed on same donors. Following our initial discoveries in cHCV patients, we were prompted to evaluate our findings in an alternative viral hepatitis disease state. We were fortunate to have access to a bio-repository of HBV samples that permitted rapid testing, however samples were indeed limited, with ~5x107 PBMCs being available per patient. We therefore restricted our analysis to: (i) Mart1-specific T cell phenotype and numbers, (ii) CD5 levels on bulk naïve T cells, and (iii) CD25 and active caspase 3 percentages after 24 hours of activation with CD3/CD28 antibodies, parameters that we identified in the cHCV cohort as the best biomarkers of a perturbed T cell repertoire. As discussed, we demonstrated lower absolute numbers and intermediate proportions of memoryphenotype Mart1-specific cells in cHBV patients, and a similar CD5 expression and T cell activation as compared to HDs. Based on these findings, we believe that different persistent viral infections can trigger distinct T cell repertoire perturbations. We have taken the advice of the reviewer to include all data from cHBV patients in a separate supplementary figure. These results will be followed up in future studies.

4) I am somewhat concerned about the approach to define naïve cells by CD45RA and CD27 alone, based on the data in Figure 1–figure supplement 1. I would argue that this figure shows a significant proportion of this population to be CD127 negative, CD45RO positive and CCR7 negative. The latter two markers are also surprisingly hard to evaluate, since the staining of the total CD8 population indicates inadequate separation between positive and negative cells.

We thank the reviewer for the careful analysis of our data, and acknowledge that the gating strategy is crucial. Regarding the gating on naïve T cells based on high levels of CD45RA and CD27, we validated our strategy using additional healthy donors. As shown in Author response image 2, CD45RA+CD27+ gated naïve CD4+ and CD8+ T cells express the highest levels of CCR7 and lowest level of HLA-DR markers. We also demonstrate a high degree of correlation between percentages of CD45RA+CD27+ and CD45RA+CCR7+ naïve CD8+ T cells. These data were confirmed in cHCV patients, with >95% bulk and Mart1- specific CD45RA+CD27+ CD8+ T cells also expressing CCR7. We now provide these data in the manuscript as a supplementary figure (Figure S1B). Regarding the concern related to Figure S1, we confirm that there is a minor population (9%) of CD127 negative cells in the displayed CD45RA+CD27+ gate. These cells are >99% CD45RO negative and CCR7 positive, which we believe allows us to be confident that they are indeed naïve T cells. Interestingly, they express lower levels of CD27 as compared to other naïve cells. Our interpretation is that this subpopulation corresponds to activated naïve T cells that have shed surface CD27, as previously described in the context of graft-versus-host disease (Poulin, 2003).

Author response image 2
Evaluation of CD45RA/CD27 gating strategy.

CD45RA+CD27+ naïve CD4+ and CD8+ T cells express the highest levels of CCR7 and lowest level of HLA-DR markers (A). Also, there is a close correlation between CD45RA+CD27+ and CD45RA+CCR7+- gated naïve CD8+ T cells (B).

https://doi.org/10.7554/eLife.07916.029

5) The data in Figures 3E and F needs more detail. In Figure 3E, was the treatment IFN? And on how many specific cells are these plots based? They look not like the best flow data.

We have revised the indicated figure, now revised Figure 4. Specifically, we (i) provide new FACS plots showing the evolution of Mart1 phenotype in one SVR patient collected during chronic phase, 6 months, 1 year and 2 years after successful IFNRBV therapy; (ii) included 4 new HLA-A0201 SVR patients that were sampled at different time points after viral clearance; and (iii) employed statistical methods to analyze longitudinal datasets (p-value represents the significance of the fixed effect of time after linearisation of the data followed by fitting of a mixed model (see Materials and methods for details). Our data confirm on a per patient basis that viral clearance results in the restoration of an expected naïve phenotype for Mart1-specific T cells. Concerning the number of cells, we agree with the reviewer that low number of Ag-specific inexperienced T cells is a challenge. As an example, in Figure 4, we gated a mean of 97 Mart1- specific T cells (range 48-126). As a general strategy, in line with recognized groups in the field (Yu et al., 2015), we interpret a tetramer-gated population as real if containing more than 20 antigen-specific enriched cells. We chose this number based on our initial evaluation of the limit of detection of our assay (please refer to Alanio et al., 2010), established using spiked numbers of CTL clones.

In Figure 2F, which patients were studied at which time-point?

The manuscript has been modified accordingly, tagging corresponding data in Figures 4B and C with patient ID. To minimize bias, we utilized data from the first available time point when performing comparisons among the different patient groups (as shown in revised Figure 4C).

The range for naïve cells in chronic HCV ranges from 25% to 90%, so the selection of patients in the different post treatment timeframes could greatly alter the results, given the small numbers of data points. Did all subjects show an increase in naïve cells from chronic to post treatment?

We evaluated the impact of previous treatment on our observations (see above, Point #2). Additionally, we show here a plot of Mart-1 memory phenotype cells as a function of time-post treatment for cHCV. Aside from the outlier patient with 25% frequency, we observe a narrow range of differentiated cells.

Author response image 3
Percentages of MP cells within Mart1-specific population is not a function of delay post-treatment in cHCV patients.
https://doi.org/10.7554/eLife.07916.030

6) For some of the analyses it would be important to know how many specific cells were actually included. For example the ICS data (Figure 4D) and the immunoscope study (Figure 4E) could be greatly influenced/skewed by low cell numbers in the analysis.

We apologize for the oversight and have amended the manuscript accordingly. As now indicated in the revised manuscript, we performed the immunoscope using 1,500 sorted naïve and memory Mart1-specific T cells. Although we agree with the reviewer that low number of cells could account for restricted repertoire diversity, we believe it is still valuable for repertoire comparison.

For the ICS experiment, flow plots should be provided as well, and the results not be reported as a multiple of unstimulated versus unstimulated cells as this not allowing a meaningful interpretation of the data.

For ICS experiments, we stimulated 107 cells in 24-well format plates with cognate peptide. Based on precursor frequencies, we expect ~2-20 inexperienced antigen-specific cells to be available in a given well for peptide re-stimulation. We made the recommended modification of the figure and added additional patients to the study (see revised Figures 6E and F). The new results confirmed our initial observations.

7) I think the data in Figure 6 needs to be analyzed differently. The focus on fold change is too dependent on the unstimulated background, which seems higher for the HD sample. The different timpanist also seems to be quite heterogeneous, yet only one time-point is used. I find these data not fully convincing.

We have revised the data analysis and figure accordingly (now revised Figure 5D and F). To extend our initial observations, we included an additional 6 cHCV patients and 6 HD. Of note, there were no significant differences in the background in HD and cHCV patients in these experiments (data not shown).

We have also evaluated the kinetics of p-ERK and CD25 upregulation. These data are provided in Author response image 4. Based on these initial observations, we chose 5 min for measurement of p-ERK and 24 hour for monitoring CD25 expression.

Author response image 4
Kinetics of P-ERK and CD25 in naïve T cells from HD and cHCV patients after CD3/CD28 stimulation.
https://doi.org/10.7554/eLife.07916.031

Were buffy coat cells used for HD? And if so, how long after blood draw were the cells collected?

As discussed above (see Point #2), functional studies on HD were performed using buffy coats from the blood bank. PBMCs were processed within 5 hours of collection.

8) Can the data in Figure 7 really be interpreted? The variation in that assay seems to be extremely high. At least it would be good to show the results as individual data points instead of bar graphs.

To address the raised concerns, we have increased the number of donors tested. Data was analyzed using paired non-parametric methods, confirming a positive effect of CD5 blockade on T cell activation (please see Figures 6G and H).

Reviewer #2:

1) Of the three antigen-specific populations studied, two were specific for self antigens – MART1 and hTERT. Thus, although the authors state that these T cells are antigen "inexperienced", the antigen is present in normal tissues and it seems feasible that chronic HCV infection leads to enhanced presentation of peptides from these self antigens, and subsequent antigen-specific T cell stimulation. In which case the acquisition of non-naïve phenotype is not a surprise. These concerns are partly offset by additional analysis of hCMV specific CD8+ T cells (Figure 4), since the frequency of this population is the lowest of all the specificities studied (Figure 4A), making it more difficult to be confident about apparent phenotypic differences. It would be useful if the authors could extend their observations to another non-self antigen (ideally, one with a higher precursor frequency), but at the very least the fact that most of the study is focused on self-specific T cells should be discussed in more depth.

We thank the reviewer for his/her interest in our study and for the careful analysis of the manuscript. Although we cannot formally rule out the possibility that cells-specific for self-peptides were activated by their cognate antigen, we believe the extension of our observations to T cells specific for non-self (foreign) antigens supports our conclusion of antigen-independent differentiation of preimmune repertoire in cHCV patients. Indeed, our new experiments measuring HIV and Ebola virus epitopes in cHCV patients – all HIV and presumably Ebola seronegative – considerably strengthens our argument (see revised Figure 3).

We have also included new functional data to assess the impact of preimmune alterations. Notably, Mart1 peptide based expansion was higher in cHCV than normal donors, and the responding T cells showed higher levels of effector molecules (see Figure 6 in revised manuscript). Future studies will address how these findings impact the in vivo capacity of cHCV patients to mount adequate CD8 T cell responses – providing direct relevance for optimization of therapeutic vaccination in this patient population. Notably, these data may be relevant in other chronic disease areas.

2) The data on TCR sensitivity and CD5 expression is interesting, and would be in line with the authors' suggestion that proliferation of naïve phenotype CD8+ T cells (as evidenced by loss of TREC – Figure 2) might lead to decreased CD5 expression levels. However, the data shown in Figure 7 do not make a strong case that CD5 expression levels are the root cause of the increased TCR sensitivity of the naïve cells from cHCV patients. The experiments presented show that blockade of CD5 leads to an enhanced response by CD8+ T cells – in line with many previous studies. What is needed is to test whether this blockade would normalize the differences in sensitivity between T cells from HD and cHCV donors. To be specific, the authors need to apply anti-CD5 blockade to both HD and cHCV T cells and determine whether TCR hypersensitivity is now equivalent between the groups (supporting the authors' idea that CD5 expression levels are the key determinant). Alternatively, they may find that T cells from cHCV patients still show the same degree of enhanced reactivity following TCR engagement – a result that would imply that CD5 blockade increases sensitivity by both populations to the same extent, and that other features of the cells from cHCV patients are responsible for their enhanced sensitivity.

We thank the reviewer for the positive comments and suggestions. To address the inter-individual variation, we have increased the number of donor tested in TCR activation experiments (see revised Figure 5). As recommended, we also tested the impact of pre-incubation with blocking CD5 antibody on both HD and cHCV groups. As shown in Author response image 5, we demonstrate a consistent positive impact of αCD5 on TCRinduced activation, with a corresponding increase in activation-induced cell death. By contrast, cHCV patients do not show evidence of enhanced T cell activation following CD5 blockade. These data support our interpretation that T cells from cHCV patients are functionally perturbed due to their lower expression of CD5, and that further inhibition does not provide additional hyper-activation.

Author response image 5
Impact of CD5 blockade on TCR activation profile in cHCV patients.

(A and B) Impact of preincubation with anti-CD5 antibodies on% CD25 and% active-caspase 3 after CD3/28 stimulation in cHCV patients. (C and D) Summary of impact of anti-CD5 on HD and cHCV patients.

https://doi.org/10.7554/eLife.07916.032

3) The authors show that the proportion of antigen-specific CD45RA+CD27+ naïve CD8 T cells is reduced in chronic HCV patients compared to healthy donors and HCV-cured patients.

We have examined the phenotype of non-naïve Mart1-, Ebola- and HIV-specific CD8 T cells. Following the suggestions made by the reviewer, we have observed an interesting pattern of expression leading us to conclude that the state of differentiation for the observed MP cells is closest to that of central memory T cells. We have included this analysis as Figure 3–figure supplement 1 in our manuscript, and added a sentence in the Results section.

The stated conclusion is that chronic HCV infection results in increased memory phenotype CD8 T cells; however, it would be useful for the authors to define the phenotypic traits of the non-naïve populations and show whether there is a consistent increase in either CD45RA-CD27+ or CD45RA-CD27- antigen specific CD8+ T cells in chronic HCV patients compared to healthy donors and HCV-cured patients. There appears to be substantial differences between groups or experiments in the flow cytometry plots – e.g. comparing the CD27 expression by non-naïve MART-specific cells in Figure 3C and Figure 3E – and it would be good to know whether this averages out with compiling samples from multiple individuals.

The reviewer is correct that the MFI of CD27 is lower in cHCV patients as compared to HD. This is the case for both naïve and MP Ag-specific populations (see Author response image 6). As mentioned above (see discussion in reply to reviewer #1 – point 4), a soluble form of CD27 is released after TCR engagement (Hintzen et al., 1991). We believe this might represent an additional feature of naïve cells that have a hyper-activated phenotype. As we did not fully explore this phenotype in our study, we have not included the data.

Author response image 6
Phenotype of Ag-specific inexperienced T cells in cHCV patients.

Percentages of naïve, CM, EM, and EMRA – phenotype Ag-specific Mart1-, Ebola-, and HIVspecific T cells enriched from chronic HCV patients.

https://doi.org/10.7554/eLife.07916.033

References:

Hadrup, S.R., Bakker, A.H., Shu, C.J., Andersen, R.S., van Veluw, J., Hombrink, P., Castermans, E., Thor Straten, P., Blank, C., Haanen, J.B., et al. (2009). Parallel detection of antigen-specific T-cell responses by multidimensional encoding of MHC multimers. Nat Meth 6, 520–526.

Hasan, M., Beitz, B., Rouilly, V., Libri, V., Urrutia, A., Duffy, D., Cassard, L., Di Santo, J.P., Mottez, E., Quintana-Murci, L., et al. (2015). Semi-automated and standardized cytometric procedures for multi-panel and multi-parametric whole blood immunophenotyping. Clin Immunol 157, 261–276.

Hintzen, R.Q., de Jong, R., Hack, C.E., Chamuleau, M., de Vries, E.F., Berge, ten, I.J., Borst, J., and van Lier, R.A. (1991). A soluble form of the human T cell differentiation antigen CD27 is released after triggering of the TCR/CD3 complex. J Immunol 147, 29–35.

Poulin, J.F. (2003). Evidence for adequate thymic function but impaired naïve T-cell survival following allogeneic hematopoietic stem cell transplantation in the absence of chronic graft-versushost disease. Blood 102, 4600–4607.

Yu, W., Jiang, N., Ebert, P.J.R., Kidd, B.A., Müller, S., Lund, P.J., Juang, J., Adachi, K., Tse, T., Birnbaum, M.E., et al. (2015). Clonal Deletion Prunes but Does Not Eliminate Self- Specific ab CD8. Immunity 42, 929–941.

[Editors’ note: the author responses to the re-review follow.]

Reviewer #1:

1) The observation that there are changes in the composition of the CD8 T cell pool based on memory markers remains intriguing. I am still not sure, however, whether this is a relative or an absolute change. The reasons are that the gates on the naïve populations are different for each subject (as seen in Figure 1A), making the quantification of the relative number of naïve T cells seem a little random.

We thank the reviewers for their positive assessment of our study. The gates were not perfectly identical in Figure 1A because the samples were acquired in different experiments. However, fluorescence measurements were consistent over time, standardized using CST Check Performance. Following the reviewer’s note, we reanalysed the same files in one single FlowJo workspace. We are now providing a sample of the resulting plots in new Figure 1a, as well as new percentages (47 in HD, 35 in SVR, and 24 in cHCV instead of 43, 33 and 22 respectively). Although we recognize that acquiring all samples in a unique experiment, and applying strict gating to all samples would have been ideal, this was not possible in our prospective study. We hope the reviewer will acknowledge the minor impact of this technical limitation on the relevance of our observations.

At the same time (if I understood the new information about absolute T cell counts correctly), absolute T cell counts were performed using different methods in HD vs SVR and cHCV patients, raising the possibility that the differences in absolute CD3 numbers as shown in Figure 1–figure supplement 2 could be a result of different methodology. These issues should be reconciled.

Regarding the differences in absolute numbers of CD3, the reviewer is right that the methodologies used are slightly different, i.e. automatic counts on whole blood in hospital laboratory for HCV and SVR patients vs. beads-based counts on whole blood for healthy donors. However, as HCV and SVR patients were collected by leukapheresis, we did not have a way to standardize the methods. Both being well calibrated, we don’t see any reason for significant discrepancies. We have now clarified this in the manuscript. We remain confident in our conclusion of lower percentages and normal absolute counts of naïve CD8 T cells in cHCV patients, especially that the first observation is in accordance with previously published studies (Shen et al., 2010).

2) I find it very difficult to interpret the TCR data. The cross sectional results on bulk naïve cells in Figure 1 show somewhat subtle differences that could be caused by a multitude of factors. Data on specific cells like from the one subject in Figure 2E should be more revealing. In this case, it seems clear that just one clonotype (already dominant in the naïve cells) is responsible for all memory cells. Would that not imply that one clonotype might have some cross-reactivity and thus have expanded?

We thank the reviewer for these comments. We acknowledge that our data on bulk naïve cells are reinforced by repertoire analysis on sorted Mart1-specific naïve and memory inexperienced T cells (Figure 2E). We agree with the reviewer that our observation of predominant Vβ18 clone within Mart1-specific memory-phenotype cells argues for it being the progeny of the dominant Vβ18 clone within Mart1-specific naïve-phenotype cells. Our interpretation is that the shift in the CD45RA/CD27 phenotype of inexperienced T cells is accompanied by clonal selection – and thus not a reflection of abnormal surface expression. However, we believe this does not inform us on the mechanism of expansion, i.e. specific vs. aspecific (homeostatic) differentiation, both being described as triggering clonal selection (Mikszta et al., 1999; Qi et al., 2014). We have now clarified this point in our manuscript.

3) The analysis of specific cells for naïve versus memory population has been significantly strengthened by the new Figure 3, justifying the overall conclusion that more T cells display a memory phenotype. I have still some concerns, especially regarding the longitudinal and cross-sectional analysis of MART responses (Figure 2 and Figure 4), given that the gates on naïve cells seem to be different in every plot. How was the "right" gate determined in each case? Where the different time-points for a patient not stained in a single experiment or why do the staining patterns/signal strength look so different?

As in point 1, the reviewer is correct that there are minor changes in naïve/memory gates (please see above for justification). With respect to Figure 2 and Figure 4, the "right" gate for tetramer-specific populations was set using naïve/memory markers on bulk CD8 T cells, acquired on the same day as given patients were analyzed.

Regarding the cross sectional analysis in Figure 4C, my concern remains that the differences seen in the few patients more than 2 years after treatment (already barely significant) might be driven by patient selection (exemplified by patients S11 and S12 with low memory percentages already pre and post treatment, potentially skewing the results for the 6 subjects included at more than 2 years post treatment). The case that treatment normalizes the phenotype remains not very strong in my opinion (did the bulk CD8 T cells show changes? more data points might be available).

Concerning recovery after SVR, we studied all frozen material available from the five longitudinally-sampled HLA A0201-positive donors. Analysis on bulk CD8 T cells, as suggested by the reviewer, was conducted and is depicted in Author response image 7. Although it shows an interesting pattern, we share with the reviewer the conviction that these findings will need to be confirmed in large longitudinal cohort studies. We have now added one sentence to the manuscript to indicate the need for replication.

Author response image 7
Percentages of memoryphenotype cells within bulk CD8+ T cells over time in SVR patients tested in Figure 4.
https://doi.org/10.7554/eLife.07916.034

4) The functional experiments using CD5 blockade remain inconclusive, given that an effect is only seen in HD. As this effect is modest and does not lead to the same level of reactivity as seen in the cHCV patients (R6) it seems unclear whether CD5 is the main mechanism here.

We agree with the reviewer that the effect of blocking CD5 is modest and does not lead to the same level of reactivity than in cHCV patients. However, and this is consistent with reviewer 2’s interpretation, we believe the selective effect on HD is an argument for CD5 being involved in the pathogenesis underlying the hyperactivation phenotype.

5) The in vitro expansion and ICS experiments in Figure 6 are a bit problematic. First, for each of the subjects the naïve MART precursor frequency should be given, since some of the cHCV subjects had relatively high frequencies as seen in Figure 2–figure supplement 2.

We thank the reviewer for careful analysis of Figure 6. We now provide precursor frequencies for patients tested in IVP experiments in Author response image 8. We did not observe any correlation between precursor frequency and Mart1 expansion after in vitro priming.

Author response image 8
Mart1 precursor frequencies for the HD and cHCV patients included in in vitro priming experiments in Figure 6.
https://doi.org/10.7554/eLife.07916.035

In any case, assuming specific cells in cHCV expand better, could this not be explained by them being memory and not naïve cells? For the functional data, the difference in granzyme B is modest and no difference is observed directly ex-vivo (Figure 6–figure supplement 3).

The difference in granzyme B is modest but statistically significant. We have clarified this in the text.

As for the IFN assay, are the authors really suggesting that populations requiring TAME for visualization can be detected via ICS directly ex-vivo? Detecting such small populations via ex-vivo ICS from 5 million PBMC seems unlikely.

For ICS experiments, we stimulated 107 PBMCs cells in 24-well format plates. Based on precursor frequencies (10-5 – 10-6 / CD8+ T cells), we expect between 2 and 20 inexperienced antigen-specific cells to be available in a given well for peptide restimulation. Given the absence of detectable IFNγ in unstimulated conditions, we believe cytokine secretion reflects a state of hyperactivation towards antigens. We therefore concluded that presumably inexperienced T cells are indeed hyperactive.

Reviewer #2:

The authors have made numerous revisions to the manuscript, which address the major concerns I raised. The only request would be to include the data shown in Author response image 5 in the manuscript. These data make, in my opinion, an important point about the impact and selectivity of the anti-CD5 blockade, and offers material support to the authors' hypothesis. This figure could be included in the supplementary material.

We thank the reviewer for his/her interest in our study and for positive assessment of the revision. We agree that Author response image 5 reinforces our hypothesis that CD5 plays a key role in lowering the threshold for TCR activation in naïve T cells from HCV patients. These results are now included in our manuscript in the Result section, and provided as Figure 5–figure supplement 4 A-D.

References:

Mikszta, J.A., McHeyzer-Williams, L.J., and McHeyzer-Williams, M.G. (1999). Antigen-driven selection of TCR In vivo: related TCR alpha-chains pair with diverse TCR beta-chains. J Immunol 163, 5978–5988.

Qi, Q., Liu, Y., Cheng, Y., Glanville, J., Zhang, D., Lee, J.Y., Olshen, R.A., Weyand, C.M., Boyd, S.D., and Goronzy, J.J. (2014). Diversity and clonal selection in the human T-cell repertoire. Proc Natl Acad Sci USA 111, 13139–13144.

https://doi.org/10.7554/eLife.07916.037

Article and author information

Author details

  1. Cécile Alanio

    1. Unités de Recherche Internationales Mixtes Pasteur, Institut Pasteur, Paris, France
    2. Centre d'Immunologie Humaine, Institut Pasteur, Paris, France
    3. Immunobiology of Dendritic Cells, Institut Pasteur, Paris, France
    Contribution
    CA, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    No competing interests declared.
  2. Francesco Nicoli

    1. Sorbonne Universités, UPMC Univ Paris 06, DNU FAST, CR7, Centre d'Immunologie et des Maladies Infectieuses (CIMI-Paris), Paris, France
    2. Emory, United States
    Contribution
    FN, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    No competing interests declared.
  3. Philippe Sultanik

    1. Unités de Recherche Internationales Mixtes Pasteur, Institut Pasteur, Paris, France
    2. Immunobiology of Dendritic Cells, Institut Pasteur, Paris, France
    Contribution
    PS, Acquisition of data, Drafting or revising the article
    Competing interests
    No competing interests declared.
  4. Tobias Flecken

    The University Medical Center Freiburg, Department of Internal Medicine II, Albert-Ludwigs-Universität, Freiberg, Germany
    Contribution
    TF, Acquisition of data, Analysis and interpretation of data, Contributed unpublished essential data or reagents
    Competing interests
    No competing interests declared.
  5. Brieuc Perot

    1. Unités de Recherche Internationales Mixtes Pasteur, Institut Pasteur, Paris, France
    2. Immunobiology of Dendritic Cells, Institut Pasteur, Paris, France
    Contribution
    BP, Acquisition of data, Analysis and interpretation of data
    Competing interests
    No competing interests declared.
  6. Darragh Duffy

    1. Unités de Recherche Internationales Mixtes Pasteur, Institut Pasteur, Paris, France
    2. Centre d'Immunologie Humaine, Institut Pasteur, Paris, France
    3. Immunobiology of Dendritic Cells, Institut Pasteur, Paris, France
    Contribution
    DD, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    No competing interests declared.
  7. Elisabetta Bianchi

    Immunoregulation Unit, Institut Pasteur, Paris, France
    Contribution
    EB, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    No competing interests declared.
  8. Annick Lim

    Plateforme d’Immunoscope, Institut Pasteur, Paris, France
    Contribution
    AL, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    No competing interests declared.
  9. Emmanuel Clave

    Hôpital Saint-Louis, Assistance publique - hôpitaux de Paris, Paris, France
    Contribution
    EC, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    No competing interests declared.
  10. Marit M van Buuren

    Department of Immunology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
    Contribution
    MMvanB, Conception and design, Drafting or revising the article, Contributed unpublished essential data or reagents
    Competing interests
    No competing interests declared.
  11. Aurélie Schnuriger

    Laboratoire de virologie, Hôpital Armand-Trousseau, Assistance publique - hôpitaux de Paris, Paris, France
    Contribution
    AS, Acquisition of data, Drafting or revising the article
    Competing interests
    No competing interests declared.
  12. Kerstin Johnsson

    Mathematics, Faculty of Engineering, Lunds University, Lund, Sweden
    Contribution
    KJ, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    No competing interests declared.
  13. Jeremy Boussier

    1. Unités de Recherche Internationales Mixtes Pasteur, Institut Pasteur, Paris, France
    2. Centre d'Immunologie Humaine, Institut Pasteur, Paris, France
    3. Immunobiology of Dendritic Cells, Institut Pasteur, Paris, France
    Contribution
    JB, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    No competing interests declared.
  14. Antoine Garbarg-Chenon

    Laboratoire de virologie, Hôpital Armand-Trousseau, Assistance publique - hôpitaux de Paris, Paris, France
    Contribution
    AGC, Acquisition of data, Contributed unpublished essential data or reagents
    Competing interests
    No competing interests declared.
  15. Laurence Bousquet

    APHP, Université Paris Descartes, Paris, France
    Contribution
    LB, Conception and design, Contributed unpublished essential data or reagents
    Competing interests
    No competing interests declared.
  16. Estelle Mottez

    Centre d'Immunologie Humaine, Institut Pasteur, Paris, France
    Contribution
    EM, Conception and design, Contributed unpublished essential data or reagents
    Competing interests
    No competing interests declared.
  17. Ton N Schumacher

    Department of Immunology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
    Contribution
    TNS, Conception and design, Drafting or revising the article, Contributed unpublished essential data or reagents
    Competing interests
    No competing interests declared.
  18. Antoine Toubert

    Hôpital Saint-Louis, Assistance publique - hôpitaux de Paris, Paris, France
    Contribution
    AT, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    No competing interests declared.
  19. Victor Appay

    1. Sorbonne Universités, UPMC Univ Paris 06, DNU FAST, CR7, Centre d'Immunologie et des Maladies Infectieuses (CIMI-Paris), Paris, France
    2. Emory, United States
    Contribution
    VA, Conception and design, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    No competing interests declared.
  20. Farhad Heshmati

    EFS, Hôpital Cochin, Paris, France
    Contribution
    FH, Conception and design, Contributed unpublished essential data or reagents
    Competing interests
    No competing interests declared.
  21. Robert Thimme

    The University Medical Center Freiburg, Department of Internal Medicine II, Albert-Ludwigs-Universität, Freiberg, Germany
    Contribution
    RT, Conception and design, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    No competing interests declared.
  22. Stanislas Pol

    APHP, Université Paris Descartes, Paris, France
    Contribution
    SP, Conception and design, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    SP: Has received consulting and lecturing fees from Bristol-Myers Squibb, Boehringer Ingelheim, Janssen, Vertex, Gilead, Roche, MSD, Novartis, Abbvie, Sanofi and Glaxo Smith Kline, and grants from Bristol-Myers Squibb, Gilead, Roche and MSD.
  23. Vincent Mallet

    APHP, Université Paris Descartes, Paris, France
    Contribution
    VM, Conception and design, Drafting or revising the article
    Competing interests
    No competing interests declared.
  24. Matthew L Albert

    1. Unités de Recherche Internationales Mixtes Pasteur, Institut Pasteur, Paris, France
    2. Centre d'Immunologie Humaine, Institut Pasteur, Paris, France
    3. Immunobiology of Dendritic Cells, Institut Pasteur, Paris, France
    Contribution
    MLA
    For correspondence
    albertm@pasteur.fr
    Competing interests
    No competing interests declared.

Funding

Institut National Du Cancer

  • Cécile Alanio
  • Philippe Sultanik
  • Brieuc Perot
  • Darragh Duffy
  • Stanislas Pol
  • Vincent Mallet
  • Matthew L Albert

European Research Council (SPHINX)

  • Cécile Alanio
  • Marit M van Buuren
  • Estelle Mottez
  • Antoine Toubert
  • Victor Appay
  • Stanislas Pol
  • Vincent Mallet
  • Matthew L Albert

Institut Pasteur

  • Darragh Duffy
  • Elisabetta Bianchi
  • Annick Lim
  • Emmanuel Clave
  • Aurélie Schnuriger
  • Kerstin Johnsson
  • Antoine Garbarg-Chenon
  • Laurence Bousquet
  • Stanislas Pol
  • Matthew L Albert

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

The authors wish to thank all patients for participating in the study. We thank the Etablissement Français du Sang (EFS, France) for healthy donors samples; P. Loiseau and the Laboratory of Immunology (Hôpital Saint Louis, Paris, France) for molecular HLA typing; M. Ahloulay, T. Buivan, K. Sperber, and A. Noble for coordination of the clinical protocol; V. Rouilly, and M. Fontes for careful reading of the manuscript and support for statistical analysis; A. Alanio and N. Dulphy for logistic assistance; and P. Bousso, F. Lemaitre, and L. Rogge for technical help and critical review of the manuscript. We would also like to acknowledge the Centre d’Immunologie Humaine (CIH, Institut Pasteur, Paris, France) for support and technical expertise, as well as technicians from the Laboratory of Virology (Hôpital Trousseau, Paris, France) for CMV serological testing.

Ethics

Clinical trial registration NCT01534728

Human subjects: 29 cHCV, 37 SVR, and 18 cHBV patients were included (Table 1). All subjects were followed in the Liver Unit of Hopital Cochin (Paris, France) or the Department of Internal Medicine II (Freiburg, Germany). French samples were obtained as part of study protocol C11-33 approved by the INSERM clinical investigation department with ethical approval from the CPP Ile-de-France II, Paris (ClinicalTrials.gov identifier: n°NCT01534728). German samples were obtained in the University Hospital Freiburg according to regulations of local ethic committee. Both study protocols conformed to the ethical guidelines of the Declaration of Helsinki, and patients provided informed consent.

Reviewing Editor

  1. Rafi Ahmed, Emory, United States

Publication history

  1. Received: April 3, 2015
  2. Accepted: November 12, 2015
  3. Accepted Manuscript published: November 14, 2015 (version 1)
  4. Version of Record published: January 19, 2016 (version 2)

Copyright

© 2015, Alanio et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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