Skip to main content

Main menu

  • Home
  • Articles
    • Current Issue
    • Next in The JI
    • Archive
    • Brief Reviews
    • Pillars of Immunology
    • Translating Immunology
    • Most Read
    • Top Downloads
    • Annual Meeting Abstracts
  • COVID-19/SARS/MERS Articles
  • Info
    • About the Journal
    • For Authors
    • Journal Policies
    • Influence Statement
    • For Advertisers
  • Editors
  • Submit
    • Submit a Manuscript
    • Instructions for Authors
    • Journal Policies
  • Subscribe
    • Journal Subscriptions
    • Email Alerts
    • RSS Feeds
    • ImmunoCasts
  • More
    • Most Read
    • Most Cited
    • ImmunoCasts
    • AAI Disclaimer
    • Feedback
    • Help
    • Accessibility Statement
  • Other Publications
    • American Association of Immunologists
    • ImmunoHorizons

User menu

  • Subscribe
  • Log in

Search

  • Advanced search
The Journal of Immunology
  • Other Publications
    • American Association of Immunologists
    • ImmunoHorizons
  • Subscribe
  • Log in
The Journal of Immunology

Advanced Search

  • Home
  • Articles
    • Current Issue
    • Next in The JI
    • Archive
    • Brief Reviews
    • Pillars of Immunology
    • Translating Immunology
    • Most Read
    • Top Downloads
    • Annual Meeting Abstracts
  • COVID-19/SARS/MERS Articles
  • Info
    • About the Journal
    • For Authors
    • Journal Policies
    • Influence Statement
    • For Advertisers
  • Editors
  • Submit
    • Submit a Manuscript
    • Instructions for Authors
    • Journal Policies
  • Subscribe
    • Journal Subscriptions
    • Email Alerts
    • RSS Feeds
    • ImmunoCasts
  • More
    • Most Read
    • Most Cited
    • ImmunoCasts
    • AAI Disclaimer
    • Feedback
    • Help
    • Accessibility Statement
  • Follow The Journal of Immunology on Twitter
  • Follow The Journal of Immunology on RSS

Deep Immune Profiling of an Arginine-Enriched Nutritional Intervention in Patients Undergoing Surgery

Nima Aghaeepour, Cindy Kin, Edward A. Ganio, Kent P. Jensen, Dyani K. Gaudilliere, Martha Tingle, Amy Tsai, Hope L. Lancero, Benjamin Choisy, Leslie S. McNeil, Robin Okada, Andrew A. Shelton, Garry P. Nolan, Martin S. Angst and Brice L. Gaudilliere
J Immunol September 15, 2017, 199 (6) 2171-2180; DOI: https://doi.org/10.4049/jimmunol.1700421
Nima Aghaeepour
*Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94121;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Cindy Kin
†Department of Surgery, Stanford University School of Medicine, Stanford, CA 94121;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Cindy Kin
Edward A. Ganio
*Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94121;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Edward A. Ganio
Kent P. Jensen
‡Division of Immunology and Rheumatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94121; and
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Kent P. Jensen
Dyani K. Gaudilliere
†Department of Surgery, Stanford University School of Medicine, Stanford, CA 94121;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Dyani K. Gaudilliere
Martha Tingle
*Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94121;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Amy Tsai
*Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94121;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hope L. Lancero
*Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94121;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Benjamin Choisy
*Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94121;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Leslie S. McNeil
*Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94121;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Robin Okada
*Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94121;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Robin Okada
Andrew A. Shelton
†Department of Surgery, Stanford University School of Medicine, Stanford, CA 94121;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Garry P. Nolan
§Department of Microbiology and Immunology, Stanford University, Stanford, CA 94121
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Martin S. Angst
*Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94121;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Brice L. Gaudilliere
*Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94121;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Brice L. Gaudilliere
  • Article
  • Figures & Data
  • Info & Metrics
  • PDF + SI
  • PDF
Loading

Abstract

Application of high-content immune profiling technologies has enormous potential to advance medicine. Whether these technologies reveal pertinent biology when implemented in interventional clinical trials is an important question. The beneficial effects of preoperative arginine-enriched dietary supplements (AES) are highly context specific, as they reduce infection rates in elective surgery, but possibly increase morbidity in critically ill patients. This study combined single-cell mass cytometry with the multiplex analysis of relevant plasma cytokines to comprehensively profile the immune-modifying effects of this much-debated intervention in patients undergoing surgery. An elastic net algorithm applied to the high-dimensional mass cytometry dataset identified a cross-validated model consisting of 20 interrelated immune features that separated patients assigned to AES from controls. The model revealed wide-ranging effects of AES on innate and adaptive immune compartments. Notably, AES increased STAT1 and STAT3 signaling responses in lymphoid cell subsets after surgery, consistent with enhanced adaptive mechanisms that may protect against postsurgical infection. Unexpectedly, AES also increased ERK and P38 MAPK signaling responses in monocytic myeloid-derived suppressor cells, which was paired with their pronounced expansion. These results provide novel mechanistic arguments as to why AES may exert context-specific beneficial or adverse effects in patients with critical illness. This study lays out an analytical framework to distill high-dimensional datasets gathered in an interventional clinical trial into a fairly simple model that converges with known biology and provides insight into novel and clinically relevant cellular mechanisms.

Introduction

Recent developments in multiplexed, high-content immune profiling technologies have enormous potential to advance our understanding of the biology that drives disease processes and restores human health. Among these technologies, mass cytometry is increasingly implemented in clinical studies for the high-resolution surveillance of human circulating immune cells in response to clinically relevant perturbations (1–6). A pertinent study recently revealed immune signatures that predicted the rate of clinical recovery in patients undergoing surgery (1, 7, 8). With the emerging promise of mass cytometry (and other high-parameter flow cytometry platforms) to discover novel molecular metrics for advancing precision medicine, demonstrating the utility of these technologies to comprehensively profile therapeutic interventions becomes paramount.

We applied mass cytometry to immune profile a pharmaconutrient that is widely used in patients undergoing surgery. Specifically, we studied a commercially available arginine-enriched dietary supplement (AES) that reduces the risk of infection in patients undergoing elective surgery, but possibly increases morbidity in critically ill patients (9, 10). Arginine plays a critical role in T cell proliferation, differentiation, and function (11–13), and in a murine model of surgical injury, surgery-induced arginine depletion has been linked to T cell dysfunction and an increased infection risk (14).

Previous studies in patients receiving perioperative AES have provided important insight on the effects of immunonutrition on aspects of the human immune response to surgery. These studies have focused on certain circulating factors (15), the distribution of pooled immune cell subsets (e.g., T cells, B cells, neutrophils) (16), changes in the expression of selected surface markers (e.g., CD3 expression, HLA-DR expression), or the functional analysis of isolated immune cells in ex vivo assays (e.g., phagocytosis of neutrophils) (17, 18). However, technological limitations did not allow for the comprehensive phenotyping of all major immune cell subsets or the functional analysis of intracellular signaling activities as they occur in vivo. Furthermore, the statistical interpretation of high-dimensional immunological data presents an analytical challenge that has thus far precluded a system-wide characterization of the immune-modifying properties of AES in patients undergoing surgery.

Chosen experimental and clinical setting were therefore appealing to examine two major questions, that is, whether bedside application of mass cytometry would allow detecting expected and potentially novel immunological effects of an accepted clinical intervention with proven benefits, and whether application of a machine learning algorithm particularly adapted to the analysis of highly correlated and complex immunological data would capture known biology and provide novel insight into cellular mechanisms that may explain the context-specific clinical effects of AES (9, 10).

Materials and Methods

Study design

The aim of this prospective, randomized clinical trial was to comprehensively characterize the molecular effect of AES on the human inflammatory response to surgical trauma from the combined proteomic and mass cytometry analysis of peripheral blood samples from patients undergoing abdominal surgery. The study was conducted between August 19, 2013 and June 3, 2015 at Stanford University School of Medicine. The study used a randomized, controlled, and open-label design, as only objective outcomes were assessed. Research Randomizer (https://www.randomizer.org) was used for patient treatment allocation. Patients randomized to arginine-rich supplements were asked to drink four containers (237 ml) of Impact every day for 5 d before surgery. One container of Impact contains 4.2 g of l-arginine. Randomization was performed by a study nurse. No adverse effects attributable to the intervention were observed. Exclusion and inclusion criteria and the consort chart are available in Supplemental Fig. 1.

Anesthesia protocol

Anesthesia care was standardized to the use of fentanyl and hydromorphone as i.v. analgesics. Analgesics were dosed to maintain the blood pressure and heart rate within 20% of baseline during surgery, and keep pain levels at ≤4 on a 10-point numerical pain rating scale after emergence from anesthesia. Anesthesia was induced with propofol and rocuronium and maintained with the volatile anesthetic sevoflurane. Medications with potential immune-modulatory effects, including steroids, ketamine, and i.v. local anesthetics, were not allowed. No violations of this protocol were noted.

Deviations from study protocol

One patient in the AES group took the supplement for 4 rather than 5 d. One patient in the AES group and on adalimumab (40 mg once every 2 wk) stopped therapy 2 rather than 4 wk before surgery, and one patient in the control group on mercaptopurine (50 mg every other day) stopped therapy 1 rather than 4 wk before surgery.

Mass cytometry analysis of perioperative whole-blood samples

Serial blood samples collected at all perioperative time points (5 d and 1 h before surgery, then 1 h, 1 d, and 3 d after surgery) were processed using a standardized protocol for fixation (Smart Tube, San Carlos, CA), storage, and Ab staining of whole-blood samples for mass cytometry analysis (1, 7, 19). Extracellular and intracellular Abs used in the analysis are described in Supplemental Table II. To minimize experimental variability, samples corresponding to an entire time series were barcoded, stained, and run simultaneously on the mass cytometry instrument (20, 21). To maximize the sensitivity of the assay for detection of differences between the AES and control group, sample time series from patients in the AES group were randomly paired with samples from patients in the control group, and paired sample time series were barcoded and run using the same barcode plate. Barcoded samples were analyzed at a flow rate of ∼500 cells/s on a CyTOF 2.0 mass cytometer (Fluidigm). Samples were normalized and debarcoded as described previously (20, 22).

Multiplex cytokine analysis of perioperative plasma samples

Multiplex analyses of plasma cytokines were performed in the Human Immune Monitoring Center (Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine) using Luminex human 63-plex kits from eBioscience/Affymetrix according to the manufacturer’s recommendations.

Derivations of immune features

Mass cytometry data from each sample were manually gated into 23 immune-cell types of interest (Supplemental Fig. 2). Immune cell subsets were selected on the basis that immune features included in the current analysis would capture at least all innate and adaptive immune responses previously detected in our mass cytometry analysis of patients’ undergoing surgery (1).

Cell frequency features.

Cell frequencies were expressed as a percentage of gated singlets in the case of granulocytes, and as a percentage of mononuclear cells (CD45+CD66−) in the case of all other cell types. For each cell type, frequency features were calculated as the difference in cell frequency between each postoperative time point and the 1 h preoperative time point.

Cell signaling features.

The signal intensity of the following functional markers was simultaneously quantified per single cell: pSTAT1, pSTAT3, pSTAT5, pNF-κB, total IκB, pMAPK-activated protein kinase 2 (pMAPKAPK2), pP38, p–ribosomal protein-S6 (rpS6), pERK1/2, and pCREB. For each cell type, signaling immune features were calculated as the difference in median signal intensity (arcsinh-transformed value) of each signaling protein between each postoperative time point and the 1 h preoperative time point.

Cytokine features.

For each of the 63 plasma analytes, cytokine features were calculated as the difference in mean fluorescence intensity between each postoperative time point and the 1 h preoperative time point.

Identification of myeloid-derived suppressor cells

A number of studies in mouse and human models have documented the accumulation of myeloid-derived suppressor cells (MDSCs; both granulocytic MDSCs [G-MDSCs] and monocytic MDSCs [M-MDSC]) during acute inflammatory processes such as traumatic stress, burn injury, and sepsis (23–27). In this study, we followed recent recommendations aiming to standardize MDSC nomenclature to define M-MDSCs (28). M-MDSCs were defined 1) phenotypically as lineage− (CD66−CD15−CD3−CD19−CD7−), CD11b+CD33+CD14+HLA-DRlow; and 2) functionally by their ability to suppress CD4+ and CD8+ T cell proliferation in a standard MDSC suppression assay (see Supplemental Fig. 3). The Ab panel used for the gating of immune cells from whole-blood samples did not allow G-MDSCs to be distinguished from neutrophils, which are also CD66+CD15+CD11b+ and CD14−HLA-DRlow. G-MDSCs were therefore not included in the analysis.

T cell suppression assays

As previously shown in the context of orthopedic surgery (1), M-MDSC frequency increased >5-fold after abdominal surgery and peaked at the 24 h time point (Supplemental Fig. 3B). T cell suppression assays were therefore performed with T cells isolated from samples collected before surgery and M-MDSCs isolated from samples collected 24 h after surgery. Briefly, PBMCs were isolated before and 24 h after surgery from blood samples collected from five patients undergoing abdominal surgery. Fresh PBMCs isolated before surgery were used as source for responder T cells. CD2+ T cells were subsequently enriched from CFSE-labeled PBMCs using a CD2 positive selection kit (Stemcell Technologies) according to the manufacturer’s protocol. Fresh PBMCs isolated 24 h after surgery were used as source of M-MDSCs. PBMCs were incubated in the presence of fluorescent mAbs (Supplemental Table III). M-MDSCs were identified as lineage−CD14+CD11b+HLA-DRlow cells and sorted on a FACSAria sorter (BD Biosciences). Enriched T cells were cultured for 5 d at 37°C in the presence of anti-CD3/CD28 microbeads (Dynabeads; Thermo Fisher Scientific) either alone or in the presence of M-MDSCs (one suppressor cell per two T cells). Cells were then collected and stained with fluorescent Abs (Supplemental Table III) to identify CD4+ and CD8+ T cells and analyzed by flow cytometry on an LSR II. Proliferation was quantified for each gated cell type as the percentage of CFSEdim cells.

Statistical analysis

Sample size.

Based on previous data documenting the activation of STAT3 and MAPK signaling pathways in M-MDSCs and their rapid expansion after surgery (1), a sample size of 10 patients in each group provided 80% power at p < 0.05 to detect an intervention-related change in STAT3 phosphorylation in M-MDSCs ≥40%.

Correlation network.

The correlation network consists of a minimum spanning tree of a graph on which the weight of each edge is the inverse of the absolute value of the Spearman correlation between the two respective immune features (Fig. 1). To visualize the modularity of the network, edges with a significant correlation p value (after Bonferroni adjustment) were added to the graph. The graph layout was calculated using the Large Graph Layout algorithm (29) as implemented by the R package (3.2.2) igraph (1.0.1).

Elastic net analysis.

Elastic net analysis was performed using the R package (3.2.2) glmnet (2.0). All parameters were set to default except for α = 0.5 (to limit the number of selected features but account for most important components of each intracorrelated module) and standardize = FALSE to enable the modifications described above.

Handling of missing values.

Two samples (the 4 h sample from one patient and the 24 h sample from another patient) did not contain a sufficient number of cells for analysis. Immune feature values for these two samples were set to the average of the respective values from the entire cohort.

Data resources.

Raw data, manually gated cell types, and plasma cytokines are available for download from http://flowrepository.org/experiments/1021.

Results

Subjects, surgical parameters, and arginine plasma concentrations

A representative sample of patients undergoing major abdominal surgery was randomized to a 5-d preoperative intervention with AES (Impact; Nestle HealthCare Nutrition, Florham Park, NJ) or routine preoperative care without supplement. The study was registered at ClinicalTrials.gov on June 18, 2013 (NCT01885728). Abdominal surgery was chosen because most compelling evidence for beneficial effects of AES on postoperative infection rates exists for this type of surgery (10).

Participant flow is summarized in Supplemental Fig. 1 according to CONSORT recommendations. Two hundred forty-one patients were screened for eligibility and 135 patients were eligible; ultimately 22 patients (16%) were randomized and included in the final analysis. Of the 135 eligible patients, 33% declined or withdrew early during the study, whereas an unexpectedly high percentage of patients (43%) could not be randomized due to logistic challenges. These predominantly included late scheduling for the first preoperative visit or the final date of surgery, which precluded preoperative treatment with AES for 5 d. Of the 22 patients completing the study, 11 patients received AES before surgery (AES group), and 11 patients served as controls (control group). Study groups were evenly matched except for age (control group, 47.7 ± 12.9 y; AES group, 61.8 ± 7.9 y). Complications, including infections within 30 d after surgery, were nominally twice as high in the control group compared with the AES group (30). Patient demographics, clinical diagnoses, surgical procedures, surgical and anesthetic parameters, and postoperative complications are listed in Table I.

View this table:
  • View inline
  • View popup
Table I. Patient and procedural characteristics

The median plasma concentration of arginine increased significantly from 91 nmol/ml (interquartile range [IQR], 87–121) 5 d before surgery to 138 nmol/ml (IQR, 111–142) 1 h before surgery in the AES group, indicating successful presurgical arginine enrichment in this patient group (p = 0.01). Corresponding plasma concentrations in the control group were 87 nmol/ml (IQR, 80–137) and 97 nmol/ml (IQR, 71–128). Observed increase in plasma arginine concentration after AES supplementation (averaging 51%) was consistent with observed increases in previous studies demonstrating clinical benefits of AES (18, 31).

Deep cellular and proteomic profiling of patients’ immune responses to surgery

Serial whole-blood and plasma samples were collected starting 5 d before surgery and ending 3 d after surgery (Fig. 1A). Deep immune and proteomic profiling of patient samples with single-cell mass cytometry and a multiplexed proteomic platform revealed surgery-induced changes in immune cell frequency, immune cell signaling, and plasma cytokine concentrations (Fig. 1B; see Supplemental Fig. 2 for gating strategy). Three hundred sixteen immune features were captured per time point, including the frequency of 23 cell types, the activity of 10 signaling proteins in each cell type, and the plasma concentration of 63 cytokines (Fig. 1C).

FIGURE 1.
  • Download figure
  • Open in new tab
  • Download powerpoint
FIGURE 1.

Experimental flowchart and analytical approach. (A) Whole blood was obtained 5 d and 1 h before surgery, and 4, 24, and 72 h after surgery. (B) Aliquots were stained with cell surface and intracellular Abs and analyzed with mass cytometry. Plasma proteins were measured using a Luminex 63-plex assay. (C) Assays produced three data layers providing information about cell frequencies (red bar), cell signaling (green bar), and plasma protein concentrations (blue bar).

Examination of selected immune features in samples from the control group indicated that abdominal surgery produced profound cell frequency, cell signaling, and cytokine response patterns (Fig. 2A–C) that recapitulated patterns previously described in the context of surgical and traumatic injury (1, 32, 33). The three-layered dataset built a correlation network that characterized each patient’s immune response to surgery (Fig. 2D). A minimum spanning tree algorithm juxtaposed immune features that were most tightly correlated. Although many correlations were observed within the same data layer, a significant number of correlations were also observed between data layers. These findings highlight the complexity and interconnectivity of surgery-induced immune changes. Construction of this network provided the structural basis for further computational analysis.

FIGURE 2.
  • Download figure
  • Open in new tab
  • Download powerpoint
FIGURE 2.

Abdominal surgery elicits canonical immune responses. Representative changes in cell frequency, intracellular signaling, and cytokine plasma concentration are shown. The same changes have previously been described in patients undergoing other types of surgery (1, 32). Depicted are changes observed in patients from the control group (n = 11). Changes are calculated as the difference in cell frequency (%CD45+CD66− cells), intracellular signaling activity (arcsinh transform of mass cytometry signal), and cytokine plasma concentration (mean fluorescence intensity) between perioperative time points (−5 d, 4, 24, 72 h) and day 0 (1 h before surgery). Box plots represent median and IQR. (A) cMCs increased, CD4+ T cells decreased, and CD8+ T cells decreased in frequency after surgery. (B) Increased STAT1, STAT3, and STAT5 phosphorylation in cMCs, CD4+ T cell, and CD8+ T cell subsets (4, 24, or 72 h depending on cell type) and increased phosphorylation of MAPK P38 (24, 72 h) in cMCs (but not in CD4+ or CD8+T cells) also recapitulate sentinel cell type–specific signaling changes previously observed in patients undergoing orthopedic surgery (1). (C) Plasma concentrations of IL-6, IL-8, and IL-10 increased after surgery. (D) The entire dataset composed of 316 immune features is represented by a minimum spanning tree emphasizing the correlations between the tree categories of immune features (red dots, cell frequencies; green dots, cell signaling; blue dots, plasma cytokines).

An elastic net analysis reveals specific immune features that are modulated by arginine-enriched supplements

An elastic net (EN) algorithm (34) was applied to extract the components of the correlation network that best differentiated the immune response to surgery between the AES and control groups. The EN is a penalized regression method particularly adapted to the analysis of highly correlated data, as it eliminates redundant parameters while retaining interrelated parameters (35–38). This approach identified a cross-validated model that separated the AES from the control group (leave-one-out cross-validated p = 0.0001, Fig. 3A). The model consisted of 20 immune features, including nine differences in cell frequency, six differences in cell signaling, and five differences in plasma cytokine concentrations. Fourteen features were increased and six features were decreased in the AES group (Fig. 3B).

FIGURE 3.
  • Download figure
  • Open in new tab
  • Download powerpoint
FIGURE 3.

An EN analysis identifies a cross-validated model of interrelated immune features separating patients treated with AES from controls. (A) An EN algorithm extracted 20 immune features from the correlation network that differentiated the immune response to surgery between patients randomized to AES from controls (cross-validated, p = 0.0001). (B) The EN features were extracted from three visually segregated modules. Fourteen EN features were higher and six features were lower in the arginine group. (C) Ten EN features projected onto module 1 (purple dots), nine features projected onto module 2 (orange dots), and one single feature projected onto module 3 (light blue dots).

EN features resided within three modules that visually segregated the correlation network into distinct sets of correlated parameters (Fig. 3C). All EN features within module 1 (purple) were increased in the AES group. They included changes in the frequency of B cells (4 h), M-MDSCs (1 d), granulocytes (3 d), and γδ T cells (3 d). They also included changes in signaling activity of MAPKAPK2 (4 h) in M-MDSCs, classical monocytes (cMCs), plasmacytoid dendritic cell (pDCs), and regulatory T cells (Tregs), and changes in signaling activity of STAT1 and STAT3 (4 h) in CD25+CD8+ memory T (Tmem) cells. EN features within module 2 (orange) included increased frequencies of nonclassical monocytes (24 h), intermediate monocytes (72 h), mDCs (72 h), and CD4+ T cells (72 h), as well as a decreased frequency of cMCs (4 h) in the AES group. They also included higher concentrations of the plasma cytokines leptin (24 h) and G-CSF (24 h), and decreased concentrations of IFN-β (4 h) and ICAM1 (4 h) in the AES group. Module 3 (blue) contained the plasma protein leptin (4 h) as the only parameter, which was decreased in the AES group.

Several broad themes became apparent when examining the EN model. First, high-dimensional mass cytometry sensitively captured the modifying effects of the preoperative intervention (AES) on endogenous cellular immune response in patients undergoing surgery. Second, the components of the EN model separating the AES group from the control group were embedded in a larger correlation network emphasizing that changes in cell frequency, cell signaling activity, and plasma cytokine concentrations are highly interrelated rather than isolated events. Third, the nutritional intervention modulated a wide array of cell types and signaling events encompassing the innate and adaptive branch of the immune system. These findings highlight the utility of high-parameter single-cell immune profiling and an EN approach to comprehensively characterize the complex modulation of the immune system with a common clinical nutritional intervention.

Elastic net features are proxies reflecting broader immunological changes

The EN analysis provided a list of 20 interrelated immune features. However, the biological interpretation of this multivariate output requires further consideration. EN analysis is a statistical approach that markedly reduced the high-dimensional correlation network to a set of stringent and interrelated but not redundant parameters that differentiate the two study groups. However, the EN may not reveal all biologically meaningful parameters that separate the two groups. As such, EN parameters can be viewed as particularly stringent proxies that can reveal broader biology upon further examination.

From a biological perspective, intracellular signaling changes are particularly informative, as they are intimately associated with cell function. All signaling changes captured by the EN occurred early (4 h) after surgery (Fig. 4A). They were prominent for STAT1 and STAT3 signaling in CD25+CD8+ Tmem cells and MAPKAPK2 signaling in M-MDSCs, cMCs, pDCs, and Tregs. These signaling changes were tightly interlinked but also correlated with changes in cell frequency (including M-MDSC frequencies) that occurred later in the postoperative course, that is, 24 and 72 h after surgery.

FIGURE 4.
  • Download figure
  • Open in new tab
  • Download powerpoint
FIGURE 4.

EN features are proxies that reveal broader immunological effects of AES. (A) EN features are grouped chronologically (color coded according to correlation network). Size of circle indicates relative statistical strength. Thickness of gray lines indicates correlation strength between features (Spearman coefficient). Signaling changes occurred 4 h after surgery and were prominent for pSTAT1/3 in CD25+CD8+ Tmem cells and pMAPKAPK2 in M-MDSCs, cMCs, pDCs, and Tregs. (B) Increased STAT3 signaling in CD25+CD8+ Tmem cells (AES group) was reflected in several CD4+ and CD8+ T cell subsets, indicating that the EN parameter “STAT3 signaling in CD25+CD8+ Tmem cells” acted as a proxy revealing broad effects of AES on STAT3 signaling in T cells. (C) The same findings applied to STAT1. (D) Increased MAPKAPK2 signaling in M-MDSCs 4 h after surgery (AES group) was reflected along the P38 and ERK1/2 MAPK signaling pathways (pP38, pERK, pS6, pCREB, and NF-κB), indicating that the net parameter “MAPKAPK2 signaling in M-MDSCs” acted as a proxy revealing consistent changes along this pathway. (E) Increased signaling in M-MDSCs along the MAPK pathway was also present at 24 h. (F) Accentuated expansion of M-MDSCs (AES group) 24 h after surgery was echoed at 72 h.

Although the EN specifically captured differences in STAT1 and STAT3 signaling in CD25+CD8+ Tmem cells, consistent directional differences were found across all phenotyped CD4+ and CD8+ T cells (Fig. 4B, 4C). These results indicate that the EN features “STAT1 and STAT3 signaling in CD25+CD8+ Tmem cells” acted as a proxy that revealed broad effects of AES on STAT1 and STAT3 signaling in multiple adaptive cell subsets. Similarly, although the EN specifically captured differences in MAPKAPK2 signaling in M-MDSCs 4 h after surgery, such differences were reflected across many components of the P38 and ERK MAPK signaling pathways, including P38, ERK, rpS6, CREB, and NF-κB 4 and/or 24 h after surgery (Fig. 4D, 4E). These results indicate that the EN parameter “MAPKAPK2 signaling in M-MDSCs” was a proxy revealing consistent directional differences along the MAPK pathway, which in turn corroborated the biological significance of the EN parameter. Interestingly, increased MAPKAPK2 signaling in M-MDSCs was linked to the expansion of M-MDSCs at 24 h, and such expansion was also seen at 72 h (Fig. 4F).

Taken together, the EN analysis identified an immune signature that specified the effect of AES on innate and adaptive immune responses to surgery. A post hoc multiple linear regression analysis estimating whether demographic (age, sex, race), clinical (preoperative diagnosis of malignancy), or surgical (open versus laparoscopic surgery) variables confounded the effect of AES indicated that the EN model remained significant after controlling for these variables (residual p = 0.001, Supplemental Table I). Similarly, after controlling for these demographic, clinical, and surgical variables the effect of AES treatment remained significant as an independent predictor of key EN model components, including the pSTAT1 and pSTAT3 signals in CD8+ T cell subsets, and the pMAPKAPK2 signal in M-MDSCs.

Discussion

This study combined the high-resolution functional profiling of circulating immune cells from patients undergoing surgery with multiplex analysis of plasma factors to capture the immunological fingerprint of AES, a preoperative intervention that decreases infectious complications after abdominal surgery. An EN algorithm extracted interrelated immune features that separated patients randomized to AES from controls and pointed to biologically relevant innate and adaptive mechanisms modified by the preoperative intervention.

EN features should be viewed as statistically stringent proxies that can be linked to broader biological information. Notably, in the absence of prior knowledge EN proxies pointed to signaling responses and frequency changes in immune cell subsets that are widely discussed in the context of AES. As such, our results integrate well with previous findings highlighting the interplay between arginine, MDSCs, and T cells in trauma and surgery: within hours of trauma, plasma arginine levels decrease (39, 40), leading to multiple T cell dysfunctions, including the downregulation of operational TCRs, decreased cell proliferation, and cytokine production (41). A sentinel role for these changes has been attributed to MDSCs. MDSCs are a heterogeneous population of immune cells with immunosuppressive properties, consisting of immature granulocytes (G-MDSCs) and monocytes (M-MDSCs and early stage MDSCs) that accumulate in the context of malignancies, sepsis, and severe trauma (27, 28, 42, 43), and metabolize arginine at a high rate (25, 44). Counteracting the surgery-induced depletion of arginine stores has been proposed as an important mechanism for the clinical observation that AES reduces infection rates in patients undergoing surgery (10, 45).

In our analysis, the proxies “STAT1 and STAT3 signaling in CD25+CD8+ Tmem cells” (elevated in the AES group) revealed that the same directional signaling changes occurred in many CD4+ and CD8+ T cell subsets. STAT1 and STAT3 regulate numerous T cell functions downstream of type I and type II cytokine receptors, including cell proliferation, survival, effector functions, and differentiation into specific Th cell subsets (46–48). The simultaneous increase in MAPKAPK2 signaling observed in dendritic cells and classical monocytes (Fig. 3C) indicates that the nutritional intervention may also upregulate shared mechanisms activated downstream of pattern recognition receptors in the setting of acute inflammation (49). Together, these enhanced innate and adaptive immune responses may be protective against postsurgical infections by improving the host’s ability to mount an efficient pathogen response (50).

Interestingly, changes in plasma cytokines engaging the JAK/STAT1 (type I IFNs and IFN-γ) or the JAK/STAT3 pathway (including IL-6, IL-8, and IL-10) could not account for increased STAT1 or STAT3 signaling in the AES group. Although plasma concentrations of IL-6, IL-8, and IL-10 increased after surgery (Fig. 2B), these increases were not different between the AES and control groups. These results suggest that other factors modulated by AES likely contributed to increases in STAT1/3 signaling. One possibility is that AES activated the mechanistic target of rapamycin (mTOR), a key metabolic regulator and sensor of amino acids (51, 52). Availability of intracellular arginine is critical for T cell survival and function (13). Recent work highlights cross-talk between mTOR and JAK/STAT pathways, particularly the JAK/STAT3 pathway, as they synergistically regulate T cell differentiation (53, 54). Perioperative AES may therefore alter T cell function by enhancing JAK/STAT signaling via mTOR activation. This hypothesis derived from an agnostic analysis of the high-dimensional dataset will guide further investigation.

The EN model also pointed to enhanced activation and expansion of M-MDSCs in surgical patients receiving AES, a novel and surprising finding. Further examination of signaling responses in M-MDSCs suggested that AES increased the phosphorylation of multiple members of the MAPK signaling pathway (including P38, ERK, MAPKAPK2, rpS6, CREB, and NF-κB), a critical component of the MyD88-mediated TLR signal transduction (55). TLR2 and TLR4 in particular are primed and activated in response to surgical trauma by endogenous ligands released from damaged tissue (56–58). In rodent models, arginine supplementation has been shown to facilitate MAPK activation downstream of TLR4 (59). Results thus indicate that AES may exacerbate the activation of the MAPK pathway downstream of TLRs in response to tissue injury, thereby facilitating the expansion of M-MDSCs after surgery (Fig. 2). Of note, the more pronounced expansion of M-MDSCs in patients receiving AES seems to contradict findings of a recent study suggesting that AES decreased M-MDSC frequency after surgery (16). However, expression of HLA-DR, a critical phenotypical marker of human M-MDSCs, was not assessed in this recent study. As such, the reported decrease in cell frequency cannot clearly be attributed to M-MDSCs.

MDSCs arise as a conserved response to acute inflammatory processes (such as trauma or sepsis) to protect the organism from an uncontrolled immune response (11). However, prolonged expansion of MDSCs can drive pathological states in chronic illness and cancer (26, 43). The observed expansion of M-MDSCs provides a mechanistic argument in response to the clinical dilemma surrounding the benefits or harm of arginine supplementation in critically ill patients (60). A review emphasizing studies of high methodological quality suggested that AES may increase mortality in critically ill patients (61). However, studies in septic patients with only moderate illness reported decreased mortality and infection rates in patients receiving AES (62), a finding further supported by animal experiments (63, 64). In contrast, a recent study in patients with severe sepsis linked the persistent elevation of MDSCs to increased infection, prolonged intensive care treatments, and poor functional status (26). It has become clear that the biological role of MDSCs is highly contextual and dependent on the type, severity, and chronicity of a disease (27). A better understanding of the interplay between AES, MDSCs, and potential beneficial or adverse clinical outcomes will require clinical trials that carefully describe the functional properties of MDSCs and clinical characteristics of the studied patient population.

The role of MDSCs in facilitating tumor growth and metastasis is well established. Ample evidence supports a close association between MDSCs and clinical outcomes in cancer patients (65, 66). The increased abundance of M-MDSCs observed in samples from patients treated with AES raises the question whether such expansion could have negative consequences in cancer patients undergoing surgery. To address this question, future studies will need to examine migration properties and functional state of MDSCs, as the sole expansion of MDSCs in peripheral blood does not necessarily imply that this cell type accumulates in tissue compartments and contributes to tumor growth and metastasis (67, 68). Nevertheless, it is noteworthy that critical functional attributes that are linked to the accumulation and immune-suppressive function of MDSCs such as the activation of STAT3 and N are prominent features of M-MDSCs retrieved from patients undergoing surgery (1, 69).

This study has several limitations. This proof-of-concept study enrolled a relatively small number of subjects, which limits the generalizability of the results and, by design, did not provide sufficient power to consolidate that the observed nominal increase in postoperative infection rate in the control group was significant. Despite this limitation, reported findings integrate well with results from previous studies, are directly pertinent to human biology, and generate several hypotheses worthy of future examination. Although the role of arginine in regulating immune cell function is well established, the reported immune-modulating effects may not solely be attributed to the use of arginine. The AES supplement contained other components with potential immune-modulating properties, such as omega-3 fatty acid and glutamine. These nutritional supplements may also modulate the inflammatory response to surgery through several mechanisms, including alteration in plasma membrane composition and modulation of eicosanoid production, cytokine biosynthesis, and immune cell signaling responses (70–73). However, the primary purpose of this study was to comprehensively monitor the immune response to a widely used nutritional intervention that has been linked to beneficial and potentially adverse clinical outcomes. Although mass cytometry allows for the phenotyping of major immune cell subsets and the functional characterization of major signaling pathways with a panel of up to 50 Abs, this number of Abs precludes deep phenotyping of all cell subsets (e.g., Th1, Th2, and Th17 CD4+ T cells) and in-depth evaluation of all pertinent signaling pathways (e.g., mTOR) in a given blood sample. In particular, the Ab panel, specifically developed for phenotyping of whole-blood immune cell subsets, did not allow G-MDSCs to be distinguished from other CD66+ neutrophil subsets. This technical limitation may have undermined the effect of AES on G-MDSCs and biased the analysis toward more readily identifiable M-MDSCs. It is therefore unlikely that all immune-modulating effects of AES have been captured. In subsequent studies, it will be interesting to introduce an Ab that recognizes the lectin-type oxidized LDL receptor-1, which was recently identified as a specific marker distinguishing G-MDSCs from neutrophils in human peripheral blood samples (74). Finally, alternative predictive algorithms including other machine leaning methods could have been used for the analysis of our high-dimensional data set. However, the systematic comparison of different algorithms for interrogating highly modular immunological data will require formal evaluation of multiple data sets from various clinical settings.

Implementing high-content immune-profiling strategies to guide the development of effective therapies is the subject of substantial interest. This study provides the analytical framework needed to comprehensively survey the peripheral immune system of patients randomized to a therapeutic intervention in the perioperative setting, and it offers a strategy generalizable to the analysis of other interventional clinical studies. Future applications in larger clinical trials will allow biology extracted from complex networks of interrelated immune features to be linked to pertinent clinical outcomes, to detect off-target immune responses, and to identify patient-specific immune signatures associated with response to therapy.

Disclosures

G.P.N. has personal financial interest in the companies Fluidigm and Becton Dickinson, the manufacturers that produce the reagents or instrumentation used in this study. The other authors have no financial conflicts of interest.

Acknowledgments

We thank Astraea Jager and Angelica Trejo for technical assistance with CyTOF experiments and William Magruder for critically editing this manuscript.

Footnotes

  • This work was supported by National Institutes of Health Grant 1K23GM111657 (to B.L.G.) and by funding from the Department of Anesthesiology, Perioperative and Pain Medicine at Stanford University (to B.L.G. and M.S.A.). This work was also supported by National Institutes of Health Grants U19AI057229 (to G.P.N.) and 1U19AI100627 (to G.P.N.), as well as by Food and Drug Administration Grant HHSF223201210194C (to G.P.N.).

  • The online version of this article contains supplemental material.

  • Abbreviations used in this article:

    AES
    arginine-enriched dietary supplement
    cMC
    classical monocyte
    EN
    elastic net
    G-MDSC
    granulocytic MDSC
    IQR
    interquartile range
    MAPKAPK2
    MAPK-activated protein kinase 2
    MDSC
    myeloid-derived suppressor cell
    M-MDSC
    monocytic MDSC
    mTOR
    mechanistic target of rapamycin
    pDC
    plasmacytoid dendritic cell
    rpS6
    ribosomal protein-S6
    Tmem
    memory T
    Treg
    regulatory T cell.

  • Received March 21, 2017.
  • Accepted July 11, 2017.
  • Copyright © 2017 by The American Association of Immunologists, Inc.

References

  1. ↵
    1. Gaudillière, B.,
    2. G. K. Fragiadakis,
    3. R. V. Bruggner,
    4. M. Nicolau,
    5. R. Finck,
    6. M. Tingle,
    7. J. Silva,
    8. E. A. Ganio,
    9. C. G. Yeh,
    10. W. J. Maloney, et al
    . 2014. Clinical recovery from surgery correlates with single-cell immune signatures. Sci. Transl. Med. 6: 255ra131.
    OpenUrlAbstract/FREE Full Text
    1. Kordasti, S.,
    2. B. Costantini,
    3. T. Seidl,
    4. P. Perez Abellan,
    5. M. Martinez Llordella,
    6. D. McLornan,
    7. K. E. Diggins,
    8. A. Kulasekararaj,
    9. C. Benfatto,
    10. X. Feng, et al
    . 2016. Deep phenotyping of Tregs identifies an immune signature for idiopathic aplastic anemia and predicts response to treatment. Blood 128: 1193–1205.
    OpenUrlAbstract/FREE Full Text
    1. Levine, J. H.,
    2. E. F. Simonds,
    3. S. C. Bendall,
    4. K. L. Davis,
    5. A. D. Amir,
    6. M. D. Tadmor,
    7. O. Litvin,
    8. H. G. Fienberg,
    9. A. Jager,
    10. E. R. Zunder, et al
    . 2015. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162: 184–197.
    OpenUrlCrossRefPubMed
    1. O’Gorman, W. E.,
    2. E. W. Hsieh,
    3. E. S. Savig,
    4. P. F. Gherardini,
    5. J. D. Hernandez,
    6. L. Hansmann,
    7. I. M. Balboni,
    8. P. J. Utz,
    9. S. C. Bendall,
    10. W. J. Fantl, et al
    . 2015. Single-cell systems-level analysis of human Toll-like receptor activation defines a chemokine signature in patients with systemic lupus erythematosus. J. Allergy Clin. Immunol. 136: 1326–1336.
    OpenUrl
    1. Kling, J.
    2015. Cytometry: measure for measure. Nature 518: 439–443.
    OpenUrlCrossRefPubMed
  2. ↵
    1. Nair, N.,
    2. H. E. Mei,
    3. S. Y. Chen,
    4. M. Hale,
    5. G. P. Nolan,
    6. H. T. Maecker,
    7. M. Genovese,
    8. C. G. Fathman,
    9. C. C. Whiting
    . 2015. Mass cytometry as a platform for the discovery of cellular biomarkers to guide effective rheumatic disease therapy. Arthritis Res. Ther. 17: 127.
    OpenUrlCrossRefPubMed
  3. ↵
    1. Fragiadakis, G. K.,
    2. B. Gaudillière,
    3. E. A. Ganio,
    4. N. Aghaeepour,
    5. M. Tingle,
    6. G. P. Nolan,
    7. M. S. Angst
    . 2015. Patient-specific immune states before surgery are strong correlates of surgical recovery. Anesthesiology 123: 1241–1255.
    OpenUrl
  4. ↵
    1. Tárnok, A.
    2015. Revisiting the crystal ball—high content single cells analysis as predictor of recovery. Cytometry A 87: 97–98.
    OpenUrl
  5. ↵
    1. Heyland, D. K.,
    2. Novak, F.
    2001. Immunonutrition in the critically ill patient: more harm than good? JPEN J. Parenter. Enteral Nutr. 25: S51–S55.
    OpenUrlCrossRefPubMed
  6. ↵
    Drover, J. W., Dhaliwal, R., Weitzel, L., Wischmeyer, P. E., Ochoa, J. B., and Heyland, D. K. 2011. Perioperative use of arginine-supplemented diets: a systematic review of the evidence. J. Am. Coll. Surg. 212: 385–399, 399.e381.
  7. ↵
    1. Bronte, V.,
    2. P. Zanovello
    . 2005. Regulation of immune responses by l-arginine metabolism. Nat. Rev. Immunol. 5: 641–654.
    OpenUrlCrossRefPubMed
    1. Rodriguez, P. C.,
    2. D. G. Quiceno,
    3. A. C. Ochoa
    . 2007. l-arginine availability regulates T-lymphocyte cell-cycle progression. Blood 109: 1568–1573.
    OpenUrlAbstract/FREE Full Text
  8. ↵
    1. Geiger, R.,
    2. Rieckmann, J. C.,
    3. Wolf, T.,
    4. Basso, C.,
    5. Feng, Y.,
    6. Fuhrer, T.,
    7. Kogadeeva, M.,
    8. Picotti, P.,
    9. Meissner, F.,
    10. Mann, et al
    . 2016. l-arginine modulates T cell metabolism and enhances survival and anti-tumor activity. Cell 167: 829–842 e813.
    OpenUrlCrossRef
  9. ↵
    1. Zhu, X.,
    2. J. P. Pribis,
    3. P. C. Rodriguez,
    4. S. M. Morris Jr..,
    5. Y. Vodovotz,
    6. T. R. Billiar,
    7. J. B. Ochoa
    . 2014. The central role of arginine catabolism in T-cell dysfunction and increased susceptibility to infection after physical injury. Ann. Surg. 259: 171–178.
    OpenUrlCrossRefPubMed
  10. ↵
    1. Weimann, A.,
    2. L. Bastian,
    3. W. E. Bischoff,
    4. M. Grotz,
    5. M. Hansel,
    6. J. Lotz,
    7. C. Trautwein,
    8. G. Tusch,
    9. H. J. Schlitt,
    10. G. Regel
    . 1998. Influence of arginine, omega-3 fatty acids and nucleotide-supplemented enteral support on systemic inflammatory response syndrome and multiple organ failure in patients after severe trauma. Nutrition 14: 165–172.
    OpenUrlCrossRefPubMed
  11. ↵
    1. Hamilton-Reeves, J. M.,
    2. M. D. Bechtel,
    3. L. K. Hand,
    4. A. Schleper,
    5. T. M. Yankee,
    6. P. Chalise,
    7. E. K. Lee,
    8. M. Mirza,
    9. H. Wyre,
    10. J. Griffin,
    11. J. M. Holzbeierlein
    . 2016. Effects of immunonutrition for cystectomy on immune response and infection rates: a pilot randomized controlled clinical trial. Eur. Urol. 69: 389–392.
    OpenUrl
  12. ↵
    Braga, M., Gianotti, L., Cestari, A., Vignali, A., Pellegatta, F., Dolci, A., and Di Carlo, V. 1996. Gut function and immune and inflammatory responses in patients perioperatively fed with supplemented enteral formulas. Arch Surg. 131: 1257–1264; discussion 1264–1255.
  13. ↵
    1. Braga, M.,
    2. L. Gianotti,
    3. A. Vignali,
    4. V. D. Carlo
    . 2002. Preoperative oral arginine and n-3 fatty acid supplementation improves the immunometabolic host response and outcome after colorectal resection for cancer. Surgery 132: 805–814.
    OpenUrlCrossRefPubMed
  14. ↵
    1. Gaudillière, B.,
    2. E. A. Ganio,
    3. M. Tingle,
    4. H. L. Lancero,
    5. G. K. Fragiadakis,
    6. Q. J. Baca,
    7. N. Aghaeepour,
    8. R. J. Wong,
    9. C. Quaintance,
    10. Y. Y. El-Sayed, et al
    . 2015. Implementing mass cytometry at the bedside to study the immunological basis of human diseases: distinctive immune features in patients with a history of term or preterm birth. Cytometry A 87: 817–829.
    OpenUrlCrossRefPubMed
  15. ↵
    1. Zunder, E. R.,
    2. R. Finck,
    3. G. K. Behbehani,
    4. A. D. Amir,
    5. S. Krishnaswamy,
    6. V. D. Gonzalez,
    7. C. G. Lorang,
    8. Z. Bjornson,
    9. M. H. Spitzer,
    10. B. Bodenmiller, et al
    . 2015. Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm. Nat. Protoc. 10: 316–333.
    OpenUrlCrossRefPubMed
  16. ↵
    1. Behbehani, G. K.,
    2. C. Thom,
    3. E. R. Zunder,
    4. R. Finck,
    5. B. Gaudilliere,
    6. G. K. Fragiadakis,
    7. W. J. Fantl,
    8. G. P. Nolan
    . 2014. Transient partial permeabilization with saponin enables cellular barcoding prior to surface marker staining. Cytometry A 85: 1011–1019.
    OpenUrlCrossRefPubMed
  17. ↵
    1. Finck, R.,
    2. E. F. Simonds,
    3. A. Jager,
    4. S. Krishnaswamy,
    5. K. Sachs,
    6. W. Fantl,
    7. D. Pe’er,
    8. G. P. Nolan,
    9. S. C. Bendall
    . 2013. Normalization of mass cytometry data with bead standards. Cytometry A 83: 483–494.
    OpenUrlCrossRefPubMed
  18. ↵
    1. Saiwai, H.,
    2. H. Kumamaru,
    3. Y. Ohkawa,
    4. K. Kubota,
    5. K. Kobayakawa,
    6. H. Yamada,
    7. T. Yokomizo,
    8. Y. Iwamoto,
    9. S. Okada
    . 2013. Ly6C+ Ly6G− myeloid-derived suppressor cells play a critical role in the resolution of acute inflammation and the subsequent tissue repair process after spinal cord injury. J. Neurochem. 125: 74–88.
    OpenUrlCrossRefPubMed
    1. Bryk, J. A.,
    2. P. J. Popovic,
    3. M. S. Zenati,
    4. V. Munera,
    5. J. P. Pribis,
    6. J. B. Ochoa
    . 2010. Nature of myeloid cells expressing arginase 1 in peripheral blood after trauma. J. Trauma 68: 843–852.
    OpenUrlPubMed
  19. ↵
    1. Makarenkova, V. P.,
    2. V. Bansal,
    3. B. M. Matta,
    4. L. A. Perez,
    5. J. B. Ochoa
    . 2006. CD11b+/Gr-1+ myeloid suppressor cells cause T cell dysfunction after traumatic stress. J. Immunol. 176: 2085–2094.
    OpenUrlAbstract/FREE Full Text
  20. ↵
    1. Mathias, B.,
    2. A. L. Delmas,
    3. T. Ozrazgat-Baslanti,
    4. Tezcan, E. L.,
    5. Vanzant, B. E.,
    6. Szpila, A. M.,
    7. Mohr, F. A.,
    8. Moore, S. C.,
    9. Brakenridge, B. A.,
    10. Brumback, et al
    . 2017. Human myeloid-derived suppressor cells are associated with chronic immune suppression after severe sepsis/septic shock. Ann. Surg. 265: 827–834
    OpenUrl
  21. ↵
    1. Cuenca, A. G.,
    2. M. J. Delano,
    3. K. M. Kelly-Scumpia,
    4. C. Moreno,
    5. P. O. Scumpia,
    6. D. M. Laface,
    7. P. G. Heyworth,
    8. P. A. Efron,
    9. L. L. Moldawer
    . 2011. A paradoxical role for myeloid-derived suppressor cells in sepsis and trauma. Mol. Med. 17: 281–292.
    OpenUrlCrossRefPubMed
  22. ↵
    1. Bronte, V.,
    2. S. Brandau,
    3. S. H. Chen,
    4. M. P. Colombo,
    5. A. B. Frey,
    6. T. F. Greten,
    7. S. Mandruzzato,
    8. P. J. Murray,
    9. A. Ochoa,
    10. S. Ostrand-Rosenberg, et al
    . 2016. Recommendations for myeloid-derived suppressor cell nomenclature and characterization standards. Nat. Commun. 7: 12150.
    OpenUrlCrossRefPubMed
  23. ↵
    1. Adai, A. T.,
    2. S. V. Date,
    3. S. Wieland,
    4. E. M. Marcotte
    . 2004. LGL: creating a map of protein function with an algorithm for visualizing very large biological networks. J. Mol. Biol. 340: 179–190.
    OpenUrlCrossRefPubMed
  24. ↵
    1. Clavien, P. A.,
    2. J. Barkun,
    3. M. L. de Oliveira,
    4. J. N. Vauthey,
    5. D. Dindo,
    6. R. D. Schulick,
    7. E. de Santibañes,
    8. J. Pekolj,
    9. K. Slankamenac,
    10. C. Bassi, et al
    . 2009. The Clavien–Dindo classification of surgical complications: five-year experience. Ann. Surg. 250: 187–196.
    OpenUrlCrossRefPubMed
  25. ↵
    1. Gianotti, L.,
    2. M. Braga,
    3. L. Nespoli,
    4. G. Radaelli,
    5. A. Beneduce,
    6. V. Di Carlo
    . 2002. A randomized controlled trial of preoperative oral supplementation with a specialized diet in patients with gastrointestinal cancer. Gastroenterology 122: 1763–1770.
    OpenUrlCrossRefPubMed
  26. ↵
    1. Lin, E.,
    2. S. E. Calvano,
    3. S. F. Lowry
    . 2000. Inflammatory cytokines and cell response in surgery. Surgery 127: 117–126.
    OpenUrlCrossRefPubMed
  27. ↵
    1. Xiao, W.,
    2. M. N. Mindrinos,
    3. J. Seok,
    4. J. Cuschieri,
    5. A. G. Cuenca,
    6. H. Gao,
    7. D. L. Hayden,
    8. L. Hennessy,
    9. E. E. Moore,
    10. J. P. Minei, et al
    . 2011. A genomic storm in critically injured humans. J. Exp. Med. 208: 2581–2590.
    OpenUrlAbstract/FREE Full Text
  28. ↵
    1. Zou, H.,
    2. T. Hastie
    . 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67: 301–320.
    OpenUrlCrossRef
  29. ↵
    1. Zou, H.,
    2. T. Hastie,
    3. R. Tibshirani
    . 2006. Sparse principal component analysis. J. Comput. Graph. Stat. 15: 265–286.
    OpenUrlCrossRef
    1. Tibshirani, R.
    1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58: 267–288.
    OpenUrl
    1. Aghaeepour, N.,
    2. G. Finak,
    3. H. Hoos,
    4. T. R. Mosmann,
    5. R. Brinkman,
    6. R. Gottardo,
    7. R. H. Scheuermann, The FlowCAP Consortium, The DREAM Consortium
    . 2013. Critical assessment of automated flow cytometry data analysis techniques. [Published erratum appears in 2013 Nat. Methods 10: 445.] Nat. Methods 10: 228–238.
    OpenUrlCrossRefPubMed
  30. ↵
    1. Aghaeepour, N.,
    2. P. K. Chattopadhyay,
    3. A. Ganesan,
    4. K. O’Neill,
    5. H. Zare,
    6. A. Jalali,
    7. H. H. Hoos,
    8. M. Roederer,
    9. R. R. Brinkman
    . 2012. Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T-cell flow cytometry assays. Bioinformatics 28: 1009–1016.
    OpenUrlCrossRefPubMed
  31. ↵
    1. Ochoa, J. B.,
    2. A. C. Bernard,
    3. W. E. O’Brien,
    4. M. M. Griffen,
    5. M. E. Maley,
    6. A. K. Rockich,
    7. B. J. Tsuei,
    8. B. R. Boulanger,
    9. P. A. Kearney,
    10. S. M. Morris Jr Jr.
    . 2001. Arginase I expression and activity in human mononuclear cells after injury. Ann. Surg. 233: 393–399.
    OpenUrlCrossRefPubMed
  32. ↵
    1. Pribis, J. P.,
    2. X. Zhu,
    3. Y. Vodovotz,
    4. J. B. Ochoa
    . 2012. Systemic arginine depletion after a murine model of surgery or trauma. JPEN J. Parenter. Enteral Nutr. 36: 53–59.
    OpenUrlCrossRefPubMed
  33. ↵
    1. Zea, A. H.,
    2. P. C. Rodriguez,
    3. K. S. Culotta,
    4. C. P. Hernandez,
    5. J. DeSalvo,
    6. J. B. Ochoa,
    7. H. J. Park,
    8. J. Zabaleta,
    9. A. C. Ochoa
    . 2004. l-arginine modulates CD3ζ expression and T cell function in activated human T lymphocytes. Cell. Immunol. 232: 21–31.
    OpenUrlCrossRefPubMed
  34. ↵
    1. Talmadge, J. E.,
    2. D. I. Gabrilovich
    . 2013. History of myeloid-derived suppressor cells. Nat. Rev. Cancer 13: 739–752.
    OpenUrlCrossRefPubMed
  35. ↵
    1. Nagaraj, S.,
    2. M. Collazo,
    3. C. A. Corzo,
    4. J. I. Youn,
    5. M. Ortiz,
    6. D. Quiceno,
    7. D. I. Gabrilovich
    . 2009. Regulatory myeloid suppressor cells in health and disease. Cancer Res. 69: 7503–7506.
    OpenUrlFREE Full Text
  36. ↵
    1. Rodriguez, P. C.,
    2. A. H. Zea,
    3. J. DeSalvo,
    4. K. S. Culotta,
    5. J. Zabaleta,
    6. D. G. Quiceno,
    7. J. B. Ochoa,
    8. A. C. Ochoa
    . 2003. l-arginine consumption by macrophages modulates the expression of CD3ζ chain in T lymphocytes. J. Immunol. 171: 1232–1239.
    OpenUrlAbstract/FREE Full Text
  37. ↵
    1. Zhu, X.,
    2. G. Herrera,
    3. J. B. Ochoa
    . 2010. Immunosupression and infection after major surgery: a nutritional deficiency. Crit. Care Clin. 26: 491–500, ix.
    OpenUrlCrossRefPubMed
  38. ↵
    1. Kaech, S. M.,
    2. W. Cui
    . 2012. Transcriptional control of effector and memory CD8+ T cell differentiation. Nat. Rev. Immunol. 12: 749–761.
    OpenUrlCrossRefPubMed
    1. Cui, W.,
    2. Y. Liu,
    3. J. S. Weinstein,
    4. J. Craft,
    5. S. M. Kaech
    . 2011. An interleukin-21-interleukin-10-STAT3 pathway is critical for functional maturation of memory CD8+ T cells. Immunity 35: 792–805.
    OpenUrlCrossRefPubMed
  39. ↵
    1. Siegel, A. M.,
    2. J. Heimall,
    3. A. F. Freeman,
    4. A. P. Hsu,
    5. E. Brittain,
    6. J. M. Brenchley,
    7. D. C. Douek,
    8. G. H. Fahle,
    9. J. I. Cohen,
    10. S. M. Holland,
    11. J. D. Milner
    . 2011. A critical role for STAT3 transcription factor signaling in the development and maintenance of human T cell memory. Immunity 35: 806–818.
    OpenUrlCrossRefPubMed
  40. ↵
    1. Beutler, B.
    2004. Inferences, questions and possibilities in Toll-like receptor signalling. Nature 430: 257–263.
    OpenUrlCrossRefPubMed
  41. ↵
    1. Medzhitov, R.
    2007. Recognition of microorganisms and activation of the immune response. Nature 449: 819–826.
    OpenUrlCrossRefPubMed
  42. ↵
    1. Chapman, N. M.,
    2. H. Chi
    . 2015. mTOR links environmental signals to T cell fate decisions. Front. Immunol. 5: 686.
    OpenUrl
  43. ↵
    1. Carroll, B.,
    2. D. Maetzel,
    3. O. D. Maddocks,
    4. G. Otten,
    5. M. Ratcliff,
    6. G. R. Smith,
    7. E. A. Dunlop,
    8. J. F. Passos,
    9. O. R. Davies,
    10. R. Jaenisch, et al
    . 2016. Control of TSC2-Rheb signaling axis by arginine regulates mTORC1 activity. ELife 5: e11058.
    OpenUrlAbstract/FREE Full Text
  44. ↵
    1. Saleiro, D.,
    2. L. C. Platanias
    . 2015. Intersection of mTOR and STAT signaling in immunity. Trends Immunol. 36: 21–29.
    OpenUrlCrossRefPubMed
  45. ↵
    1. Delgoffe, G. M.,
    2. T. P. Kole,
    3. Y. Zheng,
    4. P. E. Zarek,
    5. K. L. Matthews,
    6. B. Xiao,
    7. P. F. Worley,
    8. S. C. Kozma,
    9. J. D. Powell
    . 2009. The mTOR kinase differentially regulates effector and regulatory T cell lineage commitment. Immunity 30: 832–844.
    OpenUrlCrossRefPubMed
  46. ↵
    1. Delano, M. J.,
    2. P. O. Scumpia,
    3. J. S. Weinstein,
    4. D. Coco,
    5. S. Nagaraj,
    6. K. M. Kelly-Scumpia,
    7. K. A. O’Malley,
    8. J. L. Wynn,
    9. S. Antonenko,
    10. S. Z. Al-Quran, et al
    . 2007. MyD88-dependent expansion of an immature GR-1+CD11b+ population induces T cell suppression and Th2 polarization in sepsis. J. Exp. Med. 204: 1463–1474.
    OpenUrlAbstract/FREE Full Text
  47. ↵
    1. Paterson, H. M.,
    2. T. J. Murphy,
    3. E. J. Purcell,
    4. O. Shelley,
    5. S. J. Kriynovich,
    6. E. Lien,
    7. J. A. Mannick,
    8. J. A. Lederer
    . 2003. Injury primes the innate immune system for enhanced Toll-like receptor reactivity. J. Immunol. 171: 1473–1483.
    OpenUrlAbstract/FREE Full Text
    1. Chan, J. K.,
    2. J. Roth,
    3. J. J. Oppenheim,
    4. K. J. Tracey,
    5. T. Vogl,
    6. M. Feldmann,
    7. N. Horwood,
    8. J. Nanchahal
    . 2012. Alarmins: awaiting a clinical response. J. Clin. Invest. 122: 2711–2719.
    OpenUrlCrossRefPubMed
  48. ↵
    1. Mollen, K. P.,
    2. R. J. Anand,
    3. A. Tsung,
    4. J. M. Prince,
    5. R. M. Levy,
    6. T. R. Billiar
    . 2006. Emerging paradigm: Toll-like receptor 4-sentinel for the detection of tissue damage. Shock 26: 430–437.
    OpenUrlCrossRefPubMed
  49. ↵
    1. Mieulet, V.,
    2. L. Yan,
    3. C. Choisy,
    4. K. Sully,
    5. J. Procter,
    6. A. Kouroumalis,
    7. S. Krywawych,
    8. M. Pende,
    9. S. C. Ley,
    10. C. Moinard,
    11. R. F. Lamb
    . 2010. TPL-2–mediated activation of MAPK downstream of TLR4 signaling is coupled to arginine availability. Sci. Signal. 3: ra61.
    OpenUrlAbstract/FREE Full Text
  50. ↵
    1. Zhou, M.,
    2. R. G. Martindale
    . 2007. Arginine in the critical care setting. J. Nutr. 137(6 Suppl. 2): 1687S–1692S.
    OpenUrlAbstract/FREE Full Text
  51. ↵
    1. Heyland, D. K.,
    2. F. Novak,
    3. J. W. Drover,
    4. M. Jain,
    5. X. Su,
    6. U. Suchner
    . 2001. Should immunonutrition become routine in critically ill patients? A systematic review of the evidence. JAMA 286: 944–953.
    OpenUrlCrossRefPubMed
  52. ↵
    1. Galbán, C.,
    2. J. C. Montejo,
    3. A. Mesejo,
    4. P. Marco,
    5. S. Celaya,
    6. J. M. Sánchez-Segura,
    7. M. Farré,
    8. D. J. Bryg
    . 2000. An immune-enhancing enteral diet reduces mortality rate and episodes of bacteremia in septic intensive care unit patients. Crit. Care Med. 28: 643–648.
    OpenUrlCrossRefPubMed
  53. ↵
    1. Derive, M.,
    2. Y. Bouazza,
    3. C. Alauzet,
    4. S. Gibot
    . 2012. Myeloid-derived suppressor cells control microbial sepsis. Intensive Care Med. 38: 1040–1049.
    OpenUrlCrossRefPubMed
  54. ↵
    1. Sander, L. E.,
    2. S. D. Sackett,
    3. U. Dierssen,
    4. N. Beraza,
    5. R. P. Linke,
    6. M. Müller,
    7. J. M. Blander,
    8. F. Tacke,
    9. C. Trautwein
    . 2010. Hepatic acute-phase proteins control innate immune responses during infection by promoting myeloid-derived suppressor cell function. J. Exp. Med. 207: 1453–1464.
    OpenUrlAbstract/FREE Full Text
  55. ↵
    1. Marvel, D.,
    2. D. I. Gabrilovich
    . 2015. Myeloid-derived suppressor cells in the tumor microenvironment: expect the unexpected. J. Clin. Invest. 125: 3356–3364.
    OpenUrlCrossRefPubMed
  56. ↵
    1. Condamine, T.,
    2. I. Ramachandran,
    3. J. I. Youn,
    4. D. I. Gabrilovich
    . 2015. Regulation of tumor metastasis by myeloid-derived suppressor cells. Annu. Rev. Med. 66: 97–110.
    OpenUrlCrossRefPubMed
  57. ↵
    1. Fletcher, M.,
    2. M. E. Ramirez,
    3. R. A. Sierra,
    4. P. Raber,
    5. P. Thevenot,
    6. A. A. Al-Khami,
    7. D. Sanchez-Pino,
    8. C. Hernandez,
    9. D. D. Wyczechowska,
    10. A. C. Ochoa,
    11. P. C. Rodriguez
    . 2015. l-arginine depletion blunts antitumor T-cell responses by inducing myeloid-derived suppressor cells. Cancer Res. 75: 275–283.
    OpenUrlAbstract/FREE Full Text
  58. ↵
    1. Ortiz, M. L.,
    2. L. Lu,
    3. I. Ramachandran,
    4. D. I. Gabrilovich
    . 2014. Myeloid-derived suppressor cells in the development of lung cancer. Cancer Immunol. Res. 2: 50–58.
    OpenUrlAbstract/FREE Full Text
  59. ↵
    1. Condamine, T.,
    2. D. I. Gabrilovich
    . 2011. Molecular mechanisms regulating myeloid-derived suppressor cell differentiation and function. Trends Immunol. 32: 19–25.
    OpenUrlCrossRefPubMed
  60. ↵
    1. Fritsche, K.
    2006. Fatty acids as modulators of the immune response. Annu. Rev. Nutr. 26: 45–73.
    OpenUrlCrossRefPubMed
    1. Lochner, M.,
    2. L. Berod,
    3. T. Sparwasser
    . 2015. Fatty acid metabolism in the regulation of T cell function. Trends Immunol. 36: 81–91.
    OpenUrlCrossRefPubMed
    1. Klysz, D.,
    2. X. Tai,
    3. P. A. Robert,
    4. M. Craveiro,
    5. G. Cretenet,
    6. L. Oburoglu,
    7. C. Mongellaz,
    8. S. Floess,
    9. V. Fritz,
    10. M. I. Matias, et al
    . 2015. Glutamine-dependent α-ketoglutarate production regulates the balance between T helper 1 cell and regulatory T cell generation. Sci. Signal. 8: ra97.
    OpenUrlAbstract/FREE Full Text
  61. ↵
    1. Newsholme, P.
    2001 Why is l-glutamine metabolism important to cells of the immune system in health, postinjury, surgery or infection? J. Nutr. 131: 2515S–2522S.
    OpenUrlAbstract/FREE Full Text
  62. ↵
    1. Condamine, T.,
    2. G. A. Dominguez,
    3. J. I. Youn,
    4. A. V. Kossenkov,
    5. S. Mony,
    6. K. Alicea-Torres,
    7. E. Tcyganov,
    8. A. Hashimoto,
    9. Y. Nefedova,
    10. C. Lin, et al
    . 2016. Lectin-type oxidized LDL receptor-1 distinguishes population of human polymorphonuclear myeloid-derived suppressor cells in cancer patients. Sci. Immunol. 1: aaf8943.
    OpenUrlAbstract/FREE Full Text
View Abstract
PreviousNext
Back to top

In this issue

The Journal of Immunology: 199 (6)
The Journal of Immunology
Vol. 199, Issue 6
15 Sep 2017
  • Table of Contents
  • Table of Contents (PDF)
  • About the Cover
  • Advertising (PDF)
  • Back Matter (PDF)
  • Editorial Board (PDF)
  • Front Matter (PDF)
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word about The Journal of Immunology.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Deep Immune Profiling of an Arginine-Enriched Nutritional Intervention in Patients Undergoing Surgery
(Your Name) has forwarded a page to you from The Journal of Immunology
(Your Name) thought you would like to see this page from the The Journal of Immunology web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Deep Immune Profiling of an Arginine-Enriched Nutritional Intervention in Patients Undergoing Surgery
Nima Aghaeepour, Cindy Kin, Edward A. Ganio, Kent P. Jensen, Dyani K. Gaudilliere, Martha Tingle, Amy Tsai, Hope L. Lancero, Benjamin Choisy, Leslie S. McNeil, Robin Okada, Andrew A. Shelton, Garry P. Nolan, Martin S. Angst, Brice L. Gaudilliere
The Journal of Immunology September 15, 2017, 199 (6) 2171-2180; DOI: 10.4049/jimmunol.1700421

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Deep Immune Profiling of an Arginine-Enriched Nutritional Intervention in Patients Undergoing Surgery
Nima Aghaeepour, Cindy Kin, Edward A. Ganio, Kent P. Jensen, Dyani K. Gaudilliere, Martha Tingle, Amy Tsai, Hope L. Lancero, Benjamin Choisy, Leslie S. McNeil, Robin Okada, Andrew A. Shelton, Garry P. Nolan, Martin S. Angst, Brice L. Gaudilliere
The Journal of Immunology September 15, 2017, 199 (6) 2171-2180; DOI: 10.4049/jimmunol.1700421
del.icio.us logo Digg logo Reddit logo Twitter logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like

Jump to section

  • Article
    • Abstract
    • Introduction
    • Materials and Methods
    • Results
    • Discussion
    • Disclosures
    • Acknowledgments
    • Footnotes
    • References
  • Figures & Data
  • Info & Metrics
  • PDF + SI
  • PDF

Related Articles

Cited By...

More in this TOC Section

  • Spinal Cord Injury Impairs Lung Immunity in Mice
  • Lupus Susceptibility Loci Predispose Mice to Clonal Lymphocytic Responses and Myeloid Expansion
  • Liver Environment–Imposed Constraints Diversify Movement Strategies of Liver-Localized CD8 T Cells
Show more SYSTEMS IMMUNOLOGY

Similar Articles

Navigate

  • Home
  • Current Issue
  • Next in The JI
  • Archive
  • Brief Reviews
  • Pillars of Immunology
  • Translating Immunology

For Authors

  • Submit a Manuscript
  • Instructions for Authors
  • About the Journal
  • Journal Policies
  • Editors

General Information

  • Advertisers
  • Subscribers
  • Rights and Permissions
  • Accessibility Statement
  • FAR 889
  • Privacy Policy
  • Disclaimer

Journal Services

  • Email Alerts
  • RSS Feeds
  • ImmunoCasts
  • Twitter

Copyright © 2022 by The American Association of Immunologists, Inc.

Print ISSN 0022-1767        Online ISSN 1550-6606