Abstract

   Sepsis is one of the highest causes of mortality in hospitalized people
   and a common complication in both surgical and clinical patients
   admitted to hospital for non-infectious reasons. Sepsis is especially
   common in older people and its incidence is likely to increase
   substantially as a population ages. Despite its increased prevalence
   and mortality in older people, immune responses in the elderly during
   septic shock appear similar to that in younger patients. The purpose of
   this study was to conduct a genome-wide gene expression analysis of
   circulating neutrophils from old and young septic patients to better
   understand how aged individuals respond to severe infectious insult. We
   detected several genes whose expression could be used to differentiate
   immune responses of the elderly from those of young people, including
   genes related to oxidative phosphorylation, mitochondrial dysfunction
   and TGF-β signaling, among others. Our results identify major molecular
   pathways that are particularly affected in the elderly during sepsis,
   which might have a pivotal role in worsening clinical outcomes compared
   with young people with sepsis.

Introduction

   Sepsis is a complex syndrome triggered by infection and characterized
   by the massive deregulation of immunological networks [[39]1]. Septic
   patients have a mean age of approximately 65 years [[40]2] and the
   incidence of sepsis and its risk of mortality increase significantly
   with advanced age [[41]3, [42]4]. Factors that might contribute to the
   increased risk and incidence in the elderly include defects in the
   integrity of epithelial barriers, decreased gag and cough reflexes,
   altered levels of consciousness, immobility, concomitant medical
   diseases, a dependency on invasive medical devices, diminished
   physiological reserves, endocrine deficiencies, and malnutrition
   [[43]5, [44]6].

   Age-related defects in immunity are caused by major defects in
   cell-mediated and humoral effector functions [[45]7]. Aging causes
   changes in adaptive immunity associated with a shift of the T cell
   repertoire from a naive phenotype to memory T cells [[46]3] and from
   type 1 to type 2 responses [[47]8, [48]9]. Numbers of B and plasma
   cells gradually decrease with age, while immunoglobulin levels increase
   [[49]10].

   Innate immunity was previously considered to be well-preserved in the
   elderly [[50]11], but recent studies have also demonstrated significant
   alterations in these components [[51]3]. Studies have also indicated
   the altered expression and function of Toll-like receptors (TLRs)
   caused by aging, altering the host’s response to pathogens [[52]12].
   Elevated levels of basal inflammation [[53]13, [54]14], defective
   activation of mitogen-activated protein kinases (MAPKs) [[55]15],
   increased number of apoptotic cells [[56]16], defects in phagocytosis,
   generation of reactive oxygen species (ROS) and impaired costimulatory
   molecule expression have also been reported in the elderly [[57]17].
   Indeed, recent studies have indicated that older adults have elevated
   levels of pro-inflammatory cytokines, clotting factors and acute phase
   reactants in the steady state [[58]18–[59]20].

   The inflammatory response of the elderly following infection, however,
   remains under debate. Animal studies demonstrated that mortality,
   inflammation, hypothermia, apoptosis and disseminated intravascular
   coagulation are increased in aged animals subjected to experimental
   models of sepsis [[60]21]. It is intriguing that despite the well
   characterized aspects of immunosenescence and the exaggerated
   inflammatory response detected in septic aged rodents [[61]22, [62]23],
   clinical studies conducted in humans (including those from our group)
   observed a similar immune profile when old and young septic critically
   ill patients were compared [[63]24–[64]27].

   The purpose of this study was to perform a genome-wide gene expression
   analysis of neutrophils obtained from elderly and young patients in
   septic shock, to investigate the potential differences in cell
   activation that might explain the altered status of immune and
   inflammatory systems in advanced aged patients with sepsis. We decided
   to focus on neutrophils because recent reports suggested their function
   was particularly altered in the elderly [[65]28].

Patients and Methods

Study design

   The current study was a prospective cohort study, conducted in the
   Hospital das Clínicas Intensive Care Units (University of Sao Paulo,
   Brazil). Blood samples were obtained from six aged septic patients (age
   range 65–78 years old), six young septic patients (age range 22–35
   years old), six healthy aged volunteers (age range 60–82 years old) and
   six healthy younger individuals (age range 20–35 years old). Patients’
   profiles are described in the [66]S1 Table. All cases of sepsis were in
   patients with a clinical illness and did not include patients admitted
   for trauma or surgical reasons. Reasons for the majority of admissions
   included in this study were sepsis, stroke, altered levels of
   consciousness, pulmonary edema, and asthma and/or chronic obstructive
   pulmonary disease. Patients who were less than 18 years old, pregnant,
   HIV-positive, or in end-of-life conditions were excluded. Patients with
   disseminated malignancies or advanced hepatic disease, those receiving
   chemotherapy and those who refused to participate in this study were
   also excluded. Septic shock was defined according to the criteria of
   the American College of Chest Physicians/Society of Critical Care
   Medicine (ACCP/SCCM) Consensus Conference Committee proposed in 1992
   [[67]29].

   The study protocol was approved by the Hospital das Clínicas Ethics
   Committee. Patients (or their close relatives) received detailed
   explanations and provided written consent prior to inclusion in the
   study (Protocol # 1207/09).

RNA extraction and microarray experiments

   All blood samples were sent to our laboratory for processing
   immediately after collection. The anticoagulant-treated blood was
   layered on the Ficoll-Paque PLUS solution (GE Healthcare) and
   centrifuged for a short period of time. Differential migration during
   centrifugation results in the formation of layers containing different
   cell types. The bottom layer contains erythrocytes that have been
   aggregated by the Ficoll and, therefore, sediment completely through
   the Ficoll-Paque PLUS. The layer immediately above the erythrocyte
   layer contains the granulocytes, which at the osmotic pressure of the
   Ficoll-Paque PLUS solution attain a density great enough to migrate
   through the Ficoll-Paque PLUS layer.

   After Ficoll-Paque PLUS (GE Healthcare) density gradient
   centrifugation, we separated the second layer containing the
   granulocytes. This layer was transferred to new tubes, diluted in lysis
   buffer and kept on ice for 10 minutes. After centrifugation at 290 x g
   for 10 minutes at 4°C the pellet was resuspended in lysis buffer and
   kept on ice for additional 10 minutes. A new centrifugation step was
   performed at 2500 x g for 2 minutes at room temperature and the samples
   were washed with phosphate-buffered saline (PBS). Finally, the samples
   were centrifuged at 1500 x g for 2 minutes at room temperature and the
   pellet was resuspended in Trizol (Life Technology, Carlsbad, USA) and
   stored at -80°C.

   Total RNA was isolated following the manufacturer’s protocol and its
   integrity and concentration were assessed using the Agilent 2100
   Bioanalyzer and the RNA 6000 Nano Kit (Agilent Technologies, Santa
   Clara, CA, USA). Expression levels of protein-coding genes and long
   noncoding RNAs (lncRNA) were evaluated using the SurePrint G3 Human
   Gene Expression 8x60K v2 Microarray (design ID # 039494) and the Low
   Input Quick Amp Labeling kit, following a two-color labeling protocol
   (Agilent Technologies). Cyanine-3 labeled RNA from each sample and
   cyanine-5 labeled reference RNA (Universal Human Reference RNA,
   Agilent, cat #740000) were combined and hybridized to the microarrays
   following the manufacturer’s protocols.

Data processing and filtering

   Microarrays were scanned using the SureScan Microarray Scanner (Agilent
   Technologies) and images were processed using the Feature Extraction
   Software v12 (Agilent Technologies) for quality control, determination
   of feature intensities and ratios, and for background correction. The
   LOWESS procedure was used for data normalization and transformation
   [[68]30]. We only considered features that were considered “well above
   background” in at least five (of six) subjects in at least one study
   group (young adults with sepsis, control young adults, elderly with
   sepsis, and control elderly). After data filtering and processing,
   16,698 probes remained. A Pearson correlation between those filtered
   features was calculated for each pair of subjects. Overall, we observed
   a good correlation between any pair of samples (minimum r = 0.88) and a
   higher correlation between samples from the same group (mean intragroup
   correlation = 0.96) compared with different groups of samples (mean
   intergroup correlation = 0.93). Moreover, the samples grouped by
   disease status and age using unsupervised hierarchical clustering
   ([69]S1 Fig) further indicating the good quality of the data.

   A number of probes (8192) could not be assigned to the known or
   predicted lncRNA sequences annotated in public databases (Genbank
   RefSeq, Emsembl). These poorly annotated probes were categorized as
   “unannotated” and were not analyzed further. The raw and processed
   microarray datasets are deposited at the Gene Expression Omnibus
   (accession number [70]GSE67652 associated to platform [71]GPL16699).

Statistical analysis

   We performed two sets of differential gene expression analyses. One
   analysis was to identify genes deregulated in young and elderly sepsis
   subjects compared with matched healthy controls. A second analysis
   searched for genes deregulated in septic or healthy elderly subjects
   compared with matched young controls. In each analysis, a gene was
   considered differentially expressed when it was identified by a
   combination of two statistical methods, namely Significance Analysis of
   Microarrays [[72]31] and rank product [[73]32], using publicly
   available R packages [[74]33]. To limit the number of false-positives
   we only considered for further analysis genes identified as
   differentially expressed with a P-value of p ≤ 0.01 by both methods.

Functional annotation and pathway analysis

   Lists of differentially expressed genes were annotated and investigated
   to identify the enrichment of particular gene categories or pathways
   using the Ingenuity Pathway Analysis suite (IPA, Qiagen, Redwood City,
   [75]www.qiagen.com/ingenuity). IPA Upstream Analysis was used to
   identify putative upstream regulators and to predict whether they were
   activated or inhibited, given the observed gene expression changes in
   the experimental dataset, and the expected causal effects, which were
   compiled from the literature in Ingenuity’s Knowledge Base.

Relative Quantification of gene expression levels using Real Time-PCR

   To validate the microarray expression data, selected genes were
   subjected to quantitative Real Time-PCR using the TaqMan assay system
   (Life Technologies). Briefly, 100 ng of total RNA was subjected to
   reverse transcription using the High-Capacity cDNA Reverse
   Transcription Kit (Life Technologies) following the manufacturer’s
   protocol. Specific TaqMan Gene Expression Assays (Life Technologies)
   were used for detecting gene expression levels of TGFB1 (Hs00998133),
   SRC (Hs1082246), HDAC4 (Hs01041638), CREBBP (Hs00231733), NDUFA4
   (Hs00800172), INHBB (Hs00173582), BMP7 (Hs00233476) and SDHC
   (Hs01698067). PCR was carried out following the protocol recommended
   for the TaqMan Universal PCR Master Mix (Life Technologies) in an
   AB7500 thermocycler (Life Technologies). For each sample, the
   expression data of each candidate gene was normalized to 18S rRNA
   expression (Eukaryotic 18S rRNA Endogenous Control, Life Technologies).
   For relative quantification we used the comparative ∆Ct method
   [[76]34]. The Student’s t-test (p-value < 0.05) was used to determine
   the statistical significance of expression differences between the
   sample groups.

Results

   The microarray in this study contained 58,717 probes that included
   protein-coding mRNAs (36,075), long noncoding RNAs (14,450), and a
   number of poorly annotated transcripts (8192) ([77]Table 1).
   Approximately 28.4% of all probes were detected in at least one group
   of samples. A smaller fraction of lncRNAs (10%) was detected compared
   with protein-coding mRNAs (82%) ([78]Table 1), which is in line with
   the more tissue-specific and less abundant nature of these transcripts
   [[79]35, [80]36].

Table 1. Expressed probes in the array according to gene type and sample
group.

   Probe type Number of probes in the array Number of detected
   probes[81]^* Control vs. Sepsis[82]^** Control vs. Sepsis[83]^**
   Elderly vs. Young Adults[84]^** Elderly vs. Young Adults[85]^**
   Elderly Young Sepsis Control
   Protein-coding mRNA 36,075 13,622 1,407 1,631 375 277
   Long noncoding RNA 14,450 1,604 165 155 33 112
   Unannotated[86]^*** 8,192 1,472 230 188 13 87
   Total 58,717 16,698 1,802 1,974 421 476
   [87]Open in a new tab

   *To be considered “detected” a probe was measured with the ‘Well Above
   Background’ flag in at least five of six samples in at least one group.

   ** Number of differentially expressed probes in each group: To be
   considered “differentially expressed” a probe should have a p-value ≤
   0.01 assigned by two different methods (SAM and RankProduct).

   ***Probes were labeled as “unannotated” when not clearly categorized as
   coding or non-coding in the microarray annotation table.

   We first compared gene expression profiles of neutrophils across the
   four groups of samples, grouped by septic status and age ([88]Fig 1).
   [89]S1 Table shows the clinical and laboratory characteristics of the
   study groups. As expected, the occurrence of sepsis affected the
   expression of a greater number of genes (4- to 5-fold) compared with
   aging ([90]Table 1). We detected 421 genes that were differentially
   expressed between old and young subjects with sepsis, and 476 between
   old and young healthy controls ([91]Fig 1). Only 1.4% of genes
   deregulated in old subjects compared with young subjects were common to
   both septic and healthy subjects. We also observed a number of lncRNAs
   that were deregulated in both sepsis and aging ([92]Table 1).

Fig 1. Venn diagram of differentially expressed genes in each group according
to age and disease status.

   Fig 1
   [93]Open in a new tab

   To identify transcriptional changes that might explain the molecular
   basis of the clinical behavior in septic elderly patients, we examined
   the molecular pathways that were differentially expressed between
   elderly and young adults. First, we identified a number of canonical
   pathways that were enriched in genes deregulated in the elderly.
   Interestingly, most of these pathways were significantly enriched only
   in the sepsis group, i.e., genes were differentially expressed between
   elderly subjects with sepsis and younger adults with sepsis, with
   marginal enrichment in the healthy group ([94]Table 2). The oxidative
   phosphorylation (p = 5.3 × 10^−13) and mitochondrial dysfunction (p =
   1.4 × 10^−10) pathways were the most enriched in septic patients of
   advanced age when compared with the young septic group. Other pathways
   significantly enriched in the elderly were extracellular
   signal-regulated kinase 5 (ERK5) signaling (p = 9.4 × 10^−4) and the
   NRF2-mediated oxidative stress response (p = 5.2 × 10^−3) ([95]Table
   2). [96]Table 3 lists the enzymes and transporters involved in
   oxidative phosphorylation that were differentially expressed in elderly
   septic patients.

Table 2. Differentially enriched canonical pathways between elderly and young
individuals, with or without sepsis.

   Canonical Pathway                       Elderly vs. Young
                                           Control   Sepsis
   Oxidative phosphorylation               1.95E-001 5.28E-013
   Mitochondrial dysfunction               8.98E-002 1.41E-010
   ERK5 signaling                          –         9.37E-004
   NRF2-mediated oxidative stress response 2.58E-001 5.23E-003
   Bile acid biosynthesis neutral pathway  1.50E-002 2.14E-001
   dTMP de novo biosynthesis               1.00E+000 3.23E-003
   GADD45 signaling                        –         4.75E-003
   DNA damage-induced 14-3-3σ signaling    –         4.75E-003
   TWEAK signaling                         1.22E-002 4.57E-001
   [97]Open in a new tab

   Fisher’s Exact test p-values are shown. Significant values (p < 0.05)
   are shown.

   TWEAK: TNF related weak inducer of apoptosis.

Table 3. Genes from the oxidative phosphorylation pathway differentially
expressed between elderly and young individuals with sepsis.

   Symbol Gene name Fold- change p-value
   NDUFA4 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 4 0.69
   1.90E-04
   MT-CO1 Mitochondrially encoded cytochrome c oxidase I 0.74 2.80E-03
   SDHC Succinate dehydrogenase complex, subunit C 0.83 6.40E-03
   ATP5G3 ATP synthase, H+ transporting, mitochondrial F0 complex, subunit
   C3 0.80 1.00E-03
   COX5A Cytochrome c oxidase subunit Va 0.83 1.90E-03
   COX6C Cytochrome c oxidase subunit Vic 0.79 5.50E-03
   NDUFA8 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 8 0.81
   1.40E-03
   ATP5F1 ATP synthase, H+ transporting, mitochondrial F0 complex, subunit
   B1 0.83 3.20E-03
   NDUFB4 NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 4 0.84
   4.00E-03
   ATP5H ATP synthase, H+ transporting, mitochondrial F0 complex, subunit
   d 0.83 5.00E-03
   NDUFB9 NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 9 0.83
   1.40E-03
   COX7C Cytochrome c oxidase subunit VIIc 0.78 2.50E-03
   UQCRB Ubiquinol-cytochrome c reductase binding protein 0.83 1.70E-03
   COX11 COX11 homolog, cytochrome c oxidase assembly protein 0.81
   7.50E-03
   ATP5O ATP synthase, H+ transporting, mitochondrial F1 complex, O
   subunit 0.82 2.40E-03
   UQCRH Ubiquinol-cytochrome c reductase hinge protein 0.82 3.10E-03
   COX7A2 Cytochrome c oxidase subunit VIIa polypeptide 2 0.84 1.50E-03
   UQCRQ Ubiquinol-cytochrome c reductase, complex III subunit VII 0.85
   3.00E-03
   [98]Open in a new tab

   Next, we performed a pathway enrichment analysis of the genes
   differentially expressed in septic patients compared with healthy
   controls. Many pathways were identified, but none showed significantly
   different enrichment in the elderly compared with younger adults.
   Pathways that are modulated during sepsis such as Cdc42 signaling
   [[99]37], phospholipase C signaling [[100]38], interleukin 17 (IL-17)
   signaling [[101]39], protein ubiquitination pathway [[102]40],
   glucocorticoid receptor [[103]41] and p38 MAPK signaling [[104]42] were
   observed.

   To favor the identification of molecular pathways preferentially
   affected in the elderly with sepsis, we performed a similar analysis
   using a subset of genes differentially expressed between elderly sepsis
   and healthy subjects, but not in younger adults (944 genes, [105]S2
   Fig). Canonical pathways preferentially altered in elderly individuals
   with sepsis are shown in [106]Table 4. These pathways included
   transforming growth factor β (TGF-β) signaling, Wnt/β-catenin
   signaling, calcium signaling and other pathways of interest relevant to
   sepsis. Notably, during sepsis, the TGF-β pathway was modulated both in
   the elderly and in younger adults. However, we noted an excess of TFG-β
   signaling genes upregulated in elderly septic individuals but not in
   younger septic individuals, as well as differences in the repertoire of
   genes modulated upon sepsis ([107]Table 5).

Table 4. Canonical pathways enriched with genes altered in elderly
individuals with sepsis compared with elderly controls, but not altered in
young adult septic patients compared with young adult controls.

   Canonical Pathways p-value
   TGF-β signaling 5.8E-05
   Factors promoting cardiogenesis in vertebrates 6.8E-05
   Role of osteoblasts, osteoclasts and chondrocytes in rheumatoid
   arthritis 4.9E-04
   Gα12/13 signaling 2.1E-03
   Cardiomyocyte differentiation via BMP receptors 2.9E-03
   Wnt/β-catenin signaling 4.2E-03
   Cholecystokinin/Gastrin-mediated signaling 4.5E-03
   Role of macrophages, fibroblasts and endothelial cells in rheumatoid
   arthritis 4.5E-03
   Calcium signaling 4.7E-03
   BMP signaling pathway 5.6E-03
   NGF signaling 5.9E-03
   [108]Open in a new tab

   Significant values (Fisher’s Exact test; p < 0.05) are shown.

   NGF, nerve growth factor; TGF, transforming growth factor; BMP, bone
   morphogenetic protein.

Table 5. TFG-β signaling pathway genes exclusively differentially expressed
in elderly individuals with sepsis compared with controls.

   Symbol Gene Name Fold change
   BMP7 Bone morphogenetic protein 7 1.19
   CREBBP CREB binding protein 1.15
   INHBB Inhibin beta B 1.53
   SERPINE1 Serpin peptidase inhibitor clade E (nexin plasminogen
   activator inhibitor type 1) member 1 1.29
   SMAD4 SMAD family member 4 1.10
   SMAD9 SMAD family member 9 1.08
   TGFB1 Transforming growth factor beta 1 1.13
   [109]Open in a new tab

   Significant values (Fisher’s Exact test; p < 0.05) are shown.

   A bioinformatics analysis based on the expression profiles of known
   targets identified by microarray identified several upstream regulators
   predicted to be either activated or inhibited in aging. Among those we
   focused on were putative regulators that had opposite activity in
   elderly septic individuals compared with healthy controls ([110]Table
   6). Interestingly, these included the upstream regulators of pathways
   detected in the previous analysis including ERK and TGF-β[1], which
   were more active in septic elderly subjects compared with healthy
   elderly subjects, and when compared with matched young controls. This
   indicated that regulatory microRNAs might be predicted to be activated
   (miR-590-3p) or inhibited (miR-141-3p, miR-186-5p) in septic elders, as
   well as compounds that may exert an antagonic regulatory role in these
   patients compared with healthy controls ([111]Table 6), suggesting
   novel potential therapeutic areas.

Table 6. Putative upstream regulators with inverted activity between elderly
and young adults, with or without sepsis.

   Upstream regulators Elderly vs. Young adults
   Control Sepsis
   z- score p- value z- score p- value
   miR-590-3p (miRNAs w/seed AAUUUUA) −0.75 0.025 2.85 0.011
   INSR 1.95 0.035 −1.63 0.000
   ERK −0.79 0.003 2.74 0.012
   GnRH analog −0.90 0.011 2.50 0.003
   miR-141-3p (miRNAs w/seed AACACUG) 2.30 0.275 −0.95 0.020
   Phorbol myristate acetate −1.67 0.022 1.48 0.006
   TP63 −0.44 0.028 1.97 0.080
   Phorbol esters −0.49 0.005 1.72 0.002
   Cg (Choriogonadotropin) −0.93 0.046 0.80 0.032
   SB203580 (kinase inhibitor) 1.40 0.017 −0.16 0.012
   TGFB1 −0.44 0.030 1.00 0.034
   PD98059 (kinase inhibitor) 0.41 0.014 −0.55 0.019
   MAP2K4 −0.76 0.007 0.15 0.015
   miR-186-5p (miRNAs w/seed AAAGAAU) 0.03 0.016 −0.36 0.008
   [112]Open in a new tab

   Positive/negative z-score values indicate the upstream regulator is
   activated/inhibited in elderly relative to young adults, respectively.

   To provide independent support of the DNA microarray data analysis,
   quantitative PCR experiments were performed for the following genes: 1)
   NDUFA4 and SDHC, to investigate respiratory chain activity and 2)
   INHBB, TGFB1 and CREBBP, to investigate TGF-β signaling. HDAC4 (calcium
   signaling) and SRC (Wnt/β-catenin signaling) were also investigated
   because these genes were significantly altered in elderly individuals
   with sepsis compared with healthy elderly controls, and were not
   expressed at all in young adults ([113]Table 4). In general, we
   observed a good association (Pearson correlation = 0.68, p < 0.05)
   between qPCR and DNA microarray measurements ([114]S3 Fig). NDUFA4,
   SDHC, and INHBB were significantly altered (p < 0.02). Although not
   statistically significant, TGFB1 and HDAC4 showed transcriptional
   changes in agreement with those observed in the microarray. Only CREBBP
   and SRC showed changes that were the reverse of those observed in the
   microarray, but these were not statistically significant, and therefore
   were inconclusive. Overall, these results support our observation that
   aged people had a reduced expression of genes related to respiratory
   chain activity and an increased activation of TGF-β-related genes
   during the course of overwhelming infection.

Discussion

   Transcriptomics is a powerful technique used to identify therapeutic
   targets and biomarker signatures during infectious disease [[115]43,
   [116]44]. A number of gene expression profiling studies were previously
   performed in sepsis, revealing a persistent repression of genes
   involved in adaptive immunity and the massive activation of innate
   immune pathways during septic shock [[117]45]. Using whole
   blood-derived RNA, Wong et al. observed the upregulation of oxidative
   phosphorylation, IL-10 signaling, TLR signaling, NRF2-mediated
   oxidative stress responses, ubiquinone biosynthesis, TREM signaling,
   NF-κB signaling, protein ubiquitination pathways and IL-6 signaling in
   macrophages. They also observed the downregulation of T lymphocyte
   pathways and CCR5 signaling [[118]46]. Similarly, Cvijanovich et al.
   detected the upregulation of TLR, IL-10, IL-6 and NF-κB signaling and
   the downregulation of T lymphocyte activation. Moreover, they detected
   the upregulation of acute phase responses, p38 MAPK, the complement
   system, and some nuclear receptor signaling molecules (LXR and PPAR)
   associated with the downregulation of antigen presentation pathways
   [[119]47].

   Using whole blood-derived RNA from patients in septic shock, the global
   gene expression experiments of Shanley et al. agreed with these
   studies. However, they also observed the upregulation of integrin,
   IGF-1, GM-CSF and insulin receptor signaling [[120]48]. Similarly, Tang
   et al. observed the novel activation of many apoptotic genes, including
   CARD12, APAF1 and ELMOD2 in mononuclear cells from septic patients
   [[121]49]. Surprisingly, a recent study in severe blunt trauma patients
   reported similar findings, with the activation of a large number of
   genes involved in inflammation, pattern recognition and antimicrobial
   functions with the simultaneous suppression of genes involved in
   antigen presentation and T cell proliferation, suggesting that severe
   physiological stress, regardless of its origin, may present common
   genomic signatures [[122]50].

   Sepsis affects very different groups of patients (including aged
   people, diabetics, patients with end-stage renal disease, trauma
   victims, surgical patients’, and obese people). Historically, it has
   been accepted that each specific subset of septic patients may have its
   own characteristic inflammatory immune response. In the course of
   sepsis, a transition from inflammation to immunosuppression has been
   suggested to explain the disappointing results obtained by clinical
   trials that investigated the use of anti-inflammatory drugs in this
   population [[123]51–[124]53]. High-throughput transcriptome analysis
   has provided additional information and clustering algorithms using
   separate training and validation cohorts suggested the existence of
   gene expression subtypes in sepsis and septic shock [[125]54, [126]55].
   Furthermore, a study of gene expression in patients with sepsis
   secondary to community-acquired pneumonia distinguish survivors from
   nonsurvivors in part by variations in the expression of genes
   implicated in energy metabolism [[127]56]. In contrast, a recent
   systematic review based on DNA microarray expression data was unable to
   detect a distinctive pro-inflammatory or anti-inflammatory phase in
   early or late sepsis or differences in the expression patterns between
   groups [[128]57]. Thus, this subject remains highly controversial.

   Most transcriptome studies in sepsis were performed in a pediatric
   population, and to our knowledge, our study is the first genome-wide
   expression analysis comparing septic shock patients of advanced age
   with young adults.

   Initially, our results confirmed that both the young and the elderly
   responded alike regarding the production of tumor necrosis factor α
   (TNFα), IL-6, IL-1β, TLRs and other classical markers of cell
   activation, following severe infectious stress. Some specific pathways,
   however, appeared to be more critically affected in the elderly with
   sepsis, explaining the worse prognosis observed in these patients.
   Based on the data shown here, it is our opinion that defects in
   mitochondrial function/oxidative phosphorylation, TGF-β, Wnt/β-catenin,
   nerve growth factor (NGF) and calcium signaling may play a major role
   in the clinical features of sepsis in elderly patients.

   We observed a marked decrease in the expression of genes encoding
   components of the mitochondrial respiratory chain in the septic
   elderly. Impairment of mitochondrial function significantly contributed
   to organ failure in septic patients [[129]58]. Physiologically, small
   amounts of ROS are produced at complexes I and III of the respiratory
   chain. Sepsis is accompanied by increased oxidative stress caused by
   numerous factors, including the production of ROS by neutrophils,
   increased xanthine oxidase activity, increased nitric oxide plasma
   levels, and decreased antioxidant serum capacity [[130]59].
   Pro-inflammatory mediators and oxidative stress impair the function of
   the enzyme complexes of the respiratory chain and lead to structural
   damage to mitochondrial lipids, proteins and DNA [[131]60, [132]61],
   promoting multiple organ failure [[133]62]. Mitochondrial damage and
   dysfunction secondary to oxidative stress are also observed in aged
   cells [[134]3, [135]63, [136]64]. Indeed, mice that present with
   defective editing functions of mitochondrial DNA polymerase exhibit a
   markedly shortened lifespan and increased features of aging, including
   alopecia, kyphosis, decreased activity, and a loss of reproductive
   function [[137]65].

   TGF-β has pleiotropic effects on adaptive immunity, especially for the
   regulation of effector and regulatory CD4^+ T cell responses. TGF-β is
   a potent suppressor of Th1 and Th2 effector cell differentiation, a
   regulator of Foxp3^+ regulatory T cells, and is a critical cytokine for
   the induction of Th9 and Th17 cells [[138]66–[139]68]. TGF-β also has
   regulatory effects on CD8^+ cells [[140]69] and promotes phagocytosis
   [[141]70]. Thus, we propose that increased activation of the TGF-β
   pathway in elderly septic patients may induce a prolonged
   downregulation of adaptive immunity and an increased Th17 response.

   The Wnt/β-catenin pathway determines major developmental processes in
   the embryo and regulates maintenance, self-renewal and differentiation
   of adult mammalian tissue stem cells [[142]71]. Many organs with a high
   cell turnover require continuous replenishment by somatic stem cells.
   Ageing of hematopoietic stem cells is associated with impaired
   hematopoiesis in the elderly [[143]72, [144]73], which was attributed
   to a switch from canonical to non-canonical Wnt signaling [[145]74]. In
   addition, a recent study reported that Wnt/β-catenin signaling induced
   the aging of mesenchymal stem cells through ROS generation [[146]75].
   These mechanisms might be responsible for defects in dendritic cell
   differentiation [[147]76] and loss of T cell potential observed with
   age [[148]77]. The Wnt/β-catenin pathway participates in the regulation
   of many other pathways, including NF-κB and PI3-kinase pathways, with
   important implications in the pathophysiology of septic shock
   [[149]78]. In our opinion, the increased upregulation of the
   Wnt/β-catenin pathway in the elderly might be a critical detrimental
   factor.

   Nerve growth factor (NGF) ensures the maintenance of phenotypic and
   functional characteristics of several populations of neurons and immune
   cells. The role of NGF has never been investigated in sepsis, but might
   be involved because neuropeptides possess a broad number of regulatory
   functions in immunity [[150]79] and NGF directly affects the survival
   and differentiation of stem cells, granulocytes, lymphocytes and
   monocytes [[151]80–[152]83]. Further studies should investigate its
   role in sepsis and in specific populations of septic patients, such as
   the elderly.

   Calcium has a myriad of cellular functions, including effects on
   hormone secretion, enzyme activity, nerve conduction, and muscle
   contraction with obvious repercussions in sepsis. Calcium influx is a
   potent cell activator and, together with phosphate, alters
   electrostatic fields and protein conformation, dictating the majority
   of signal transduction [[153]84].

   The mechanisms that lead to the dysfunction of specific pathways in the
   immune responses of older people are complex and might involve multiple
   factors. We propose that environmental factors [[154]85, [155]86],
   microRNAs [[156]87] and epigenetic changes [[157]88] might play a key
   role as modulating factors of the immune pathways that are particularly
   affected in older people.

   Our data confirm previous reports that aging is accompanied by changes
   in the expression of genes related to immune responses [[158]89], and
   further studies are required to support the involvement of pathways we
   identified and their relevance in sepsis. Interestingly, our analysis
   also suggests that a number of upstream regulators of molecular
   pathways display an inverted pattern of expression in elderly patients
   with sepsis compared with healthy controls, including microRNAs
   miR-590-3p (predicted activation in the septic elderly), miR-141-3p
   (predicted inhibition in the septic elderly) and miR-186-5p (predicted
   inhibition in the septic elderly). Another distinctive feature of this
   study was the observation for the first time that subsets of long
   noncoding RNAs are deregulated in the immune system in sepsis and
   aging, warranting additional studies aimed at investigating the
   biological roles exerted by this class of transcript in septic shock in
   patients of advanced age.

Conclusions

   There is great hope that high-throughput screening technology will
   result in a better understanding of the complexity of systemic
   inflammation, identifying new therapeutic targets and groups of
   patients that may benefit from specific interventions.

   Here, we identified major pathways preferentially deregulated in the
   elderly following severe infection, providing evidence that the
   systemic inflammatory response differs according to patient age. Thus,
   oxidative stress might have a major role in inducing multiple
   dysfunctions in the elderly. Furthermore, our study identified
   additional important genes and molecular pathways, thus helping to
   elucidate the mechanisms involved in signaling crosstalk during
   infection and in aging.

Supporting Information

   S1 Fig. Hierarchical clustering of samples from sepsis (S) and healthy
   (H) subjects based on gene expression measurements.

   After background filtering (see “[159]Methods” for details), valid
   measurements from 16,698 genes were used to group samples using UPGMA
   hierarchical clustering and Pearson correlation as a distance
   measurement. Dendrograms are colored according to disease status
   (sepsis in red, healthy controls in blue) and age (elderly subjects are
   shown in darker colors whereas those from young adults are shown in
   lighter colors). Samples are well grouped by the disease status
   sepsis/healthy and moderately grouped by age.

   (DOCX)
   [160]Click here for additional data file.^ (35KB, docx)
   S2 Fig. Venn diagram showing all possible intersections in
   differentially expressed genes between all groups.

   (DOCX)
   [161]Click here for additional data file.^ (76.6KB, docx)
   S3 Fig. Confirmation of gene expression changes in sepsis and age
   detected by microarray using Real-Time PCR.

   Genes with comparable changes in gene expression are shown in green and
   with an inverted pattern in red. Statistically significant results are
   shown in dark green. Values that follow a trend, but do not reach
   statistical significance are shown in light green.

   (DOCX)
   [162]Click here for additional data file.^ (34.3KB, docx)
   S1 Table. Patient characteristics.

   (DOCX)
   [163]Click here for additional data file.^ (16.3KB, docx)

Acknowledgments