Abstract

Background

   Host immune responses during late-onset sepsis (LOS) in very preterm
   infants are poorly characterised due to a complex and dynamic
   pathophysiology and challenges in working with small available blood
   volumes. We present here an unbiased transcriptomic analysis of whole
   peripheral blood from very preterm infants at the time of LOS.

Methods

   RNA-Seq was performed on peripheral blood samples (6–29 days postnatal
   age) taken at the time of suspected LOS from very preterm infants <30
   weeks gestational age. Infants were classified based on blood culture
   positivity and elevated C-reactive protein concentrations as having
   confirmed LOS (n = 5), possible LOS (n = 4) or no LOS (n = 9).
   Bioinformatics and statistical analyses performed included pathway
   over-representation and protein-protein interaction network analyses.
   Plasma cytokine immunoassays were performed to validate differentially
   expressed cytokine pathways.

Results

   The blood leukocyte transcriptional responses of infants with confirmed
   LOS differed significantly from infants without LOS (1,317
   differentially expressed genes). However, infants with possible LOS
   could not be distinguished from infants with no LOS or confirmed LOS.
   Transcriptional alterations associated with LOS included genes involved
   in pathogen recognition (mainly TLR pathways), cytokine signalling
   (both pro-inflammatory and inhibitory responses), immune and
   haematological regulation (including cell death pathways), and
   metabolism (altered cholesterol biosynthesis). At the
   transcriptional-level cytokine responses during LOS were characterised
   by over-representation of IFN-α/β, IFN-γ, IL-1 and IL-6 signalling
   pathways and up-regulation of genes for inflammatory responses. Infants
   with confirmed LOS had significantly higher levels of IL-1α and IL-6 in
   their plasma.

Conclusions

   Blood responses in very preterm infants with LOS are characterised by
   altered host immune responses that appear to reflect unbalanced
   immuno-metabolic homeostasis.

Introduction

   Neonatal sepsis results in an estimated 0.5–1 million infant deaths
   globally each year. Preterm infants, particularly those born very
   preterm (<32 weeks gestation), account for more than a third of the
   mortality within the first month of life [[40]1–[41]3]. Late-onset
   sepsis (LOS), with onset after 72h of age, is the most common form of
   sepsis affecting preterm infants, occurring in more than 20% of those
   born very preterm. The majority of LOS is due to ubiquitous and often
   low-virulence commensals of the gastrointestinal tract and skin,
   especially coagulase negative staphylococci such as Staphylococcus
   epidermidis, suggesting that the preterm immune system may be
   developmentally immature [[42]4–[43]6].

   The preterm infant immune system has both quantitative and functional
   impairments; with diminished protective function due to anatomical
   barriers, impaired innate immune defences including reduced
   pro-inflammatory cytokine production, antigen presentation and
   phagocytosis of innate cells, and delayed adaptive immune responses due
   to limited antigenic exposure prior to birth [[44]7–[45]11]. In
   addition, immune cells of preterm infants have also shown impairments
   in cellular metabolism that limit innate immune responses during
   infection [[46]12]. These immune-metabolic deficiencies contribute to
   an increased risk of preterm infants acquiring infection and
   ultimately, demonstrating dysregulated inflammatory responses that lead
   to neonatal sepsis [[47]7, [48]13–[49]15].

   There has been recent interest in understanding neonatal sepsis
   pathophysiology at the molecular level. Smith et al (2014) identified a
   52-gene immune-metabolic network that could predict bacterial
   infections in preterm and term neonates. Cernada and colleagues found
   554 differentially expressed genes that discriminated VLBW septic cases
   from controls (who showed no signs of infection)[[50]16, [51]17]. Wynn
   and colleagues found significant differences in transcriptional host
   responses between patients with early-onset sepsis and LOS, suggesting
   that accounting for timing of sepsis is critical in transcriptional
   profiling studies of neonates [[52]18]. However, no study to date has
   specifically used transcriptome sequencing (RNA-Seq) to investigate
   immuno-metabolic host transcriptional responses during LOS in very
   preterm infants, in whom the incidence of sepsis is highest.

   In this pilot study, we used RNA-Seq to determine whole blood
   transcriptional responses of very preterm infants with suspected or
   confirmed LOS. Our findings are the first to characterise the
   underlying functional biology of blood leukocyte transcriptional
   responses associated specifically with LOS immunopathology in very
   preterm infants.

Methods

Ethics statement

   The study was approved by the institutional Human Research Ethics
   Committee at King Edward Memorial Hospital for Women, Perth, Western
   Australia (HREC reference: 2014091EW). Written informed consent from
   parents or guardians was obtained prior to study participation.

Study design

   A cohort of 20 very preterm infants (<30 weeks gestational age, GA) was
   recruited as part of a larger prospective study of 57 infants born <42
   weeks GA. Infants were prospectively recruited from the neonatal
   intensive care unit of King Edward Memorial Hospital, Perth, Australia
   over a period of 18 months (July 2015 –December 2016). The inclusion
   criterion for this study was informed consent by parent/guardian for an
   infant being investigated for suspected sepsis within the first 42 days
   of life. Relevant demographic (GA, birth weight and postnatal age) and
   laboratory (blood culture results, serial C-reactive protein (CRP)
   measurements and differential counts) information of recruited infants
   was collected from patient records and electronic hospital databases.

   Study patients born very preterm (n = 20) were retrospectively
   classified, based on blood culture results using a recommended minimum
   volume of 0.5 mL for infants <28 weeks GA and plasma CRP immuno-assay
   measurements (Vitros, Ortho-Clinical Diagnostics), as having confirmed
   LOS (positive blood culture and CRP >20mg/L within 72 hours of blood
   culture), possible LOS (negative blood culture and CRP >20mg/L within
   72 hours of blood culture) or no LOS (negative blood culture and CRP
   <20mg/L within 72 hours of blood culture) [[53]19]. All infants with
   confirmed LOS received >5 days of antibiotics. Two very preterm infants
   classified as having a possible blood culture contamination, i.e. those
   with positive blood culture and CRP <20mg/L, were excluded from further
   analyses, leaving a final cohort of 18 ([54]Fig 1).

Fig 1. Study design.

   [55]Fig 1
   [56]Open in a new tab

   Summary of samples included in bioinformatics and statistical analyses
   (bolded boxes). Infants were classified as having confirmed late-onset
   sepsis (LOS), possible LOS or no LOS based on blood culture and CRP
   measurement results within 72 hours of blood culture.

Blood sampling and processing

   Neonatal peripheral blood was collected either at the time of septic
   screen (n = 13) or within fifteen hours, at the next blood draw for
   clinical purposes (n = 7, median 10 hours), by venipuncture into a
   lithium heparin tube, following decontamination of the skin with 70%
   ethanol and 1% chlorhexidine swab. Blood (0.2–0.5 mL) was stabilized in
   RNase/DNase-free Eppendorf tubes containing pre-aliquoted PAXgene™
   reagent (blood:reagent ratio of 1:2.76) according to manufacturer’s
   guidelines (PreAnalytix; Qiagen/Becton Dickson). Sample tubes were
   gently inverted ten times and stored at -80°C until batch RNA
   extraction. A separate aliquot of blood (0.5 mL) was centrifuged at
   3,000g for 20 min at 4°C to extract plasma, with collected plasma
   samples stored at -80°C for immunoassays. All blood samples from very
   preterm infants were transported on ice to the research laboratory and
   processed within two hours from blood collection, with the exception of
   blood samples from two infants with no LOS, which were processed within
   7 hours.

RNA extraction, quantification and quality assessment

   RNA was extracted from stabilised blood using an amended version of the
   PAXgene™ Blood RNA System Kit (PreAnalytix; Qiagen/Becton Dickson).
   Briefly, samples were thawed at room temperature for up to 2 hours.
   Samples containing a total blood and PAXgene™ reagent volume of less
   than 1880μL had RNase-free water added to a final volume of 1880μL to
   standardise extraction volumes. All samples were centrifuged and had
   supernatant removed as per manufacturer’s instructions. RNase-free
   water (500μL) was then added to the pellet. All subsequent steps were
   performed according to the manufacturer’s protocol. The RNA
   concentration and RNA integrity number (RIN) were measured using the
   Agilent 2100 Bioanalyzer (Agilent Technologies). Samples included in
   bioinformatic and statistical analyses (n = 18) had total RNA
   concentrations from 14–262 μg/μL and RIN of 8.3–9.6.

RNA-Seq

   PolyA-enrichment of total RNA was performed using the NEBNext Poly(A)
   mRNA Magnetic Isolation Kit (New England Biolabs), followed by cDNA
   library synthesis using the KAPA Stranded RNA-seq Library Preparation
   Kit as per the manufacturer’s instructions (Kapa Biosystems).
   Quantification of the cDNA libraries was performed using a Quant-iT
   dsDNA Assay Kit (Invitrogen) and normalized to 4 nM. To derive the
   transcriptome for each infant, samples were labelled with a unique
   barcode, multiplexed, and sequenced on a HiSeq 2500 sequencer
   (Illumina), using the High Output 100-bp single-end run, at the
   University of British Columbia Sequencing Centre.

   The deconvoluted sequences from each infant were analysed and the
   quality of FASTQ reads were assessed using FastQC(v0.0.15), and
   summarized using MultiQC(v0.8.dev0) [[57]20, [58]21]. FASTQ reads were
   aligned to the GRCh37(hg19) human reference genome [[59]22] using the
   STAR(v2.5) aligner, followed by generation of read counts using
   htseq-count(v0.6.1p1) [[60]23]. Globin transcripts and low count genes
   were bioinformatically removed from the count matrix. The median
   library size of uniquely mapped reads for very preterm infant samples
   was 6.8 million reads, and a range of 4.1–8.9 million reads for the
   entire study.

Cytokine immunoassays

   The concentrations of interferon (IFN)-α, IFN-β, IFN-γ, interleukin
   (IL)-1α, IL-1β, IL-10 and IL-6 in plasma were measured using
   ProcartaPlex™ immunoassay kits (Invitrogen, Thermo Fisher Scientific)
   according to the manufacturer’s instructions.

Bioinformatics and statistical analysis

   Bioinformatics and statistical analyses were performed on the 18
   samples included in the final study design ([61]Fig 1). Descriptive
   statistics were conducted in PRISM 7 (GraphPad Software Inc.,
   California, USA) and included the Mann-Whitney test to determine
   statistical differences between clinical groups. All packages used for
   bioinformatics analyses were performed in R (v3.3.1) [[62]24].
   Differential gene expression analysis was conducted using DESeq2
   (v1.12.4), whereby counts were normalised using variance stabilising
   transformation then visualised using principal component analysis
   [[63]25] with plots generated using ggplot2 (v2.2.1) [[64]26].
   Differentially expressed genes, based on pair-wise comparison between
   condition groups, were identified based on an adjusted p-value of ≤0.05
   and ±1.5 fold-change. Statistically significant pathways with
   Bonferroni-corrected hypergeometric test p-value <0.05 were identified
   from each differentially expressed gene list using Sigora (v2.0.1)
   using Reactome database annotations [[65]27] based on over-represented
   gene-pair signatures. NetworkAnalyst [[66]28] was used for
   protein-protein interaction (PPI) based network visualisations of gene
   expression changes based on the InnateDB interactome database [[67]29].
   NetworkAnalyst was also used to identify pathway enrichment based on
   Reactome database pathway and reaction annotations
   ([68]www.reactome.org) [[69]27, [70]28]. Sub-networks were extracted
   based on significantly enriched pathways and were grouped into
   functional categories. BiomaRt(v2.28.0) was used to convert Ensembl
   gene identifications to gene symbols [[71]30, [72]31].

Results

Patient demographics

   The demographic information of very preterm infants in this study is
   shown in [73]Table 1. Overall, infants from all clinical groups were
   similar with no significant differences in GA, birth weight or
   postnatal age (p>0.05) ([74]S1 Table). Infants with confirmed LOS were
   infected with either Gram-positive (Staphylococcus capitis and/or
   Staphylococcus epidermidis) or Gram-negative (Escherichia coli or
   Enterobacter asburiae) pathogens ([75]Table 1).

Table 1. Demographics of very preterm infants in each analysis cohort with
confirmed possible and no Late-Onset Sepsis (LOS); with additional details on
confirmed LOS infants.

   Variable Median (Range)
   Confirmed LOS; n = 5 Possible LOS; n = 4 No LOS; n = 9
   GA (weeks)[76]^* 26.9 (24.3–29.9) 26.4 (24.4–27.0) 26.4 (23.7–29.8)
   Birth weight (grams)[77]^* 1286 (620–1650) 645 (560–790) 900 (575–1340)
   Postnatal age (days)[78]^* 11 (6–37) 21 (14–29) 12 (6–25)
   Sex (male) 4/5 3/4 4/9
   Septic infants
   Infant ID Gestation (weeks) Birth weight (g) Sex Postnatal age (days)
   Postmenstrual age (days)[79]^† Infecting organism
   5 29.9 1650 M 11 220 S. capitis
   12 28.6 1340 M 37 237 S. epidermidis & S. capitis
   16 24.3 620 F 9 179 E. coli
   17 24.6 770 M 14 186 S. aureus
   18 26.9 1286 M 6 194 E. asburiae
   [80]Open in a new tab

   *Median (Minimum—Maximum).

   †Gestation plus postnatal age.

Differential gene expression and cell counts between clinical groups

   We conducted RNA-Seq to determine possible differences in gene
   expression between infants with confirmed LOS, possible LOS and no LOS.
   To determine the major factors contributing to overall differences
   principal component analysis was first conducted on normalised gene
   count data ([81]Fig 2). This demonstrated that 4 of the 5 infants with
   confirmed LOS had similar overall transcriptional responses that were
   distinct from responses in infants with possible or no LOS. Of note,
   the single infant with confirmed LOS who did not cluster with the other
   4 confirmed LOS patients was distinct based on the presence of
   polymicrobial infection and a higher postmenstrual age of 237 days at
   the time of sample collection ([82]Table 1).

Fig 2. Principal component analysis of global gene expression.

   [83]Fig 2
   [84]Open in a new tab

   The graph shows the first two principal components based on overall
   gene expression between very preterm infants with confirmed late-onset
   sepsis (LOS; red triangles), possible LOS (green squares) or no LOS
   (blue circles). The number of DEGs between infants with confirmed
   LOS/no LOS, 1317; confirmed LOS/possible LOS, 21; and possible LOS/no
   LOS, 0.

   Differential gene expression analysis was subsequently performed to
   identify genes that were significantly different between clinical
   groups using as cut-offs an adjusted P-value of ≤0.05 and ±1.5
   fold-change. We found that the transcriptional responses of infants
   with confirmed LOS were substantially different from those with no LOS,
   with 1,317 differentially expressed genes (995 up-regulated and 322
   down-regulated; [85]Fig 2 and [86]S2 Table). In contrast, the
   differences in transcriptional responses of infants with confirmed LOS
   and possible LOS were markedly lower, with only 21 differentially
   expressed genes identified (19 up-regulated and 2 down-regulated;
   [87]S3 Table). Interestingly, there were no differentially expressed
   genes identified when comparing transcriptional responses of very
   preterm infants with possible LOS or no LOS. Analysis of differential
   counts for white cells, platelets, red cells, neutrophils, lymphocytes,
   monocytes and eosinophils ([88]S1 Table) showed that there were no
   significant differences (p>0.05) in cell proportions between the
   clinical groups except for platelet count between infants with no LOS
   and confirmed LOS (p = 0.01; [89]S4 Table).

Transcriptional changes during confirmed LOS

   Comparing infants with confirmed LOS to those without LOS, a biological
   pathway over-representation analysis was performed using SIGORA on all
   differentially expressed genes. In contrast to traditional pathway
   enrichment analysis, where pathways are classified as sets of genes
   with individual genes being equally informative, gene-pair signature
   pathway analysis accounts for only statistically significant gene-pairs
   that, taken together, are unique to a single pathway. This reduces the
   risk of identifying redundant pathways (in which the same genes appear
   repeatedly in multiple pathways) and allows more appropriate
   examination of pathways associated with disease [[90]27]. Overall, 39
   pathways that function in a broad range of host responses, were
   significantly (p<0.05) altered in infants with confirmed LOS ([91]Table
   2). Of these over-represented biological pathways, 31 comprised only
   up-regulated genes, 1 had only down-regulated genes and 6 included both
   up- and down-regulated genes. To better understand the relationship
   between these pathways and their role during confirmed LOS in very
   preterm infants, we investigated these pathways based on their
   involvement in key processes involved in host responses to infection,
   namely: pathogen recognition, cytokine signalling,
   immune/haematological regulation, and metabolism. For each of these key
   processes, expression was investigated and visualised by plotting the
   underlying interactions between the protein products of these genes, by
   generating zero-order PPI sub-networks using NetworkAnalyst ([92]Fig
   3).

Table 2. Functional classification of statistically significant
over-represented pathways (determined using the Sigora gene-pair signature
method) in infants with Late-Onset Sepsis (LOS).

   Pathway description P value[93]^† Differentially expressed genes in
   pathway
   Pathogen/Pattern Recognition
   Toll Like Receptor TLR6:TLR2 cascade 2.82E-20 Up-regulated
   Toll Like Receptor TLR1:TLR2 cascade 5.03E-08 Up-regulated
   MyD88-independent TLR3/TLR4 cascade 5.12E-06 Up-regulated
   MyD88:Mal cascade initiated on plasma membrane 1.24E-05 Up-regulated
   Toll Like Receptor 3 (TLR3) cascade 4.25E-05 Up-regulated
   TRIF-mediated TLR3/TLR4 signalling 1.29E-02 Up-regulated
   Toll Like Receptor 9 (TLR9) cascade 2.86E-02 Up-regulated
   Dectin-2 family 8.20E-04 Up-regulated
   Cytokine Signalling
   Interferon alpha/beta signalling 5.33E-27 Up-regulated
   Interferon signalling 8.20E-19 Up-regulated
   Interleukin-6 signalling 2.12E-12 Up-regulated
   Interleukin-1 signalling 2.86E-06 Up-regulated
   Negative regulators of RIG-I/MDA5 signalling 7.40E-06 Up-regulated
   Interferon gamma signalling* 7.48E-05 Up-regulated
   Signalling by interleukins* 7.57E-05 Up-regulated
   Immune/Haematological Regulation
   Alternative complement activation 1.32E-03 Up- and down-regulated
   Hemostasis
   Platelet degranulation 2.34E-11 Up-regulated
   Cell surface interactions at the vascular wall 3.31E-03 Up-regulated
   Signal transduction
   RHO GTPases activate WASPs and WAVEs 2.05E-03 Up-regulated
   Signalling to RAS 5.02E-03 Up-regulated
   Hedgehog "on" state 4.74E-03 Down-regulated
   Cellular responses to external stimuli
   Diseases of immune system 1.68E-08 Up-regulated
   Oxygen-dependent proline hydroxylation of Hypoxia-inducible Factor
   Alpha 1.32E-04 Up-regulated
   Programmed cell death
   Activation of BAD and translocation to mitochondria 3.22E-03
   Up-regulated
   Activation of BH3-only proteins 1.74E-02 Up-regulated
   Metabolism
   Activation of gene expression by SREBF (SREBP) 1.23E-04 Up-regulated
   Regulation of cholesterol biosynthesis by SREBP (SREBF) 2.07E-02
   Up-regulated
   Cholesterol biosynthesis 3.23E-02 Up-regulated
   Metabolism of vitamins and cofactors 1.60E-02 Up- and down-regulated
   PPARA activates gene expression 2.95E-02 Up- and down-regulated
   Metabolism of proteins
   O-linked glycosylation of mucins 6.02E-15 Up-regulated
   Metabolism of RNA
   KSRP (KHSRP) binds and destabilizes mRNA 6.20E-03 Up-regulated
   Developmental Biology
   Sema4D in semaphorin signalling 8.65E-04 Up- and down-regulated
   Constitutive signalling by NOTCH1 HD domain mutants 4.96E-03 Up- and
   down-regulated
   [94]Open in a new tab

   †Statistical significance was based on the hypergeometric test with
   Bonferroni correction.

Fig 3. PPI network visualisations associated with four main functions
transcriptionally altered in infants with confirmed Late-Onset Sepsis (LOS).

   [95]Fig 3
   [96]Open in a new tab

   Zero-order networks were generated in NetworkAnalyst from
   differentially expressed genes comparing very preterm infants with
   confirmed LOS to those with no LOS. The sub-networks shown were
   extracted based on pathways identified from the over-representation
   analysis ([97]Table 2) that were associated with (A) pathogen/pattern
   recognition with innate immune signalling nodes highlighted in blue,
   cytokine signalling and metabolism, and (B) immune/haematological
   regulation. The nodes were colour coded based on up-regulation (red) or
   down-regulation (black).

Pathogen recognition and cytokine signalling

   Many transcriptional responses in LOS patients were associated with TLR
   signalling pathways ([98]Table 2). The zero-order PPI sub-network of
   these functionally enriched TLR pathways showed up-regulation of key
   interacting genes involved in innate immune signalling including
   NFKBIA, MYD88, CEBPB, STAT1, IRF7, IRAK2, IRAK4 and TBK1 ([99]Fig 3A),
   which are known to drive the production of pro-inflammatory cytokines
   and type I IFNs [[100]17, [101]32]. Genes from the dendritic
   cell-associated C-type lectin-2 (Dectin-2) family of CLR were also
   up-regulated ([102]Table 2 and [103]S5 Table), although the CLR
   signalling pathway was not itself functionally enriched in the
   zero-order PPI network.

   With regards to cytokine responses in LOS patients, it was found that
   the IFN-α/β, IFN-γ, IL-1 and IL-6 pathways were over-represented, with
   128 up-regulated and 13 down-regulated nodes in a large cytokine
   signalling sub-network ([104]Table 2 and [105]Fig 3A). In addition,
   immune inhibitory signalling genes for IL-1 receptor antagonist
   proteins (IL1R2 and IL1RN) and suppressor of cytokine signalling
   proteins (SOCS1 and SOCS3) were also up-regulated. Interestingly, SOCS1
   was part of the unique gene-set associated with all IFN signalling
   pathways, whereas SOCS3 was part of the gene-set for the IL-6
   signalling and IFN signalling pathways ([106]S5 Table).

Immune and haematological processes

   Transcriptional regulation of immune and haematological processes
   during confirmed LOS occurred on multiple levels. Significantly
   over-represented pathways were identified that were involved in
   haemostasis, signal transduction, cellular responses to external
   stimuli and programmed cell death ([107]Table 2 and [108]S5 Table). All
   pathways comprised up-regulated genes, except for the alternative
   complement activation (up- and down-regulated genes) and hedgehog “on”
   state (down-regulated genes) pathways. It was also found that the
   oxygen-dependent proline HIF1A pathway, commonly associated with
   hypoxia, was over-represented, with up-regulation of HIF1A in the
   immune/haematological PPI sub-network of differentially expressed genes
   associated with LOS ([109]Fig 3B). The gene for lactate dehydrogenase A
   (LDHA), which is induced by HIF1α, was also up-regulated along with
   SLC16A3, the gene coding for monocarboxylate transporter 4 (MCT4)
   [[110]33].

   Interestingly, pathways associated with programmed cell death were also
   identified with the over-representation of the Bcl-2 homology (BH3)
   protein and Bcl-2-associated death (BAD) pathways ([111]Table 2).
   Importantly, our immune/haematological PPI sub-network revealed that
   despite up-regulation of the majority of genes in the network, BCL2 and
   IL-7R, two key genes involved in controlling apoptosis, were
   significantly down-regulated ([112]Fig 3B).

Changes in metabolic pathways

   Significant transcriptional changes during confirmed LOS were
   identified that were associated with lipid metabolism and more
   specifically, activation and regulation of cholesterol biosynthesis
   ([113]Table 2). The relationship between cholesterol metabolism, type I
   IFNs and suppression of IL-1 has previously been elucidated [[114]34,
   [115]35], while paediatric sepsis patients also have major alterations
   in lipid metabolism [[116]36]. We therefore explored gene expression
   and interaction during metabolism by generating an immuno-metabolism
   sub-network consisting of functionally enriched genes and pathways
   involved in the regulation of cholesterol biosynthesis, cytokine
   signalling and pathogen/pattern recognition ([117]Fig 3A). The sterol
   regulatory element-binding proteins (SREBP) pathway, involved in
   responding to low concentrations of cholesterol and subsequent
   promotion of cholesterol biosynthesis [[118]37, [119]38], was
   over-represented along with significant up-regulation of cholesterol
   biosynthesis genes SREBF2, DHCR7, INSIG1, ERLIN2, SQLE, IDI1 and LDLR
   ([120]Fig 3, [121]Table 2 and [122]S5 Table). Immune-metabolic
   regulatory genes, PPARG (encoding the proliferator-activated receptor
   gamma protein, a nuclear receptor involved in controlling
   beta-oxidation of fatty acids and regulation of glucose metabolism)
   [[123]39, [124]40] and CEBPB (a transcription factor important for
   regulating immune and inflammatory response genes) were also
   up-regulated. Importantly, this metabolism sub-network indicated
   connections between cholesterol biosynthesis genes (i.e. SREBF2, INSIG1
   and ERLIN2), regulatory genes (i.e. PPARG and CEBPB) and genes involved
   in the pro-inflammatory response (e.g. IL1B).

Validation of transcriptional responses in infants with confirmed LOS

   To validate the transcriptional findings associated with
   over-represented cytokine signalling pathways ([125]Table 2), we
   measured plasma concentrations of IFN-α, IFN-β, IFN-γ, IL-1α, IL-1β and
   IL-6 at the protein-level ([126]Fig 4). The concentrations of all IFNs
   were below detection limits. However, consistent with our findings at
   the transcriptional-level, infants with confirmed LOS had significantly
   higher levels of IL-1α and IL-6 (p<0.05) compared to infants with no
   LOS. Infants with confirmed LOS did not have significantly different
   levels of IL-1β compared to infants with no LOS (median of 5.19 pg/mL
   in confirmed LOS versus 0.87 pg/mL in no LOS; p = 0.14). However,
   comparison between 4 of the 5 infants with confirmed LOS that had
   similar overall transcriptional responses ([127]Fig 2) showed
   significantly higher levels of IL-1β compared to all infants with no
   LOS (median of 9.03 pg/mL in confirmed LOS versus 0.87 pg/mL in no LOS;
   p<0.05). Infants with confirmed LOS also had up-regulated expression of
   S100A12, which encodes for the S100A12 alarmin previously implicated
   during neonatal and adult sepsis [[128]41–[129]43]. In line with our
   differential gene expression findings, the IL-1α and IL-1β levels of
   possible LOS infants were similar to those in infants with no LOS.
   However, infants with possible LOS also had significantly higher IL-6
   protein concentrations than infants without LOS, consistent with the
   elevated CRP levels in these infants [[130]44, [131]45]. Plasma
   concentrations of IL-10 were significantly higher in infants with
   confirmed (p<0.005) or possible LOS (p<0.05) compared to those with no
   LOS. There were however no significant differences in IL-10 or
   IL-6-related gene expression in infants with possible LOS compared to
   those with no LOS. Although the IL-10 signalling pathway was not
   significantly over-represented in our pathway analysis, infants with
   confirmed LOS had up-regulated expression of IL10 compared to no LOS
   infants.

Fig 4. Cytokine expression of IL-1α, IL-1β, IL-6 and IL-10.

   [132]Fig 4
   [133]Open in a new tab

   Data represent the concentrations of cytokines IL-1α, IL-1β, IL-6 and
   IL-10 in infants with confirmed late-onset sepsis (LOS), possible LOS
   and no LOS. The box plots show the median and interquartile range and
   statistically significant differences based on the Mann-Whitney Test
   (*, p<0.05 and **, p<0.005).

Discussion

   Very preterm infants are at highest risk of acquiring LOS, however, our
   understanding of their underlying leukocyte transcriptional responses
   during sepsis remains limited. Using a combined approach of
   differential gene expression, pathway over-representation and network
   analyses, RNA-Seq was employed to examine the whole blood
   transcriptional responses of very preterm infants with confirmed or
   suspected LOS. Our findings showed that: (i) infants with confirmed LOS
   had significantly altered regulation of host immune responses at the
   transcriptional-level and significantly higher IL-6 and IL-10 plasma
   concentrations compared to infants without LOS; (ii) infants with
   confirmed LOS had changes in immuno-metabolism that were associated
   with LOS pathophysiology; and (iii) infants with possible LOS had no
   differentially expressed genes compared to infants without LOS at the
   transcriptional-level but had significantly higher IL-6 and IL-10
   plasma concentrations at the protein-level.

   Transcriptional profiling of blood leukocyte responses revealed that
   infants with confirmed LOS, when compared to infants without LOS, had
   1,317 differentially expressed genes associated with 39
   over-represented biological pathways that were involved in a broad
   range of biological processes from pathogen recognition to metabolism.
   We found that both immuno-stimulatory and inhibitory transcriptional
   responses occurred simultaneously during confirmed LOS, indicative of
   the dynamic pathophysiology of sepsis that is not marked by an initial
   hyper-inflammatory response followed by a later immunosuppressive
   anti-inflammatory phase [[134]15, [135]46, [136]47]. Immune stimulatory
   responses included up-regulated genes involved in pro-inflammatory
   responses including MYD88, STAT1, IRF7, NFKBIA and IL1R1, which are
   associated with over-represented cytokine signalling pathways for
   IFN-α/β, IL-1 and IL-6. These genes were also found to be increased in
   expression during neonatal infection in a transcriptional study of
   preterm and term infants [[137]17]. However, although the IFN-α/β, IL-1
   and IL-6 signalling pathways were over-represented and consisted of
   only up-regulated genes, cytokine immunoassays showed that at the
   protein-level, only IL-1α and IL-6 concentrations were significantly
   higher in confirmed LOS compared to no LOS infants. This is consistent
   with the concept that the type I interferon signalling pathways invoke
   other host defence mechanisms other than just interferons [[138]48].

   The higher IL-6 plasma concentrations in our study are in line with
   previous studies of preterm infants that showed increased plasma IL-6
   concentrations during sepsis compared to non-infected or control groups
   [[139]45, [140]49, [141]50]. Separately, IL-1 signalling is mediated by
   both IL-1α and IL-1β binding to the IL-1R1 receptor. Although IL-1β has
   been implicated during neonatal sepsis, less is known about IL-1α in
   human neonates [[142]51–[143]54]. The elevated IL-1α levels in our
   study were consistent with a recent murine study that also showed
   increased IL-1α during polymicrobial sepsis [[144]51]. The study
   identified that IL-1α, and not IL-1β, was responsible for
   IL-1R1-dependent lethality during neonatal sepsis. In keeping with
   this, although we found up-regulation of IL1B in infants with confirmed
   LOS, this did not translate to significant differences in IL-1β plasma
   concentrations compared to infants with no LOS. Our findings were
   consistent with other neonatal studies that found IL-1β concentrations
   were not different during sepsis [[145]55–[146]57]. However, our
   findings differed from studies that reported significantly elevated
   IL-1β plasma and serum levels in infected compared to non-infected
   infants [[147]53, [148]54, [149]58], except when comparing the 4 of 5
   infants with confirmed LOS that had similar transcriptional responses
   to all infants with no LOS. Overall, the discrepancies between our
   study and previous studies [[150]53, [151]54, [152]58] could be due to
   multiple factors including different laboratory methods used, sample
   sizes, sample collection times and neonatal populations.

   Importantly, we found up-regulation of IL1R2 and IL1RN in infants with
   confirmed LOS, with these genes directly connected to IL1B in the
   cytokine signalling sub-network. These results were consistent with two
   independent transcriptional studies showing up-regulation of IL1R2 and
   IL1RN during sepsis in preterm/term infants [[153]17] as well as very
   preterm infants [[154]16] with a role in immune inhibitory signalling.
   These genes encode for the IL-1 receptor type-2 (IL1R2) and IL-1
   receptor antagonist proteins (IL1RN), where IL1R2 acts as a decoy
   receptor that competitively binds to IL-1α/β and preventing binding to
   IL1R1, whereas IL1RN prevents IL-1 from binding to the IL1RAP receptor,
   inhibiting IL-1 signalling [[155]17, [156]59]. These findings suggest
   that IL-1β plasma concentrations may not have increased during
   confirmed LOS in our study due to negative regulation by both IL1R2 and
   IL1RN.

   In parallel with the induction of pro-inflammatory cytokine responses,
   we also found up-regulation of anti-inflammatory IL10, whereby plasma
   IL-10 concentrations were significantly higher in infants with
   confirmed LOS compared to infants with no LOS. This was consistent with
   previous studies where preterm infants with sepsis had significantly
   increased IL-10 levels compared to those who were non-infected
   [[157]45, [158]60]. In addition to IL10, we showed up-regulation of
   SOCS1 and SOCS3, which form part of the IFN signalling pathways. This
   finding concurs with a previous study showing that septic preterm/term
   infants had up-regulated SOCS1 that functioned in immune inhibitory
   signalling [[159]17]. SOCS1 and SOCS3 code for the suppressor of
   cytokine signalling (SOCS) family of proteins that play a critical role
   in the maintenance of immune homeostasis [[160]61]. SOCS proteins,
   which can be induced by cytokines (e.g. IL-10) and bacterial products
   (e.g. lipopolysaccharide, LPS), negatively regulate the signalling of
   cytokines, such as type I IFNs, through direct inhibitory modulation of
   cytokine receptors via the JAK-STAT pathway. This in turn has profound
   regulatory effects on inflammatory responses and immunity that affects
   a range of immune cells including T cells, macrophages and neutrophils
   [[161]48, [162]62–[163]65]. Although the direct mechanisms of the SOCS
   family of proteins have not been established for neonatal sepsis, our
   findings suggest a potential role for SOCS1 and SOCS3 in dampening
   interferon responses. In particular, IL-10 may inhibit IFN-γ signalling
   and subsequent IFN-γ production via SOCS1, resulting in undetectable
   IFN-γ protein. The role of SOCS1 and SOCS3 for immunomodulation during
   neonatal sepsis warrants further investigation.

   Another marker of sepsis in both neonates and adults that was
   up-regulated here is S100A12 (also called calgranulin C), which is an
   alarmin involved in regulating innate immune/inflammatory responses
   [[164]41, [165]42]. S100A12 is overexpressed during inflammation and
   might serve as a general marker of inflammatory diseases, as well as
   one of the markers of sepsis immunosuppression through endotoxin
   tolerance [[166]43].

   Overall, our findings are consistent with an emerging view describing
   neonatal sepsis pathophysiology as being characterised by simultaneous
   hyper- and hypo-inflammatory immune responses [[167]15] and suggest
   that impaired immuno-regulation may be a key feature of LOS in very
   preterm infants.

   The regulation of immune responses and maintenance of immune
   homeostasis also depends on tight control of programmed cell death, an
   essential cellular self-destruction mechanism to ensure tissue
   homeostasis and elimination of damaged and/or infected cells [[168]66].
   In confirmed LOS, we found down-regulation of BCL2 and IL-7R, two key
   genes important for controlling apoptosis [[169]67, [170]68]. In two
   separate mouse models, the over-expression of BCL2 during sepsis was
   protective against the death of both lymphoid (thymocytes and splenic T
   cells) or myeloid cells (dendritic cells and macrophages), and was
   important for host immune responses during sepsis [[171]69, [172]70].
   Accordingly, the significant down-regulation of BCL2 could potentially
   contribute to increased cell death of immune cells important for host
   defences during confirmed LOS. Our study did not include examination of
   the frequencies of the cell populations in blood, however, based on our
   transcriptional findings and the presently limited understanding of
   BCL2 for sepsis pathophysiology [[173]71], investigation of the effects
   of BCL2 for regulation of immune cell death during neonatal sepsis is
   warranted. We also found down-regulation of IL-7R, the receptor
   important for mediating the activity of IL-7, a hematopoietic growth
   factor crucial for immune system development and lymphocyte survival
   [[174]68, [175]72]. In experimental models of sepsis, IL-7 was
   responsible for regulating BCL2 to block lymphocyte apoptosis,
   restoring IFN-γ production and improving recruitment of immune cells to
   the infection site [[176]73, [177]74]. Further, lymphocyte depletion
   has been reported in post-mortem EOS and LOS studies of preterm and
   term infants, with results suggesting that severe neonatal sepsis could
   be associated with sepsis-induced immune cell apoptosis [[178]15,
   [179]75–[180]77]. Therefore, the disruption of IL-7/IL-7R signalling
   during confirmed LOS has potentially detrimental implications for
   regulation of immune and cellular homeostasis that contributes to
   sepsis pathophysiology. The potential of using IL-7 as a
   lymphostimulating therapy for septic patients has been described in
   previous studies [[181]73, [182]74] and could also be explored in the
   context of neonatal sepsis. We found an intersection between host
   immune responses and metabolism at the transcriptional level in infants
   with LOS. Infants with confirmed LOS had multiple changes in gene
   expression and pathways associated with cholesterol metabolism and
   biosynthesis. Several genes identified as up-regulated (SQLE, IDI1,
   DHCR7 and LDLR) in infants with confirmed LOS were consistent with a
   study of preterm and term infants that also found transcriptional
   changes to these genes involved in cholesterol biosynthesis and
   homeostasis during neonatal sepsis [[183]17]. Specifically, DHCR7, IDI1
   and LDLR were also found to be up-regulated during bacterial sepsis in
   VLBW infants [[184]16]. The cross-regulation between metabolism and the
   immune system is increasingly recognised as important for host
   responses during infection [[185]13, [186]17, [187]78].

   Numerous studies in both humans and mouse models have explored the role
   of cholesterol, both high-density lipoproteins (HDL) and low-density
   lipoproteins, in relation to sepsis [[188]79–[189]86]. Adult patients
   with severe sepsis typically have lower cholesterol levels, and high
   cholesterol levels are suggested to be important for protection during
   sepsis, whereby lipoproteins play a crucial role in regulating immune
   responses to infection such as controlling pro-inflammatory cytokine
   release, exerting anti-inflammatory effects by neutralising LPS and
   facilitating clearance of bacterial toxin [[190]79, [191]84, [192]87].
   Our study did not measure total cholesterol, HDL or low-density
   lipoproteins levels, however, LOS has been linked to lower total
   cholesterol and HDL levels in preterm and term infants, with impaired
   LPS neutralisation and effects of cytokines suggested to contribute to
   reduced HDL levels [[193]86, [194]88].

   The metabolism sub-network identified demonstrated connections between
   metabolic (SREBF2, INSIG1 and ERLIN2), regulatory (PPARG and CEBPB) and
   inflammatory (IL1B) genes, which were all up-regulated, suggesting
   potential inflammasome activation by sterols during confirmed LOS in
   our study [[195]38]. Acute infections and sepsis have been linked to
   diminished ability of HDL to mediate cholesterol efflux from
   macrophages to plasma [[196]38, [197]89], which could explain lower
   total plasma cholesterol and HDL levels during LOS reported in a
   preterm and term infant study [[198]86]. Specifically, this leads to
   cholesterol accumulation in macrophages, which promotes inflammasome
   activation that leads to IL-1β production, and that drive inflammatory
   responses during microbial infection [[199]34, [200]35, [201]38].
   Although altered cholesterol homeostasis could contribute to
   pro-inflammatory responses, we did not find significantly increased
   plasma IL-1β concentrations in infants with confirmed LOS compared to
   infants with no LOS at the protein-level. In accordance with a study of
   neonatal monocytes, this could be due to low expression of NLRP3 in
   very preterm infants, where NLRP3 has a central role in regulating
   inflammasome activation and subsequent IL-1β secretion [[202]38,
   [203]90].

   We also found over-representation of the oxygen-dependent proline HIF1A
   pathway and up-regulation of HIF1A (transcription factor
   HIF1α)[[204]91]. Our findings were consistent with studies showing
   significant overexpression of HIF1A during EOS in preterm infants (<32
   weeks gestation) [[205]92] and during bacterial sepsis in very preterm
   infants [[206]16]. The induction of HIF1α in response to
   sepsis-associated cytopathic hypoxia has also been shown in previous
   human adult and mouse sepsis studies [[207]33, [208]93–[209]96].
   Importantly, HIF1α plays a crucial role in the metabolic switch from
   oxidative phosphorylation to glycolysis to meet increased energy
   demands for inflammatory responses during sepsis [[210]33, [211]91,
   [212]97]. Further, HIF1α induces genes for proteins associated with
   glycolytic processes including lactate dehydrogenase (LDHA) and MCT4
   (SLC16A3) [[213]33, [214]91], where both genes were up-regulated in
   infants with confirmed LOS. Therefore, consistent with previous sepsis
   studies [[215]16, [216]33, [217]91, [218]97, [219]98], our findings
   suggest that LOS is also associated with a shift towards glycolysis to
   meet the increased energy requirements for host immune responses in
   very preterm infants.

   Finally, we found that host immune responses of infants with possible
   LOS could not be clearly distinguished from those with either confirmed
   LOS or no LOS–there were no differentially expressed genes between
   infants with possible LOS or no LOS, and only 21 differentially
   expressed genes when compared to infants with confirmed LOS. The small
   sample size of infants with possible LOS could potentially contribute
   to the lack of differentially expressed genes identified in comparison
   to infants with confirmed and no LOS. However, at the protein-level,
   infants with possible LOS did have significantly higher plasma levels
   of IL-6 and IL-10 compared to infants with no LOS, and similar levels
   to infants with confirmed LOS. Infants with possible LOS were
   identified by their elevated CRP levels in the absence of positive
   blood cultures. The production of plasma CRP by hepatocytes is
   controlled in part by IL-6 [[220]44] and consistent with a study of
   preterm infants with suspected sepsis, our findings are consistent with
   those showing a positive relationship between CRP and IL-6 plasma
   levels [[221]45]. We identified several genes associated with IL-6
   (e.g. NFKBIA, MYD88, IRAK3 and IRKA4) and IL-10 (e.g. IL10RA and IL10)
   production, although these were not significantly different in infants
   with possible LOS compared to those with no LOS which could be due to
   the differential expression criteria used and the small sample size of
   infants with possible LOS. The septic state of infants with possible
   LOS among a population of infants with suspected LOS remains
   challenging due to ambiguous host responses and examination of
   additional systems-level responses, such as at the metabolic levels,
   could allow us to better comprehend the underlying functional biology
   associated with possible LOS [[222]99–[223]101].

Limitations

   The limitations of our study included small sample sizes with no formal
   power calculations and no inclusion of the severity of illness score to
   describe preterm infants with LOS within this analysis [[224]102],
   however, we have begun collecting this information and will report it
   in future studies. Our study demonstrates the ability to use RNA from
   small blood volumes (less than 0.5 mL) for transcriptional profiling at
   the systems-level using an unbiased RNA-Seq approach. The capacity to
   stabilise nucleic acids at the time of sample collection from low
   volumes of blood is also clinically relevant due to the difficulties
   associated with obtaining large blood volumes from neonates, especially
   those born preterm [[225]16].

Conclusion

   We found that confirmed LOS is characterised by host immune
   transcriptional responses that reflect unbalanced immunometabolic
   homeostasis. The findings of this study represent a starting point for
   systems-level investigations of neonatal sepsis in very preterm infants
   and warrant validation in a statistically powered study. Investigation
   of LOS pathophysiology at the systems-level could lead to
   identification of novel gene signatures and gene products that could
   improve diagnosis of neonatal sepsis.

Supporting information

   S1 Table. Metadata of study participants.

   (CSV)
   [226]Click here for additional data file.^ (2KB, csv)
   S2 Table. Differentially expressed gene list between confirmed LOS and
   no LOS groups.

   (CSV)
   [227]Click here for additional data file.^ (155KB, csv)
   S3 Table. Differentially expressed gene list between confirmed LOS and
   possible LOS groups.

   (CSV)
   [228]Click here for additional data file.^ (3.6KB, csv)
   S4 Table. Differential cell count analysis between clinical groups.

   (DOCX)
   [229]Click here for additional data file.^ (17.3KB, docx)
   S5 Table. Genes associated with over-represented pathways identified
   using the Sigora gene-pair signature method.

   (DOCX)
   [230]Click here for additional data file.^ (19.7KB, docx)

Acknowledgments