1. Immunology and Inflammation

The leukocyte non-coding RNA landscape in critically ill patients with sepsis

  1. Brendon P Scicluna  Is a corresponding author
  2. Fabrice Uhel
  3. Lonneke A van Vught
  4. Maryse A Wiewel
  5. Arie J Hoogendijk
  6. Ingelore Baessman
  7. Marek Franitza
  8. Peter Nürnberg
  9. Janneke Horn
  10. Olaf L Cremer
  11. Marc J Bonten
  12. Marcus J Schultz
  13. Tom van der Poll
  14. Molecular Diagnosis and Risk Stratification in Sepsis (MARS) consortium
  1. Amsterdam UMC, University of Amsterdam, Center for Experimental Molecular Medicine, Amsterdam Infection & Immunity, Netherlands
  2. Amsterdam UMC, University of Amsterdam, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Netherlands
  3. Cologne Center for Genomics, University of Cologne, Germany
  4. Center for Molecular Medicine Cologne, University of Cologne, Germany
  5. Amsterdam UMC, University of Amsterdam, Department of Intensive Care Medicine, Netherlands
  6. Department of Intensive Care, University Medical Center Utrecht, Netherlands
  7. Department of Medical Microbiology, University Medical Center Utrecht, Netherlands
  8. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Netherlands
  9. Amsterdam UMC, University of Amsterdam, Division of Infectious Diseases, Netherlands
https://doi.org/10.7554/eLife.58597
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Cite this article
  1. Brendon P Scicluna
  2. Fabrice Uhel
  3. Lonneke A van Vught
  4. Maryse A Wiewel
  5. Arie J Hoogendijk
  6. Ingelore Baessman
  7. Marek Franitza
  8. Peter Nürnberg
  9. Janneke Horn
  10. Olaf L Cremer
  11. Marc J Bonten
  12. Marcus J Schultz
  13. Tom van der Poll
  14. Molecular Diagnosis and Risk Stratification in Sepsis (MARS) consortium
(2020)
The leukocyte non-coding RNA landscape in critically ill patients with sepsis
eLife 9:e58597.
https://doi.org/10.7554/eLife.58597
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Abstract

The extent of non-coding RNA alterations in patients with sepsis and their relationship to clinical characteristics, soluble mediators of the host response to infection, as well as an advocated in vivo model of acute systemic inflammation is unknown. Here we obtained whole blood from 156 patients with sepsis and 82 healthy subjects among whom eight were challenged with lipopolysaccharide in a clinically controlled setting (human endotoxemia). Via next-generation microarray analysis of leukocyte RNA we found that long non-coding RNA and, to a lesser extent, small non-coding RNA were significantly altered in sepsis relative to health. Long non-coding RNA expression, but not small non-coding RNA, was largely recapitulated in human endotoxemia. Integrating RNA profiles and plasma protein levels revealed known as well as previously unobserved pathways, including non-sensory olfactory receptor activity. We provide a benchmark dissection of the blood leukocyte ‘regulome’ that can facilitate prioritization of future functional studies.

Introduction

Sepsis is a multifaceted syndrome that develops as the consequence of an abnormal host response to infection leading to organ failure and high risk of death (Angus and van der Poll, 2013; Cecconi et al., 2018). It is estimated that 2–5 million deaths worldwide are attributable to sepsis (Fleischmann et al., 2016). Despite empirical antimicrobial therapy and advances in intensive care, it is expected that sepsis will remain a major healthcare problem. As such, sepsis has been recognized as a global health priority in 2017 by the World Health Assembly and WHO (World Health Organization, 2017). In spite of more than 100 clinical trials having evaluated drugs targeting specific components of the host response to infection (Marshall, 2014), no specific treatment for sepsis has been approved (Angus and van der Poll, 2013; Cecconi et al., 2018). This argues for a deeper understanding of sepsis immunopathology to identify veritable drug targets (Marshall, 2014; Tse, 2013).

Protein-coding RNA expression profiling of blood leukocytes from sepsis patients has helped to broaden our understanding of sepsis immunopathology (van der Poll et al., 2017), for example, by unmasking defects in leukocyte energy metabolism of sepsis patients (Cheng et al., 2016), and by classifying sepsis patients as transcriptomic endotypes with prognostic and pathophysiological value (Scicluna et al., 2017; Davenport et al., 2016; Wong et al., 2009). From fruit flies to man, the protein-coding part of genomes from different species is remarkably similar in numbers and functions (Liu et al., 2013), which suggests that numerous aspects of complex biology in eukaryotes might stem from non-protein-coding regions of the genome. The increase in genomic coverage of tiled microarrays and massive cDNA sequencing undertaken by the Functional Annotation of the Mammalian genome (FANTOM) consortium revealed pervasive transcription outside of the known gene loci (Kapranov et al., 2002; Carninci et al., 2005). Moreover, such studies facilitated the demonstration that non-coding RNAs were under negative evolutionary selection, which implied functionality rather than plain ‘transcriptional noise’ (Ponjavic et al., 2007). Indeed, a substantial proportion of non-coding RNA, by general convention defined as long (>200 nucleotides) or small (<200 nucleotides) non-coding RNAs, yields clear phenotypic effects in both in vitro and in vivo functional studies (Zhu et al., 2016; Gebert and MacRae, 2019; Atianand et al., 2016; Carpenter et al., 2013). Ever-growing numbers of small non-coding RNAs, for example micro (mi)RNAs (20–24 nucleotides), or long non-coding RNAs such as long intergenic non-coding (linc)RNAs, have been linked to human diseases (Bao et al., 2019; Esteller, 2011). An important aspect of non-coding RNAs is their capacity for precise regulation of cellular biological processes via epigenetic mechanisms, including complex immune system processes (Carpenter, 2018; Atianand and Fitzgerald, 2014; Mehta and Baltimore, 2016).

Knowledge of the non-coding RNA landscape in patients with sepsis is limited. Here we report a comprehensive screen of non-coding RNA expression patterns in blood leukocytes of patients with sepsis and their relation to clinical characteristics and soluble mediators of the host response. In addition, by using a guilt-by-association approach we positioned non-coding RNAs in network modules encompassing protein-coding RNA reflecting distinct cellular biological pathways.

Results

Protein-coding and non-coding blood transcriptomes

In order to build a comprehensive map of RNA expression in the context of sepsis, we evaluated protein-coding, long and small non-coding RNA expression in whole blood leukocytes from 156 sepsis patients and 82 healthy subjects (median age (Q1–Q3), 54 (42 – 60); 26% male). Patient characteristics are tabulated in Table 1, causative pathogens in Supplementary file 1. Principal component (PC) analysis of the most abundant protein-coding RNAs (n = 18,063) and long non-coding RNAs (n = 16,087) showed clear partitioning of patients with sepsis distinct from healthy subjects (Figure 1A). In contrast, small non-coding RNAs (n = 4949) showed only minimal separation between patients and healthy subjects. We observed similar patterns after calculating the molecular distance to health (MDTH)(Berry et al., 2010; Dunning et al., 2018) index, a measure of transcript-level expression perturbation relative to health, with significantly higher MDTH indices in sepsis (Figure 1B). Notably, long non-coding RNA transcripts exhibited the broadest expression perturbations in healthy participants and sepsis patients, exemplified by the highest overall MDTH indices (Figure 1B).

Table 1
Baseline characteristics and outcomes of critically ill patients with sepsis.
ParameterSepsis patients
(n = 156)
Age, years62 (50 - 70)
Male sex98 (62.8)
White ethnicity140 (89.7)
Medical admission117 (75.0)
Immune suppression45 (28.8)
Cardiovascular insufficiency43 (27.6)
Malignancy45 (28.8)
Renal insufficiency18 (11.5)
Respiratory insufficiency37 (23.7)
Charlson comorbidity index4 (2 - 6)
APACHE IV score72 [58 - 92]
SOFA score7 (4 - 9)
Shock86 (55.1)
Mechanical ventilation128 (82.1)
Primary diagnosis
Pneumonia99 (63.5)
Community-acquired68 (43.6)
Hospital-acquired31 (19.9)
Abdominal sepsis57 (36.5)
Outcome
28-day mortality48 (30.8)
90-day mortality59 (37.8)
1-year mortality77 (49.4)

  1. Data presented as median [Q1–Q3], or n (%).

    Abbreviations: APACHE, Acute Physiology and Chronic Health Evaluation; ICU, Intensive care unit; GI, gastrointestinal; SOFA, Sequential Organ Failure Assessment.

Table 1—source data 1

Baseline characteristics and outcomes of critically ill patients with sepsis.

https://cdn.elifesciences.org/articles/58597/elife-58597-table1-data1-v2.txt
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Coding and non-coding RNA expression in leukocytes of sepsis patients and healthy individuals.

(A) Principal component (PC) plot depicting PC1 and PC2, and (B) the molecular distance to health (MDTH) index of protein-coding (n = 18,063), long non-coding (n = 16,087), and small non-coding RNAs (n = 4949) in healthy subjects and sepsis patients. **p<0.01; ***p<0.001. (C) Volcano plot representation of differences in coding and non-coding RNA expression between sepsis patients and healthy subjects. Horizontal (black) line denotes −log10 transformed adjusted p-value of 0.01. (D) Pie chart showing the subclass distribution of significant long non-coding RNA (adjusted p<0.01). LincRNA, long intergenic non-coding RNA; rRNA, ribosomal RNA; TEC, to be experimentally confirmed; Mt tRNA, mitochondrial transfer RNA; Mt rRNA, mitochondrial ribosomal RNA. (E) Pie chart showing the subclass distribution of significant small non-coding RNA (adjusted p<0.01). miRNA, microRNA; snoRNA, small nucleolar RNA; C/D box snoRNA, C/D box small nucleolar RNA; H/ACA box snoRNA, H/ACA box small nucleolar RNA; scaRNA, small cajal body-specific RNA.

Comparing sepsis patients to healthy subjects identified 15,097, 13,158, and 635 significantly altered (adjusted p-value <0.01) protein-coding, long non-coding, and small non-coding RNAs, respectively (Figure 1C). Ingenuity pathway analysis of the significantly altered protein-coding RNA transcripts revealed associations with various canonical signaling pathways that included elevated pro- and anti-inflammatory pathways, cell cycle, DNA damage response, and metabolic pathways (Figure 1—figure supplement 1). Transcripts with reduced expression were predominantly associated with T helper cell activation, antigen presentation, and B cell responses. Results on protein-coding RNA profiles are in agreement with previous reports from our and other groups (van der Poll et al., 2017). LincRNAs, antisense, and pseudogene RNA transcripts represented the most highly altered long non-coding RNA biotypes in sepsis relative to health (Figure 1D). Micro (mi)RNAs, stem loop RNAs, and small nucleolar (sno)RNAs were the most abundant small non-coding RNA biotypes (Figure 1E).

Protein-coding and non-coding blood transcriptomes, demographics, and clinical characteristics

In order to understand inter-individual variation in RNA expression profiles, we set out to determine the contribution of demographics and clinical characteristics to protein-coding and non-coding RNA expression variation in sepsis patients (Figure 2), as well as healthy subjects. Using a variance partition (multivariable) approach (Hoffman and Schadt, 2016), differences in gender and age of healthy subjects explained 5%, 4%, and 4% of the variation in protein-coding, long non-coding, and small non-coding RNA expression, respectively (Figure 2—figure supplement 1A). Specific transcripts had high percentages of explainable variance, in particular long non-coding RNAs against gender. Not surprisingly, expression of long non-coding RNAs positioned on the X and Y chromosomes, for example TXLNGY, LINC00278, and XIST had 98%, 97%, and 94% of variance explained by gender, respectively (Figure 2—figure supplement 1B). In sepsis patients, a multivariable model that incorporated demographics and common clinical characteristics, including APACHE IV, SOFA scores, shock and Charlson comorbidity indices, cumulatively explained 18%, 13%, and 8% of protein-coding, long non-coding, and small non-coding RNA expression variance, respectively (Figure 2A). Specifically, sepsis primary site of infection (lung or abdomen) and place of acquisition (community or hospital) explained the highest proportion of variation in protein-coding (6.7%) and long non-coding (4.4%) RNA expression (Figure 2A). Despite overall low proportions of variance explained, outlier RNA transcripts could be detected. For example, some specific transcripts demonstrated high individual explained variance against primary sepsis diagnosis, including protein-coding RNA encoding basic leucine zipper and W2 domains 1 (BZW1); long non-coding RNA SUMO2 pseudogene 1 (SUMO2P1); and small non-coding RNA miRNA hsa-miR-7855–5 p (Figure 2B). Septic shock explained low proportions of variation in RNA expression (Figure 2A), and directly comparing patients with septic shock to patients without shock resulted in 837 and 80 significantly altered protein-coding and long non-coding RNA, respectively (Figure 2C). High expression protein-coding RNA included matrix metalloproteinase 8 (MMP8), resistin (RETN), and lipocalin 2 (LCN2). Low expression protein-coding RNA included a Na+/Ca2+ exchanger (SLC8A1), membrane metalloendopeptidase (MME), and interleukin (IL-) six receptor (IL6R). Long non-coding RNA included lincRNA lung cancer-associated transcript 1 (LUCAT1; low expression) and antisense RNA (LRRC75A-AS1; high expression) (Figure 2C). No significant alterations were identified in small non-coding RNA expression profiles. Evaluating RNA expression in patients discordant for survival after 28 days identified 146 significantly altered protein-coding RNA (Figure 2—figure supplement 1C). No significant differences were uncovered in non-coding RNA expression profiles, suggesting that non-coding RNA profiles obtained on ICU admission may not be suitable as mortality predictors.

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Variance in coding and non-coding RNA expression attributed to demographics and clinical characteristics of sepsis patients.

(A) Violin plots of percent variation in protein-coding, long non-coding, and small non-coding RNA expression explained by sepsis patient demographics and clinical variables. Black dots depict outlier RNA transcripts. (B) Percent variance of select protein-coding and long non-coding RNA partitioned into the segment attributable to each demographic and clinical variable ranked by percent variation (>20%) for primary diagnosis (site of infection and place of acquisition). (C) Volcano plots depicting the changes in protein-coding and long non-coding RNA in patients discordant for septic shock on ICU admission. Horizontal (black) line denotes the adjusted p-value threshold for significance (adjusted p≤0.01). Abbreviations: BC+, blood culture positive microbiology; diagnosis, infection site (lung or abdomen) and source (community or hospital); Charlson, Charlson comorbidity index; Apache IV, Acute Physiology and Chronic Health Evaluation; ICU, Intensive care unit; SOFA, Sequential Organ Failure Assessment.

Protein-coding and non-coding RNA profiles of sepsis patients relative to human endotoxemia

Previous studies have compared the protein-coding RNA response in patients with sepsis or trauma (non-septic) to the response after lipopolysaccharide (LPS) administration to healthy volunteers in a controlled clinical setting (human endotoxemia) (Cheng et al., 2016; Calvano et al., 2005; Scicluna et al., 2013; Perlee et al., 2018; Seok et al., 2013; Xiao et al., 2011; Takao and Miyakawa, 2015). Here we sought to extend on those observations by evaluating long and small non-coding RNA expression in sepsis relative to temporal leukocyte responses in human endotoxemia (Figure 3). As previously reported in this model (Cheng et al., 2016; Calvano et al., 2005; Scicluna et al., 2013; Perlee et al., 2018), robust alterations in protein-coding RNA expression were noted after 2, 4, and 6 hr of LPS administration (Figure 3—figure supplement 1). Fold expression in sepsis (relative to health) was directly correlated with fold expression after 2, 4, and 6 hr LPS (Figure 3A). Long non-coding RNA expression was robustly altered in endotoxemia, with 2361, 5053, 2925, and 43 significant differences after 2, 4, 6, and 24 hr endotoxemia, respectively (Figure 3—figure supplement 2A). Pseudogenes, lincRNA, and antisense RNA were the most abundant long non-coding RNA biotypes (Figure 3B). Small non-coding RNA were modestly altered in human endotoxemia (Figure 3—figure supplement 2B). The most abundant biotypes of small RNA were miRNA (Figure 3C). Comparing fold expression in sepsis (relative to health) to human endotoxemia revealed significant correlations after 2, 4, and 6 hr of endotoxemia (Figure 3D). The highest r2 was found for sepsis and 4 hr post-LPS (r2 = 0.51). Correlation analysis of small RNA fold expression during endotoxemia against fold expression in sepsis revealed indirect correlations (Figure 3E).

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Comparison of the coding and non-coding transcriptome in sepsis to human endotoxemia.

(A) Dot plots depicting the correlation between protein-coding RNA fold expression indices in sepsis (compared to health) and fold expression after 2, 4, 6, and 24 hr lipopolysaccharide (LPS) infusion relative to pre-LPS. (B) Pie chart illustrating the biotypes of significantly altered long non-coding RNA (adjusted p<0.01) across endotoxemia time points (2, 4, 6, and 24 hr after 2 ng/kg LPS). LincRNA, long intergenic non-coding RNA; rRNA, ribosomal RNA; TEC, to be experimentally confirmed; Mt tRNA, mitochondrial transfer RNA; Mt rRNA, mitochondrial ribosomal RNA. (C) Pie chart showing the biotypes of significantly altered small non-coding RNA (adjusted p<0.05) in human endotoxemia. miRNA, microRNA; snoRNA, small nucleolar RNA; C/D box snoRNA, C/D box small nucleolar RNA; H/ACA box snoRNA, H/ACA box small nucleolar RNA; scaRNA, small cajal body-specific RNA. (D) Dot plots illustrating the correlation between long non-coding RNA fold expression indices in sepsis (compared to health) and fold expression of 2, 4, 6, and 24 hr after LPS relative to pre-LPS. rho, Spearman’s coefficient. (E) Dot plots depicting the correlation between small non-coding RNA fold expression indices in sepsis (compared to health) and 2, 4, 6, and 24 hr after LPS relative to pre-LPS. rho, Spearman’s coefficient.

Functional inference of non-coding RNA

To better understand the functional organization of the non-coding leukocyte transcriptome in sepsis, particularly long non-coding RNA, we undertook a guilt-by-association approach. On the basis of a bi-weight midcorrelation matrix of the most variable protein-coding and long non-coding RNA (n = 8539; coefficient of variation >5%) in sepsis patients only (Figure 4), a weighted network was built with scale-free topology (Figure 4—figure supplement 1A; Langfelder and Horvath, 2008; Langfelder and Horvath, 2012; Scicluna et al., 2015a). Hierarchical clustering uncovered 23 network modules (clusters) each harboring more than 100 inter-correlating RNA transcripts (Figure 4A and Figure 4—figure supplement 1B). Of the 8539 RNA transcripts, 158 transcripts did not cluster (designated as a gray module). Seventeen modules were associated with specific gene ontologies or canonical signaling pathways that included cell death/olfactory receptor activity/cell-cycle G2/M DNA damage checkpoint and regulation (turquoise module, n = 1001 transcripts) and RNA biosynthesis/RNA binding (yellow module, n = 579 transcripts) (Figure 4A). Eight modules in the co-expression network were significantly enriched for long non-coding RNA relative to protein-coding RNA (Fisher’s adjusted p<0.01; Figure 4B). This suggests that the leukocyte long non-coding transcriptome of sepsis patients is primarily co-expressed with protein-coding RNA, but 34% of non-coding RNA modules were organized into distinct units. Evaluation of total and intra-module connectivities, which measure the importance of each module relative to the overall structure of co-expression networks (Langfelder and Horvath, 2008), identified two ‘driver’ modules, namely the cell death/olfactory receptor activity/cell-cycle G2/M DNA damage checkpoint and regulation (turquoise module, n = 1001 transcripts) and RNA biosynthesis/RNA binding (yellow module, n = 579 transcripts) modules (Figure 4C and D and Figure 4—figure supplement 1C). The former module included protein-coding RNA encoding ATM serine/threonine kinase (ATM), TNF alpha-induced protein 3 (TNFAIP3 or A20), histone deacetylase 2 (HDAC2), and mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1) paracaspase (Figure 4D). Non-coding RNA included GABPB1-AS1, THAP9-AS1, and SCARNA9. We subsequently focused our attention on integrating miRNA profiles to the co-expression network. Considering miRNA profiles that were significantly altered in sepsis patients relative to health (Figure 1C), and miRNA-to-gene interactions (miRWalk method), we detected 49 small RNAs in five network modules with explained variance estimated >20%, including hsa-miR-200c-3p (translation initiation module), SNORD84 (regulation of cytokine secretion/Toll-like receptor [TLR] signaling module), HBII-276 (translation initiation module), hsa-miR-1275 (sensory perception of chemical stimulus/olfactory receptor activity module), and hsa-miR-664b-3p (neutrophil degranulation/extracellular exosome module) (Figure 4E). Of note, hsa-miR-200c-3p has been shown to modify TLR4 signaling efficiency dependent on MYD88-mediated pathways in an embryonic kidney cell line (HEK293) (Wendlandt et al., 2012).

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Network analysis of coding and non-coding RNA expression.

(A) Circular plot of protein-coding and long non-coding co-expression network modules characterized by significantly associated (Fisher’s adjusted p<0.01) gene ontologies and Ingenuity canonical signaling pathways. Seventeen modules were associated with specific ontologies or canonical signaling pathways. (B) Bar plot depicting the distribution of protein coding and long non-coding RNA in each network module. *Fisher’s Benjamini–Hochberg adjusted p<0.01. (C) Dot plot illustrating the correlation between intramodular and total connectivities of each RNA transcript in their respective network module. Yellow dots illustrate protein-coding and long non-coding RNA in the RNA biosynthesis/RNA binding module; Turquoise dots depict the cell death and olfactory receptor activity module. (D) Diagrammatic representation of Ingenuity’s biofunctions (z-score <2 or >2 and adjusted p<0.05) together with predicted long intergenic non-coding RNA (lincRNA) and antisense RNA in the cell death/olfactory receptor activity/cell-cycle G2/M DNA damage checkpoint and regulation module (turquoise). Blue, reduced expression; red, elevated expression in sepsis relative to health (fold change ≥1.2 or ≤−1.2; adjusted p-value<0.01). (E) Violin plots of network module eigengene (first principal component) percent variance attributable to small non-coding RNA.

Next, we evaluated the association of network modules with soluble mediators of the host response and clinical severity scores. Neutrophil degranulation (secretory; red), protein ubiquitination (pink), and mitotic cell cycle (tan) modules correlated with soluble mediators of inflammation (C reactive protein [CRP], IL-6, IL-10, IL-8), endothelial responses (E-selectin and angiopoietin-2 [ANG2]), coagulation (D-Dimer), and clinical variables of disease severity (Figure 5A). In contrast, antigen presentation/Th1-Th2 cell activation (green module), regulation of cytokine secretion/TLR signaling (black module), and type-I interferon signaling/double stranded RNA binding (salmon module) were indirectly correlated with various soluble mediators and clinical severity indices. Patients with septic shock showed significantly higher neutrophil degranulation (secretory) expression patterns (Figure 5B). Protein-coding RNA transcripts in the neutrophil degranulation (secretory) module included matrix metalloproteinases (MMP8 and MMP9), neutrophil activation cluster of differentiation 177 (CD177), lipocalin 2 (LCN2), and arginase 1 (ARG1) (Figure 5C). LincRNA and antisense RNA included an inducer of differentiation MYOSLID (myocardin-induced smooth muscle LncRNA, inducer of differentiation), cell proliferation, and metastasis-associated antisense RNA of the titin gene (TTN-AS1) and an IL10 receptor beta subunit antisense RNA, IL10RB-AS1. Calculating intra-modular connectivities enabled us to define ‘hub’ transcripts, which are understood to represent cogs in the functional output of a network module (Langfelder and Horvath, 2008; Zhao et al., 2010), and identified MYOSLID (neutrophil degranulation; red module) and LUCAT1 (Lung Cancer Associated Transcript 1) in the TLR-signaling (black) module, as module ‘hubs’. In line with their respective module eigengene correlations with inflammatory response markers, MYOSLID expression was directly correlated with levels of inflammatory response markers IL-6, IL-8, IL-10, and acute phase response protein CRP (Figure 5D). In contrast, LUCAT1 expression was indirectly correlated with soluble mediators of inflammation, except for CRP (Figure 5E).

Figure 5
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Relationship of protein-coding, non-coding RNA network modules to soluble mediators and clinical severity.

(A) Heatmap representation of Pearson correlation coefficients (adjusted p<0.05) calculated for each network module eigengene (first principal component) against soluble mediators of inflammation, endothelial function, coagulation, as well as clinical parameters of disease severity. APACHE IV, Acute Physiology and Chronic Health Evaluation; SOFA, Sequential Organ Failure Assessment. Red denotes direct correlations and blue denotes indirect correlations (B) Boxplot showing differences in neutrophil degranulation (red) module eigengene values in sepsis patients discordant for septic shock on intensive care unit admission. High module eigengene values mean overall elevated RNA expression; low module eigengene values mean reduced expression. (C) Diagrammatic representation of the neutrophil degranulation (secretory; red) module (Ingenuity’s biofunction z-score <2 or >2; adjusted p<0.05) together with predicted long intergenic non-coding RNA (lincRNA) and antisense RNA. Red or blue nodes denote high or low expression RNA transcripts in sepsis relative to health, respectively. ***Mann–Whitney p<0.001. (D and E) Dot plots of (D) MYOSLID expression and (E) LUCAT1 expression against soluble mediators of inflammation IL-6, IL-8, and IL-10, as well as the acute phase response protein CRP. Rho, Spearman’s coefficient.

Discussion

In this study we found that the transcriptional changes in critically ill patients with sepsis are not exclusive to protein-coding RNAs. Whole blood long non-coding RNAs, and to a lesser extent small non-coding RNAs, were significantly altered in sepsis patients relative to healthy subjects. The pattern of protein-coding and long non-coding RNA profiles in sepsis was mimicked by expression profiles in a human endotoxemia model, notably at a time point indicative of endotoxin tolerance. Small non-coding RNA profiles in sepsis patients were not recapitulated in human endotoxemia. In general, common clinical characteristics explained low proportions of variation in protein-coding and non-coding RNA profiles, suggesting that variation in leukocyte responses are largely not explained by clinical parameters. Leveraging on the concepts of network biology, protein-coding and non-coding RNA were clustered as functional biological units with RNA binding/RNA biosynthesis and cell death/olfactory receptor activity/cell-cycle G2-M DNA damage checkpoint and regulation modules central to network architecture.

Advances in genomics, notably massively parallel cDNA sequencing, have shown that active transcription is not exclusive to protein-coding RNA regions (Carninci et al., 2005). Regions of the genome void of protein-coding genes have since been shown to be actively transcribed in the context of various diseases (Esteller, 2011). Small non-coding RNAs, mainly microRNAs, as well as long non-coding RNAs were linked to specific immune processes (Mehta and Baltimore, 2016; Fitzgerald and Caffrey, 2014). While microRNAs have been established as veritable epigenetic modifiers of transcriptional outputs, studies on the functional aspects of long non-coding RNAs have only recently begun. However, those studies were centered primarily on mouse models (Atianand et al., 2016; Carpenter et al., 2013). This presents a problem for translation to human physiology because non-coding RNA sequences are typically not conserved between species (Diederichs, 2014). Furthermore, expression of non-coding RNAs was shown to exhibit substantially higher inter-individual variation in healthy subjects as compared to protein-coding RNAs alone (Kornienko et al., 2016). In line with those observations our data showed that long non-coding RNA expression patterns were far more variable across individuals (healthy or sepsis) than protein-coding and small non-coding RNAs. The sources of increased inter-individual variation in long non-coding RNA expression relative to protein-coding and small non-coding RNAs are as yet unknown. Lower conservation coupled with faster evolution rates of long non-coding RNA regions, which seemingly harbor more single nucleotide polymorphisms (SNPs) than protein-coding genes (Necsulea and Kaessmann, 2014), as well as the possibility of their relatively higher susceptibility to environmental and lifestyle factors (Dumeaux et al., 2010), may be at the basis of the extensive variation in long non-coding RNA expression.

In line with previous studies (Seok et al., 2013; Takao and Miyakawa, 2015), we found that protein-coding RNA alterations during endotoxemia mimicked those that ensue in sepsis patients. The human endotoxemia model is a highly relevant in vivo model of acute systemic inflammation in the context of a controlled clinical setting (Lowry, 2005). In general, the model is characterized by a robust systemic response, including leukocyte transcriptional responses, exhibiting shared and unique temporal changes that resolve within 24 hr of bolus administration (Calvano et al., 2005; Perlee et al., 2018). In extension to the previously reported data, based on a single time-point of human endotoxemia (Seok et al., 2013; Takao and Miyakawa, 2015), we found that the correlation between sepsis and human endotoxemia was also dependent, at least in part, on timing of the response to LPS. The highest correlation was found at 4 hr, a time point at which the capacity of cytokine production by leukocytes is typically reduced in the human endotoxemia model, indicative of endotoxin tolerance (Cheng et al., 2016; de Vos et al., 2009). Long non-coding RNA alterations in human endotoxemia also mimicked those in sepsis, with similar time dependencies as protein-coding RNA. In contrast, small non-coding RNA expression profiles in sepsis patients were not reliably recapitulated in human endotoxemia, primarily showing indirect correlations. This may be due to typically low expression patterns of miRNA, compared to protein-coding and long non-coding RNA, and reported high specificities of miRNA to developmental stage and cell-type (Bernstein et al., 2003). The host response during infection is characterized by a balance between resistance (seeking to limit the pathogen load) and tolerance (aiming to retain cell and organ functions) (Schneider and Ayres, 2008). In sepsis both mechanisms can become uncontrolled, wherein aberrant activation of resistance pathways results in tissue damage and inadequate tolerance can cause immune suppression with enhanced susceptibility to secondary infections (Bauer and Wetzker, 2020). While our time-sequential data in healthy humans injected with LPS suggest that coding and long non-coding RNA profiles in blood leukocytes of sepsis patients particularly reflect a tolerant state, time course studies in patients are needed to increase the insight into the role of distinct RNA species in the interplay between resistance and tolerance.

A substantial proportion of variance in protein-coding and non-coding RNA expression in critically ill patients with sepsis remained unexplained. Other sources of variation, not assessed in this study, include patient genetics and time between the onset of sepsis and ICU admission (Schadt et al., 2003; Maslove and Wong, 2014). The former represents an important source of inter-individual variation where SNPs segregating in populations are in part tightly related to RNA expression variability (Schadt et al., 2003). This was shown in a recent prospective study in sepsis due to community-acquired pneumonia (CAP), wherein SNPs influencing gene expression patterns were identified (Davenport et al., 2016). The time of onset of sepsis is a current ‘black box’ in the field as it cannot be accurately determined, thereby resulting in considerable uncertainty since patients are presumably admitted to the ICU at various stages of the sepsis syndrome. Overall, we determined that clinical characteristics and outcome explained low proportions of variation in RNA expression; however, specific protein-coding and long non-coding RNA transcripts had high percent variation attributable to, particularly, primary diagnosis that included infections site (lung or abdomen) and place of acquisition (community or hospital), which may constitute important proxies to discern organ-specific infections that are typically caused by different causal pathogens (van Vught et al., 2016a; van Vught et al., 2016b; Sartelli, 2010).

Ascribing long non-coding RNA function to cellular biological pathways is a major challenge. To address this challenge, we undertook a guilt-by-association strategy that sought to position long non-coding RNA in co-expression modules of tightly correlating protein-coding RNA, thereby infer on functional outputs of long non-coding RNA by virtue of the pathways that associate with protein-coding RNA in each module. By leveraging on the concepts of scale free networks (Barabási, 2009), we built a map of protein-coding and non-coding RNA relationships that pointed to cell death/olfactory receptor activity/cell-cycle G2/M DNA damage checkpoint and regulation (turquoise module) and RNA biosynthesis/RNA binding (yellow module) as central to the organization of the co-expression network. Cell death or exhaustion, particularly in lymphocytes, have been proposed as causal features of immunosuppression and lethality in sepsis (Hotchkiss et al., 2013). Our findings further strengthen this hypothesis and position previously unknown non-coding RNA, including an autophagy and chemical stress responder GABPB1-AS1 (Tani et al., 2014; Luan et al., 2019), as putative regulators of cell death in the context of sepsis. Interestingly, protein-coding RNA in the cell death (turquoise) module also included olfactory receptors and cell-cycle DNA damage regulators. Modulation of DNA damage responses was demonstrated as a potential therapeutic path that might be exploited to confer protection to severe sepsis (Figueiredo et al., 2013). Little is known about olfactory receptors in non-chemosensory cells, but a growing body of evidence suggests they are not exclusive to the nose (Kang and Koo, 2012). They have been shown to be involved in cell–cell recognition, migration, proliferation, and apoptosis (Maßberg and Hatt, 2018).

In conclusion, we here describe the non-coding RNA landscape in blood leukocytes of sepsis patients upon admission to the ICU. By considering non-coding RNA expression patterns in relation to protein-coding RNA we provide an important layer to the blood leukocyte ‘regulome’ in a clinical context, which may facilitate prioritization of non-coding RNA in future functional studies.

Materials and methods

Key resources table
Reagent type
(species) or
resource		DesignationSource or
reference		IdentifiersAdditional
information
Biological sample (Homo sapiens)Total RNALeukocytes
Commercial assay or kitPAXgene Blood miRNA kitQiagenCat no./ID: 763134
Commercial assay or kitHuman Transcriptome Array 2.0Affymetrix; Thermo Fishermicroarray
Commercial assay or kitmiRNA 4.1 96-array plateAffymetrix; Thermo Fishermicroarray
Commercial assay or kitFlexSet cytometric bead arraysBD Biosciences
Commercial assay or kitImmunoturbidimetric assayRoche diagnostics
Commercial assay or kitLuminex Flow Cytometry AnalyzerLuminex Corp.RRID:SCR_018025
Commercial assay or kitSysmex CA-1500 SystemSiemens Healthineers
Chemical compound, drugLipopolysaccharide- Escherichia coli, 100 ng/ml, UltrapureInvivogenCat#0111:B4
Software, algorithmR Project for Statistical Computing, (version 3.5.0)R Development Core TeamRRID:SCR_001905
Software, algorithmOligo (version 1.44)Bioconductor (Carvalho and Irizarry, 2010)RRID:SCR_015729
Software, algorithmSVA (version 3.28)Bioconductor (Leek and Storey, 2007)RRID:SCR_012836
Software, algorithmgenefilter (version 1.62)Bioconductor (Bourgon et al., 2010)
Software, algorithmarrayQualityMetricsBioconductor (Kauffmann et al., 2009)RRID:SCR_001335
Software, algorithmAffymetrix Transcriptome Analysis ConsoleAffymetrixRRID:SCR_018718
Software, algorithmlimma (version 3.36)Bioconductor (Smyth, 2005)
RRID:SCR_010943
Software, algorithmIngenuity pathway analysis softwareQiagenRRID:SCR_008653
Software, algorithmWGCNA (version 1.64)Bioconductor (Langfelder and Horvath, 2008)RRID:SCR_003302
Software, algorithmmiR-Walk 2.0University of Heidelberg, Germany (Dweep et al., 2011)
Software, algorithmvariancePartition (version 1.10)Bioconductor (Hoffman and Schadt, 2016)
Software, algorithmmixOmicsBioconductor (Rohart et al., 2017)RRID:SCR_016889
OtherDeposited data super-seriesGene Expression OmnibusGSE134364

Patient population and inclusion criteria

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This study was part of the Molecular Diagnosis and Risk Stratification of sepsis (MARS) project, a prospective observational study in the mixed ICUs of two tertiary teaching hospitals in the Netherlands (Academic Medical Center, Amsterdam and University Medical Center Utrecht, Utrecht) (ClinicalTrials.gov identifier NCT01905033) (van Vught et al., 2016a; Klein Klouwenberg et al., 2013; Scicluna et al., 2015b). For the current study, we selected consecutive patients with sepsis from the MARS biorepository who were older than 18 years of age and had been admitted to the ICU between July 2012 and January 2014. Sepsis (n = 156) was defined as the presence of CAP, hospital-acquired pneumonia (HAP), or intra-abdominal infection diagnosed within 24 hr of ICU admission with a culture proven or probable likelihood using criteria as described (Zimmerman et al., 2006), accompanied by at least one additional general, inflammatory, hemodynamic, organ dysfunction, or tissue perfusion variable described in the third international consensus definitions for sepsis and septic shock (Singer et al., 2016). Patients with aspiration pneumonia, with multiple sites of infection, and patients admitted to the ICU more than 2 days after the initiation of antibiotics were excluded. All readmissions and patients transferred from another ICU were also excluded, except when patients were referred to one of the study centers on the same day of presentation to the first ICU. Severity was assessed by APACHE IV (Zimmerman et al., 2006) and SOFA score excluding the central nervous system component (Vincent et al., 1996). Shock was qualified by the use of vasopressors (norepinephrine, epinephrine, or dopamine) for hypotension in a norepinephrine-equivalent dose of more than 0.1 µg/kg/min in patients with a SOFA score of at least 2 (Singer et al., 2016). Blood was collected in PAXgene tubes (Becton-Dickinson, Breda, The Netherlands) and ethylenediaminetetraacetic acid (EDTA) vacutainer tubes within 24 hr of ICU admission. Definitions of comorbid and immunocompromised conditions are reported in the online data supplement.

Healthy participants and endotoxemia

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PAXgene and EDTA tubes were also obtained from 82 healthy subjects. Eight male subjects were exposed to intravenous LPS in a Phase I, randomized, single-blind, parallel group, placebo controlled study (Clinicaltrials.gov identifier NCT02328612); the subjects who received placebo were used in the current study (Perlee et al., 2018). Subjects were infused with LPS over 1 min (2 ng/kg; from Escherichia [E. coli], US standard reference endotoxin, kindly provided by Anthony Suffredini, National Institute of Health, Bethesda, MD). Whole blood was collected in PaxGene Blood tubes (Qiagen) before and 2, 4, 6, and 24 hr after LPS administration.

Immunological markers

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EDTA-anticoagulated blood plasma collected on ICU admission was used for soluble mediator measurements. IL-6, IL-8, IL-10, soluble intercellular adhesion molecule-1 (ICAM-1), soluble E-selectin, and fractalkine were measured using FlexSet cytometric bead arrays (BD Biosciences, San Jose, CA) using a FACS Calibur (Becton Dickinson, Franklin Lakes, NJ, NJ, USA). Neutrophil gelatinase-associated lipocalin (NGAL), ANG-1, ANG-2, protein C, antithrombin, matrix metalloproteinase (MMP)−8 (R and D Systems, Abingdon, UK), and D-dimer (Procartaplex, eBioscience, San Diego, CA) were measured by Luminex multiplex assay using a BioPlex 200 (BioRas, Hercules, CA). CRP was determined by an immunoturbidimetric assay (Roche diagnostics). Platelet counts were determined by hemocytometry, prothrombin time (PT), and activated partial thromboplastin time (aPTT) by using a photometric method with Dade Innovin Reagent or by Dade Actin FS Activated PTT Reagent, respectively (Siemens Healthcare Diagnostics). Normal biomarker values were obtained from 27 age- and sex-matched healthy subjects, except for CRP, platelet counts, PT, and aPTT (routine laboratory reference values).

Microarrays and data processing

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Total RNA was isolated by means of PaxGene blood miRNA isolation kit (Thermo-Fisher) as per manufacturer’s instructions. Quality RNA (Agilent 2100 Bioanalyzer, Agilent Technologies; RIN > 6) was processed and hybridized to either the GeneChip Human Transcriptome Array (HTA) 2.0 (Thermo-Fisher) or the miRNA 4.1 96-array plate (Thermo-Fisher) following manufacturer’s instructions. Both arrays were done on all samples (sepsis patients, controls, and healthy subjects injected with LPS). Microarrays were scanned at the Cologne Center for Genomics, Cologne, Germany.

The HTA 2.0 scans (.CEL) were processed in the R language and environment for statistical computing version 3.5.0 (R Development Core Team, Foundation for Statistical Computing, Vienna, Austria). Following robust multi-average (RMA) background-correction, quantile normalization, and log2-transformation using the oligo method (version 1.44) (Carvalho and Irizarry, 2010) data were evaluated for non-experimental chip effects by means of surrogate variable analysis (SVA; version 3.28) and adjusted using the combat method (Leek and Storey, 2007). Probes were annotated using biomart (version 2.36.1) (Smedley et al., 2015), and low expression probes were filtered by means of the genefilter method (version 1.62) (Bourgon et al., 2010). The miRNA-4.1 scans (.CEL) were analyzed by means of Affymetrix Expression Console software (Thermo-Fisher). Probes were normalized using the RMA method and detection above background (DABG) probe level detection. Homo sapiens annotated probes with detection p-value <0.05 in at least one sample were considered for downstream analyses. Quality of HTA2.0 and miRNA-4.1 arrays was evaluated by means of the arrayqualitymetrics R package (Kauffmann et al., 2009). Comparisons between study groups were done using the limma method (version 3.36) (Smyth, 2005) and significance was demarcated by Benjamini–Hochberg multiple test adjusted probabilities (adjusted p<0.01). The linear model included age and sex as additive covariates. The MDTH index was calculated as described previously (Berry et al., 2010; Dunning et al., 2018). Ingenuity Pathway Analysis (Ingenuity systems, Qiagen) was used to determine the most significant canonical signaling pathways for elevated and reduced protein-coding RNA transcripts considering adjusted Fisher’s probabilities (adjusted p<0.05) specifying the Ingenuity knowledgebase as reference and human species. All other parameters were default.

The novelty of our study, that is, profiling non-coding RNA expression in leukocytes of patients with sepsis, precludes an adequate study power estimation. However, considering known co-regulation with protein-coding RNA expression, we provide study power estimates based on previous observations in typical gene expression studies (Cheng et al., 2016; Scicluna et al., 2017; Davenport et al., 2016). Considering a false discovery rate of 5%, beta error level 5% (95% power), and typical effect sizes greater than 0.25 in sepsis relative to health, a sample size of 42 per group was estimated. In addition, eight healthy volunteers in a human endotoxemia challenge would have more than 95% power to detect differences relative to pre-challenge (baseline) samples (Cheng et al., 2016; Davenport et al., 2016; Calvano et al., 2005; Scicluna et al., 2013; Perlee et al., 2018; Seok et al., 2013; Xiao et al., 2011; Takao and Miyakawa, 2015). Using a continuous model, we estimated that 156 patients would have more than 98% power to detect significant associations with demographic or clinical variables (false-discovery rates of 5%).

Co-expression network and pathway analysis

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The weighted gene co-expression network analysis (WGCNA) method (version 1.64) was used to build the leukocyte co-expression network as described previously (Langfelder and Horvath, 2008; Scicluna et al., 2015a; Zhao et al., 2010). A pair-wise biweight midcorrelation matrix of the most variable transcripts (coefficient of variation >5%) was transformed into an adjacency matrix by using a ‘soft’ power function of 8 ensuring scale-free topology (Langfelder and Horvath, 2008; Zhao et al., 2010). The adjacency matrix was further transformed into a topological overlap matrix to enable the identification of modules (clusters) encompassing highly inter-correlating RNA transcripts by using a dynamic tree cut method (version 1.63) (Langfelder and Horvath, 2008; Zhao et al., 2010). Modules were summarized by means of the eigengene value, defined as the first PC of the module expression matrix and the module membership measure. Protein-coding RNA in each module were analyzed for enrichment of gene ontologies for biological processes (GO:BP), molecular function (GO:MF), and cellular compartment (GO:CC) using the Gene Ontology Consortium database with significance defined by adjusted p-value <0.05 (http://www.geneontology.org) (Ashburner et al., 2000). Biofunctions were predicted using Ingenuity Pathways software (Ingenuity pathway analysis, Qiagen Bioinformatics) specifying activation z-score <2 or >2 and adjusted p-value <0.05. The miR-Walk atlas of gene-miRNA-target interactions was used to evaluate predicted interactions of miRNA with module-specific genes by specifying the miR-Walk algorithm (Dweep et al., 2011; Mills et al., 2017). Human species annotations and 3’ untranslated region (UTR) interactions as well as a minimum seed length equating to seven were specified. All other parameters were default.

Statistics

Statistical analysis was performed in the R statistical environment (v 3.5.0). Comparison of continuous data between categories was done with the Wilcoxon rank sum test. Correlation analysis of continuous data was performed using Pearson’s method unless otherwise stated as well as the coefficient of determination (r2). Categorical data were analyzed by Fisher exact tests or chi-squared tests. Multiple comparison (Benjamini–Hochberg) adjusted p-values <0.05 defined significance. The proportion of variance in RNA expression explained by demographics and clinical characteristics was calculated using a multivariate approach implemented in the variancePartition method (version 1.10) (Hoffman and Schadt, 2016). A multivariate linear model was fit including age, gender, primary diagnosis, total SOFA, APACHE IV scores, shock, and Charlson comorbidity indices. PC analysis was done using the mixOmics package, specifying 10 components (Rohart et al., 2017). Data is presented in the form of volcano plots, pie charts, dot plots, bar charts, and circular and violin plots.

Appendix 1

Patients

Comorbidities were defined as follows: cardiovascular compromise was defined as a medical history of congestive heart failure, chronic cardiovascular disease, myocardial infarction, peripheral vascular disease, or cerebrovascular disease. Malignancy was defined as a medical history of either metastatic or not metastatic solid tumor, or hemodynamic malignancy. Patients with a history of chronic renal insufficiency or treated with chronic intermittent hemodialysis or continuous ambulatory peritoneal dialysis were marked as renal insufficient. Respiratory insufficiency included patients with a history of chronic respiratory insufficiency, chronic obstructive pulmonary disease, or treated at home with oxygen or ventilator support. Patients with a history of immune deficiency, human immunodeficiency virus (HIV) infection, acquired immune deficiency syndrome (AIDS), asplenia, or chronically treated with corticosteroids, antineoplastic or other immune suppressive medications were deemed immunocompromised.

Data availability

The datasets generated and analyzed in the current study are available in the Gene Expression Omnibus of the National Center for Biotechnology Information repository with primary data accession numbers GSE134364 (super-series), GSE134347 for patients and healthy volunteers (HTA 2.0 microarray), GSE134356 for the human endotoxemia model samples (HTA 2.0 microarray) and GSE134358 for all patients, healthy volunteers and human endotoxemia samples (miRNA-4.1 microarray).

The following data sets were generated
    1. Scicluna BP
    (2020) NCBI Gene Expression Omnibus
    ID GSE134364. GEO super-series of patients and healthy volunteers.
    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134364
    1. Scicluna BP
    (2020) NCBI Gene Expression Omnibus
    ID GSE134347. HTA2.0 microarray data of patients and healthy volunteers.
    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134347
    1. Scicluna BP
    (2020) NCBI Gene Expression Omnibus
    ID GSE134356. HTA2.0 microarray data of human endotoxemia volunteers.
    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134356
    1. Scicluna BP
    (2020) NCBI Gene Expression Omnibus
    ID GSE134358. MicroRNA microarray data of patients, healthy volunteers and human endotoxemia volunteers.
    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134358

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Decision letter

  1. Evangelos J Giamarellos-Bourboulis
    Reviewing Editor; Attikon University Hospital, Greece
  2. Jos WM van der Meer
    Senior Editor; Radboud University Medical Centre, Netherlands
  3. Evangelos J Giamarellos-Bourboulis
    Reviewer; Attikon University Hospital, Greece
  4. Michael Bauer
    Reviewer; Jena University Hospital, Germany

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

Sepsis is a serious clinical condition, in which an abnormal host response to infection leads to organ failure with a high risk of death. This study by Scicluna et al. assesses the role of non-coding RNA in patients with sepsis. Using advanced molecular analysis and bioinformatics the authors compare non-coding RNA patterns in sepsis patients and healthy subjects. In addition, the authors have treated healthy volunteers with bacterial endotoxin, which is considered an important factor in bacterial sepsis, to investigate the role non-coding RNAs. They have found that long non-coding RNAs, and to a lesser extent small non-coding RNAs, were significantly altered in sepsis patients as compared to healthy controls. Thereby, this paper significantly adds to the current concepts of the regulatory responses in sepsis.

Decision letter after peer review:

Thank you for submitting your article "The leukocyte non-coding RNA landscape in critically ill patients with sepsis" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Evangelos J Giamarellos-Bourboulis as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Jos van der Meer as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Michael Bauer (Reviewer #3).

The reviewers have discussed the reviews with one another and the Senior Editor has drafted this decision to help you prepare a revised submission.

We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.

Consensus review proposal:

Non-coding RNA have been recently discovered as a new component of regulatory complexity of gene Expression in eukaryotic cells. The present study by Scicluna et al. is aimed to assess the role of non-coding RNA in sepsis patients. Using advanced molecular analysis and bioinformatics the authors compare non-coding RNA patterns in sepsis patients and healthy subjects. Additional, healthy volunteers have been treated with lipopolysaccharide to investigate effects of endotoxemia on non-coding RNAs. Resulting data are of novelty and significantly add to the current knowledge on sepsis related regulatory responses. The RNA preparation and analysis has been accomplished by up-to-date technology.

The paper is descriptive, well-written, and with reasonable conclusions. It is a reasonable step forward in the field, but to some degree the paper suffers from a lack of context. Other authors have written on this topic and no attempt is made to discuss, reproduce, or bioinformatically include/compare to those studies.

Essential revisions:

1) The authors might consider PMID: 29657277 (data on GEO). A very small study is here: https://doi.org/10.1089/gtmb.2017.0061. They also might consider mining their own dataset GSE65682 as others have done recently: https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-020-02372-2. Many microarrays measure lncRNAs and the data are available, just not examined, online. Shouldn't the authors try to validate their findings externally?

2) As a brief aside, much of the above is also true for endotoxin studies. There are also a huge number of studies looking at miRNAs in sepsis in a mechanistic fashion; nice review here: PMC6255943. All of this could be contextualized better.

3) Why is the analysis of non-coding RNAs in this submission accompanied by an analysis of protein-coding regions? We understand that the authors try to investigate how the expression of coding regions is modulated by non-coding regions in terms of pathways analysis, but the way data are analysed does not support such conclusions.

4) Although there is a positive association of coding-RNAs between sepsis patients and the LPS endotoxemia model, this is at the best marginal for the non-coding RNAs. This should be highlighted and the data of Figure 3—figure supplement 1 showing this should be moved in the core of the manuscript.

5) For Figure 1C and the resulting pathways analysis, was an effect size threshold also used? Or just significance?

6) Remarkable that the endotoxemia model had so few genes differently expressed from sepsis at 24 hours (just 43). Worth a comment on the model?

7) Across which data was the functional inference done? Sepsis only? Sepsis and healthy?

8) Figure 5 should be revised and demonstrate the correlation of biomarkers with non-coding RNAs.

9) Figure 5B examines shock vs. non-shock; can Figure 1 add this subclassification?

10) We welcome the authors to present Volcano plots and analysis of non-coding RNAs between survivors and non-survivors. Since data on 1-year survival are available, can the authors shifts in expression of non-coding RNAs between original after 28-days who become survivors and non-survivors after one year?

11) Materials and methods: do any patients profiled here overlap with this group's prior microarray studies?

12) Materials and methods: why were two different microarray types used, and how was this technical difference accounted for in the sepsis-vs.-endotoxemia analysis?

13) As the results indicating correlation of non-coding RNA profiles in sepsis patients and expression profiles in the human endotoxemia model simultaneously with endotoxin tolerance is of extraordinary importance and interest, we suggest discussion of the (accidental) interplay of resistance and tolerance responses during endoxemia and sepsis.

https://doi.org/10.7554/eLife.58597.sa1

Author response

Essential revisions:

1) The authors might consider PMID: 29657277 (data on GEO). A very small study is here: https://doi.org/10.1089/gtmb.2017.0061. They also might consider mining their own dataset GSE65682 as others have done recently: https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-020-02372-2. Many microarrays measure lncRNAs and the data are available, just not examined, online. Shouldn't the authors try to validate their findings externally?

Absolutely, we have certainly searched the public domain for independent datasets to provide a level of robustness to the patterns we observed. The three studies noted by the reviewers were indeed shortlisted in our search for potential use as external validation cohorts. However, we found certain issues with the studies that precluded their use in validation of our findings. The study by da Silva Pellegrini, et al. (PMID: 29657277) was done on a granulocyte fraction after Ficoll-Paque density gradient centrifugation of whole blood, that is, only polymorphonuclear cells were assessed. This contrasts tremendously with our study on whole blood leukocytes. The data used in the study by Dai, et al. (PMID: 28872921) is not available in the public domain, and severely underpowered since the authors performed microarray analysis of 3 septic patients relative to 3 healthy controls. The study by Cheng, et al. (PMID: 32471511), which also included our own previous data (not on non-coding RNA expression), was done by inferring the potential interaction of long non-coding RNA with coding gene expression profiles, that is, not one long non-coding RNA mentioned was measured. Thus, while of high interest, these earlier studies unfortunately cannot be used to validate our findings.

2) As a brief aside, much of the above is also true for endotoxin studies. There are also a huge number of studies looking at miRNAs in sepsis in a mechanistic fashion; nice review here: PMC6255943. All of this could be contextualized better.

We concur with the reviewers that a body of work has identified specific microRNA as potential modulators of the immune response, particularly endotoxin tolerance, as kindly noted by the reviewers in the article by Vergadi and colleagues (PMC6255943). However, interpretation of our findings in the context of previous studies on microRNA is complicated by the fact that the majority of clinical studies focused on extracellular (circulating) microRNA, whereas more mechanistic studies employed predominantly mouse macrophages or a pro-monocytic cell line THP1 or human embryonic kidney cell lines. In order to accommodate the reviewer’s suggestion, we opted to specifically discuss those miRNA that were unmasked as potential co-expression network modulators. We found that hsa-miR-200c (Figure 4E) was shown to modify the capacity of HEK293 cells for TLR-signaling in a MYD88-dependent fashion (see Wendlandt et al., 2012). We greatly appreciated the reviewers for raising this question as it has certainly helped in raising the confidence level of our network findings. The main text has been revised accordingly:

“Of note, hsa-miR-200c-3p has been shown to modify TLR4 signaling efficiency dependent on MYD88-mediated pathways in an embryonic kidney cell line (HEK293).(Wendlandt et al., 2012)”

3) Why is the analysis of non-coding RNAs in this submission accompanied by an analysis of protein-coding regions? We understand that the authors try to investigate how the expression of coding regions is modulated by non-coding regions in terms of pathways analysis, but the way data are analysed does not support such conclusions.

We analyzed protein-coding and non-coding RNA in combination because we sought to mainly infer on the functional connotations of long non-coding RNA. Functional inference of long non-coding RNA is currently limited to a handful of transcripts, unlike those of protein-coding RNA. To address this problem, we reasoned that by identifying modules (or clusters) of tightly correlating protein-coding and non-coding RNA we can then infer on functional outputs of non-coding RNA by virtue of the pathways that associate with protein-coding RNA in each module. Using this “guilt-by-association” approach we were able to position a substantial proportion of non-coding RNA with otherwise no known functional annotation. We discuss this aspect of our analysis in the Discussion section, while avoiding the topic of protein-coding RNA modulation by non-coding RNA since, as the reviewers rightly pointed out, the analytical strategy we undertook would not support it. To clarify this issue in the revised manuscript we modified the following section:

“To address this challenge, we undertook a guilt-by-association strategy that sought to position long non-coding RNA in co-expression modules of tightly correlating protein-coding RNA, thereby infer on functional outputs of long non-coding RNA by virtue of the pathways that associate with protein-coding RNA in each module.”

4) Although there is a positive association of coding-RNAs between sepsis patients and the LPS endotoxemia model, this is at the best marginal for the non-coding RNAs. This should be highlighted and the data of Figure 3—figure supplement 1 showing this should be moved in the core of the manuscript.

It is definitely remarkable that non-coding RNA expression in sepsis patients was marginally correlated to that in the human endotoxemia model. In particular, small non-coding RNA mainly showed indirect correlations. We agree with the reviewers to restructure Figure 3 by including data originally found in Figure 3—figure supplement 1. Specifically, we moved Figure 3—figure supplement 1E to Figure 3A, showing the extent of the correlation in protein-coding RNA expression between sepsis and endotoxemia.

5) For Figure 1C and the resulting pathways analysis, was an effect size threshold also used? Or just significance?

We used a fold change cutoff (≤ -1.2 or ≥ 1.2) and adjusted P-value ≤ 0.01. We included this information as a legend beneath volcano plots in Figure 1C. In addition, all other volcano plots throughout our manuscript were clarified by including legends for fold change cutoffs.

6) Remarkable that the endotoxemia model had so few genes differently expressed from sepsis at 24 hours (just 43). Worth a comment on the model?

Yes, definitely worth a comment and to accommodate the reviewer’s suggestion we have now expanded on the human endotoxemia model in the Discussion:

“The human endotoxemia model is a highly relevant in vivo model of acute systemic inflammation in the context of a controlled clinical setting (Lowry, 2005). In general, the model is characterized by a robust systemic response, including leukocyte transcriptional responses, exhibiting shared and unique temporal changes that resolve within 24 hours of bolus administration (Calvano et al., 2005; Perlee et al., 2018).”

7) Across which data was the functional inference done? Sepsis only? Sepsis and healthy?

Functional inference was done with sepsis patients only. We reasoned that in so doing we could test the co-expression modules against clinical characteristics, that is, without the confounding signal of the dramatic changes in sepsis relative to health. In order to clarify this point, we added the following to the main text:

“On the basis of a bi-weight midcorrelation matrix of the most variable protein-coding and long non-coding RNA (n=8539; coefficient of variation > 5%) in sepsis patients only, …”

8) Figure 5 should be revised and demonstrate the correlation of biomarkers with non-coding RNAs.

To address this comment, we calculated the connectivity of each transcript (sum of transcript adjacencies with respect to other module transcripts) in the co-expression network as described in the Materials and methods and illustrated in Figure 4. By ranking the intra-module connectivities we identified non-coding RNA as module “hubs”. Correlation analysis of all “hub” non-coding RNA against soluble mediators showed strong correlations, particularly for markers of systemic inflammation. Of note, these patterns fully reflected the module eigengene correlations to soluble mediators shown in Figure 5A. To illustrate these findings, we focused our attention on modules that correlate substantially to levels of different soluble mediators, that is, neutrophil degranulation (red) and TLR signaling (black). Figure 5D and E now depict dot plots of MYOSLID (Myocardin-Induced Smooth Muscle LncRNA, Inducer Of Differentiation) expression (red module hub) and LUCAT1 (Lung Cancer Associated Transcript 1) expression (black module hub), respectively, against levels of inflammatory response markers IL-6, IL-8, IL-10, and acute phase response protein CRP. MYOSLID expression correlated directly with inflammatory markers, whereas in contrast LUCAT1 expression was indirectly correlated. We thank the reviewers for motivating this analysis as it has provided specificity to formulating testable hypotheses on the functions of non-coding RNA thereby strengthening our study. The Results section and figure legend have been revised as follows:

“LincRNA and antisense RNA included an inducer of differentiation MYOSLID (Myocardin-Induced Smooth Muscle LncRNA, Inducer Of Differentiation), cell proliferation and metastasis associated antisense RNA of the titin gene (TTN-AS1) and a IL10 receptor beta subunit antisense RNA, IL10RB-AS1. […] In contrast, LUCAT1 expression was indirectly correlated to soluble mediators of inflammation, except for CRP (Figure 5E).”

Figure 5D and E legend: “(D and E) Dot plots of (D) MYOSLID expression and (E) LUCAT1 expression against soluble mediators of inflammation IL-6, IL-8 and IL-10, as well as the acute phase response protein CRP. Rho, Spearman’s coefficient.”

9) Figure 5B examines shock vs. non-shock; can Figure 1 add this subclassification?

Definitely, and since the question is related to a source of transcriptomic variation in sepsis patients, we would prefer including the analysis motivated by the reviewer in Figure 2. Figure 2 illustrates an analysis of variance in blood transcriptomes of sepsis patients against various demographic and clinical characteristics, including shock. Figure 2C and D now depict the global blood transcriptomic changes in septic shock patients relative to no shock. In the Results section we have included the following sentences to report the new findings:

“Septic shock explained low proportions of variation in RNA expression (Figure 2A), and directly comparing patients with septic shock to patients without shock resulted in 837 and 80 significantly altered protein-coding and long non-coding RNA, respectively (Figure 2C). […] Low expression protein-coding RNA included a Na+/Ca2+ exchanger (SLC8A1), membrane metalloendopeptidase (MME) and interleukin (IL-) 6 receptor (IL6R). Long non-coding RNA included lincRNA lung cancer associated transcript 1 (LUCAT1; low expression) and antisense RNA (LRRC75A-AS1; high expression) (Figure 2C). No significant alterations were identified in small non-coding RNA expression profiles.”

The legend of Figure 2 has also been revised to include the following details: “(C) Volcano plots depicting the changes in protein-coding and long non-coding RNA in patients discordant for septic shock on ICU admission. Horizontal (black) line denotes the adjusted p-value threshold for significance (adjusted p ≤ 0.01).”

10) We welcome the authors to present Volcano plots and analysis of non-coding RNAs between survivors and non-survivors. Since data on 1-year survival are available, can the authors shifts in expression of non-coding RNAs between original after 28-days who become survivors and non-survivors after one year?

Based on the reviewer’s comments, we firstly report results pertaining to long non-coding RNA expression in sepsis patients discordant for survival after 28 days of ICU admission. Secondly, considering only 28-day survivors we compared non-survivors to survivors after one year follow-up. We uncovered differences in protein-coding RNA profiles, albeit minimal, but no differences were observed for non-coding RNA profiles. This analysis suggests that non-coding RNA expression may not be a suitable source of signal for mortality prediction. No differences were found for 28-day survivors followed up to one year. Since our data showed no added value of RNA expression in discriminating sepsis survivors from non-survivors, we opted to only include a few sentences to report the results of the 28-day mortality analysis. However, if the reviewer would prefer us to incorporate the one year follow up analysis of 28-day survivors, we will gladly add them to the main text. The Results section was revised accordingly:

“Evaluating RNA expression in patients discordant for survival after 28 days, identified 146 significantly altered protein-coding RNA (Figure 2—figure supplement 2C). No significant differences were uncovered in non-coding RNA expression profiles, suggesting that non-coding RNA profiles obtained on ICU admission may not be suitable as mortality predictors.”

Figure 2—figure supplement 1 legend: “(C) Volcano plot of significantly altered protein-coding RNA in non-survivors relative to survivors after 28 days since ICU admission. Horizontal (black) line denotes -log10 transformed adjusted p-value thresholds.”

11) Materials and methods: do any patients profiled here overlap with this group's prior microarray studies?

None of the patients profiled here overlap with our previous microarray studies.

12) Materials and methods: why were two different microarray types used, and how was this technical difference accounted for in the sepsis-vs.-endotoxemia analysis?

Two types of arrays were used in all samples (sepsis patients and human endotoxemia) based on their specificity for measuring the different types of non-coding RNA expression. The human transcriptome array (HTA) 2.0 was designed for exon-level protein-coding RNA and mainly long non-coding RNA expression studies. The miRNA 4.1 array was used to comprehensively assay small non-coding RNA including types of small RNA (precursor miRNA, stem loop RNA, etc.) that are not covered in the HTA 2.0 microarrays. To clarify this we added the following to the Materials and methods section:

“Both arrays were done on all samples (sepsis patients, controls and healthy subjects injected with LPS)”.

13) As the results indicating correlation of non-coding RNA profiles in sepsis patients and expression profiles in the human endotoxemia model simultaneously with endotoxin tolerance is of extraordinary importance and interest, we suggest discussion of the (accidental) interplay of resistance and tolerance responses during endoxemia and sepsis.

In accordance with the suggestion of the reviewer we added the following to the Discussion:

“The host response during infection is characterized by a balance between resistance (seeking to limit the pathogen load) and tolerance (aiming to retain cell and organ functions)(Schneider and Ayres, 2008). […] While our time-sequential data in healthy humans injected with LPS suggest that coding and long non-coding RNA profiles in blood leukocytes of sepsis patients particularly reflect a tolerant state, time course studies in patients are needed to increase the insight into the role of distinct RNA species in the interplay between resistance and tolerance”.

https://doi.org/10.7554/eLife.58597.sa2

Article and author information

Author details

  1. Brendon P Scicluna

    1. Amsterdam UMC, University of Amsterdam, Center for Experimental Molecular Medicine, Amsterdam Infection & Immunity, Amsterdam, Netherlands
    2. Amsterdam UMC, University of Amsterdam, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam, Netherlands
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft
    For correspondence
    b.scicluna@amc.uva.nl
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2826-0341

  2. Fabrice Uhel

    Amsterdam UMC, University of Amsterdam, Center for Experimental Molecular Medicine, Amsterdam Infection & Immunity, Amsterdam, Netherlands
    Contribution
    Formal analysis, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4946-8184

  3. Lonneke A van Vught

    Amsterdam UMC, University of Amsterdam, Center for Experimental Molecular Medicine, Amsterdam Infection & Immunity, Amsterdam, Netherlands
    Contribution
    Resources, Writing - review and editing
    Competing interests
    No competing interests declared

  4. Maryse A Wiewel

    Amsterdam UMC, University of Amsterdam, Center for Experimental Molecular Medicine, Amsterdam Infection & Immunity, Amsterdam, Netherlands
    Contribution
    Resources, Writing - review and editing
    Competing interests
    No competing interests declared

  5. Arie J Hoogendijk

    Amsterdam UMC, University of Amsterdam, Center for Experimental Molecular Medicine, Amsterdam Infection & Immunity, Amsterdam, Netherlands
    Contribution
    Resources, Writing - review and editing
    Competing interests
    No competing interests declared

  6. Ingelore Baessman

    Cologne Center for Genomics, University of Cologne, Cologne, Germany
    Contribution
    Resources
    Competing interests
    No competing interests declared

  7. Marek Franitza

    Cologne Center for Genomics, University of Cologne, Cologne, Germany
    Contribution
    Resources
    Competing interests
    No competing interests declared

  8. Peter Nürnberg

    1. Cologne Center for Genomics, University of Cologne, Cologne, Germany
    2. Center for Molecular Medicine Cologne, University of Cologne, Cologne, Germany
    Contribution
    Resources
    Competing interests
    No competing interests declared

  9. Janneke Horn

    Amsterdam UMC, University of Amsterdam, Department of Intensive Care Medicine, Amsterdam, Netherlands
    Contribution
    Resources
    Competing interests
    No competing interests declared

  10. Olaf L Cremer

    Department of Intensive Care, University Medical Center Utrecht, Utrecht, Netherlands
    Contribution
    Resources, Writing - review and editing
    Competing interests
    No competing interests declared

  11. Marc J Bonten

    1. Department of Medical Microbiology, University Medical Center Utrecht, Utrecht, Netherlands
    2. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands
    Contribution
    Resources, Funding acquisition, Writing - review and editing
    Competing interests
    No competing interests declared

  12. Marcus J Schultz

    Amsterdam UMC, University of Amsterdam, Department of Intensive Care Medicine, Amsterdam, Netherlands
    Contribution
    Resources, Writing - review and editing
    Competing interests
    No competing interests declared

  13. Tom van der Poll

    1. Amsterdam UMC, University of Amsterdam, Center for Experimental Molecular Medicine, Amsterdam Infection & Immunity, Amsterdam, Netherlands
    2. Amsterdam UMC, University of Amsterdam, Division of Infectious Diseases, Amsterdam, Netherlands
    Contribution
    Conceptualization, Funding acquisition, Writing - original draft
    Competing interests
    No competing interests declared

  14. Molecular Diagnosis and Risk Stratification in Sepsis (MARS) consortium

    Contribution
    Project administration
    Competing interests
    No competing interests declared

Funding

Innovative Medicines Initiative Joint Undertaking (115523)

  • Marc J Bonten

Innovative Medicines Initiative Joint Undertaking (115620)

  • Marc J Bonten

Innovative Medicines Initiative Joint Undertaking (115737)

  • Marc J Bonten

Center for Translational Molecular Medicine (04I-201)

  • Tom van der Poll

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

Acknowledgements

The authors thank all the patients and healthy volunteers who participated in this study, as well as the critical care nursing staff at both the AMC and UMCU ICUs. Members of the MARS consortium were: from Amsterdam University Medical Centers, location Academic Medical Center, University of Amsterdam, the Netherlands: Friso M de Beer, Lieuwe D J Bos, Gerie J Glas, Roosmarijn T M van Hooijdonk, Janneke Horn, Mischa A Huson, Laura R A Schouten, Marleen Straat, Luuk Wieske, Maryse A Wiewel, and Esther Witteveen; from University Medical Center Utrecht, Utrecht, the Netherlands: David SY Ong, Jos F Frencken, Maria E Koster-Brouwer, Kirsten van de Groep, and Diana M Verboom.

Ethics

Human subjects: The institutional review boards of both participating centers approved an opt-out consent method (IRB No. 10-056C). The Dutch Central Committee on Research Involving Human Subjects and the Medical Ethics Committee of the Academic Medical Center, Amsterdam, the Netherlands, approved the study. Written informed consent was obtained from all healthy participants.

Senior Editor

  1. Jos WM van der Meer, Radboud University Medical Centre, Netherlands

Reviewing Editor

  1. Evangelos J Giamarellos-Bourboulis, Attikon University Hospital, Greece

Reviewers

  1. Evangelos J Giamarellos-Bourboulis, Attikon University Hospital, Greece
  2. Michael Bauer, Jena University Hospital, Germany

Version history

  1. Received: May 5, 2020
  2. Accepted: December 10, 2020
  3. Accepted Manuscript published: December 11, 2020 (version 1)
  4. Version of Record published: December 31, 2020 (version 2)

Copyright

© 2020, Scicluna et al.

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

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  1. Brendon P Scicluna
  2. Fabrice Uhel
  3. Lonneke A van Vught
  4. Maryse A Wiewel
  5. Arie J Hoogendijk
  6. Ingelore Baessman
  7. Marek Franitza
  8. Peter Nürnberg
  9. Janneke Horn
  10. Olaf L Cremer
  11. Marc J Bonten
  12. Marcus J Schultz
  13. Tom van der Poll
  14. Molecular Diagnosis and Risk Stratification in Sepsis (MARS) consortium
(2020)
The leukocyte non-coding RNA landscape in critically ill patients with sepsis
eLife 9:e58597.
https://doi.org/10.7554/eLife.58597

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