medRxiv preprint doi: https://doi.org/10.1101/2020.02.15.20022772; this version posted February 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
1 Gene expression signatures identify pediatric patients with multiple
2 organ dysfunction who require advanced life support in the intensive
3 care unit
4 Running title: Transcriptional signature for multiple organ dysfunction
5 syndrome trajectory
6 7 Rama Shankar ([email protected])1,2, Mara L. Leimanis 8 ([email protected])1,3, Patrick A. Newbury ([email protected])1,2, Ke Liu 9 ([email protected])1,2, Jing Xing ([email protected])1,2, Derek Nedveck 10 ([email protected]) 4, Eric J. Kort ([email protected])1,5,6, Jeremy W 11 Prokop ([email protected])1,2, Guoli Zhou ([email protected])7, André S Bachmann 12 ([email protected])1, Bin Chen ([email protected])1,2*, Surender Rajasekaran 13 ([email protected])1,3,4*
14
15 16 1 Depar tment of Pediatrics and Human Development, College of Human Medicine, Michigan State 17 University, Grand Rapids, MI 49503, USA 18 2 Depar tment of Pharmacology and Toxicology, College of Human Medicine, Michigan State University, 19 Grand Rapids, MI 49503, USA 20 3 Pediatr ic Intensive Care Unit, Helen DeVos Children’s Hospital, 100 Michigan Street NE, Grand Rapids, 21 MI, 49503, USA 22 4 Office of Research, Spectrum Health, 15 Michigan Street NE, Grand Rapids, MI 49503, USA 23 5 DeV os Cardiovascular Program, Van Andel Research Institute and Fredrik Meijer Heart and Vascular 24 Institute/Spectrum Health, Grand Rapids, MI 49503, USA 25 6 Pediatr ic Hospitalist Medicine, Helen DeVos Children’s Hospital, 100 Michigan Street NE, Grand Rapids, 26 MI, 49503, USA 27 7 Biom edical Research Informatics Core (BRIC), Clinical and Translational Sciences Institute (CTSI), 28 Michigan State University, East Lansing, MI 48824, USA 29 30 * Correspondence: Dr. Bin Chen ([email protected]) and Dr. Surender Rajasekaran 31 (sur [email protected]) 32
NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. 1 medRxiv preprint doi: https://doi.org/10.1101/2020.02.15.20022772; this version posted February 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
33 Abstract
34 Background: Multiple organ dysfunction syndrome (MODS) occurs in the setting of a
35 variety of pathologies including infection and trauma. Some of these patients will further
36 decompensate and require extra corporeal membrane oxygenation (ECMO) as a
37 palliating maneuver to allow time for recovery of cardiopulmonary function. The
38 molecular mechanisms driving progression from MODS to cardiopulmonary collapse
39 remain incompletely understood, and no biomarkers have been defined to identify those
40 MODS patients at highest risk for progression to requiring ECMO support. We
41 hypothesize that molecular features derived from whole blood transcriptomic profiling
42 either alone or in combination with traditional clinical and laboratory markers can
43 prospectively identify these high risk MODS patients in the pediatric intensive care unit
44 (PICU).
45
46 Design/Methods: Whole blood RNA-seq profiling was performed for 23 MODS patients
47 at three time points during their ICU stay (at diagnosis of MODS, 72 hours after, and 8
48 days later), as well as four healthy controls undergoing routine sedation. Of the 23
49 MODS patients, six required ECMO support (ECMO patients). The predictive power of
50 conventional demographic and clinical features was quantified for differentiating the
51 MODS and ECMO patients. We then compared the performance of markers derived
52 from transcriptomic profiling including (1) transcriptomically imputed leukocyte subtype
53 distribution, (2) relevant published gene signatures and (3) a novel differential gene
54 expression signature computed from our data set. The predictive power of our novel
55 gene expression signature was then validated using independently published datasets.
2 medRxiv preprint doi: https://doi.org/10.1101/2020.02.15.20022772; this version posted February 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
56 Results: None of the five demographic characteristics and 14 clinical features, including
57 The Pediatric Logistic Organ Dysfunction (PELOD) score, could predict deterioration of
58 MODS to ECMO at baseline. From previously published sepsis signatures, only the
59 signatures positively associated with patients mortality could differentiate ECMO
60 patients from MODS patients, when applied to our transcriptomic dataset (P-value
61 ranges from 0.01 to 0.04). Deconvolution of bulk RNA-Seq samples suggested that
62 lower neutrophil counts were associated with increased risk of progression from MODS
63 to ECMO (P-value = 0.03, OR=2.82 [95% CI 0.63– 12.45]). A total of 28 genes were
64 differentially expressed between ECMO and MODS patients at baseline (log2 fold
65 change ≥ 1 or ≤ -1 with false discovery rate ≤ 0.2). These genes are involved in protein
66 maintenance and epigenetic-related processes. Further univariate analysis of these 28
67 genes suggested a signature of six DE genes associated with ECMO (OR > 3.0, P-
68 value ≤ 0.05). Notably, this contains a set of histone marker genes, including H1F0,
69 HIST2H3C, HIST1H2AI, HIST1H4, and HIST1H1B, that were highly expressed in
70 ECMO. A risk score derived from expression of these genes differentiated ECMO and
71 MODS patients in our dataset (AUC = 0.91, 95% CI 0.79-0.1.00, P-value = 7e-04) as
72 well as validation dataset (AUC= 0.73,95% CI 0.53-0.93, P-value = 2e-02).
73
74 Conclusions: This study identified lower neutrophils and upregulation of specific
75 histone related genes as a putative signature for deterioration of MODS to ECMO. This
76 study demonstrates that transcriptomic features may be superior to traditional clinical
77 methods of ascertaining severity in patients with MODS.
78
3 medRxiv preprint doi: https://doi.org/10.1101/2020.02.15.20022772; this version posted February 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
79 Author summary
80 Why was this study done?
81 ● Multiple organ dysfunction syndrome (MODS) is a major cause of mortality and
82 morbidity in critically ill pediatric patients who survive the initial physical insult.
83 ● A variety of triggers including trauma and infections can lead to MODS in
84 pediatric patients.
85 ● The clinical condition of some MODS patients improve while others deteriorate,
86 needing resource-intensive life support such as extracorporeal membrane
87 oxygenation (ECMO).
88 ● Mortality is uncommon in PICUs and the need for advanced life support devices,
89 such as ECMO can serve as proxy for mortality.
90 ● The decision to initiate ECMO in pediatric patients is often subjective made by a
91 committee of physicians that include surgeons, intensivists and a variety of other
92 subspecialists often in the absence of objective data.
93 ● Despite decades of research, no diagnostic criteria or biomarker has been
94 identified that comprehensively assesses severity in MODS patients who may
95 need subsequent ECMO support in the hyperacute phase of injury.
96 ● We systematically assessed clinical and transcriptional features as biomarkers
97 for the prediction of the ECMO patients.
98
99 What did the researcher do and find?
100 ● We investigated various clinical and transcriptional features in 27 patients with
101 MODS at multiple time points (4 CT, 17 MODS, 6 ECMO) at baseline (0h).
4 medRxiv preprint doi: https://doi.org/10.1101/2020.02.15.20022772; this version posted February 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
102 ● We observed that immune response pathways (monocytes, cytokines, NF-kB,
103 and inflammation) were activated in the initiation of MODS, whereas neutrophil
104 level was decreased during deterioration of MODS to ECMO.
105 ● A total of 51 DE genes were identified in MODS compared to CT and 28 DE in
106 ECMO compared to MODS at baseline (0h).
107 ● We identified the enrichment of immune-related and glycogenolysis processes in
108 MODS compared to CT and enrichment of protein maintenance, DNA repair and
109 epigenetic-related processes in ECMO compared to MODS at baseline (0h).
110 ● Logistic regression was used to identify a signature of 6 genes strongly
111 associated with ECMO decision and this signature could help to diagnose MODS
112 patients requiring ECMO.
113 ● The transcriptomic signature-based risk scores were further evaluated in an
114 independent cohort.
115
116 What do these findings mean?
117 ● The compromised level of neutrophils and activation of gene markers including a
118 few histone genes could be used as putative signature for diagnosing the
119 deterioration of MODS to ECMO.
120 ● A risk score derived from signature genes could be used to predict the need for
121 ECMO.
122 ● This score is superior to traditional clinical criteria and severity scores used in the
123 Pediatric ICU.
5 medRxiv preprint doi: https://doi.org/10.1101/2020.02.15.20022772; this version posted February 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
124 ● The transcriptional signature derived in this study could potentially be used to
125 identify patients in the hyperacute phase of injury that may need higher levels of
126 support like ECMO enabling the selection of an appropriate treatment plan.
127
128 Abbreviation: Multiple organ dysfunction syndrome, MODS; Extracorporeal Membrane
129 Oxygenation, ECMO; patients did not develop MODS, no-MODS; pediatric intensive
130 care unit, PICU; Differentially expressed, DE; False discovery rate, FDR; Area under
131 curve, AUC; principal component analysis, PCA; Odds ratio, OR.
132
133 Introduction
134 Multiple organ dysfunction syndrome (MODS) is common in the pediatric intensive care
135 unit (PICU), being diagnosed in the majority of patients with sepsis as well as many
136 trauma patients [1]. MODS complicates a wide range of pathologies including severe
137 hypoxemia, cardiorespiratory arrest, shock, trauma, acute pancreatitis, gut
138 malperfusion, acute leukemia, solid organ or hematopoietic stem cell transplantation,
139 hemophagocytic lymphohistiocytosis, and thrombotic microangiopathy [2].
140 Contemporary management of MODS is entirely supportive, and focused on addressing
141 the underlying disease process.
142 Some pediatric patients who develop MODS deteriorate and require intensive life
143 support in the form of Extracorporeal Membrane Oxygenation (ECMO). It has been
144 observed that pediatrics patients requiring ECMO have a 50-60% mortality rate [3]. The
145 decision to initiate ECMO remains subjective based on the empirical experience of the
146 multidisciplinary care team. Establishment of objective markers of what patients will
6 medRxiv preprint doi: https://doi.org/10.1101/2020.02.15.20022772; this version posted February 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
147 require ECMO support would simplify the decision making process and potentially
148 enable earlier intervention for these patients. However, no clinical scoring tool or
149 molecular biomarker has been developed to identify the patients who may require
150 subsequent advanced support; therefore, developing biomarkers for identifying MODS
151 patients at high risk of requiring ECMO support is a critical unmet need.
152 Whole blood transcriptomic profiling has been evaluated to perform risk-
153 stratification of sepsis patients, predict mortality in sepsis and better understand the
154 pathogenesis of MODS [4]. A number of published gene expression signatures shed
155 light on the molecular mechanism of MODS [5,6]. However, none of the signatures were
156 developed with a view towards identifying patients that require ECMO support.
157 In this work, we present a cohort of MODS patients, a subset of whom
158 progressed to requiring ECMO support (MODS vs ECMO) and healthy controls (CT).
159 Here we use the term MODS to denote those MODS patients that did not require ECMO
160 and ECMO for MODS patients deteriorated to needing ECMO support. These patients
161 were assessed using a combination of conventional demographic and clinical markers,
162 as well as whole blood transcriptomic profiling in an effort to identify diagnostic markers
163 that can distinguish between the MODS and ECMO patient population.
164
165 Methods
166 Patients and blood sampling
167 The IRB of this study (2016-062-SH/HDVCH) was approved by Spectrum Health on
168 May 17, 2016. All the patients were minors and their parents were consented prior to
169 recruitment into our study. Every parent gave consent. After IRB approval, a short-term
7 medRxiv preprint doi: https://doi.org/10.1101/2020.02.15.20022772; this version posted February 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
170 longitudinal design was adopted to assess the transcriptomic profiles of patients from
171 the PICU at Helen DeVos Children’s Hospital, Michigan. Critically ill patients, meeting
172 criteria for MODS as determined by clinical observations, were screened for eligibility
173 and consented. Blood samples were collected at three time points: at recognition of
174 MODS (0h), 72 hours after, and 8 days later (N=27). Samples were collected in
175 PaxGene® tubes and stored at -80oC. Healthy controls (N=4) were patients that
176 presented for same day sedation. Samples from each control patient were obtained only
177 once and were reported as 0h. Of the 23 MODS patients, 6 required ECMO support.
178 From admission to day 8, 47% of the MODS patients were discharged to home or out of
179 the ICU to a medical floor. Patients who left the ICU did not have further blood draws.
180 One patient from the ECMO group died during the study and two other MODS patients
181 died six months later.
182
183 Sequencing
184 RNA samples were prepared using KAPA RNA HyperPrep Kit, and sequenced on
185 an Illumina NextSeq500. Using ribosomal reduction RNAseq methodology, we were able
186 to capture both cellular and acellular RNA signatures of all PICU patients.
187
188 Validation Data Sets
189 For validation, we were unable to identify any analogous publicly available gene
190 expression datasets that included pediatric MODS patients at multiple time points. We
191 therefore chose a dataset describing an adult cohort (23-63 years) developed MODS in
192 the hyperacute phase of trauma [7]. This dataset was used as an independent cohort to
8 medRxiv preprint doi: https://doi.org/10.1101/2020.02.15.20022772; this version posted February 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
193 validate our signature genes. The MODS patients in this validation dataset were
194 categorized into MODS and noMODS (patients did not develop MODS) as described in
195 patient demographics [7]. In addition, a single cell RNA-Seq dataset was also available
196 for adult ECMO patients [8]. We used the immune cell markers from this dataset to
197 validate our immune response analysis.
198
199 Bioinformatics analysis
200 RNA-Seq data analysis. All the sequencing reads were mapped on Hg38
201 transcriptome using the ENSEMBL GRCh38.p3 annotation with the STAR aligner [9].
202 The edgeR package [10] was used for quantification of differentially expressed (DE)
203 genes with criteria: log2 fold change ≥ 1 or ≤ -1 with adjusted P-value (False Discover
204 Rate) < 0.20. DE genes were identified between the two groups in all the three-time
205 points separately. The DE genes were used for co-expression network analysis using
206 CEMiTools package [11]. The gene ontology (GO) enrichment of DE genes was
207 performed using the clusterProfiler R package [12]. Biological processes with adjusted
208 P-value ≤ 0.01 were considered as significantly enriched. Dotplot function provided in
209 clusterProfiler was used to visualize enriched pathways. In addition, gene interaction
210 network was visualized using STRING: functional protein association network
211 (https://string-db.org/).
212
213 Immune Cell Deconvolution. CIBERSORT was used to estimate the relative
214 composition of immune cells in bulk RNA-Seq samples [13] using a machine learning
215 model named as nu–support vector regression (ν-SVR) [14]. For each patient, a
9 medRxiv preprint doi: https://doi.org/10.1101/2020.02.15.20022772; this version posted February 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license .
216 complete blood count (CBC) was obtained upon presentation as part of their standard of
217 care clinical evaluation. We were therefore able to calculate estimated absolute counts
218 for each leukocyte subpopulation. This was done by multiplying the proportion for each
219 subpopulation as determined by CIBERSORT to the total white blood cell count from
220 the CBC. This analysis was validated by comparing the absolute neutrophil counts
221 (ANC) as estimated by CIBERSORT with the ANC reported by the clinical laboratory.
222
223 Statistical Analysis
224 All plots and statistical analyses were carried out using R programming language
225 (v3.5.1) (https://www.r-project.org/). By default, two-sided student’s t-test was performed
226 to compute the significance between two groups. The generalized linear model function
227 (glm) was used to calculate odds ratio (OR). Principal component analysis (PCA) of
228 gene expression profiles was performed using the prcomp function. The risk score was
229 estimated using the signature gene expression for each patient based on the geometric
230 mean. The geometric mean for x1, x2, ..., xn was calculated as follows: