Dissecting inflammatory complications in critically injured patients by within-patient gene expression changes: a longitudinal clinical genomics study

GeTeSEPdb: A comprehensive database and online tool for the identification and analysis of gene profiles with temporal-specific expression patterns

Gene expression is dynamic and varies at different stages of processes. The identification of gene profiles with temporal-specific expression patterns can provide valuable insights into ongoing biological processes, such as the cell cycle, cell development, circadian rhythms, or responses to external stimuli such as drug treatments or viral infections. However, currently, no database defines, identifies or archives gene profiles with temporal-specific expression patterns. Here, using a high-throughput regression analysis approach, eight linear and nonlinear parametric models were fitted to gene expression profiles from time-series experiments to identify eight types of gene profiles with temporal-specific expression patterns. We curated 2684 time-series transcriptome datasets and identified 2644,370 gene profiles exhibiting temporal-specific expression patterns. The results were stored in the database GeTeSEPdb (gene profiles with temporal-specific expression patterns database, http://www.inbirg.com/GeTeSEPdb/). Moreover, we implemented an online tool to identify gene profiles with temporal-specific expression patterns from user-submitted data. In summary, GeTeSEPdb is a comprehensive web service that can be used to identify and analyse gene profiles with temporal-specific expression patterns. This approach facilitates the exploration of transcriptional changes and temporal patterns of responses. We firmly believe that GeTeSEPdb will become a valuable resource for biologists and bioinformaticians.

A Transcriptomic Approach to Understand Patient Susceptibility to Pneumonia After Abdominal Surgery

OBJECTIVE

To describe immune pathways and gene networks altered following major abdominal surgery and to identify transcriptomic patterns associated with postoperative pneumonia.

BACKGROUND

Nosocomial infections are a major healthcare challenge, developing in over 20% of patients aged 45 or over undergoing major abdominal surgery, with postoperative pneumonia associated with an almost 5-fold increase in 30-day mortality.

METHODS

From a prospective consecutive cohort (n=150) undergoing major abdominal surgery, whole-blood RNA was collected preoperatively and at 3 time-points postoperatively (2-6, 24, and 48 h). Twelve patients diagnosed with postoperative pneumonia and 27 matched patients remaining infection-free were identified for analysis with RNA-sequencing.

RESULTS

Compared to preoperative sampling, 3639 genes were upregulated and 5043 downregulated at 2 to 6 hours. Pathway analysis demonstrated innate-immune activation with neutrophil degranulation and Toll-like-receptor signaling upregulation alongside adaptive-immune suppression. Cell-type deconvolution of preoperative RNA-sequencing revealed elevated S100A8/9-high neutrophils alongside reduced naïve CD4 T-cells in those later developing pneumonia. Preoperatively, a gene-signature characteristic of neutrophil degranulation was associated with postoperative pneumonia acquisition ( P =0.00092). A previously reported Sepsis Response Signature (SRSq) score, reflecting neutrophil dysfunction and a more dysregulated host response, at 48 hours postoperatively, differed between patients subsequently developing pneumonia and those remaining infection-free ( P =0.045). Analysis of the novel neutrophil gene-signature and SRSq scores in independent major abdominal surgery and polytrauma cohorts indicated good predictive performance in identifying patients suffering later infection.

CONCLUSIONS

Major abdominal surgery acutely upregulates innate-immune pathways while simultaneously suppressing adaptive-immune pathways. This is more prominent in patients developing postoperative pneumonia. Preoperative transcriptomic signatures characteristic of neutrophil degranulation and postoperative SRSq scores may be useful predictors of subsequent pneumonia risk.

Improving liver transplant outcomes with transplant-omics and network biology

PURPOSE OF REVIEW

Molecular omics data is increasingly ubiquitous throughout medicine. In organ transplantation, recent large-scale research efforts are generating the 'transplant-ome' - the entire set of molecular omics data, including the genome, transcriptome, proteome, and metabolome. Importantly, early studies in anesthesiology have demonstrated how perioperative interventions alter molecular profiles in various patient populations. The next step for anesthesiologists and intensivists will be to tailor perioperative care to the transplant-ome of individual liver transplant patients.

RECENT FINDINGS

In liver transplant patients, elements of the transplant-ome predict complications and point to novel interventions. Importantly, molecular profiles of both the donor organ and recipient contribute to this risk, and interventions like normothermic machine perfusion influence these profiles. As we can now measure various omics molecules simultaneously, we can begin to understand how these molecules interact to form molecular networks and emerging technologies offer noninvasive and continuous ways to measure these networks throughout the perioperative period. Molecules that regulate these networks are likely mediators of complications and actionable clinical targets throughout the perioperative period.

SUMMARY

The transplant-ome can be used to tailor perioperative care to the individual liver transplant patient. Monitoring molecular networks continuously and noninvasively would provide new opportunities to optimize perioperative management.

Obesity Does Not Increase Risk for Mortality in Severe Sepsis Trauma Patients

INTRODUCTION

The prevalence of obesity in the United States is up to 40% in adults. Obese patients with severe sepsis have a lower mortality rate compared with normal body mass index (BMI) patients. We hypothesized that trauma patients with severe sepsis and obese BMI will have a decreased mortality risk in comparison with normal BMI patients.

METHODS

The Trauma Quality Improvement Program (2017) was queried for adult trauma patients with documented BMI and severe sepsis. Patients were grouped based on BMI: non-obese trauma patients (nOTP) BMI <30 kg/m2 and obese trauma patients (OTP) ≥30 kg/m2. A multivariable logistic regression model was used for analysis of mortality.

RESULTS

From 1246 trauma patients with severe sepsis, 566 (42.4%) were nOTP and 680 (57.6%) were OTP. OTP had increased length of stay (LOS) (19 vs 21 days, P < .001), intensive care unit (ICU) LOS (13 vs 18 days, P < .001) and ventilator days (10 vs 11 days, P < .001). After adjusting for covariates, when compared to normal BMI patients, patients who were overweight (OR 1.11 CI .875-1.41 P = .390), obese (OR .797 CI .59-1.06 P = .126), severely obese (OR .926 CI .63-1.36 P = .696) and morbidly obese (OR 1.448 CI 1.01-2.07 P = .04) all had a similar associated risk for mortality compared to patients with normal BMI.

CONCLUSION

In adult trauma patients with severe sepsis, this national analysis demonstrated OTP had increased LOS, ICU LOS, and ventilator days compared to nOTP. However, patients with increasing degrees of obesity had similar associated risk of mortality compared to trauma patients with severe sepsis and a normal BMI.

Multi-omic molecular profiling and network biology for precision anaesthesiology: a narrative review

Technological advancement, data democratisation, and decreasing costs have led to a revolution in molecular biology in which the entire set of DNA, RNA, proteins, and various other molecules - the 'multi-omic' profile - can be measured in humans. Sequencing 1 million bases of human DNA now costs US$0.01, and emerging technologies soon promise to reduce the cost of sequencing the whole genome to US$100. These trends have made it feasible to sample the multi-omic profile of millions of people, much of which is publicly available for medical research. Can anaesthesiologists use these data to improve patient care? This narrative review brings together a rapidly growing literature in multi-omic profiling across numerous fields that points to the future of precision anaesthesiology. Here, we discuss how DNA, RNA, proteins, and other molecules interact in molecular networks that can be used for preoperative risk stratification, intraoperative optimisation, and postoperative monitoring. This literature provides evidence for four fundamental insights: (1) Clinically similar patients have different molecular profiles and, as a consequence, different outcomes. (2) Vast, publicly available, and rapidly growing molecular datasets have been generated in chronic disease patients and can be repurposed to estimate perioperative risk. (3) Multi-omic networks are altered in the perioperative period and influence postoperative outcomes. (4) Multi-omic networks can serve as empirical, molecular measurements of a successful postoperative course. With this burgeoning universe of molecular data, the anaesthesiologist-of-the-future will tailor their clinical management to an individual's multi-omic profile to optimise postoperative outcomes and long-term health.

Immune modulation after traumatic brain injury

Traumatic brain injury (TBI) induces instant activation of innate immunity in brain tissue, followed by a systematization of the inflammatory response. The subsequent response, evolved to limit an overwhelming systemic inflammatory response and to induce healing, involves the autonomic nervous system, hormonal systems, and the regulation of immune cells. This physiological response induces an immunosuppression and tolerance state that promotes to the occurrence of secondary infections. This review describes the immunological consequences of TBI and highlights potential novel therapeutic approaches using immune modulation to restore homeostasis between the nervous system and innate immunity.

Cytokine Pathways in Cardiac Dysfunction following Burn Injury and Changes in Genome Expression

In 2016, an estimated 486,000 individuals sustained burn injuries requiring medical attention. Severe burn injuries lead to a persistent, hyperinflammatory response that may last up to 2 years. The persistent release of inflammatory mediators contributes to end-organ dysfunction and changes in genome expression. Burn-induced cardiac dysfunction may lead to heart failure and changes in cardiac remodeling. Cytokines promote the inflammatory cascade and promulgate mechanisms resulting in cardiac dysfunction. Here, we review the mechanisms by which TNFα, IL-1 beta, IL-6, and IL-10 cause cardiac dysfunction in post-burn injuries. We additionally review changes in the cytokine transcriptome caused by inflammation and burn injuries.

Chronic Critical Illness Elicits a Unique Circulating Leukocyte Transcriptome in Sepsis Survivors

Surgical sepsis has evolved into two major subpopulations: patients who rapidly recover, and those who develop chronic critical illness (CCI). Our primary aim was to determine whether CCI sepsis survivors manifest unique blood leukocyte transcriptomes in late sepsis that differ from transcriptomes among sepsis survivors with rapid recovery. In a prospective cohort study of surgical ICU patients, genome-wide expression analysis was conducted on total leukocytes in human whole blood collected on days 1 and 14 from sepsis survivors who rapidly recovered or developed CCI, defined as ICU length of stay ≥ 14 days with persistent organ dysfunction. Both sepsis patients who developed CCI and those who rapidly recovered exhibited marked changes in genome-wide expression at day 1 which remained abnormal through day 14. Although summary changes in gene expression were similar between CCI patients and subjects who rapidly recovered, CCI patients exhibited differential expression of 185 unique genes compared with rapid recovery patients at day 14 (p < 0.001). The transcriptomic patterns in sepsis survivors reveal an ongoing immune dyscrasia at the level of the blood leukocyte transcriptome, consistent with persistent inflammation and immune suppression. Furthermore, the findings highlight important genes that could compose a prognostic transcriptomic metric or serve as therapeutic targets among sepsis patients that develop CCI.

Coupling sparse Cox models with clustering of longitudinal transcriptomics data for trauma prognosis

Background

Longitudinal gene expression analysis and survival modeling have been proved to add valuable biological and clinical knowledge. This study proposes a novel framework to discover gene signatures and patterns in a high-dimensional time series transcriptomics data and to assess their association with hospital length of stay.

Methods

We investigated a longitudinal and high-dimensional gene expression dataset from 168 blunt-force trauma patients followed during the first 28 days after injury. To model the length of stay, an initial dimensionality reduction step was performed by applying Cox regression with elastic net regularization using gene expression data from the first hospitalization days. Also, a novel methodology to impute missing values to the genes selected previously was proposed. We then applied multivariate time series (MTS) clustering to analyse gene expression over time and to stratify patients with similar trajectories. The validation of the patients’ partitions obtained by MTS clustering was performed using Kaplan-Meier curves and log-rank tests.

Results

We were able to unravel 22 genes strongly associated with hospital’s discharge. Their expression values in the first days after trauma showed to be good predictors of the length of stay. The proposed mixed imputation method allowed to achieve a complete dataset of short time series with a minimum loss of information for the 28 days of follow-up. MTS clustering enabled to group patients with similar genes trajectories and, notably, with similar discharge days from the hospital. Patients within each cluster have comparable genes’ trajectories and may have an analogous response to injury.

Conclusion

The proposed framework was able to tackle the joint analysis of time-to-event information with longitudinal multivariate high-dimensional data. The application to length of stay and transcriptomics data revealed a strong relationship between gene expression trajectory and patients’ recovery, which may improve trauma patient’s management by healthcare systems. The proposed methodology can be easily adapted to other medical data, towards more effective clinical decision support systems for health applications.

BMP-2 delivery strategy modulates local bone regeneration and systemic immune responses to complex extremity trauma

Bone nonunions arising from large bone defects and composite injuries remain compelling challenges for orthopedic surgeons. Biological changes associated with nonunions, such as systemic immune dysregulation, can contribute to an adverse healing environment. Bone morphogenetic protein 2 (BMP-2), an osteoinductive and potentially immunomodulatory growth factor, is a promising strategy; however, burst release from the clinical standard collagen sponge delivery vehicle can result in adverse side effects such as heterotopic ossification (HO) and irregular bone structure, especially when using supraphysiological BMP-2 doses for complex injuries at high risk for nonunion. To address this challenge, biomaterials that strongly bind BMP-2, such as heparin methacrylamide microparticles (HMPs), may be used to limit exposure and spatially constrain proteins within the injury site. Here, we investigate moderately high dose BMP-2 delivered in HMPs within an injectable hydrogel system in two challenging nonunion models exhibiting characteristics of systemic immune dysregulation. The HMP delivery system increased total bone volume and decreased peak HO compared to collagen sponge delivery of the same BMP-2 dose. Multivariate analyses of systemic immune markers showed the collagen sponge group correlated with markers that are hallmarks of systemic immune dysregulation, including immunosuppressive myeloid-derived suppressor cells, whereas the HMP groups were associated with immune effector cells, including T cells, and cytokines linked to robust bone regeneration. Overall, our results demonstrate that HMP delivery of moderately high doses of BMP-2 promotes repair of complex bone nonunion injuries and that local delivery strategies for potent growth factors like BMP-2 may positively affect the systemic immune response to traumatic injury.

Modulation of Brain Transcriptome by Combined Histone Deacetylase Inhibition and Plasma Treatment Following Traumatic Brain Injury and Hemorrhagic Shock

INTRODUCTION

We previously showed that the addition of valproic acid (VPA), a histone deacetylase inhibitor, to fresh frozen plasma (FFP) resuscitation attenuates brain lesion size and swelling following traumatic brain injury (TBI) and hemorrhagic shock (HS). The goal of this study was to use computational biology tools to investigate the effects of FFP+VPA on the brain transcriptome following TBI+HS.

METHODS

Swine underwent TBI+HS, kept in shock for 2 h, and resuscitated with FFP or FFP + VPA (n = 5/group). After 6 h of observation, brain RNA was isolated and gene expression was analyzed using a microarray. iPathwayGuide, Gene Ontology (GO), Gene-Set Enrichment Analysis, and Enrichment Mapping were used to identify significantly impacted genes and transcriptomic networks.

RESULTS

Eight hundred differentially expressed (DE) genes were identified out of a total of 9,118 genes. Upregulated genes were involved in promotion of cell division, proliferation, and survival, while downregulated genes were involved in autophagy, cell motility, neurodegenerative diseases, tumor suppression, and cell cycle arrest. Seven hundred ninety-one GO terms were significantly enriched. A few major transcription factors, such as TP53, NFKB3, and NEUROD1, were responsible for modulating hundreds of other DE genes. Network analysis revealed attenuation of interconnected genes involved in inflammation and tumor suppression, and an upregulation of those involved in cell proliferation and differentiation.

CONCLUSION

Overall, these results suggest that VPA treatment creates an environment that favors production of new neurons, removal of damaged cells, and attenuation of inflammation, which could explain its previously observed neuroprotective effects.

Multi-Biomarker Prediction Models for Multiple Infection Episodes Following Blunt Trauma

Severe trauma predisposes patients to multiple independent infection episodes (MIIEs), leading to augmented morbidity and mortality. We developed a method to identify increased MIIE risk before clinical signs appear, which is fundamentally different from existing approaches entailing infections' detection after their establishment. Applying machine learning algorithms to genome-wide transcriptome data from 128 adult blunt trauma patients' (42 MIIE cases and 85 non-cases) leukocytes collected ≤48 hr of injury and ≥3 days before any infection, we constructed a 15-transcript and a 26-transcript multi-biomarker panel model with the least absolute shrinkage and selection operator (LASSO) and Elastic Net, respectively, which accurately predicted MIIE (Area Under Receiver Operating Characteristics Curve [AUROC] [95% confidence intervals, CI]: 0.90 [0.84–0.96] and 0.92 [0.86–0.96]) and significantly outperformed clinical models. Gene Ontology and network analyses found various pathways to be relevant. External validation found our model to be generalizable. Our unique precision medicine approach can be applied to a wide range of patient populations and outcomes.

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We describe a method for predicting multiple independent infection episodes (MIIEs).

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We applied machine learning algorithms to transcriptome data to develop models

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The biomarker prediction models significantly outperformed clinical models

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External validation in another trauma cohort found evidence of generalizability

Linking Big Data and Prediction Strategies: Tools, Pitfalls, and Lessons Learned

OBJECTIVES

Modern critical care amasses unprecedented amounts of clinical data-so called "big data"-on a minute-by-minute basis. Innovative processing of these data has the potential to revolutionize clinical prognostics and decision support in the care of the critically ill but also forces clinicians to depend on new and complex tools of which they may have limited understanding and over which they have little control. This concise review aims to provide bedside clinicians with ways to think about common methods being used to extract information from clinical big datasets and to judge the quality and utility of that information.

DATA SOURCES

We searched the free-access search engines PubMed and Google Scholar using the MeSH terms "big data", "prediction", and "intensive care" with iterations of a range of additional potentially associated factors, along with published bibliographies, to find papers suggesting illustration of key points in the structuring and analysis of clinical "big data," with special focus on outcomes prediction and major clinical concerns in critical care.

STUDY SELECTION

Three reviewers independently screened preliminary citation lists.

DATA EXTRACTION

Summary data were tabulated for review.

DATA SYNTHESIS

To date, most relevant big data research has focused on development of and attempts to validate patient outcome scoring systems and has yet to fully make use of the potential for automation and novel uses of continuous data streams such as those available from clinical care monitoring devices.

CONCLUSIONS

Realizing the potential for big data to improve critical care patient outcomes will require unprecedented team building across disparate competencies. It will also require clinicians to develop statistical awareness and thinking as yet another critical judgment skill they bring to their patients' bedsides and to the array of evidence presented to them about their patients over the course of care.

The greater inflammatory pathway-high clinical potential by innovative predictive, preventive, and personalized medical approach

BACKGROUND AND LIMITATIONS

Impaired wound healing (WH) and chronic inflammation are hallmarks of non-communicable diseases (NCDs). However, despite WH being a recognized player in NCDs, mainstream therapies focus on (un)targeted damping of the inflammatory response, leaving WH largely unaddressed, owing to three main factors. The first is the complexity of the pathway that links inflammation and wound healing; the second is the dual nature, local and systemic, of WH; and the third is the limited acknowledgement of genetic and contingent causes that disrupt physiologic progression of WH.

PROPOSED APPROACH

Here, in the frame of Predictive, Preventive, and Personalized Medicine (PPPM), we integrate and revisit current literature to offer a novel systemic view on the cues that can impact on the fate (acute or chronic inflammation) of WH, beyond the compartmentalization of medical disciplines and with the support of advanced computational biology.

CONCLUSIONS

This shall open to a broader understanding of the causes for WH going awry, offering new operational criteria for patients' stratification (prediction and personalization). While this may also offer improved options for targeted prevention, we will envisage new therapeutic strategies to reboot and/or boost WH, to enable its progression across its physiological phases, the first of which is a transient acute inflammatory response versus the chronic low-grade inflammation characteristic of NCDs.

A conceptual time window-based model for the early stratification of trauma patients

Progress in the testing of therapies targeting the immune response following trauma, a leading cause of morbidity and mortality worldwide, has been slow. We propose that the design of interventional trials in trauma would benefit from a scheme or platform that could support the identification and implementation of prognostic strategies for patient stratification. Here, we propose a stratification scheme based on defined time periods or windows following the traumatic event. This 'time-window' model allows for the incorporation of prognostic variables ranging from circulating biomarkers and clinical data to patient-specific information such as gene variants to predict adverse short- or long-term outcomes. A number of circulating biomarkers, including cell injury markers and damage-associated molecular patterns (DAMPs), and inflammatory mediators have been shown to correlate with adverse outcomes after trauma. Likewise, several single nucleotide polymorphisms (SNPs) associate with complications or death in trauma patients. This review summarizes the status of our understanding of the prognostic value of these classes of variables in predicting outcomes in trauma patients. Strategies for the incorporation of these prognostic variables into schemes designed to stratify trauma patients, such as our time-window model, are also discussed.

Temporal progression of gene regulation of peripheral white blood cells explains gender dimorphism of critically ill patients after trauma

BACKGROUND

The immune response of the critically ill after severe trauma is sex-specific and may explain the different progression of the disease. This may be explained by a different gene regulatory program of their peripheral immune cells. We investigated the progression of the transcription profiles of peripheral immune cells of the patients to elucidate their distinct physiological response and clinical course.

METHODS

We compared transcription profiles of whole blood of male and female patients from a larger longitudinal study of critically ill patients after trauma. We developed a statistical analysis pipeline that synchronized the time lapse of the profiles based on the temporal severity score of each patient.

RESULTS

This enabled to categorize the temporal progression of the disease into two pre-acute, an acute and two post-acute phases. Comparing gene regulation of male and female patients at each phase, we identified distinctively regulated molecular processes mainly in the immune response, but also in the regulation of metabolism allowing to cluster these discriminative gene sets into sets of highly related cellular processes. Compared to male patients and healthy controls, female patients showed upregulation of gene sets of innate immunity in the early phase, upregulation of wound healing processes during the acute phase and upregulation of adaptive immunity in the late phase indicating early recovery. In turn, during the pre-acute and acute phase, male patients showed less suppression of gene sets coding for enzymes of energy metabolism and anabolism, most prominently the tricarboxylic acid cycle and β-oxidation, and cellular maintenance, such as cell cycle, DNA replication and damage response, and RNA metabolism.

CONCLUSIONS

A stronger innate immune response at the very early phase of the disease may support early clearance of the pathogen and its associated molecular patterns. Upregulation of wound healing processes may explain reduced multiple organ failure during the acute phase. Down regulated energy metabolism during the acute phase may make female patients less susceptible to oxidative stress, the upregulated adaptive immune system reflects an earlier recovery and rebuilding of the adaptive immune system that may protect them from secondary infections. Follow up studies need to be performed confirming these observations experimentally.

The effect of fluid resuscitation strategy on monocyte and T-cell surface markers

BACKGROUND

Despite initial lifesaving benefits, posttraumatic resuscitation strategies have been associated with immunologic complications leading to systemic inflammatory response syndrome, sepsis, multiple organ failure, and late trauma death. Nevertheless, the direct effect on immunologic surface markers remains inadequately described. We hypothesized that changes in monocyte and T-cell surface markers were associated with initial posttraumatic fluid resuscitation.

MATERIALS AND METHODS

Data were extracted from the inflammation and host response to injury (Glue Grant) study. Blood samples were drawn from 492 patients on days 0, 1, 4, 7, 14, and 28 and analyzed for 31 monocyte and T-cell surface markers. Resuscitation strategies during the initial 48 h were quantified, including transfusion of packed red blood cells (PRBCs), fresh frozen plasma (FFP), platelets, and crystalloids. Longitudinal surface marker concentration changes were quantified by the calculation of a within-patient signal intensity change and were associated with resuscitation strategy while controlling confounders. P-values were post hoc corrected using the false detection rate q-value.

RESULTS

The monocyte surface marker (CD83) trajectory (as measured by a within-patient signal intensity change) was found to be positively associated with volume of PRBCs transfused (q = 0.002) and negatively associated with the transfused volume of FFP (q = 0.004). T-cell surface marker (CD3) was found to be negatively associated with volume of PRBCs transfused (q = 854 × 10-9) and positively associated with the transfused volume of FFP (q = 0.022). Platelets and crystalloid transfusion volumes were not associated with any surface marker trajectories.

CONCLUSIONS

PRBC and FFP transfusion was associated with opposing effects on CD3 and CD83 trajectories, which may in part explain some of the protective effects of a high FFP:PRBC ratio in trauma-related resuscitation.

The effect of resuscitation strategy on the longitudinal immuno-inflammatory response to blunt trauma

INTRODUCTION

Resuscitation strategies following blunt trauma have been linked to immuno-inflammatory complications leading to systemic inflammatory syndrome (SIRS), sepsis and multiple organ failure (MOF). The effect of resuscitation strategy on longitudinal inflammation marker trajectories is, however, unknown. We hypothesized that the effect of resuscitation strategy extends beyond the trauma-related coagulopathy, perhaps affecting the longitudinal immuno-inflammatory response to injury.

METHODS

We analyzed data prospectively collected for the Inflammation and Host Response to Injury (Glue Grant) study. Blood sampling for inflammation marker analyses from blunt trauma patients was done on admission days 0, 1, 4, 7, 14, 21 and 28 where applicable. Total volume transfused of packed red blood cells (PRBC), fresh frozen plasma (FFP), platelets (PLT), and crystalloids during the initial 48h was extracted, along with an analysis for an array of cytokines by Enzyme Linked Immunosorbent Assay (ELISA) technique. A within patient concentration change (WPCC) was calculated to quantify longitudinal alterations in cytokine levels, while controlling for potential confounders. To account for the multiple comparisons performed, p-values obtained from the multivariate regression model were post-hoc corrected by the false detection rate (FDR) q-value.

RESULTS

No longitudinal trajectories of inflammatory markers were found to be associated with PRBC- or PLT transfusion. Three proinflammatory cytokines (Il-1β, MIP-1β, and TNFR2) were negatively associated with volume of FFP transfused (q=0.02, q<0.001 and q=0.007 respectively), and one proinflammatory cytokine (MIP-1β) was positively associated with crystalloid infusion (q=0.005).

CONCLUSIONS

Resuscitation strategy employed following blunt trauma has limited association to longitudinal inflammation marker trajectories, with a potential association between the strategy employed and IL-1β, TNFR2, and MIP-1β trajectories, respectively.

Strategic Targeting of Multiple BMP Receptors Prevents Trauma-Induced Heterotopic Ossification

Trauma-induced heterotopic ossification (tHO) is a condition of pathologic wound healing, defined by the progressive formation of ectopic bone in soft tissue following severe burns or trauma. Because previous studies have shown that genetic variants of HO, such as fibrodysplasia ossificans progressiva (FOP), are caused by hyperactivating mutations of the type I bone morphogenetic protein receptor (T1-BMPR) ACVR1/ALK2, studies evaluating therapies for HO have been directed primarily toward drugs for this specific receptor. However, patients with tHO do not carry known T1-BMPR mutations. Here we show that, although BMP signaling is required for tHO, no single T1-BMPR (ACVR1/ALK2, BMPR1a/ALK3, or BMPR1b/ALK6) alone is necessary for this disease, suggesting that these receptors have functional redundancy in the setting of tHO. By utilizing two different classes of BMP signaling inhibitors, we developed a translational approach to treatment, integrating treatment choice with existing diagnostic options. Our treatment paradigm balances either immediate therapy with reduced risk for adverse effects (Alk3-Fc) or delayed therapy with improved patient selection but greater risk for adverse effects (LDN-212854).

Signatures of inflammation and impending multiple organ dysfunction in the hyperacute phase of trauma: A prospective cohort study

BACKGROUND

Severe trauma induces a widespread response of the immune system. This "genomic storm" can lead to poor outcomes, including Multiple Organ Dysfunction Syndrome (MODS). MODS carries a high mortality and morbidity rate and adversely affects long-term health outcomes. Contemporary management of MODS is entirely supportive, and no specific therapeutics have been shown to be effective in reducing incidence or severity. The pathogenesis of MODS remains unclear, and several models are proposed, such as excessive inflammation, a second-hit insult, or an imbalance between pro- and anti-inflammatory pathways. We postulated that the hyperacute window after trauma may hold the key to understanding how the genomic storm is initiated and may lead to a new understanding of the pathogenesis of MODS.

METHODS AND FINDINGS

We performed whole blood transcriptome and flow cytometry analyses on a total of 70 critically injured patients (Injury Severity Score [ISS] ≥ 25) at The Royal London Hospital in the hyperacute time period within 2 hours of injury. We compared transcriptome findings in 36 critically injured patients with those of 6 patients with minor injuries (ISS ≤ 4). We then performed flow cytometry analyses in 34 critically injured patients and compared findings with those of 9 healthy volunteers. Immediately after injury, only 1,239 gene transcripts (4%) were differentially expressed in critically injured patients. By 24 hours after injury, 6,294 transcripts (21%) were differentially expressed compared to the hyperacute window. Only 202 (16%) genes differentially expressed in the hyperacute window were still expressed in the same direction at 24 hours postinjury. Pathway analysis showed principally up-regulation of pattern recognition and innate inflammatory pathways, with down-regulation of adaptive responses. Immune deconvolution, flow cytometry, and modular analysis suggested a central role for neutrophils and Natural Killer (NK) cells, with underexpression of T- and B cell responses. In the transcriptome cohort, 20 critically injured patients later developed MODS. Compared with the 16 patients who did not develop MODS (NoMODS), maximal differential expression was seen within the hyperacute window. In MODS versus NoMODS, 363 genes were differentially expressed on admission, compared to only 33 at 24 hours postinjury. MODS transcripts differentially expressed in the hyperacute window showed enrichment among diseases and biological functions associated with cell survival and organismal death rather than inflammatory pathways. There was differential up-regulation of NK cell signalling pathways and markers in patients who would later develop MODS, with down-regulation of neutrophil deconvolution markers. This study is limited by its sample size, precluding more detailed analyses of drivers of the hyperacute response and different MODS phenotypes, and requires validation in other critically injured cohorts.

CONCLUSIONS

In this study, we showed how the hyperacute postinjury time window contained a focused, specific signature of the response to critical injury that led to widespread genomic activation. A transcriptomic signature for later development of MODS was present in this hyperacute window; it showed a strong signal for cell death and survival pathways and implicated NK cells and neutrophil populations in this differential response.

DTWscore: differential expression and cell clustering analysis for time-series single-cell RNA-seq data

BACKGROUND

The development of single-cell RNA sequencing has enabled profound discoveries in biology, ranging from the dissection of the composition of complex tissues to the identification of novel cell types and dynamics in some specialized cellular environments. However, the large-scale generation of single-cell RNA-seq (scRNA-seq) data collected at multiple time points remains a challenge to effective measurement gene expression patterns in transcriptome analysis.

RESULTS

We present an algorithm based on the Dynamic Time Warping score (DTWscore) combined with time-series data, that enables the detection of gene expression changes across scRNA-seq samples and recovery of potential cell types from complex mixtures of multiple cell types.

CONCLUSIONS

The DTWscore successfully classify cells of different types with the most highly variable genes from time-series scRNA-seq data. The study was confined to methods that are implemented and available within the R framework. Sample datasets and R packages are available at https://github.com/xiaoxiaoxier/DTWscore .

Fresh Frozen Plasma Modulates Brain Gene Expression in a Swine Model of Traumatic Brain Injury and Shock: A Network Analysis

BACKGROUND

Resuscitation with fresh frozen plasma (FFP) decreases brain lesion size and swelling in a swine model of traumatic brain injury and hemorrhagic shock. We hypothesized that brain gene expression profiles after traumatic brain injury and hemorrhagic shock would be modulated by FFP resuscitation.

STUDY DESIGN

Fifteen swine underwent a protocol of traumatic brain injury and hemorrhagic shock and 2 hours of shock followed by resuscitation with FFP, normal saline, or hetastarch (5/group). After 6 hours, brain RNA was isolated and hybridized onto a porcine gene ST 1.1 microarray. Weighted gene correlation network analysis was used to identify clusters of highly coexpressed genes. Principal component analysis identified cluster eigenvectors, indicating overall direction and magnitude of cluster gene expression. Using linear regression, cluster eigenvectors were associated with treatment as well as brain lesion size and swelling. Results were post-hoc corrected using false discovery rate. Relevant gene clusters were subjected to pathway analysis using the Reactome tool.

RESULTS

Network analysis identified 322 gene expression clusters (total of 12,462 coexpressed genes). Fresh frozen plasma resuscitation (but not normal saline or hetastarch) was positively associated with 2 distinct gene clusters (termed A and B) comprising 493 genes. Gene expression in both clusters was negatively associated with brain swelling, and cluster B was also negatively associated with lesion size. Pathway analysis revealed an upregulation of genes involved in metabolic and platelet signaling, as well as collagen formation and downregulation of inflammation.

CONCLUSIONS

Fresh frozen plasma resuscitation in this model was associated with downregulation of inflammatory pathway genes and expression of gene clusters mapping to increased metabolic and platelet signaling, which, in turn, was reversely associated with brain swelling.

The diagnostic and prognostic value of systems biology research in major traumatic and thermal injury: a review

As secondary complications remain a significant cause of morbidity and mortality amongst hospitalised trauma patients, the need to develop novel approaches by which to identify patients at risk of adverse outcome is becoming increasingly important. Centred on the idea that patients who experience “poor” outcome post trauma elicit a response to injury that is distinct from those who experience “good” outcome, tailored therapeutics is an emerging concept aimed at improving current treatment regimens by promoting patient-specific therapies. Making use of recent advancements in the fields of genomics, proteomics and metabolomics, numerous groups have undertaken a systems-based approach to analysing the acute immune and inflammatory response to major traumatic and thermal injury in an attempt to uncover a single or combination of biomarkers that can identify patients at risk of adverse outcome. Early results are encouraging, with all three approaches capable of discriminating patients with “good” outcome from those who develop nosocomial infections, sepsis and multiple organ failure, with differences apparent in blood samples acquired as early as 2 h post injury. In particular, genomic data is proving to be highly informative, identifying patients at risk of “poor” outcome with a higher degree of sensitivity and specificity than statistical models built upon data obtained from existing anatomical and physiological scoring systems. Here, focussing predominantly upon human-based research, we provide an overview of the findings of studies that have investigated the immune and inflammatory response to major traumatic and thermal injury at the genomic, protein and metabolite level, and consider both the diagnostic and prognostic potential of these approaches.

Risk Stratification and Prognosis in Sepsis: What Have We Learned from Microarrays?

Sepsis mortality rates have decreased in recent years but remain unacceptably high. Risk stratification and prognostication is of particular importance because high-risk patients may benefit from earlier clinical interventions, whereas low-risk patients may benefit from not undergoing unnecessary procedures. Prognostication is currently done mostly via clinical criteria and blood lactate levels. This article summarizes the literature on the complexity of changes at the molecular level for the casual reader.

A Transcriptomic Biomarker to Quantify Systemic Inflammation in Sepsis - A Prospective Multicenter Phase II Diagnostic Study

Development of a dysregulated immune response discriminates sepsis from uncomplicated infection. Currently used biomarkers fail to describe simultaneously occurring pro- and anti-inflammatory responses potentially amenable to therapy.

Marker candidates were screened by microarray and, after transfer to a platform allowing point-of-care testing, validated in a confirmation set of 246 medical and surgical patients. We identified up-regulated pathways reflecting innate effector mechanisms, while down-regulated pathways related to adaptive lymphocyte functions. A panel of markers composed of three up- (Toll-like receptor 5; Protectin; Clusterin) and 4 down-regulated transcripts (Fibrinogen-like 2; Interleukin-7 receptor; Major histocompatibility complex class II, DP alpha1; Carboxypeptidase, vitellogenic-like) described the magnitude of immune alterations. The created gene expression score was significantly greater in patients with definite as well as with possible/probable infection than with no infection (median (Q25/Q75): 80 (60/101)) and 81 (58/97 vs. 49 (27/66), AUC-ROC = 0.812 (95%-CI 0.755–0.869), p < 0.0001). Down-regulated lymphocyte markers were associated with prognosis with good sensitivity but limited specificity.

Quantifying systemic inflammation by assessment of both pro- and anti-inflammatory innate and adaptive immune responses provides a novel option to identify patients-at-risk and may facilitate immune interventions in sepsis.

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Pro- and anti-inflammatory signaling occurs simultaneously in the host response to infection.

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This response is currently monitored using biomarkers restricted to the pro-inflammatory component of innate immunity.

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We developed a biomarker panel consisting of 7 transcripts that can assess both facets at the point of care.

The concept that a selective, overwhelming systemic inflammation, the “Systemic Inflammatory Response Syndrome (SIRS)”, triggers organ failure subsequent to infection has lately been abandoned as it neglects parallel occurring anti-inflammatory responses or defects in the adaptive immune system.

The present findings suggest that a compound panel of nucleic acid biomarkers that was developed in independent training and verification cohorts and transferred to a point-of-care platform can more comprehensively describe the host response. Quantification of an enhanced innate immunity might inform studies of anti-inflammatory therapies, while measurement of derangements in specific immunity might guide strategies to restore immune effector functions.

A comprehensive time-course-based multicohort analysis of sepsis and sterile inflammation reveals a robust diagnostic gene set

Although several dozen studies of gene expression in sepsis have been published, distinguishing sepsis from a sterile systemic inflammatory response syndrome (SIRS) is still largely up to clinical suspicion. We hypothesized that a multicohort analysis of the publicly available sepsis gene expression data sets would yield a robust set of genes for distinguishing patients with sepsis from patients with sterile inflammation. A comprehensive search for gene expression data sets in sepsis identified 27 data sets matching our inclusion criteria. Five data sets (n = 663 samples) compared patients with sterile inflammation (SIRS/trauma) to time-matched patients with infections. We applied our multicohort analysis framework that uses both effect sizes and P values in a leave-one-data set-out fashion to these data sets. We identified 11 genes that were differentially expressed (false discovery rate ≤1%, inter-data set heterogeneity P > 0.01, summary effect size >1.5-fold) across all discovery cohorts with excellent diagnostic power [mean area under the receiver operating characteristic curve (AUC), 0.87; range, 0.7 to 0.98]. We then validated these 11 genes in 15 independent cohorts comparing (i) time-matched infected versus noninfected trauma patients (4 cohorts), (ii) ICU/trauma patients with infections over the clinical time course (3 cohorts), and (iii) healthy subjects versus sepsis patients (8 cohorts). In the discovery Glue Grant cohort, SIRS plus the 11-gene set improved prediction of infection (compared to SIRS alone) with a continuous net reclassification index of 0.90. Overall, multicohort analysis of time-matched cohorts yielded 11 genes that robustly distinguish sterile inflammation from infectious inflammation.

Inhibition of Hif1α prevents both trauma-induced and genetic heterotopic ossification

Pathologic extraskeletal bone formation, or heterotopic ossification (HO), occurs following mechanical trauma, burns, orthopedic operations, and in patients with hyperactivating mutations of the type I bone morphogenetic protein receptor ACVR1 (Activin type 1 receptor). Extraskeletal bone forms through an endochondral process with a cartilage intermediary prompting the hypothesis that hypoxic signaling present during cartilage formation drives HO development and that HO precursor cells derive from a mesenchymal lineage as defined by Paired related homeobox 1 (Prx). Here we demonstrate that Hypoxia inducible factor-1α (Hif1α), a key mediator of cellular adaptation to hypoxia, is highly expressed and active in three separate mouse models: trauma-induced, genetic, and a hybrid model of genetic and trauma-induced HO. In each of these models, Hif1α expression coincides with the expression of master transcription factor of cartilage, Sox9 [(sex determining region Y)-box 9]. Pharmacologic inhibition of Hif1α using PX-478 or rapamycin significantly decreased or inhibited extraskeletal bone formation. Importantly, de novo soft-tissue HO was eliminated or significantly diminished in treated mice. Lineage-tracing mice demonstrate that cells forming HO belong to the Prx lineage. Burn/tenotomy performed in lineage-specific Hif1α knockout mice (Prx-Cre/Hif1α(fl:fl)) resulted in substantially decreased HO, and again lack of de novo soft-tissue HO. Genetic loss of Hif1α in mesenchymal cells marked by Prx-cre prevents the formation of the mesenchymal condensations as shown by routine histology and immunostaining for Sox9 and PDGFRα. Pharmacologic inhibition of Hif1α had a similar effect on mesenchymal condensation development. Our findings indicate that Hif1α represents a promising target to prevent and treat pathologic extraskeletal bone.

[Microarrays as biomarkers in trauma]

BACKGROUND

The traditional hypothesis-driven scientific approach cannot so far sufficiently elucidate complex pathophysiologies, such as posttraumatic systemic inflammation and subsequent multiple organ failure. This complex system includes different biological and functional levels, the genome, the transcriptome, the proteome, the biome (cells), the organs and finally the whole organism.

METHODS

Microarray techniques enable a simultaneous search for these different biological levels and their functional relationships on a large scale and to discover new functional pathways and networks and potentially new biomarkers for different pathologies. Microarray technologies lead to a new paradigm in science, the hypothesis-generating approach.

AIM

This article reviews important microarray findings in trauma and systemic inflammation research and discusses potentials and limitations of these biotechnological screening methods.

Innate immunity gene expression changes in critically ill patients with sepsis and disease-related malnutrition

The aim of this study was an attempt to determine whether the expression of genes involved in innate antibacterial response (TL R2, NOD 1, TRAF6, HMGB 1 and Hsp70) in peripheral blood leukocytes in critically ill patients, may undergo significant changes depending on the severity of the infection and the degree of malnutrition. The study was performed in a group of 128 patients with infections treated in the intensive care and surgical ward. In 103/80.5% of patients, infections had a severe course (sepsis, severe sepsis, septic shock, mechanical ventilation of the lungs). Clinical monitoring included diagnosis of severe infection (according to the criteria of the ACC P/SCC M), assessment of severity of the patient condition and risk of death (APACHE II and SAPS II), nutritional assessment (NRS 2002 and SGA scales) and the observation of the early results of treatment. Gene expression at the mRNA level was analyzed by real-time PCR. The results of the present study indicate that in critically ill patients treated in the IC U there are significant disturbances in the expression of genes associated with innate antimicrobial immunity, which may have a significant impact on the clinical outcome. The expression of these genes varies depending on the severity of the patient condition, severity of infection and nutritional status. Expression disorders of genes belonging to innate antimicrobial immunity should be diagnosed as early as possible, monitored during the treatment and taken into account during early therapeutic treatment (including early nutrition to support the functions of immune cells).

Hypertrophic burn scar management: what does the evidence show? A systematic review of randomized controlled trials

INTRODUCTION

Hypertrophic scars (HTS) are a source of morbidity for burn survivors and can present with a range of lifestyle-limiting problems. These include pruritus, pain, burning, stiffness, and contractures. Many solutions have been developed, but few have been studied in the form of a prospective, randomized control trial (RCT). Given the importance these RCTs carry in shaping the treatment of burn patients, we sought to systematically and critically review this portion of the burn literature.

METHODS

PubMed was used to perform 2 separate searches with limits that included Humans, English, and Randomized Controlled Trial. A keyword search using "hypertrophic," "Scar," "burn," and "treatment" was cross-referenced with a MeSH subject-heading search using "Cicatrix, Hypertrophic" AND "Burn." Studies were then reviewed and excluded if they did not address management of burn HTS in the non-acute setting.

RESULTS

Two literature searches resulted in a total of 32 articles. Twelve articles were excluded because they were not relevant to the topic (n = 10) or could not be obtained (n = 2). The remaining 20 articles contained 882 patients treated for hypertrophic scars. Breakdown based on topics included laser therapy (58 patients, 2 articles), silicone gel (204 patients, 7 articles), compression garment (236 patients, 4 articles), silicone + pressure (226 patients, 3 articles), topical emollients (58 patients, 2 articles), systemic therapy (62 patients, 1 article), intralesional therapy (18 patients, 1 article), and surgical treatment (20 patients, 1 article). While some articles had favorable conclusions (laser, emollients, surgical, and intralesional therapy) or unfavorable conclusions (systemic therapy), there were conflicting results on silicone and/or compression.

CONCLUSIONS

Despite hypertrophic scars being a common occurrence in burn survivors, both the number of studies and consensus for treatment are limited. Efforts to perform larger, adequately powered RCTs are needed, specifically in the areas of silicone, compression garments, and combination therapy.

Inflammatory response to trauma: implications for coagulation and resuscitation

PURPOSE OF REVIEW

Recent studies have changed our understanding of the timing and interactions of the inflammatory processes and coagulation cascade following severe trauma. This review highlights this information and correlates its impact on the current clinical approach for fluid resuscitation and treatment of coagulopathy for trauma patients.

RECENT FINDINGS

Severe trauma is associated with a failure of multiple biologic emergency response systems that includes imbalanced inflammatory response, acute coagulopathy of trauma, and endovascular glycocalyx degradation with microcirculatory compromise. These abnormalities are all interlinked and related. Recent observations show that after severe trauma: proinflammatory and anti-inflammatory responses are concomitant, not sequential and resolution of the inflammatory response is an active process, not a passive one. Understanding these interrelated processes is considered extremely important for the development of future therapies for severe trauma in humans.

SUMMARY

Traumatic injuries continue to be a significant cause of mortality worldwide. Recent advances in understanding the mechanisms of end-organ failure, and modulation of the inflammatory response has important clinical implications regarding fluid resuscitation and treatment of coagulopathy.

Statistical significance of variables driving systematic variation in high-dimensional data

Motivation: There are a number of well-established methods such as principal component analysis (PCA) for automatically capturing systematic variation due to latent variables in large-scale genomic data. PCA and related methods may directly provide a quantitative characterization of a complex biological variable that is otherwise difficult to precisely define or model. An unsolved problem in this context is how to systematically identify the genomic variables that are drivers of systematic variation captured by PCA. Principal components (PCs) (and other estimates of systematic variation) are directly constructed from the genomic variables themselves, making measures of statistical significance artificially inflated when using conventional methods due to over-fitting.

Results: We introduce a new approach called the jackstraw that allows one to accurately identify genomic variables that are statistically significantly associated with any subset or linear combination of PCs. The proposed method can greatly simplify complex significance testing problems encountered in genomics and can be used to identify the genomic variables significantly associated with latent variables. Using simulation, we demonstrate that our method attains accurate measures of statistical significance over a range of relevant scenarios. We consider yeast cell-cycle gene expression data, and show that the proposed method can be used to straightforwardly identify genes that are cell-cycle regulated with an accurate measure of statistical significance. We also analyze gene expression data from post-trauma patients, allowing the gene expression data to provide a molecularly driven phenotype. Using our method, we find a greater enrichment for inflammatory-related gene sets compared to the original analysis that uses a clinically defined, although likely imprecise, phenotype. The proposed method provides a useful bridge between large-scale quantifications of systematic variation and gene-level significance analyses.

Availability and implementation: An R software package, called jackstraw, is available in CRAN.

Contact: jstorey@princeton.edu

Identifying proteins controlling key disease signaling pathways

MOTIVATION

Several types of studies, including genome-wide association studies and RNA interference screens, strive to link genes to diseases. Although these approaches have had some success, genetic variants are often only present in a small subset of the population, and screens are noisy with low overlap between experiments in different labs. Neither provides a mechanistic model explaining how identified genes impact the disease of interest or the dynamics of the pathways those genes regulate. Such mechanistic models could be used to accurately predict downstream effects of knocking down pathway members and allow comprehensive exploration of the effects of targeting pairs or higher-order combinations of genes.

RESULTS

We developed methods to model the activation of signaling and dynamic regulatory networks involved in disease progression. Our model, SDREM, integrates static and time series data to link proteins and the pathways they regulate in these networks. SDREM uses prior information about proteins' likelihood of involvement in a disease (e.g. from screens) to improve the quality of the predicted signaling pathways. We used our algorithms to study the human immune response to H1N1 influenza infection. The resulting networks correctly identified many of the known pathways and transcriptional regulators of this disease. Furthermore, they accurately predict RNA interference effects and can be used to infer genetic interactions, greatly improving over other methods suggested for this task. Applying our method to the more pathogenic H5N1 influenza allowed us to identify several strain-specific targets of this infection.

AVAILABILITY

SDREM is available from http://sb.cs.cmu.edu/sdrem.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Multiplatform single-sample estimates of transcriptional activation

Over the past two decades, many biotechnology platforms have been developed for high-throughput gene expression profiling. However, because each platform is subject to technology-specific biases and produces distinct raw-data distributions, researchers have experienced difficulty in integrating data across platforms. Data integration is crucial to data-generating consortiums, researchers transitioning to newer profiling technologies, and individuals seeking to aggregate data across experiments. We address this need with our Universal exPression Code (UPC) approach, which corrects for platform-specific background noise using models that account for the genomic base composition and length of target regions; this approach also uses a mixture model to estimate whether a gene is active in a particular profiling sample. The latter produces standardized UPC values on a zero-to-one scale, so that they can be interpreted consistently, irrespective of profiling technology, thus enabling downstream analysis pipelines to be developed in a platform-agnostic manner. The UPC method can be applied to one- and two-channel expression microarrays and to next-generation sequencing data (RNA sequencing). Furthermore, UPCs are derived using information from within a given sample only--no ancillary samples are required at processing time. Thus, UPCs are suitable for personalized-medicine workflows where samples must be processed individually rather than in batches. In a variety of analyses and comparisons, UPCs perform comparably to other methods designed specifically for microarrays or RNA sequencing in most settings. Software for calculating UPCs is freely available at www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html.

Development of a genomic metric that can be rapidly used to predict clinical outcome in severely injured trauma patients

OBJECTIVE

Many patients have complicated recoveries following severe trauma due to the development of organ injury. Physiological and anatomical prognosticators have had limited success in predicting clinical trajectories. We report on the development and retrospective validation of a simple genomic composite score that can be rapidly used to predict clinical outcomes.

DESIGN

Retrospective cohort study.

SETTING

Multi-institutional level 1 trauma centers.

PATIENTS

Data were collected from 167 severely traumatized (injury severity score >15) adult (18-55 yr) patients.

METHODS

Microarray-derived genomic data obtained from 167 severely traumatized patients over 28 days were assessed for differences in messenger RNA abundance among individuals with different clinical trajectories. Once a set of genes was identified based on differences in expression over the entire study period, messenger RNA abundance from these subjects obtained in the first 24 hours was analyzed in a blinded fashion using a rapid multiplex platform, and genomic data reduced to a single metric.

RESULTS

From the existing genomic dataset, we identified 63 genes whose leukocyte expression differed between an uncomplicated and complicated clinical outcome over 28 days. Using a multiplex approach that can quantitate messenger RNA abundance in less than 12 hours, we reassessed total messenger RNA abundance from the first 24 hours after trauma and reduced the genomic data to a single composite score using the difference from reference. This composite score showed good discriminatory capacity to distinguish patients with a complicated outcome (area under a receiver-operator curve, 0.811; p <0.001). This was significantly better than the predictive power of either Acute Physiology and Chronic Health Evaluation II or new injury severity score scoring systems.

CONCLUSIONS

A rapid genomic composite score obtained in the first 24 hours after trauma can retrospectively identify trauma patients who are likely to develop complicated clinical trajectories. A novel platform is described in which this genomic score can be obtained within 12 hours of blood collection, making it available for clinical decision making.

Effect of normalization on statistical and biological interpretation of gene expression profiles

An under-appreciated aspect of the genetic analysis of gene expression is the impact of post-probe level normalization on biological inference. Here we contrast nine different methods for normalization of an Illumina bead-array gene expression profiling dataset consisting of peripheral blood samples from 189 individual participants in the Center for Health Discovery and Well Being study in Atlanta, quantifying differences in the inference of global variance components and covariance of gene expression, as well as the detection of variants that affect transcript abundance (eSNPs). The normalization strategies, all relative to raw log2 measures, include simple mean centering, two modes of transcript-level linear adjustment for technical factors, and for differential immune cell counts, variance normalization by interquartile range and by quantile, fitting the first 16 Principal Components, and supervised normalization using the SNM procedure with adjustment for cell counts. Robustness of genetic associations as a consequence of Pearson and Spearman rank correlation is also reported for each method, and it is shown that the normalization strategy has a far greater impact than correlation method. We describe similarities among methods, discuss the impact on biological interpretation, and make recommendations regarding appropriate strategies.

A non-lethal traumatic/hemorrhagic insult strongly modulates the compartment-specific PAI-1 response in the subsequent polymicrobial sepsis

Introduction

Plasminogen activator inhibitor 1 (PAI-1) is a key factor in trauma- and sepsis-induced coagulopathy. We examined how trauma-hemorrhage (TH) modulates PAI-1 responses in subsequent cecal ligation and puncture (CLP)-induced sepsis, and the association of PAI-1 with septic outcomes.

Methods

Mice underwent TH and CLP 48 h later in three separate experiments. In experiment 1, mice were sacrificed pre- and post-CLP to characterize the trajectory of PAI-1 in plasma (protein) and tissues (mRNA). Post-CLP dynamics in TH-CLP (this study) and CLP-Only mice (prior study) were then compared for modulatory effects of TH. In experiment 2, to relate PAI-1 changes to outcome, mice underwent TH-CLP and were sampled daily and followed for 14 days to compare non-survivors (DEAD) and survivors (SUR). In experiment 3, plasma and tissue PAI-1 expression were compared between mice predicted to die (P-DIE) and to live (P-LIVE).

Results

In experiment 1, an early post-TH rise of circulating PAI-1 was contrasted by a delayed (post-TH) decrease of PAI-1 mRNA in organs. In the post-CLP phase, profiles of circulating PAI-1 were similar between TH-CLP and CLP-Only mice. Conversely, PAI-1 mRNA declined in the liver and heart of TH-CLP mice versus CLP-Only. In experiment 2, there were no DEAD/SUR differences in circulating PAI-1 prior to CLP. Post-CLP, circulating PAI-1 in DEAD was 2–4-fold higher than in SUR. PAI-1 increase heralded septic deaths up to 48 h prior but DEAD/SUR thrombomodulin (endothelial injury marker) levels were identical. In experiment 3, levels of circulating PAI-1 and its hepatic gene expression were higher in P-DIE versus P-LIVE mice and those increases closely correlated with liver dysfunction.

Conclusions

Trauma modulated septic PAI-1 responses in a compartment-specific fashion. Only post-CLP increases in circulating PAI-1 predicted septic outcomes. In posttraumatic sepsis, pre-lethal release of PAI-1 was mostly of hepatic origin and was independent of endothelial injury.

Advances and future directions for management of trauma patients with musculoskeletal injuries

Musculoskeletal injuries are the most common reason for operative procedures in severely injured patients and are major determinants of functional outcomes. In this paper, we summarise advances and future directions for management of multiply injured patients with major musculoskeletal trauma. Improved understanding of fracture healing has created new possibilities for management of particularly challenging problems, such as delayed union and non union of fractures and large bone defects. Optimum timing of major orthopaedic interventions is guided by increased knowledge about the immune response after injury. Individual treatment should be guided by trading off the benefits of early definitive skeletal stabilisation, and the potentially life-threatening risks of systemic complications such as fat embolism, acute lung injury, and multiple organ failure. New methods for measurement of fracture healing and function and quality of life outcomes pave the way for landmark trials that will guide the future management of musculoskeletal injuries.

Haemorrhage control in severely injured patients

Most surgeons have adopted damage control surgery for severely injured patients, in which the initial operation is abbreviated after control of bleeding and contamination to allow ongoing resuscitation in the intensive-care unit. Developments in early resuscitation that emphasise rapid control of bleeding, restrictive volume replacement, and prevention or early management of coagulopathy are making definitive surgery during the first operation possible for many patients. Improved topical haemostatic agents and interventional radiology are becoming increasingly useful adjuncts to surgical control of bleeding. Better understanding of trauma-induced coagulopathy is paving the way for the replacement of blind, unguided protocols for blood component therapy with systemic treatments targeting specific deficiencies in coagulation. Similarly, treatments targeting dysregulated inflammatory responses to severe injury are under investigation. As point-of-care diagnostics become more suited to emergency environments, timely targeted intervention for haemorrhage control will result in better patient outcomes and reduced demand for blood products. Our Series paper describes how our understanding of the roles of the microcirculation, inflammation, and coagulation has shaped new and emerging treatment strategies.

Studying and modelling dynamic biological processes using time-series gene expression data

Biological processes are often dynamic, thus researchers must monitor their activity at multiple time points. The most abundant source of information regarding such dynamic activity is time-series gene expression data. These data are used to identify the complete set of activated genes in a biological process, to infer their rates of change, their order and their causal effects and to model dynamic systems in the cell. In this Review we discuss the basic patterns that have been observed in time-series experiments, how these patterns are combined to form expression programs, and the computational analysis, visualization and integration of these data to infer models of dynamic biological systems.

Cell surface localization and release of the candidate tumor suppressor Ecrg4 from polymorphonuclear cells and monocytes activate macrophages

We identified fresh human leukocytes as an abundant source of the candidate epithelial tumor suppressor gene, Ecrg4, an epigenetically regulated gene, which unlike other tumor suppressor genes, encodes an orphan-secreted, ligand-like protein. In human cell lines, Ecrg4 gene expression was low, Ecrg4 protein undetectable, and Ecrg4 promoter hypermethylation high (45-90%) and reversible by the methylation inhibitor 5-AzaC. In contrast, Ecrg4 gene expression in fresh, normal human PBMCs and PMNs was 600-800 times higher than in cultured cell lines, methylation of the Ecrg4 promoter was low (<3%), and protein levels were readily detectable in lysates and on the cell surface. Flow cytometry, immunofluorescent staining, and cell surface biotinylation established that full-length, 14-kDa Ecrg4 was localized on PMN and monocyte cell surfaces, establishing that Ecrg4 is a membrane-anchored protein. LPS treatment induced processing and release of Ecrg4, as detected by flow and immunoblotting, whereas an effect of fMLF treatment on Ecrg4 on the PMN cell surface was detected on the polarized R2 subpopulation of cells. This loss of cell surface Ecrg4 was associated with the detection of intact and processed Ecrg4 in the conditioned media of fresh leukocytes and was shown to be associated with the inflammatory response that follows severe, cutaneous burn injury. Furthermore, incubation of macrophages with a soluble Ecrg4-derived peptide increased the P-p65, suggesting that processing of an intact sentinel Ecrg4 on quiescent circulating leukocytes leads to processing from the cell surface following injury and macrophage activation.