Identification and interpretation of longitudinal gene expression changes in trauma

Genetic regulation of gene expression and splicing during a 10-year period of human aging

BACKGROUND

Molecular and cellular changes are intrinsic to aging and age-related diseases. Prior cross-sectional studies have investigated the combined effects of age and genetics on gene expression and alternative splicing; however, there has been no long-term, longitudinal characterization of these molecular changes, especially in older age.

RESULTS

We perform RNA sequencing in whole blood from the same individuals at ages 70 and 80 to quantify how gene expression, alternative splicing, and their genetic regulation are altered during this 10-year period of advanced aging at a population and individual level. We observe that individuals are more similar to their own expression profiles later in life than profiles of other individuals their own age. We identify 1291 and 294 genes differentially expressed and alternatively spliced with age, as well as 529 genes with outlying individual trajectories. Further, we observe a strong correlation of genetic effects on expression and splicing between the two ages, with a small subset of tested genes showing a reduction in genetic associations with expression and splicing in older age.

CONCLUSIONS

These findings demonstrate that, although the transcriptome and its genetic regulation is mostly stable late in life, a small subset of genes is dynamic and is characterized by a reduction in genetic regulation, most likely due to increasing environmental variance with age.

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.

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.

Time-Course Gene Set Analysis for Longitudinal Gene Expression Data

Gene set analysis methods, which consider predefined groups of genes in the analysis of genomic data, have been successfully applied for analyzing gene expression data in cross-sectional studies. The time-course gene set analysis (TcGSA) introduced here is an extension of gene set analysis to longitudinal data. The proposed method relies on random effects modeling with maximum likelihood estimates. It allows to use all available repeated measurements while dealing with unbalanced data due to missing at random (MAR) measurements. TcGSA is a hypothesis driven method that identifies a priori defined gene sets with significant expression variations over time, taking into account the potential heterogeneity of expression within gene sets. When biological conditions are compared, the method indicates if the time patterns of gene sets significantly differ according to these conditions. The interest of the method is illustrated by its application to two real life datasets: an HIV therapeutic vaccine trial (DALIA-1 trial), and data from a recent study on influenza and pneumococcal vaccines. In the DALIA-1 trial TcGSA revealed a significant change in gene expression over time within 69 gene sets during vaccination, while a standard univariate individual gene analysis corrected for multiple testing as well as a standard a Gene Set Enrichment Analysis (GSEA) for time series both failed to detect any significant pattern change over time. When applied to the second illustrative data set, TcGSA allowed the identification of 4 gene sets finally found to be linked with the influenza vaccine too although they were found to be associated to the pneumococcal vaccine only in previous analyses. In our simulation study TcGSA exhibits good statistical properties, and an increased power compared to other approaches for analyzing time-course expression patterns of gene sets. The method is made available for the community through an R package.

Gene set analysis methods use prior biological knowledge to analyze gene expression data. This prior knowledge takes the form of predefined groups of genes, linked through their biological function. Gene set analysis methods have been successfully applied in transversal studies, their results being more sensitive and interpretable than those of methods investigating genomic data one gene at a time. The time-course gene set analysis (TcGSA) introduced here is an extension of such gene set analysis to longitudinal data. This method identifies a priori defined groups of genes whose expression is not stable over time, taking into account the potential heterogeneity between patients and between genes. When biological conditions are compared, it identifies the gene sets that have different expression dynamics according to these conditions. Data from 2 studies are analyzed: data from an HIV therapeutic vaccine trial, and data from a recent study on influenza and pneumococcal vaccines. In both cases, TcGSA provided new insights compared to standard approaches thanks to an increased sensitivity compared to other approaches. Those results highlight the benefits of the TcGSA method for analyzing gene expression dynamics.

Algebraic Statistical Model for Biochemical Network Dynamics Inference

With modern molecular quantification methods, like, for instance, high throughput sequencing, biologists may perform multiple complex experiments and collect longitudinal data on RNA and DNA concentrations. Such data may be then used to infer cellular level interactions between the molecular entities of interest. One method which formalizes such inference is the stoichiometric algebraic statistical model (SASM) of [2] which allows to analyze the so-called conic (or single source) networks. Despite its intuitive appeal, up until now the SASM has been only heuristically studied on few simple examples. The current paper provides a more formal mathematical treatment of the SASM, expanding the original model to a wider class of reaction systems decomposable into multiple conic subnetworks. In particular, it is proved here that on such networks the SASM enjoys the so-called sparsistency property, that is, it asymptotically (with the number of observed network trajectories) discards the false interactions by setting their reaction rates to zero. For illustration, we apply the extended SASM to in silico data from a generic decomposable network as well as to biological data from an experimental search for a possible transcription factor for the heat shock protein 70 (Hsp70) in the zebrafish retina.

A genomic analysis of Clostridium difficile infections in blunt trauma patients

BACKGROUND

Evidence demonstrates that susceptibility to Clostridium difficile infection is related to host risk factors as much as bacterial potency. Using blood leukocyte genome-wide expression patterns of severe blunt trauma patients obtained by the National Institute of General Medical Sciences-sponsored Glue Grant Inflammation and the Host Response to Injury, we examined leukocyte genomic profiles of patients with C. difficile infection to determine preinfection and postinfection gene expression changes.

METHODS

The genomic responses of 21 severe trauma patients were analyzed (5 C. difficile, 16 controls matched for age and severity of injury). After elimination of probe sets whose expression was below baseline or were unchanged, remaining probe sets underwent hierarchical clustering and principal component analysis. Molecular pathways were generated through Ingenuity Pathways Analysis.

RESULTS

Supervised analysis demonstrated 118 genes whose expression in patients with C. difficile infection varied before and after their infection. Supervised analysis comparing patients with C. difficile infection with matched non-C. difficile patients before infection suggested that the expression of 501 genes were different in the two groups with up to 87% class prediction (p < 0.05). Many of these genes are related to cell-mediated immune responses, signaling, and interaction.

CONCLUSION

Genomic analysis of severe blunt trauma patients reveals a distinct leukocyte expression profile of C. difficile both before and after infection. We conclude that an association may exist between a severe trauma patient's leukocyte genomic expression profile and subsequent susceptibility to C. difficile infection. Further prospective expression analysis of this C. difficile population may reveal potential therapeutic interventions and allow early identification of C. difficile-susceptible patients.

LEVEL OF EVIDENCE

Prognostic/diagnostic study, level III.

Life or death? A physiogenomic approach to understand individual variation in responses to hemorrhagic shock

Severe hemorrhage due to trauma is a major cause of death throughout the world. It has often been observed that some victims are able to withstand hemorrhage better than others. For decades investigators have attempted to identify physiological mechanisms that distinguish survivors from nonsurvivors for the purpose of providing more informed therapies. As an alternative approach to address this issue, we have initiated a research program to identify genes and genetic mechanisms that contribute to this phenotype of survival time after controlled hemorrhage. From physiogenomic studies using inbred rat strains, we have demonstrated that this phenotype is a heritable quantitative trait, and is therefore a complex trait regulated by multiple genes. Our work continues to identify quantitative trait loci as well as potential epigenetic mechanisms that might influence survival time after severe hemorrhage. Our ultimate goal is to improve survival to traumatic hemorrhage and attendant shock via regulation of genetic mechanisms and to provide knowledge that will lead to genetically-informed personalized treatments.