Transcriptomic response to injury sheds light on the physiological costs of reproduction in ant queens.

The trade-off between reproduction and longevity is widespread among multicellular organisms. As an important exception, the reproductive females of perennial social insects (ants, honeybees, termites) are simultaneously highly fertile and very long-lived relative to their nonreproductive nestmates. The observation that increased fecundity is not coupled with decreased lifespan suggests that social insect queens do not have to reallocate resources between reproduction and self-maintenance. If queens have to compensate for the costs of reproduction on the level of the individual, the activation of other energy-demanding physiological processes might force them to reduce the production of eggs. To test this hypothesis in ant queens, we increased immunity costs by injury and measured the effect of this treatment on egg-laying rates and genomewide gene expression. Amputation of both middle legs led to a temporary decrease in egg-laying rates and affected the expression of 947 genes corresponding to 9% of the transcriptome. The changes comprised the upregulation of the immune and wound healing response on the one hand, and the downregulation of germ cell development, central nervous system development and learning ability on the other hand. Injury strongly influenced metabolism by inducing catabolism and repressing amino acid and nitrogen compound metabolism. By comparing our results to similar transcriptomic studies in insects, we found a highly consistent upregulation of immune genes due to sterile and septic wounding. The gene expression changes, complemented by the temporary decline of egg-laying rates, clearly reveal a trade-off between reproduction and the immune response in social insect queens.

Outcome-Driven Cluster Analysis with Application to Microarray Data.

One goal of cluster analysis is to sort characteristics into groups (clusters) so that those in the same group are more highly correlated to each other than they are to those in other groups. An example is the search for groups of genes whose expression of RNA is correlated in a population of patients. These genes would be of greater interest if their common level of RNA expression were additionally predictive of the clinical outcome. This issue arose in the context of a study of trauma patients on whom RNA samples were available. The question of interest was whether there were groups of genes that were behaving similarly, and whether each gene in the cluster would have a similar effect on who would recover. For this, we develop an algorithm to simultaneously assign characteristics (genes) into groups of highly correlated genes that have the same effect on the outcome (recovery). We propose a random effects model where the genes within each group (cluster) equal the sum of a random effect, specific to the observation and cluster, and an independent error term. The outcome variable is a linear combination of the random effects of each cluster. To fit the model, we implement a Markov chain Monte Carlo algorithm based on the likelihood of the observed data. We evaluate the effect of including outcome in the model through simulation studies and describe a strategy for prediction. These methods are applied to trauma data from the Inflammation and Host Response to Injury research program, revealing a clustering of the genes that are informed by the recovery outcome.

Lessons of War: Turning Data Into Decisions.

BACKGROUND: Recent conflicts in Afghanistan and Iraq produced a substantial number of critically wounded service-members. We collected biomarker and clinical information from 73 patients who sustained 116 life-threatening combat wounds, and sought to determine if the data could be used to predict the likelihood of wound failure. METHODS: From each patient, we collected clinical information, serum, wound effluent, and tissue prior to and at each surgical débridement. Inflammatory cytokines were quantified in both the serum and effluent, as were gene expression targets. The primary outcome was successful wound healing. Computer intensive methods were used to derive prognostic models that were internally validated using target shuffling and cross-validation methods. A second cohort of eighteen critically injured civilian patients was evaluated to determine if similar inflammatory responses were observed. FINDINGS: The best-performing models enhanced clinical observation with biomarker data from the serum and wound effluent, an indicator that systemic inflammatory conditions contribute to local wound failure. A Random Forest model containing ten variables demonstrated the highest accuracy (AUC 0.79). Decision Curve Analysis indicated that the use of this model would improve clinical outcomes and reduce unnecessary surgical procedures. Civilian trauma patients demonstrated similar inflammatory responses and an equivalent wound failure rate, indicating that the model may be generalizable to civilian settings. INTERPRETATION: Using advanced analytics, we successfully codified clinical and biomarker data from combat patients into a potentially generalizable decision support tool. Analysis of inflammatory data from critically ill patients with acute injury may inform decision-making to improve clinical outcomes and reduce healthcare costs. FUNDING: United States Department of Defense Health Programs.

Donor site healing dynamics: molecular, histological, and noninvasive imaging assessment in a porcine model.

Understanding the physiology of donor site healing will lead to advances in how these wounds are treated and may ultimately allow faster healing, more frequent autografting, and more effective care of the burn-injured patient. Unfortunately, a paucity of data exists regarding perfusion metrics over the course of donor site healing. Furthermore, there are no studies that interrelate indices of perfusion with the molecular and cellular processes of donor site healing. Male Duroc pigs were anesthetized and donor site wounds were created using a Zimmer dermatome at a depth of 0.060 inch (1.52 mm). Digital photographs, laser Doppler images, and punch biopsies were obtained before and after excision and on days 2, 4, 7, 9, 11, 14, and 16 until wounds were healed. RNA isolation was performed and quantitative polymerase chain reaction was used to examine differential gene expression over the time course. Formalin-fixed biopsies were embedded in paraffin, sectioned, stained, and examined. Wound surfaces were 83% re-epithelialized by day 16. Perfusion peaked on day 2 then declined, but it remained significantly elevated compared to before excision (P < .05). From day 9 onward, mean perfusion units were not significantly different from baseline (P < .05). Twenty-two representative genes were selected for examination. RNA expression of collagen, tenascin-cytoactin, inflammatory cytokines, remodeling enzymes, growth factors, and Wnt was increased. Inflammatory cells and cytokines were demonstrated histologically. Nuclei per high powered field peaked at day 7 and neodermal thickness increased daily to day 14. A novel porcine model for donor site wound healing that interrelates re-epithelilaizationand perfusion with molecular and cellular indices has been demonstrated.

Mortality in insured Swedish dogs: rates and causes of death in various breeds.

Data on over 222,000 Swedish dogs enrolled in life insurance in 1992 and 1993 were analysed. There were approximately 260 deaths per 10,000 dog-years at risk. Breed-specific mortality rates and causes of death are presented for breeds with more than 500 dogs at risk that had consistently high or low rates. Breed-specific mortality ranged from less than 1 per cent to more than 11 per cent. True rates and proportional statistics for the cause of death were calculated for the entire insured population (250 breeds) and cause-specific mortality rates were calculated for the breeds with the highest risk of dying of the most common causes. Trauma, tumours and problems related to the locomotor system together accounted for more than 40 per cent of all deaths or euthanasias. Although limited to insured dogs, these data cover approximately one-third of all Swedish dogs and provide baseline mortality data for further population-based studies on health and disease.