The influence of microbial colonization on inflammatory versus pro-healing trajectories in combat extremity wounds.

A combination of improved body armor, medical transportation, and treatment has led to the increased survival of warfighters from combat extremity injuries predominantly caused by blasts in modern conflicts. Despite advances, a high rate of complications such as wound infections, wound failure, amputations, and a decreased quality of life exist. To study the molecular underpinnings of wound failure, wound tissue biopsies from combat extremity injuries had RNA extracted and sequenced. Wounds were classified by colonization (colonized vs. non-colonized) and outcome (healed vs. failed) status. Differences in gene expression were investigated between timepoints at a gene level, and longitudinally by multi-gene networks, inferred proportions of immune cells, and expression of healing-related functions. Differences between wound outcomes in colonized wounds were more apparent than in non-colonized wounds. Colonized/healed wounds appeared able to mount an adaptive immune response to infection and progress beyond the inflammatory stage of healing, while colonized/failed wounds did not. Although, both colonized and non-colonized failed wounds showed increasing inferred immune and inflammatory programs, non-colonized/failed wounds progressed beyond the inflammatory stage, suggesting different mechanisms of failure dependent on colonization status. Overall, these data reveal gene expression profile differences in healing wounds that may be utilized to improve clinical treatment paradigms.

Central role for neurally dysregulated IL-17A in dynamic networks of systemic and local inflammation in combat casualties.

Dynamic Network Analysis (DyNA) and Dynamic Hypergraphs (DyHyp) were used to define protein-level inflammatory networks at the local (wound effluent) and systemic circulation (serum) levels from 140 active-duty, injured service members (59 with TBI and 81 non-TBI). Interleukin (IL)-17A was the only biomarker elevated significantly in both serum and effluent in TBI vs. non-TBI casualties, and the mediator with the most DyNA connections in TBI wounds. DyNA combining serum and effluent data to define cross-compartment correlations suggested that IL-17A bridges local and systemic circulation at late time points. DyHyp suggested that systemic IL-17A upregulation in TBI patients was associated with tumor necrosis factor-α, while IL-17A downregulation in non-TBI patients was associated with interferon-γ. Correlation analysis suggested differential upregulation of pathogenic Th17 cells, non-pathogenic Th17 cells, and memory/effector T cells. This was associated with reduced procalcitonin in both effluent and serum of TBI patients, in support of an antibacterial effect of Th17 cells in TBI patients. Dysregulation of Th17 responses following TBI may drive cross-compartment inflammation following combat injury, counteracting wound infection at the cost of elevated systemic inflammation.

Metagenomic features of bioburden serve as outcome indicators in combat extremity wounds.

Battlefield injury management requires specialized care, and wound infection is a frequent complication. Challenges related to characterizing relevant pathogens further complicates treatment. Applying metagenomics to wounds offers a comprehensive path toward assessing microbial genomic fingerprints and could indicate prognostic variables for future decision support tools. Wound specimens from combat-injured U.S. service members, obtained during surgical debridements before delayed wound closure, were subjected to whole metagenome analysis and targeted enrichment of antimicrobial resistance genes. Results did not indicate a singular, common microbial metagenomic profile for wound failure, instead reflecting a complex microenvironment with varying bioburden diversity across outcomes. Genus-level Pseudomonas detection was associated with wound failure at all surgeries. A logistic regression model was fit to the presence and absence of antimicrobial resistance classes to assess associations with nosocomial pathogens. A. baumannii detection was associated with detection of genomic signatures for resistance to trimethoprim, aminoglycosides, bacitracin, and polymyxin. Machine learning classifiers were applied to identify wound and microbial variables associated with outcome. Feature importance rankings averaged across models indicated the variables with the largest effects on predicting wound outcome, including an increase in P. putida sequence reads. These results describe the microbial genomic determinants in combat wound bioburden and demonstrate metagenomic investigation as a comprehensive tool for providing information toward aiding treatment of combat-related injuries.

To Scan or Not to Scan: Development of a Clinical Decision Support Tool to Determine if Imaging Would Aid in the Diagnosis of Appendicitis.

BACKGROUND: Appendicitis is one of the most common surgically treated diseases in the world. CT scans are often over-utilized and ordered before a surgeon has evaluated the patient. Our aim was to develop a tool using machine learning (ML) algorithms that would help determine if there would be benefit in obtaining a CT scan prior to surgeon consultation. METHODS: Retrospective chart review of 100 randomly selected cases who underwent appendectomy and 100 randomly selected controls was completed. Variables included components of the patient's history, laboratory values, CT readings, and pathology. Pathology was used as the gold standard for appendicitis diagnosis. All variables were then used to build the ML algorithms. Random Forest (RF), Support Vector Machine (SVM), and Bayesian Network Classifiers (BNC) models with and without CT scan results were trained and compared to CT scan results alone and the Alvarado score using area under the Receiver Operator Curve (ROC), sensitivity, and specificity measures as well as calibration indices from 500 bootstrapped samples. RESULTS: Among the cases that underwent appendectomy, 88% had pathology-confirmed appendicitis. All the ML algorithms had better sensitivity, specificity, and ROC than the Alvarado score. SVM with and without CT had the best indices and could predict if imaging would aid in appendicitis diagnosis. CONCLUSION: This study demonstrated that SVM with and without CT results can be used for selective imaging in the diagnosis of appendicitis. This study serves as the initial step and proof-of-concept to externally validate these results with larger and more diverse patient population.

Context-dependent genetic architecture of Drosophila life span.

Understanding the genetic basis of variation in life span is a major challenge that is difficult to address in human populations. Evolutionary theory predicts that alleles affecting natural variation in life span will have properties that enable them to persist in populations at intermediate frequencies, such as late-life-specific deleterious effects, antagonistic pleiotropic effects on early and late-age fitness components, and/or sex- and environment-specific or antagonistic effects. Here, we quantified variation in life span in males and females reared in 3 thermal environments for the sequenced, inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) and an advanced intercross outbred population derived from a subset of DGRP lines. Quantitative genetic analyses of life span and the micro-environmental variance of life span in the DGRP revealed significant genetic variance for both traits within each sex and environment, as well as significant genotype-by-sex interaction (GSI) and genotype-by-environment interaction (GEI). Genome-wide association (GWA) mapping in both populations implicates over 2,000 candidate genes with sex- and environment-specific or antagonistic pleiotropic allelic effects. Over 1,000 of these genes are associated with variation in life span in other D. melanogaster populations. We functionally assessed the effects of 15 candidate genes using RNA interference (RNAi): all affected life span and/or micro-environmental variance of life span in at least one sex and environment and exhibited sex-and environment-specific effects. Our results implicate novel candidate genes affecting life span and suggest that variation for life span may be maintained by variable allelic effects in heterogeneous environments.

Identification of gene-specific polymorphisms and association with capsaicin pathway metabolites in Capsicum annuum L. collections.

Pepper (Capsicum annuum L.) is an economically important crop with added nutritional value. Production of capsaicin is an important quantitative trait with high environmental variance, so the development of markers regulating capsaicinoid accumulation is important for pepper breeding programs. In this study, we performed association mapping at the gene level to identify single nucleotide polymorphisms (SNPs) associated with capsaicin pathway metabolites in a diverse Capsicum annuum collection during two seasons. The genes Pun1, CCR, KAS and HCT were sequenced and matched with the whole-genome sequence draft of pepper to identify SNP locations and for further characterization. The identified SNPs for each gene underwent candidate gene association mapping. Association mapping results revealed Pun1 as a key regulator of major metabolites in the capsaicin pathway mainly affecting capsaicinoids and precursors for acyl moieties of capsaicinoids. Six different SNPs in the promoter sequence of Pun1 were found associated with capsaicin in plants from both seasons. Our results support that CCR is an important control point for the flux of p-coumaric acid to specific biosynthesis pathways. KAS was found to regulate the major precursors for acyl moieties of capsaicinoids and may play a key role in capsaicinoid production. Candidate gene association mapping of Pun1 suggested that the accumulation of capsaicinoids depends on the expression of Pun1, as revealed by the most important associated SNPs found in the promoter region of Pun1.