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.

ClotCatcher: a novel natural language model to accurately adjudicate venous thromboembolism from radiology reports.

INTRODUCTION: Accurate identification of venous thromboembolism (VTE) is critical to develop replicable epidemiological studies and rigorous predictions models. Traditionally, VTE studies have relied on international classification of diseases (ICD) codes which are inaccurate - leading to misclassification bias. Here, we developed ClotCatcher, a novel deep learning model that uses natural language processing to detect VTE from radiology reports. METHODS: Radiology reports to detect VTE were obtained from patients admitted to Emory University Hospital (EUH) and Grady Memorial Hospital (GMH). Data augmentation was performed using the Google PEGASUS paraphraser. This data was then used to fine-tune ClotCatcher, a novel deep learning model. ClotCatcher was validated on both the EUH dataset alone and GMH dataset alone. RESULTS: The dataset contained 1358 studies from EUH and 915 studies from GMH (n = 2273). The dataset contained 1506 ultrasound studies with 528 (35.1%) studies positive for VTE, and 767 CT studies with 91 (11.9%) positive for VTE. When validated on the EUH dataset, ClotCatcher performed best (AUC = 0.980) when trained on both EUH and GMH dataset without paraphrasing. When validated on the GMH dataset, ClotCatcher performed best (AUC = 0.995) when trained on both EUH and GMH dataset with paraphrasing. CONCLUSION: ClotCatcher, a novel deep learning model with data augmentation rapidly and accurately adjudicated the presence of VTE from radiology reports. Applying ClotCatcher to large databases would allow for rapid and accurate adjudication of incident VTE. This would reduce misclassification bias and form the foundation for future studies to estimate individual risk for patient to develop incident VTE.

Targeted metagenomic assessment reflects critical colonization in battlefield injuries.

IMPORTANCE: Microbial contamination in combat wounds can lead to opportunistic infections and adverse outcomes. However, current microbiological detection has a limited ability to capture microbial functional genes. This work describes the application of targeted metagenomic sequencing to profile wound bioburden and capture relevant wound-associated signatures for clinical utility. Ultimately, the ability to detect such signatures will help guide clinical decisions regarding wound care and management and aid in the prediction of wound outcomes.

Host CD3+ T-cells can significantly modulate phage treatment effects on bacterial bioburden in mouse models.

Wound healing is a complex system including such key players as host, microbe, and treatments. However, little is known about their dynamic interactions. Here we explored the interplay between: (1) bacterial bioburden and host immune responses, (2) bacterial bioburden and wound size, and (3) treatments and wound size, using murine models and various treatment modalities: Phosphate buffer saline (PBS or vehicle, negative control), doxycycline, and two doses of A. baumannii phage mixtures. We uncovered that the interplay between bacterial bioburden and host immune system may be bidirectional, and that there is an interaction between host CD3+ T-cells and phage dosage, which significantly impacts bacterial bioburden. Furthermore, the bacterial bioburden and wound size association is significantly modulated by the host CD3+ T-cells. When the host CD3+ T-cells (x on log10 scale) are in the appropriate range (1.35 < x < = 1.5), we observed a strong association between colony forming units (CFU) and wound size, indicating a hallmark of wound healing. On the basis of the findings and our previous work, we proposed an integrated parallel systems biology model.

Systemic inflammation induced from remote extremity trauma is a critical driver of secondary brain injury.

Blast exposure, commonly experienced by military personnel, can cause devastating life-threatening polysystem trauma. Despite considerable research efforts, the impact of the systemic inflammatory response after major trauma on secondary brain injury-inflammation is largely unknown. The aim of this study was to identify markers underlying the susceptibility and early onset of neuroinflammation in three rat trauma models: (1) blast overpressure exposure (BOP), (2) complex extremity trauma (CET) involving femur fracture, crush injury, tourniquet-induced ischemia, and transfemoral amputation through the fracture site, and (3) BOP+CET. Six hours post-injury, intact brains were harvested and dissected to obtain biopsies from the prefrontal cortex, striatum, neocortex, hippocampus, amygdala, thalamus, hypothalamus, and cerebellum. Custom low-density microarray datasets were used to identify, interpret and visualize genes significant (p < 0.05 for differential expression [DEGs]; 86 neuroinflammation-associated) using a custom python-based computer program, principal component analysis, heatmaps and volcano plots. Gene set and pathway enrichment analyses of the DEGs was performed using R and STRING for protein-protein interaction (PPI) to identify and explore key genes and signaling networks. Transcript profiles were similar across all regions in naïve brains with similar expression levels involving neurotransmission and transcription functions and undetectable to low-levels of inflammation-related mediators. Trauma-induced neuroinflammation across all anatomical brain regions correlated with injury severity (BOP+CET > CET > BOP). The most pronounced differences in neuroinflammatory-neurodegenerative gene regulation were between blast-associated trauma (BOP, BOP+CET) and CET. Following BOP, there were few DEGs detected amongst all 8 brain regions, most were related to cytokines/chemokines and chemokine receptors, where PPI analysis revealed Il1b as a potential central hub gene. In contrast, CET led to a more excessive and diverse pro-neuroinflammatory reaction in which Il6 was identified as the central hub gene. Analysis of the of the BOP+CET dataset, revealed a more global heightened response (Cxcr2, Il1b, and Il6) as well as the expression of additional functional regulatory networks/hub genes (Ccl2, Ccl3, and Ccl4) which are known to play a critical role in the rapid recruitment and activation of immune cells via chemokine/cytokine signaling. These findings provide a foundation for discerning pathophysiological consequences of acute extremity injury and systemic inflammation following various forms of trauma in the brain.

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.

Transcriptomic, Proteomic, and Morphologic Characterization of Healing in Volumetric Muscle Loss.

Skeletal muscle has a robust, inherent ability to regenerate in response to injury from acute to chronic. In severe trauma, however, complete regeneration is not possible, resulting in a permanent loss of skeletal muscle tissue referred to as volumetric muscle loss (VML). There are few consistently reliable therapeutic or surgical options to address VML. A major limitation in investigation of possible therapies is the absence of a well-characterized large animal model. In this study, we present results of a comprehensive transcriptomic, proteomic, and morphologic characterization of wound healing following VML in a novel canine model of VML which we compare to a nine-patient cohort of combat-associated VML. The canine model is translationally relevant as it provides both a regional (spatial) and temporal map of the wound healing processes that occur in human VML. Collectively, these data show the spatiotemporal transcriptomic, proteomic, and morphologic properties of canine VML healing as a framework and model system applicable to future studies investigating novel therapies for human VML. Impact Statement The spatiotemporal transcriptomic, proteomic, and morphologic properties of canine volumetric muscle loss (VML) healing is a translational framework and model system applicable to future studies investigating novel therapies for human VML.

Cellular microRNAs correlate with clinical parameters in multiple injury patients.

INTRODUCTION: The pathophysiology of the inflammatory response after major trauma is complex, and the magnitude correlates with severity of tissue injury and outcomes. Study of infection-mediated immune pathways has demonstrated that cellular microRNAs may modulate the inflammatory response. The authors hypothesize that the expression of microRNAs would correlate to complicated recoveries in polytrauma patients (PtPs). METHODS: Polytrauma patients enrolled in the prospective observational Tissue and Data Acquisition Protocol with Injury Severity Score of >15 were selected for this study. Polytrauma patients were divided into complicated recoveries and uncomplicated recovery groups. Polytrauma patients' blood samples were obtained at the time of admission (T0). Established biomarkers of systemic inflammation, including cytokines and chemokines, were measured using multiplexed Luminex-based methods, and novel microRNAs were measured in plasma samples using multiplex RNA hybridization. RESULTS: Polytrauma patients (n = 180) had high Injury Severity Score (26 [20-34]) and complicated recovery rate of 33%. MicroRNAs were lower in PtPs at T0 compared with healthy controls, and bivariate analysis demonstrated that variations of microRNAs correlated with age, race, comorbidities, venous thromboembolism, pulmonary complications, complicated recovery, and mortality. Positive correlations were noted between microRNAs and interleukin 10, vascular endothelial growth factor, Acute Physiology and Chronic Health Evaluation, and Sequential Organ Failure Assessment scores. Multivariable Lasso regression analysis of predictors of complicated recovery based on microRNAs, cytokines, and chemokines revealed that miR-21-3p and monocyte chemoattractant protein-1 were predictive of complicated recovery with an area under the curve of 0.78. CONCLUSION: Systemic microRNAs were associated with poor outcomes in PtPs, and results are consistent with previously described trends in critically ill patients. These early biomarkers of inflammation might provide predictive utility in early complicated recovery diagnosis and prognosis. Because of their potential to regulate immune responses, microRNAs may provide therapeutic targets for immunomodulation. LEVEL OF EVIDENCE: Diagnostic Tests/Criteria; Level II.

Patient-Specific Precision Injury Signatures to Optimize Orthopaedic Interventions in Multiply Injured Patients (PRECISE STUDY).

SUMMARY: Optimal timing and procedure selection that define staged treatment strategies can affect outcomes dramatically and remain an area of major debate in the treatment of multiply injured orthopaedic trauma patients. Decisions regarding timing and choice of orthopaedic procedure(s) are currently based on the physiologic condition of the patient, resource availability, and the expected magnitude of the intervention. Surgical decision-making algorithms rarely rely on precision-type data that account for demographics, magnitude of injury, and the physiologic/immunologic response to injury on a patient-specific basis. This study is a multicenter prospective investigation that will work toward developing a precision medicine approach to managing multiply injured patients by incorporating patient-specific indices that quantify (1) mechanical tissue damage volume; (2) cumulative hypoperfusion; (3) immunologic response; and (4) demographics. These indices will formulate a precision injury signature, unique to each patient, which will be explored for correspondence to outcomes and response to surgical interventions. The impact of the timing and magnitude of initial and staged surgical interventions on patient-specific physiologic and immunologic responses will be evaluated and described. The primary goal of the study will be the development of data-driven models that will inform clinical decision-making tools that can be used to predict outcomes and guide intervention decisions.

Viral Micro-RNAs Are Detected in the Early Systemic Response to Injury and Are Associated With Outcomes in Polytrauma Patients.

OBJECTIVES: To evaluate early activation of latent viruses in polytrauma patients and consider prognostic value of viral micro-RNAs in these patients. DESIGN: This was a subset analysis from a prospectively collected multicenter trauma database. Blood samples were obtained upon admission to the trauma bay (T0), and trauma metrics and recovery data were collected. SETTING: Two civilian Level 1 Trauma Centers and one Military Treatment Facility. PATIENTS: Adult polytrauma patients with Injury Severity Scores greater than or equal to 16 and available T0 plasma samples were included in this study. Patients with ICU admission greater than 14 days, mechanical ventilation greater than 7 days, or mortality within 28 days were considered to have a complicated recovery. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Polytrauma patients (n = 180) were identified, and complicated recovery was noted in 33%. Plasma samples from T0 underwent reverse transcriptase-quantitative polymerase chain reaction analysis for Kaposi's sarcoma-associated herpesvirus micro-RNAs (miR-K12_10b and miRK-12-12) and Epstein-Barr virus-associated micro-RNA (miR-BHRF-1), as well as Luminex multiplex array analysis for established mediators of inflammation. Ninety-eight percent of polytrauma patients were found to have detectable Kaposi's sarcoma-associated herpesvirus and Epstein-Barr virus micro-RNAs at T0, whereas healthy controls demonstrated 0% and 100% detection rate for Kaposi's sarcoma-associated herpesvirus and Epstein-Barr virus, respectively. Univariate analysis revealed associations between viral micro-RNAs and polytrauma patients' age, race, and postinjury complications. Multivariate least absolute shrinkage and selection operator analysis of clinical variables and systemic biomarkers at T0 revealed that interleukin-10 was the strongest predictor of all viral micro-RNAs. Multivariate least absolute shrinkage and selection operator analysis of systemic biomarkers as predictors of complicated recovery at T0 demonstrated that miR-BHRF-1, miR-K12-12, monocyte chemoattractant protein-1, and hepatocyte growth factor were independent predictors of complicated recovery with a model complicated recovery prediction area under the curve of 0.81. CONCLUSIONS: Viral micro-RNAs were detected within hours of injury and correlated with poor outcomes in polytrauma patients. Our findings suggest that transcription of viral micro-RNAs occurs early in the response to trauma and may be associated with the biological processes involved in polytrauma-induced complicated recovery.

Clinical risk factors and inflammatory biomarkers of post-traumatic acute kidney injury in combat patients.

BACKGROUND: Post-traumatic acute kidney injury has occurred in every major military conflict since its initial description during World War II. To ensure the proper treatment of combat casualties, early detection is critical. This study therefore aimed to investigate combat-related post-traumatic acute kidney injury in recent military conflicts, used machine learning algorithms to identify clinical and biomarker variables associated with the development of post-traumatic acute kidney injury, and evaluated the effects of post-traumatic acute kidney injury on wound healing and nosocomial infection. METHODS: We conducted a retrospective clinical cohort review of 73 critically injured US military service members who sustained major combat-related extremity wounds and had collected injury characteristics, assayed serum and tissue biopsy samples for the expression of protein and messenger ribonucleic acid biomarkers. Bivariate analyses and random forest recursive feature elimination classification algorithms were used to identify associated injury characteristics and biomarker variables. RESULTS: The incidence of post-traumatic acute kidney injury was 20.5%. Of that, 86% recovered baseline renal function and only 2 (15%) of the acute kidney injury group required renal replacement therapy. Random forest recursive feature elimination algorithms were able to estimate post-traumatic acute kidney injury with the area under the curve of 0.93, sensitivity of 0.91, and specificity of 0.91. Post-traumatic acute kidney injury was associated with injury severity score, serum epidermal growth factor, and tissue activin A type receptor 1, matrix metallopeptidase 10, and X-C motif chemokine ligand 1 expression. Patients with post-traumatic acute kidney injury exhibited poor wound healing and increased incidence of nosocomial infections. CONCLUSION: The occurrence of acute kidney injury in combat casualties may be estimated using injury characteristics and serum and tissue biomarkers. External validations of these models are necessary to generalize for all trauma patients.

Utilizing Precision Medicine to Estimate Timing for Surgical Closure of Traumatic Extremity Wounds.

BACKGROUND: Both the frequency and high complication rates associated with extremity wounds in recent military conflicts have highlighted the need for clinical decision support tools (CDST) to decrease time to wound closure and wound failure rates. METHODS: Machine learning was used to estimate both successful wound closure (based on penultimate debridement biomarker data) and the necessary number of surgical debridements (based on presentation biomarkers) in 73 service members treated according to military guidelines based on clinical data and the local/systemic level of 32 cytokines. Models were trained to estimate successful closure including an additional 8 of 80 civilian patients with similar injury patterns. Previous analysis has demonstrated the potential to reduce the number of operative debridements by 2, with resulting decreases in ICU and hospital LOS, while decreasing the rate of wound failure. RESULTS: Analysis showed similar cytokine responses when civilians followed a military-like treatment schedule with surgical debridements every 24 to 72 hours. A model estimating successful closure had AUC of 0.89. Model performance in civilians degraded when these had a debridement interval > 72 hours (73 of the 80 civilians). A separate model estimating the number of debridements required to achieve successful closure had a multiclass AUC of 0.81. CONCLUSION: CDSTs can be developed using biologically compatible civilian and military populations as cytokine response is highly influenced by surgical treatment. Our CDSTs may help identify who may require serial debridements versus early closure, and precisely when traumatic wounds should optimally be closed.

Nonsteroidal anti-inflammatory drugs may affect cytokine response and benefit healing of combat-related extremity wounds.

BACKGROUND: After adequate operative debridement and antimicrobial therapies, combat-related extremity wounds that either heal or fail are both associated with a distinct inflammatory response. Short-term use of nonsteroidal anti-inflammatory drugs in postoperative pain management may affect this response and, by consequence, the healing potential of these wounds. We investigated whether patients treated with nonsteroidal anti-inflammatory drugs had a distinct inflammatory response; different rates of critical colonization, defined as >105 colony forming units on quantitative bacteriology; and healing potential. METHODS: We retrospectively reviewed the records of 73 patients with combat-related extremity wounds. Patients were separated into 2 groups: those who received nonsteroidal anti-inflammatory drugs during the debridement period (nonsteroidal anti-inflammatory drugs group, N = 17) and those who did not (control group; N = 56). Serum and wound tissue samples collected during each operative debridement were measured for 32 known cytokines and tested for quantitative bacteriology, respectively. We compared cytokine concentrations between groups and then designed a logistic regression model to identify variables associated with successful wound healing, while controlling for known confounders. RESULTS: Despite similar demographics and wound characteristics, the nonsteroidal anti-inflammatory drugs group had significant lesser concentrations of inflammatory cytokines, interleukin-2, interleukin-6, interleukin-8, and monocyte chemoattractant protein-1. On multivariate analysis, nonsteroidal anti-inflammatory drug treatment emerged as a predictor of successful wound healing after controlling for known confounders such as wound size, tobacco use, Acute Physiology and Chronic Health Evaluation II score, and critical colonization. CONCLUSION: Treatment with nonsteroidal anti-inflammatory drugs for postoperative pain management after major combat-related extremity trauma is associated with lesser concentrations of inflammatory cytokines and may contribute to a more favorable inflammatory response leading to successful wound healing.

Introduction, Evolution, and Dissemination of Influenza A Viruses in Exhibition Swine in the United States during 2009 to 2013.

The swine-human interface created at agricultural fairs, along with the generation of and maintenance of influenza A virus diversity in exhibition swine, presents an ongoing threat to public health. Nucleotide sequences of influenza A virus isolates collected from exhibition swine in Ohio (n = 262) and Indiana (n = 103) during 2009 to 2013 were used to investigate viral evolution and movement within this niche sector of the swine industry. Phylogenetic and Bayesian analyses were employed to identify introductions of influenza A virus to exhibition swine and study viral population dynamics. In 2013 alone, we identified 10 independent introductions of influenza A virus into Ohio and/or Indiana exhibition swine. Frequently, viruses from the same introduction were identified at multiple fairs within the region, providing evidence of rapid and widespread viral movement within the exhibition swine populations of the two states. While pigs moving from fair to fair to fair is possible in some locations, the concurrent detection of nearly identical strains at several fairs indicates that a common viral source was more likely. Importantly, we detected an association between the high number of human variant H3N2 (H3N2v) virus infections in 2012 and the widespread circulation of influenza A viruses of the same genotype in exhibition swine in Ohio fairs sampled that year. The extent of viral diversity observed in exhibition swine and the rapidity with which it disseminated across long distances indicate that novel strains of influenza A virus will continue to emerge and spread within exhibition swine populations, presenting an ongoing threat to humans. IMPORTANCE: Understanding the underlying population dynamics of influenza A viruses in commercial and exhibition swine is central to assessing the risk for human infections with variant viruses, including H3N2v. We used viral genomic sequences from isolates collected from exhibition swine during 2009 to 2013 to understand how the peak of H3N2v cases in 2012 relates to long-term trends in the population dynamics of pandemic viruses recently introduced into commercial and exhibition swine in the United States. The results of our spatial analysis underscore the key role of rapid viral dispersal in spreading multiple genetic lineages throughout a multistate network of agricultural fairs, providing opportunities for divergent lineages to coinfect, reassort, and generate new viral genotypes. The higher genetic diversity of genotypes cocirculating in exhibition swine since 2013 could facilitate the evolution of new reassortants, potentially with even greater ability to cause severe infections in humans or cause human-to-human transmission, highlighting the need for continued vigilance.

Respiratory Syncytial Virus whole-genome sequencing identifies convergent evolution of sequence duplication in the C-terminus of the G gene.

Respiratory Syncytial Virus (RSV) is responsible for considerable morbidity and mortality worldwide and is the most important respiratory viral pathogen in infants. Extensive sequence variability within and between RSV group A and B viruses and the ability of multiple clades and sub-clades of RSV to co-circulate are likely mechanisms contributing to the evasion of herd immunity. Surveillance and large-scale whole-genome sequencing of RSV is currently limited but would help identify its evolutionary dynamics and sites of selective immune evasion. In this study, we performed complete-genome next-generation sequencing of 92 RSV isolates from infants in central Tennessee during the 2012-2014 RSV seasons. We identified multiple co-circulating clades of RSV from both the A and B groups. Each clade is defined by signature N- and O-linked glycosylation patterns. Analyses of specific RSV genes revealed high rates of positive selection in the attachment (G) gene. We identified RSV-A viruses in circulation with and without a recently reported 72-nucleotide G gene sequence duplication. Furthermore, we show evidence of convergent evolution of G gene sequence duplication and fixation over time, which suggests a potential fitness advantage of RSV with the G sequence duplication.

Long-term surveillance of H7 influenza viruses in American wild aquatic birds: are the H7N3 influenza viruses in wild birds the precursors of highly pathogenic strains in domestic poultry?

The emergence of influenza A virus (IAV) in domestic avian species and associated transmissions to mammals is unpredictable. In the Americas, the H7 IAVs are of particular concern, and there have been four separate outbreaks of highly pathogenic (HP) H7N3 in domestic poultry in North and South America between 2002 and 2012, with occasional spillover into humans. Here, we use long-term IAV surveillance in North American shorebirds at Delaware Bay, USA, from 1985 to 2012 and in ducks in Alberta, Canada, from 1976 to 2012 to determine which hemagglutinin (HA)-neuraminidase (NA) combinations predominated in Anseriformes (ducks) and Charadriiformes (shorebirds) and whether there is concordance between peaks of H7 prevalence and transmission in wild aquatic birds and the emergence of H7 IAVs in poultry and humans. Whole-genome sequencing supported phylogenetic and genomic constellation analyses to determine whether HP IAVs emerge in the context of specific internal gene segment sequences. Phylogenetic analysis of whole-genome sequences of the H7N3 influenza viruses from wild birds and HP H7N3 outbreaks in the Americas indicate that each HP outbreak was an independent emergence event and that the low pathogenic (LP) avian influenza precursors were most likely from dabbling ducks. The different polybasic cleavage sites in the four HP outbreaks support independent origins. At the 95% nucleotide percent identity-level phylogenetic analysis showed that the wild duck HA, PB1, and M sequences clustered with the poultry and human outbreak sequences. The genomic constellation analysis strongly suggests that gene segments/virus flow from wild birds to domestic poultry.