Implications of an Altered Gut Microbiome in Rat Experimental Vascularized Composite Transplantation.

OBJECTIVES: Vascularized composite allotransplantation is a reconstructive option after severe injury but is fraught with complications, including transplant rejection due to major histocompatibility complex mismatch in the context of allogeneic transplant, which in turn is due to altered immuno-inflammation secondary to transplant. The immunosuppressant tacrolimus can prevent rejection. Because tacrolimus is metabolized predominantly by the gut, this immunosuppressant alters the gut microbiome in multiple ways, thereby possibly affecting immunoinflammation. MATERIALS AND METHODS: We performed either allogeneic or syngeneic transplant with or without tacrolimus in rats. We quantified protein-level inflammatory mediators in the skin, muscle, and plasma and assessed the diversity of the gut microbiome through 16S RNA analysis at several timepoints over 31 days posttransplant. RESULTS: Statistical analysis highlighted a complex interaction between major histocompatibility complex and tacrolimus therapy on the relative diversity of the microbiome. Time-interval principal component analysis indicated numerous significant differences in the tissue characteristics of inflammation and gut microbiome that varied over time and across experimental conditions. Classification and regression tree analysis suggested that both inflammatory mediators in specific tissues and changes in the gut microbiome are useful in characterizing the temporal dynamics of posttransplant inflammation. Dynamic network analysis highlighted unique changes in Methanosphaera that were correlated with Peptococcusin allogeneic transplants with and without tacrolimus versus Prevotella in syngeneic transplant with tacrolimus, suggesting that alterations in Methanosphaera might be a biomarker of vascularized composite allotransplant rejection. CONCLUSIONS: Our results suggest a complex interaction among major histocompatibility complex, local and systemic immuno-inflammation, and tacrolimus therapy and highlight the potential for novel insights into vascularized composite allotransplant from computational approaches.

Patient, family caregiver, and economic outcomes of an integrated screening and novel stepped collaborative care intervention in the oncology setting in the USA (CARES): a randomised, parallel, phase 3 trial.

BACKGROUND: The current standard of care of screening and referring patients for treatment for symptoms, such as depression, pain, and fatigue, is not effective. This trial aimed to test the efficacy of an integrated screening and novel stepped collaborative care intervention versus standard of care for patients with cancer and at least one of the following symptoms: depression, pain, or fatigue. METHODS: This randomised, parallel, phase 3 trial was conducted in 29 oncology outpatient clinics associated with the UPMC Hillman Cancer Center in the USA. Patients (aged ≥21 years) with any cancer type and clinical levels of depression, pain, or fatigue (or all of these) were eligible. Eligible family caregivers were aged 21 years or older and providing care to a patient diagnosed with cancer who consented for this study. Patients were randomly assigned (1:1) to stepped collaborative care or standard of care using a central, permuted block design (sizes of 2, 4, and 6) stratified by sex and prognostic status. The biostatistician, oncologists, and outcome assessors were masked to treatment assignment. Stepped collaborative care was once-weekly cognitive behavioural therapy for 50-60 min from a care coordinator via telemedicine (eg, telephone or videoconferencing). Pharmacotherapy for symptoms might be initiated or changed if recommended by the treatment team or preferred by the patient. Standard of care was screening and referral to a health-care provider for treatment of symptoms. The primary outcome was health-related quality of life in patients at 6 months. Maintenance of the treatment benefits was assessed at 12 months. Participants included in the primary analysis were per intention to treat, which included patients missing one or both follow-up assessments. This trial was registered with ClinicalTrials.gov (NCT02939755). FINDINGS: Between Dec 5, 2016, and April 8, 2021, 459 patients and 190 family caregivers were enrolled. 222 patients were assigned to standard of care and 237 to stepped collaborative care. Of 459 patients, 201 (44%) were male and 258 (56%) were female. Patients in the stepped collaborative care group had a greater 0-6-month improvement in health-related quality of life than patients in the standard-of-care group (p=0·013, effect size 0·09). Health-related quality of life was maintained for the stepped collaborative care group (p=0·74, effect size 0·01). Patients in the stepped collaborative care group had greater 0-6-month improvements than the standard-of-care group in emotional (p=0·012), functional (p=0·042), and physical (p=0·033) wellbeing. No adverse events were reported by patients in either group and deaths were considered unrelated to the study. INTERPRETATION: An integrated screening and novel stepped collaborative care intervention, compared with the current standard of care, is recommended to improve health-related quality of life. The findings of this study will advance the implementation of guideline concordant care (screening and treatment) and has the potential to shift the practice of screening and treatment paradigm nationwide, improving outcomes for patients diagnosed with cancer. FUNDING: US National Cancer Institute.

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.

Unraveling the complex relationship between mRNA and protein abundances: a machine learning-based approach for imputing protein levels from RNA-seq data.

The correlation between messenger RNA (mRNA) and protein abundances has long been debated. RNA sequencing (RNA-seq), a high-throughput, commonly used method for analyzing transcriptional dynamics, leaves questions about whether we can translate RNA-seq-identified gene signatures directly to protein changes. In this study, we utilized a set of 17 widely assessed immune and wound healing mediators in the context of canine volumetric muscle loss to investigate the correlation of mRNA and protein abundances. Our data reveal an overall agreement between mRNA and protein levels on these 17 mediators when examining samples from the same experimental condition (e.g. the same biopsy). However, we observed a lack of correlation between mRNA and protein levels for individual genes under different conditions, underscoring the challenges in converting transcriptional changes into protein changes. To address this discrepancy, we developed a machine learning model to predict protein abundances from RNA-seq data, achieving high accuracy. Our approach also effectively corrected multiple extreme outliers measured by antibody-based protein assays. Additionally, this model has the potential to detect post-translational modification events, as shown by accurately estimating activated transforming growth factor ß1 levels. This study presents a promising approach for converting RNA-seq data into protein abundance and its biological significance.

A common single nucleotide polymorphism is associated with inflammation and critical illness outcomes.

Acute inflammation is heterogeneous in critical illness and predictive of outcome. We hypothesized that genetic variability in novel, yet common, gene variants contributes to this heterogeneity and could stratify patient outcomes. We searched algorithmically for significant differences in systemic inflammatory mediators associated with any of 551,839 SNPs in one derivation (n = 380 patients with blunt trauma) and two validation (n = 75 trauma and n = 537 non-trauma patients) cohorts. This analysis identified rs10404939 in the LYPD4 gene. Trauma patients homozygous for the A allele (rs10404939AA; 27%) had different trajectories of systemic inflammation along with persistently elevated multiple organ dysfunction (MOD) indices vs. patients homozygous for the G allele (rs10404939GG; 26%). rs10404939AA homozygotes in the trauma validation cohort had elevated MOD indices, and non-trauma patients displayed more complex inflammatory networks and worse 90-day survival compared to rs10404939GG homozygotes. Thus, rs10404939 emerged as a common, broadly prognostic SNP in critical illness.

Defining spatiotemporal gene modules in liver regeneration using Analytical Dynamic Visual Spatial Omics Representation (ADViSOR).

BACKGROUND: The liver is the only organ with the ability to regenerate following surgical or toxicant insults, and partial hepatectomy serves as an experimental model of liver regeneration (LR). Dynamic changes in gene expression occur from the periportal to pericentral regions of the liver following partial hepatectomy; thus, spatial transcriptomics, combined with a novel computational pipeline (ADViSOR [Analytic Dynamic Visual Spatial Omics Representation]), was employed to gain insights into the spatiotemporal molecular underpinnings of LR. METHODS: ADViSOR, comprising Time-Interval Principal Component Analysis and sliding dynamic hypergraphs, was applied to spatial transcriptomics data on 100 genes assayed serially through LR, including key components of the Wnt/ß-catenin pathway at critical timepoints after partial hepatectomy. RESULTS: This computational pipeline identified key functional modules demonstrating cell signaling and cell-cell interactions, inferring shared regulatory mechanisms. Specifically, ADViSOR analysis suggested that macrophage-mediated inflammation is a critical component of early LR and confirmed prior studies showing that Ccnd1, a hepatocyte proliferative gene, is regulated by the Wnt/ß-catenin pathway. These findings were subsequently validated through protein localization, which provided further confirmation and novel insights into the spatiotemporal changes in the Wnt/ß-catenin pathway during LR. CONCLUSIONS: Thus, ADViSOR may yield novel insights in other complex, spatiotemporal contexts.

Predictors and Consequences of Cancer and Non-Cancer-Related Pain in Those Diagnosed with Primary and Metastatic Cancers.

OBJECTIVES: The aims of the study were to (1) describe types of pain in cancer patients, (2) examine the predictors and consequences of pain, (3) investigate the association between type of pain and survival, and (4) examine potential biological mediators of pain and survival. METHODS: This was a secondary analysis of baseline data from patients diagnosed with cancer. Patients answered questionnaires that assessed sociodemographic characteristics, pain, depression, sleep, and fatigue. Blood was collected and cytokine assays were performed. Analysis of variance, Kaplan-Meier, and Cox regression survival analyses were used to test the aims. RESULTS: Of the 779 patients diagnosed with cancer, the mean age was 63.5 years, 57.8% male, and 90.6% White. Of those who reported pain (total 70.3%), 46.5% stated their pain was cancer-related while 53.5% stated their pain was non-cancer-related. While both cancer and non-cancer-related pain was associated with depressive symptoms, fatigue, and sleep duration, those with cancer-related pain had significantly higher rates of depressive symptoms (F(1,516) = 21.217, p < 0.001) and fatigue (F(1,516) = 30.973, p < 0.001) but not poorer sleep (F(1,497) = 0.597, p = 0.440). After adjusting for sociodemographic, disease-related characteristics, depression, sleep duration, and morphine milligram equivalent, patient reports of cancer-related pain were significantly associated with poorer survival (HR = 0.646, 95% CI = 0.459-0.910, p = 0.012) compared to those with non-cancer-related pain, which was not associated with survival (HR = 1.022, 95% CI = 0.737-1.418, p = 0.896). Cytokines did not significantly mediate the link between pain and survival. CONCLUSION: While nearly half of the pain reported was cancer-related, both types of pain resulted in greater symptom burden, but only cancer-related pain was associated with survival.

Temporally complex inflammatory networks in an animal model reveal signatures for interstitial cystitis and bladder pain syndrome phenotype.

INTRODUCTION AND OBJECTIVE: Interstitial cystitis and bladder pain syndrome (IC/BPS) presents with symptoms of debilitating bladder pain and is typically a diagnosis of exclusion. The cystoscopic detection of Hunner's lesions increases the likelihood of detecting tissue inflammation on bladder biopsy and increases the odds of therapeutic success with anti-inflammatory drugs. However, the identification of this subgroup remains challenging with the current lack of surrogate biomarkers of IC/BPS. On the path towards identifying biomarkers of IC/BPS, we modeled the dynamic evolution of inflammation in an experimental IC/BPS rodent model using computational biological network analysis of inflammatory mediators (cytokines and chemokines) released into urine. The use of biological network analysis allows us to identify urinary proteins that could be drivers of inflammation and could therefore serve as therapeutic targets for the treatment of IC/BPS. METHODS: Rats subjected to cyclophosphamide (CYP) injection (150 mg/kg) were used as an experimental model for acute IC/BPS (n = 8). Urine from each void was collected from the rats over a 12-h period and was assayed for 13 inflammatory mediators using Luminex™. Time-interval principal component analysis (TI-PCA) and dynamic network analysis (DyNA), two biological network algorithms, were used to identify biomarkers of inflammation characteristic of IC/BPS over time. RESULTS: Compared to vehicle-treated rats, nearly all inflammatory mediators were elevated significantly (p < 0.05) in the urine of CYP treated rats. TI-PCA highlighted that GRO-KC, IL-5, IL-18, and MCP-1 account for the greatest variance in the inflammatory response. At early time points, DyNA indicated a positive correlation between IL-4 and IL-1ß and between TNF-α and IL-1ß. Analysis of TI-PCA and DyNA at later time points showed the emergence of IL-5, IL-6, and IFNγ as additional key mediators of inflammation. Furthermore, DyNA network complexity rose and fell before peaking at 9.5 h following CYP treatment. This pattern of inflammation may mimic the fluctuating severity of inflammation associated with IC/BPS flares. CONCLUSIONS: Computational analysis of inflammation networks in experimental IC/BPS analysis expands on the previously accepted inflammatory signatures of IC by adding IL-5, IL-18, and MCP-1 to the prior studies implicating IL-6 and GRO as IC/BPS biomarkers. This analysis supports a complex evolution of inflammatory networks suggestive of the rise and fall of inflammation characteristic of IC/BPS flares.

Artificial Intelligence Optical Biopsy for Evaluating the Functional State of Wounds.

INTRODUCTION: The clinical characterization of the functional status of active wounds in terms of their driving cellular and molecular biology remains a considerable challenge that currently requires excision via a tissue biopsy. In this pilot study, we use convolutional Siamese neural network (SNN) architecture to predict the functional state of a wound using digital photographs of wounds in a canine model of volumetric muscle loss (VML). METHODS: Digital images of VML injuries and tissue biopsies were obtained in a standardized fashion from an established canine model of VML. Gene expression profiles for each biopsy site were obtained using RNA sequencing. These profiles were converted to functional profiles by a manual review of validated gene ontology databases in which we determined a hierarchical representation of gene functions based on functional specificity. An SNN was trained to regress functional profile expression values, informed by an image segment showing the surface of a small tissue biopsy. RESULTS: The SNN was able to predict the functional expression of a range of functions based with error ranging from ∼5% to ∼30%, with functions that are most closely associated with the early state of wound healing to be those best-predicted. CONCLUSIONS: These initial results suggest promise for further research regarding this novel use of machine learning regression on medical images. The regression of functional profiles, as opposed to specific genes, both addresses the challenge of genetic redundancy and gives a deeper insight into the mechanistic configuration of a region of tissue in wounds.

A mathematical model of SARS-CoV-2 immunity predicts paxlovid rebound.

Nirmatrelvir/ritonavir (Paxlovid), an oral antiviral medication targeting SARS-CoV-2, remains an important treatment for COVID-19. Initial studies of nirmatrelvir/ritonavir were performed in SARS-CoV-2 unvaccinated patients without prior confirmed SARS-CoV-2 infection; however, most individuals have now either been vaccinated and/or have experienced SARS-CoV-2 infection. After nirmatrelvir/ritonavir became widely available, reports surfaced of "Paxlovid rebound," a phenomenon in which symptoms (and SARS-CoV-2 test positivity) would initially resolve, but after finishing treatment, symptoms and test positivity would return. We used a previously described parsimonious mathematical model of immunity to SARS-CoV-2 infection to model the effect of nirmatrelvir/ritonavir treatment in unvaccinated and vaccinated patients. Model simulations show that viral rebound after treatment occurs only in vaccinated patients, while unvaccinated (SARS-COV-2 naïve) patients treated with nirmatrelvir/ritonavir do not experience any rebound in viral load. This work suggests that an approach combining parsimonious models of the immune system could be used to gain important insights in the context of emerging pathogens.

Effects of Technology Assisted Stepped Collaborative Care Intervention to Improve Symptoms in Patients Undergoing Hemodialysis: The TACcare Randomized Clinical Trial.

Importance: Patients with end-stage kidney disease (ESKD) undergoing long-term hemodialysis often experience a high burden of debilitating symptoms for which effective treatment options are limited. Objective: To compare the effectiveness of a stepped collaborative care intervention vs attention control for reducing fatigue, pain, and depression among patients with ESKD undergoing long-term hemodialysis. Design, Setting, and Participants: Technology Assisted Stepped Collaborative Care (TACcare) was a parallel-group, single-blinded, randomized clinical trial of adult (≥18 years) patients undergoing long-term hemodialysis and experiencing clinically significant levels of fatigue, pain, and/or depression for which they were considering treatment. The trial took place in 2 US states (New Mexico and Pennsylvania) from March 1, 2018, to June 31, 2022. Data analyses were performed from July 1, 2022, to April 10, 2023. Interventions: The intervention group received 12 weekly sessions of cognitive behavioral therapy delivered via telehealth in the hemodialysis unit or patient home, and/or pharmacotherapy using a stepped approach in collaboration with dialysis and primary care teams. The attention control group received 6 telehealth sessions of health education. Main Outcomes and Measures: The coprimary outcomes were changes in fatigue (measured using the Functional Assessment of Chronic Illness Therapy Fatigue), average pain severity (Brief Pain Inventory), and/or depression (Beck Depression Inventory-II) scores at 3 months. Patients were followed up for 12 months to assess maintenance of intervention effects. Results: There were 160 participants (mean [SD] age, 58 [14] years; 72 [45%] women and 88 [55%] men; 21 [13%] American Indian, 45 [28%] Black, 28 [18%] Hispanic, and 83 [52%] White individuals) randomized, 83 to the intervention and 77 to the control group. In the intention-to-treat analyses, when compared with controls, patients in the intervention group experienced statistically and clinically significant reductions in fatigue (mean difference [md], 2.81; 95% CI, 0.86 to 4.75; P = .01) and pain severity (md, -0.96; 95% CI, -1.70 to -0.23; P = .02) at 3 months. These effects were sustained at 6 months (md, 3.73; 95% CI, 0.87 to 6.60; P = .03; and BPI, -1.49; 95% CI, -2.58 to -0.40; P = .02). Improvement in depression at 3 months was statistically significant but small (md -1.73; 95% CI, -3.18 to -0.28; P = .02). Adverse events were similar in both groups. Conclusions and Relevance: This randomized clinical trial found that a technology assisted stepped collaborative care intervention delivered during hemodialysis led to modest but clinically meaningful improvements in fatigue and pain at 3 months vs the control group, with effects sustained until 6 months. Trial Registration: ClinicalTrials.gov Identifier: NCT03440853.

Peripheral nerve repair is associated with augmented cross-tissue inflammation following vascularized composite allotransplantation.

Introduction: Vascularized composite allotransplantation (VCA), with nerve repair/coaptation (NR) and tacrolimus (TAC) immunosuppressive therapy, is used to repair devastating traumatic injuries but is often complicated by inflammation spanning multiple tissues. We identified the parallel upregulation of transcriptional pathways involving chemokine signaling, T-cell receptor signaling, Th17, Th1, and Th2 pathways in skin and nerve tissue in complete VCA rejection compared to baseline in 7 human hand transplants and defined increasing complexity of protein-level dynamic networks involving chemokine, Th1, and Th17 pathways as a function of rejection severity in 5 of these patients. We next hypothesized that neural mechanisms may regulate the complex spatiotemporal evolution of rejection-associated inflammation post-VCA. Methods: For mechanistic and ethical reasons, protein-level inflammatory mediators in tissues from Lewis rats (8 per group) receiving either syngeneic (Lewis) or allogeneic (Brown-Norway) orthotopic hind limb transplants in combination with TAC, with and without sciatic NR, were compared to human hand transplant samples using computational methods. Results: In cross-correlation analyses of these mediators, VCA tissues from human hand transplants (which included NR) were most similar to those from rats undergoing VCA + NR. Based on dynamic hypergraph analyses, NR following either syngeneic or allogeneic transplantation in rats was associated with greater trans-compartmental localization of early inflammatory mediators vs. no-NR, and impaired downregulation of mediators including IL-17A at later times. Discussion: Thus, NR, while considered necessary for restoring graft function, may also result in dysregulated and mis-compartmentalized inflammation post-VCA and therefore necessitate mitigation strategies. Our novel computational pipeline may also yield translational, spatiotemporal insights in other contexts.

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.

Towards systems immunology of critical illness at scale: from single cell 'omics to digital twins.

Single-cell 'omics methodology has yielded unprecedented insights based largely on data-centric informatics for reducing, and thus interpreting, massive datasets. In parallel, parsimonious mathematical modeling based on abstractions of pathobiology has also yielded major insights into inflammation and immunity, with these models being extended to describe multi-organ disease pathophysiology as the basis of 'digital twins' and in silico clinical trials. The integration of these distinct methods at scale can drive both basic and translational advances, especially in the context of critical illness, including diseases such as COVID-19. Here, I explore achievements and argue the challenges that are inherent to the integration of data-driven and mechanistic modeling approaches, highlighting the potential of modeling-based strategies for rational immune system reprogramming.

MetfOrmin BenefIts Lower Extremities with Intermittent Claudication (MOBILE IC): randomized clinical trial protocol.

BACKGROUND: Peripheral artery disease (PAD) affects over 230 million people worldwide and is due to systemic atherosclerosis with etiology linked to chronic inflammation, hypertension, and smoking status. PAD is associated with walking impairment and mobility loss as well as a high prevalence of coronary and cerebrovascular disease. Intermittent claudication (IC) is the classic presenting symptom for PAD, although many patients are asymptomatic or have atypical presentations. Few effective medical therapies are available, while surgical and exercise therapies lack durability. Metformin, the most frequently prescribed oral medication for Type 2 diabetes, has salient anti-inflammatory and promitochondrial properties. We hypothesize that metformin will improve function, retard the progression of PAD, and improve systemic inflammation and mitochondrial function in non-diabetic patients with IC. METHODS: 200 non-diabetic Veterans with IC will be randomized 1:1 to 180-day treatment with metformin extended release (1000 mg/day) or placebo to evaluate the effect of metformin on functional status, PAD progression, cardiovascular disease events, and systemic inflammation. The primary outcome is 180-day maximum walking distance on the 6-min walk test (6MWT). Secondary outcomes include additional assessments of functional status (cardiopulmonary exercise testing, grip strength, Walking Impairment Questionnaires), health related quality of life (SF-36, VascuQoL), macro- and micro-vascular assessment of lower extremity blood flow (ankle brachial indices, pulse volume recording, EndoPAT), cardiovascular events (amputations, interventions, major adverse cardiac events, all-cause mortality), and measures of systemic inflammation. All outcomes will be assessed at baseline, 90 and 180 days of study drug exposure, and 180 days following cessation of study drug. We will evaluate the primary outcome with linear mixed-effects model analysis with covariate adjustment for baseline 6MWT, age, baseline ankle brachial indices, and smoking status following an intention to treat protocol. DISCUSSION: MOBILE IC is uniquely suited to evaluate the use of metformin to improve both systematic inflammatory responses, cellular energetics, and functional outcomes in patients with PAD and IC. TRIAL REGISTRATION: The prospective MOBILE IC trial was publicly registered (NCT05132439) November 24, 2021.

Interleukin-17 as a spatiotemporal bridge from acute to chronic inflammation: Novel insights from computational modeling.

A systematic review of several acute inflammatory diseases ranging from sepsis and trauma/hemorrhagic shock to the relevant pathology of the decade, COVID-19, points to the cytokine interleukin (IL)-17A as being centrally involved in the propagation of inflammation. We summarize the role of IL-17A in acute inflammation, leveraging insights made possible by biological network analysis and novel computational methodologies aimed at defining the spatiotemporal spread of inflammation in both experimental animal models and humans. These studies implicate IL-17A in the cross-tissue spread of inflammation, a process that appears to be in part regulated through neural mechanisms. Although acute inflammatory diseases are currently considered distinct from chronic inflammatory pathologies, we suggest that chronic inflammation may represent repeated, cyclical episodes of acute inflammation driven by mechanisms involving IL-17A. Thus, insights from computational modeling of acute inflammatory diseases may improve diagnosis and treatment of chronic inflammation; in turn, therapeutics developed for chronic/autoimmune disease may be of benefit in acute inflammation. This article is categorized under: Immune System Diseases > Computational Models.

A Prospective Cohort Protocol for the Remnant Investigation in Sepsis Study.

BACKGROUND: Sepsis is a common and deadly syndrome, accounting for more than 11 million deaths annually. To mature a deeper understanding of the host and pathogen mechanisms contributing to poor outcomes in sepsis, and thereby possibly inform new therapeutic targets, sophisticated, and expensive biorepositories are typically required. We propose that remnant biospecimens are an alternative for mechanistic sepsis research, although the viability and scientific value of such remnants are unknown. METHODS AND RESULTS: The Remnant Biospecimen Investigation in Sepsis study is a prospective cohort study of 225 adults (age ≥ 18 yr) presenting to the emergency department with community sepsis, defined as sepsis-3 criteria within 6 hours of arrival. The primary objective was to determine the scientific value of a remnant biospecimen repository in sepsis linked to clinical phenotyping in the electronic health record. We will study candidate multiomic readouts of sepsis biology, governed by a conceptual model, and determine the precision, accuracy, integrity, and comparability of proteins, small molecules, lipids, and pathogen sequencing in remnant biospecimens compared with paired biospecimens obtained according to research protocols. Paired biospecimens will include plasma from sodium-heparin, EDTA, sodium fluoride, and citrate tubes. CONCLUSIONS: The study has received approval from the University of Pittsburgh Human Research Protection Office (Study 21120013). Recruitment began on October 25, 2022, with planned release of primary results anticipated in 2024. Results will be made available to the public, the funders, critical care societies, laboratory medicine scientists, and other researchers.

Plasma proteomics reveals early, broad release of chemokine, cytokine, TNF, and interferon mediators following trauma with delayed increases in a subset of chemokines and cytokines in patients that remain critically ill.

Severe injury is known to cause a systemic cytokine storm that is associated with adverse outcomes. However, a comprehensive assessment of the time-dependent changes in circulating levels of a broad spectrum of protein immune mediators and soluble immune mediator receptors in severely injured trauma patients remains uncharacterized. To address this knowledge gap, we defined the temporal and outcome-based patterns of 184 known immune mediators and soluble cytokine receptors in the circulation of severely injured patients. Proteomics (aptamer-based assay, SomaLogic, Inc) was performed on plasma samples drawn at 0, 24, and 72 hours (h) from time of admission from 150 trauma patients, a representative subset from the Prehospital Plasma during Air Medical Transport in Trauma Patients at Risk for Hemorrhagic Shock (PAMPer) trial. Patients were categorized into outcome groups including Early Non-Survivors (died within 72 h; ENS; n=38), Non-Resolvers (died after 72 h or required ≥7 days of intensive care; NR; n=78), and Resolvers (survivors that required < 7 days of intensive care; R; n=34), with low Injury Severity Score (ISS) patients from the Tranexamic Acid During Prehospital Transport in Patients at Risk for Hemorrhage After Injury (STAAMP) trial as controls. The major findings include an extensive release of immune mediators and cytokine receptors at time 0h that is more pronounced in ENS and NR patients. There was a selective subset of mediators elevated at 24 and 72 h to a greater degree in NR patients, including multiple cytokines and chemokines not previously described in trauma patients. These findings were validated in a quantitative fashion using mesoscale discovery immunoassays (MSD) from an external validation cohort (VC) of samples from 58 trauma patients matched for R and NR status. This comprehensive longitudinal description of immune mediator patterns associated with trauma outcomes provides a new level of characterization of the immune response that follows severe injury.

Lipidomic signatures align with inflammatory patterns and outcomes in critical illness.

Alterations in lipid metabolism have the potential to be markers as well as drivers of pathobiology of acute critical illness. Here, we took advantage of the temporal precision offered by trauma as a common cause of critical illness to identify the dynamic patterns in the circulating lipidome in critically ill humans. The major findings include an early loss of all classes of circulating lipids followed by a delayed and selective lipogenesis in patients destined to remain critically ill. The previously reported survival benefit of early thawed plasma administration was associated with preserved lipid levels that related to favorable changes in coagulation and inflammation biomarkers in causal modelling. Phosphatidylethanolamines (PE) were elevated in patients with persistent critical illness and PE levels were prognostic for worse outcomes not only in trauma but also severe COVID-19 patients. Here we show selective rise in systemic PE as a common prognostic feature of critical illness.

Multi-Omic Admission-Based Prognostic Biomarkers Identified by Machine Learning Algorithms Predict Patient Recovery and 30-Day Survival in Trauma Patients.

Admission-based circulating biomarkers for the prediction of outcomes in trauma patients could be useful for clinical decision support. It is unknown which molecular classes of biomolecules can contribute biomarkers to predictive modeling. Here, we analyzed a large multi-omic database of over 8500 markers (proteomics, metabolomics, and lipidomics) to identify prognostic biomarkers in the circulating compartment for adverse outcomes, including mortality and slow recovery, in severely injured trauma patients. Admission plasma samples from patients (n = 129) enrolled in the Prehospital Air Medical Plasma (PAMPer) trial were analyzed using mass spectrometry (metabolomics and lipidomics) and aptamer-based (proteomics) assays. Biomarkers were selected via Least Absolute Shrinkage and Selection Operator (LASSO) regression modeling and machine learning analysis. A combination of five proteins from the proteomic layer was best at discriminating resolvers from non-resolvers from critical illness with an Area Under the Receiver Operating Characteristic curve (AUC) of 0.74, while 26 multi-omic features predicted 30-day survival with an AUC of 0.77. Patients with traumatic brain injury as part of their injury complex had a unique subset of features that predicted 30-day survival. Our findings indicate that multi-omic analyses can identify novel admission-based prognostic biomarkers for outcomes in trauma patients. Unique biomarker discovery also has the potential to provide biologic insights.