Background Measuring host gene expression is a promising diagnostic strategy to discriminate bacterial and viral infections. Multiple signatures of varying size, complexity, and target populations have been described. However, there is little information to indicate how the performance of various published signatures compare to one another. Methods This systematic comparison of host gene expression signatures evaluated the performance of 28 signatures, validating them in 4589 subjects from 51 publicly available datasets. Thirteen COVID-specific datasets with 1416 subjects were included in a separate analysis. Individual signature performance was evaluated using the area under the receiving operating characteristic curve (AUC) value. Overall signature performance was evaluated using median AUCs and accuracies. Results Signature performance varied widely, with median AUCs ranging from 0.55 to 0.96 for bacterial classification and 0.69–0.97 for viral classification. Signature size varied (1–398 genes), with smaller signatures generally performing more poorly (P < 0.04). Viral infection was easier to diagnose than bacterial infection (84% vs. 79% overall accuracy, respectively; P < .001). Host gene expression classifiers performed more poorly in some pediatric populations (3 months–1 year and 2–11 years) compared to the adult population for both bacterial infection (73% and 70% vs. 82%, respectively; P < .001) and viral infection (80% and 79% vs. 88%, respectively; P < .001). We did not observe classification differences based on illness severity as defined by ICU admission for bacterial or viral infections. The median AUC across all signatures for COVID-19 classification was 0.80 compared to 0.83 for viral classification in the same datasets. Conclusions In this systematic comparison of 28 host gene expression signatures, we observed differences based on a signature’s size and characteristics of the validation population, including age and infection type. However, populations used for signature discovery did not impact performance, underscoring the redundancy among many of these signatures. Furthermore, differential performance in specific populations may only be observable through this type of large-scale validation.

Background Appropriately modulating inflammation after traumatic brain injury (TBI) may prevent disabilities for the millions of those inflicted annually. In TBI, cellular mediators of inflammation, including macrophages and microglia, possess a range of phenotypes relevant for an immunomodulatory therapeutic approach. It is thought that early phenotypic modulation of these cells will have a cascading healing effect. In fact, an anti-inflammatory, “M2-like” macrophage phenotype after TBI has been associated with neurogenesis, axonal regeneration, and improved white matter integrity (WMI). There already exist clinical trials seeking an M2-like bias through mesenchymal stem/stromal cells (MSCs). However, MSCs do not endogenously synthesize key signals that induce robust M2-like phenotypes such as interleukin-4 (IL-4). Methods To enrich M2-like macrophages in a clinically relevant manner, we augmented MSCs with synthetic IL-4 mRNA to transiently express IL-4. These IL-4 expressing MSCs (IL-4 MSCs) were characterized for expression and functionality and then delivered in a modified mouse TBI model of closed head injury. Groups were assessed for functional deficits and MR imaging. Brain tissue was analyzed through flow cytometry, multi-plex ELISA, qPCR, histology, and RNA sequencing. Results We observed that IL-4 MSCs indeed induce a robust M2-like macrophage phenotype and promote anti-inflammatory gene expression after TBI. However, here we demonstrate that acute enrichment of M2-like macrophages did not translate to improved functional or histological outcomes, or improvements in WMI on MR imaging. To further understand whether dysfunctional pathways underlie the lack of therapeutic effect, we report transcriptomic analysis of injured and treated brains. Through this, we discovered that inflammation persists despite acute enrichment of M2-like macrophages in the brain. Conclusion The results demonstrate that MSCs can be engineered to induce a stronger M2-like macrophage response in vivo. However, they also suggest that acute enrichment of only M2-like macrophages after diffuse TBI cannot orchestrate neurogenesis, axonal regeneration, or improve WMI. Here, we also discuss our modified TBI model and methods to assess severity, behavioral studies, and propose that IL-4 expressing MSCs may also have relevance in other cavitary diseases or in improving biomaterial integration into tissues.

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Appropriately modulating inflammation after traumatic brain injury (TBI) may prevent disabilities for the millions of those inflicted annually. In TBI, cellular mediators of inflammation, including macrophages and microglia, possess a range of phenotypes relevant for an immunomodulatory therapeutic approach. It is thought that early phenotypic modulation of these cells will have a cascading healing effect. In fact, an anti-inflammatory, "M2-like" macrophage phenotype after TBI has been associated with neurogenesis, axonal regeneration, and improved white matter integrity. There already exists clinical trials seeking an M2-like bias through mesenchymal stem/stromal cells (MSCs). However, MSCs do not endogenously synthesize key signals that induce robust M2-like phenotypes such as Interleukin-4 (IL-4). To enrich M2-like macrophages in a clinically relevant manner, we augmented MSCs to transiently express IL-4 via synthetic IL-4 mRNA. We observed that these IL-4 expressing MSCs indeed induce a robust M2like macrophage phenotype and promote anti-inflammatory gene expression in a modified TBI model of closed head injury. However, here we demonstrate that acute enrichment of M2-like macrophages did not translate to improved functional or histological outcomes. This suggests that an acute enrichment of M2like macrophages cannot solely orchestrate the neurogenesis, axonal regeneration, and improved WMI after diffuse TBI. To further understand whether dysfunctional pathways underlie the lack of therapeutic effect, we report transcriptomic analysis of injured and treated brains. Through this, we discovered that inflammation persists in spite of acute enrichment of M2-like macrophages in the brain. Last, we comment on our modified TBI model, behavioral studies, and propose that IL-4 expressing MSCs may also have relevance in other cavitary diseases or in improving biomaterial integration into tissues.

Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene can trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, to date, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and contexts. Moreover, how the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear. Here we combine computational analysis of existing datasets with stochastic mathematical modeling and machine learning to uncover the widespread prevalence of transcriptional adaptation in mammalian systems and the diverse single-cell manifestations of minimal compensatory gene networks. Regulon gene expression analysis of a pooled single-cell genetic perturbation dataset recapitulates our model predictions. Our integrative approach uncovers several putative hits-genes demonstrating possible transcriptional adaptation-to follow up on experimentally, and provides a formal quantitative framework to test and refine models of transcriptional adaptation.

Background Host gene expression has emerged as a promising diagnostic strategy to discriminate bacterial and viral infection. Multiple gene signatures of varying size and complexity have been developed in various clinical populations. However, there has been no systematic comparison of these signatures. It is also unclear how these signatures apply to different clinical populations. This meta-analysis examined 19 published signatures, validated in 49 publicly available datasets for a total of 4750 patients. The objectives were to understand how the signatures compared to each other with respect to composition and performance, and to evaluate their performance in different patient subgroups. Methods Signatures were characterized with respect to size, platform, and discovery population. For each of the 19 signatures, we ran leave-one-out cross-validation to generate AUCs for bacterial classification and viral classification. We then applied dataset-specific thresholds to generate accuracies, pooling patients across datasets. Results Signature performance varied significantly with a median AUC across all validation datasets ranging from 0.55 to 0.94 for bacterial classification and 0.79 to 0.96 for viral classification. Signature size varied (1- 341 genes) with smaller signatures generally performing more poorly for both bacterial classification (P &lt; .001) and for viral classification (P = 0.02). Viral infection was easier to diagnose than bacterial infection (85% vs. 80% overall accuracy, respectively; P &lt; .001). Host gene expression classifiers performed more poorly in children &lt; 12-years compared to those older than 12-years for both bacterial infection (77% vs. 83%, respectively; P &lt; .001) and for viral infection (82% vs. 89%, respectively; P &lt; .001). We did not observe differences based on illness severity as defined by ICU care for either bacterial or viral infections. Conclusion We observed significant differences among gene expression signatures for bacterial/viral discrimination, though these were not due to variations in the discovery methods or populations. Signature size directly correlated with test performance, which was generally better for the diagnosis of viral infection and in populations &gt;12-years. Disclosures Ephraim L. Tsalik, MD, MHS, PhD, Predigen (Shareholder, Other Financial or Material Support, Founder)