Systematic comparison of published host gene expression signatures for bacterial/viral discrimination.

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.

VB10, a new blood biomarker for differential diagnosis and recovery monitoring of acute viral and bacterial infections.

BACKGROUND: Precise differential diagnosis between acute viral and bacterial infections is important to enable appropriate therapy, avoid unnecessary antibiotic prescriptions and optimize the use of hospital resources. A systems view of host response to infections provides opportunities for discovering sensitive and robust molecular diagnostics. METHODS: We combine blood transcriptomes from six independent datasets (n = 756) with a knowledge-based human protein-protein interaction network, identifies subnetworks capturing host response to each infection class, and derives common response cores separately for viral and bacterial infections. We subject the subnetworks to a series of computational filters to identify a parsimonious gene panel and a standalone diagnostic score that can be applied to individual samples. We rigorously validate the panel and the diagnostic score in a wide range of publicly available datasets and in a newly developed Bangalore-Viral Bacterial (BL-VB) cohort. FINDING: We discover a 10-gene blood-based biomarker panel (Panel-VB) that demonstrates high predictive performance to distinguish viral from bacterial infections, with a weighted mean AUROC of 0.97 (95% CI: 0.96-0.99) in eleven independent datasets (n = 898). We devise a new stand-alone patient-wise score (VB10) based on the panel, which shows high diagnostic accuracy with a weighted mean AUROC of 0.94 (95% CI 0.91-0.98) in 2996 patient samples from 56 public datasets from 19 different countries. Further, we evaluate VB10 in a newly generated South Indian (BL-VB, n = 56) cohort and find 97% accuracy in the confirmed cases of viral and bacterial infections. We find that VB10 is (a) capable of accurately identifying the infection class in culture-negative indeterminate cases, (b) reflects recovery status, and (c) is applicable across different age groups, covering a wide spectrum of acute bacterial and viral infections, including uncharacterized pathogens. We tested our VB10 score on publicly available COVID-19 data and find that our score detected viral infection in patient samples. INTERPRETATION: Our results point to the promise of VB10 as a diagnostic test for precise diagnosis of acute infections and monitoring recovery status. We expect that it will provide clinical decision support for antibiotic prescriptions and thereby aid in antibiotic stewardship efforts. FUNDING: Grand Challenges India, Biotechnology Industry Research Assistance Council (BIRAC), Department of Biotechnology, Govt. of India.

The Emerging Role of Epigenetic Mechanisms in the Causation of Aberrant MMP Activity during Human Pathologies and the Use of Medicinal Drugs.

Matrix metalloproteinases (MMPs) cleave extracellular matrix proteins, growth factors, cytokines, and receptors to influence organ development, architecture, function, and the systemic and cell-specific responses to diseases and pharmacological drugs. Conversely, many diseases (such as atherosclerosis, arthritis, bacterial infections (tuberculosis), viral infections (COVID-19), and cancer), cholesterol-lowering drugs (such as statins), and tetracycline-class antibiotics (such as doxycycline) alter MMP activity through transcriptional, translational, and post-translational mechanisms. In this review, we summarize evidence that the aforementioned diseases and drugs exert significant epigenetic pressure on genes encoding MMPs, tissue inhibitors of MMPs, and factors that transcriptionally regulate the expression of MMPs. Our understanding of human pathologies associated with alterations in the proteolytic activity of MMPs must consider that these pathologies and their medicinal treatments may impose epigenetic pressure on the expression of MMP genes. Whether the epigenetic mechanisms affecting the activity of MMPs can be therapeutically targeted warrants further research.

Eating the unknown: Xenophagy and ER-phagy are cytoprotective defenses against pathogens.

Autophagy is an evolutionary conserved catabolic process devoted to the removal of unnecessary and harmful cellular components. In its general form, autophagy governs cellular lifecycle through the formation of double membrane vesicles, termed autophagosomes, that enwrap and deliver unwanted intracellular components to lysosomes. In addition to this omniscient role, forms of selective autophagy, relying on specialized receptors for cargo recognition, exert fine-tuned control over cellular homeostasis. In this regard, xenophagy plays a pivotal role in restricting the replication of intracellular pathogens, thus acting as an ancient innate defense system against infections. Recently, selective autophagy of the endoplasmic reticulum (ER), more simply ER-phagy, has been uncovered as a critical mechanism governing ER network shape and function. Six ER-resident proteins have been characterized as ER-phagy receptors and their orchestrated function enables ER homeostasis and turnover overtime. Unfortunately, ER is also the preferred site for viral replication and several viruses hijack ER machinery for their needs. Thus, it is not surprising that some ER-phagy receptors can act to counteract viral replication and minimize the spread of infection throughout the organism. On the other hand, evolutionary pressure has armed pathogens with strategies to evade and subvert xenophagy and ER-phagy. Although ER-phagy biology is still in its infancy, the present review aims to summarize recent ER-phagy literature, with a special focus on its role in counteracting viral infections. Moreover, we aim to offer some hints for future targeted approaches to counteract host-pathogen interactions by modulating xenophagy and ER-phagy pathways.