Escalating Threat Levels of Bacterial Infection Can Be Discriminated by Distinct MAPK and NF-κB Signaling Dynamics in Single Host Cells.

During an infection, immune cells must identify the specific level of threat posed by a given bacterial input in order to generate an appropriate response. Given that they use a general non-self-recognition system, known as Toll-like receptors (TLRs), to detect bacteria, it remains unclear how they transmit information about a particular threat. To determine whether host cells can use signaling dynamics to transmit contextual information about a bacterial stimulus, we use live-cell imaging to make simultaneous quantitative measurements of host MAPK and NF-κB signaling, two key pathways downstream of TLRs, and bacterial infection and load. This combined, single-cell approach reveals that NF-κB and MAPK signaling dynamics are sufficient to discriminate between (1) pathogen-associated molecular patterns (PAMPs) versus bacteria, (2) extracellular versus intracellular bacteria, (3) pathogenic versus non-pathogenic bacteria, and (4) the presence or absence of features indicating an active intracellular bacterial infection, such as replication and effector secretion.

Western diet regulates immune status and the response to LPS-driven sepsis independent of diet-associated microbiome.

Sepsis is a deleterious immune response to infection that leads to organ failure and is the 11th most common cause of death worldwide. Despite plaguing humanity for thousands of years, the host factors that regulate this immunological response and subsequent sepsis severity and outcome are not fully understood. Here we describe how the Western diet (WD), a diet high in fat and sucrose and low in fiber, found rampant in industrialized countries, leads to worse disease and poorer outcomes in an LPS-driven sepsis model in WD-fed mice compared with mice fed standard fiber-rich chow (SC). We find that WD-fed mice have higher baseline inflammation (metaflammation) and signs of sepsis-associated immunoparalysis compared with SC-fed mice. WD mice also have an increased frequency of neutrophils, some with an "aged" phenotype, in the blood during sepsis compared with SC mice. Importantly, we found that the WD-dependent increase in sepsis severity and higher mortality is independent of the microbiome, suggesting that the diet may be directly regulating the innate immune system through an unknown mechanism. Strikingly, we could predict LPS-driven sepsis outcome by tracking specific WD-dependent disease factors (e.g., hypothermia and frequency of neutrophils in the blood) during disease progression and recovery. We conclude that the WD is reprogramming the basal immune status and acute response to LPS-driven sepsis and that this correlates with alternative disease paths that lead to more severe disease and poorer outcomes.

EMPOWERING MULTI-COHORT GENE EXPRESSION ANALYSIS TO INCREASE REPRODUCIBILITY.

A major contributor to the scientific reproducibility crisis has been that the results from homogeneous, single-center studies do not generalize to heterogeneous, real world populations. Multi-cohort gene expression analysis has helped to increase reproducibility by aggregating data from diverse populations into a single analysis. To make the multi-cohort analysis process more feasible, we have assembled an analysis pipeline which implements rigorously studied meta-analysis best practices. We have compiled and made publicly available the results of our own multi-cohort gene expression analysis of 103 diseases, spanning 615 studies and 36,915 samples, through a novel and interactive web application. As a result, we have made both the process of and the results from multi-cohort gene expression analysis more approachable for non-technical users.

Integrated, Multi-cohort Analysis Identifies Conserved Transcriptional Signatures across Multiple Respiratory Viruses.

Respiratory viral infections are a significant burden to healthcare worldwide. Many whole genome expression profiles have identified different respiratory viral infection signatures, but these have not translated to clinical practice. Here, we performed two integrated, multi-cohort analyses of publicly available transcriptional data of viral infections. First, we identified a common host signature across different respiratory viral infections that could distinguish (1) individuals with viral infections from healthy controls and from those with bacterial infections, and (2) symptomatic from asymptomatic subjects prior to symptom onset in challenge studies. Second, we identified an influenza-specific host response signature that (1) could distinguish influenza-infected samples from those with bacterial and other respiratory viral infections, (2) was a diagnostic and prognostic marker in influenza-pneumonia patients and influenza challenge studies, and (3) was predictive of response to influenza vaccine. Our results have applications in the diagnosis, prognosis, and identification of drug targets in viral infections.