Joseph Lucas

A Host-Based RT-PCR Gene Expression Signature to Identify Acute Respiratory Viral Infection

To improve the diagnosis of respiratory viral infection, a multiplex RT-PCR assay based on the host response was derived from experimentally infected subjects and validated in patients with febrile illness. Diagnosing the Cause of Coughs and Sneezes Diagnosis of viral respiratory infections remains a challenge. Early differentiation between a viral and bacterial etiology of respiratory symptoms would help to direct therapy more appropriately and prevent overuse of antibiotics. Measuring the host immune response to infection is an alternative to pathogen-based diagnostic testing and may improve diagnostic accuracy. Now, Zaas et al. have developed a reverse transcription polymerase chain reaction (RT-PCR) assay for blood RNA that can classify respiratory viral infections based on the host immune response. They developed their assay using two groups of individuals experimentally infected with either influenza A H3N2/Wisconsin or influenza A H1N1/Brisbane. They then validated their RT-PCR diagnostic in a sample of adults presenting to the emergency department with fever, who had microbiologically confirmed viral or bacterial illness. The sensitivity of the RT-PCR assay was 89% [95% confidence interval (CI), 72 to 98%], and the specificity was 94% (95% CI, 86 to 99%). These data establish an important “proof of concept” that host expression of a relatively small set of genes measured by RT-PCR can be used to classify viral respiratory illness in unselected individuals presenting at an emergency department for evaluation of fever. The development of this new assay and its validation in an independent “real-world” patient population is an important step on the translational pathway to establishing this platform for diagnostic testing in the clinic. Improved ways to diagnose acute respiratory viral infections could decrease inappropriate antibacterial use and serve as a vital triage mechanism in the event of a potential viral pandemic. Measurement of the host response to infection is an alternative to pathogen-based diagnostic testing and may improve diagnostic accuracy. We have developed a host-based assay with a reverse transcription polymerase chain reaction (RT-PCR) TaqMan low-density array (TLDA) platform for classifying respiratory viral infection. We developed the assay using two cohorts experimentally infected with influenza A H3N2/Wisconsin or influenza A H1N1/Brisbane, and validated the assay in a sample of adults presenting to the emergency department with fever (n = 102) and in healthy volunteers (n = 41). Peripheral blood RNA samples were obtained from individuals who underwent experimental viral challenge or who presented to the emergency department and had microbiologically proven viral respiratory infection or systemic bacterial infection. The selected gene set on the RT-PCR TLDA assay classified participants with experimentally induced influenza H3N2 and H1N1 infection with 100 and 87% accuracy, respectively. We validated this host gene expression signature in a cohort of 102 individuals arriving at the emergency department. The sensitivity of the RT-PCR test was 89% [95% confidence interval (CI), 72 to 98%], and the specificity was 94% (95% CI, 86 to 99%). These results show that RT-PCR–based detection of a host gene expression signature can classify individuals with respiratory viral infection and sets the stage for prospective evaluation of this diagnostic approach in a clinical setting.

Host gene expression classifiers diagnose acute respiratory illness etiology.

Pathogen-specific host gene expression changes may combat inappropriate antibiotic use and emerging antibiotic resistance. Resisting antibiotics No matter the cause, acute respiratory infections can be miserable. Indeed, these infections are one of the most common reasons for seeking medical care. A clear diagnostic can help medical practitioners resist the patient-induced pressure to prescribe antibiotics as a catch-all therapy, which increases the risk of bacteria developing antibiotic resistance. Now, Tsalik et al. report clear differences in host gene expression induced by bacterial and viral infection as well as by noninfectious illness. These differences can be used to discriminate between these groups, and a host gene expression classifier may be a helpful diagnostic platform to curb unnecessary antibiotic use. Acute respiratory infections caused by bacterial or viral pathogens are among the most common reasons for seeking medical care. Despite improvements in pathogen-based diagnostics, most patients receive inappropriate antibiotics. Host response biomarkers offer an alternative diagnostic approach to direct antimicrobial use. This observational cohort study determined whether host gene expression patterns discriminate noninfectious from infectious illness and bacterial from viral causes of acute respiratory infection in the acute care setting. Peripheral whole blood gene expression from 273 subjects with community-onset acute respiratory infection (ARI) or noninfectious illness, as well as 44 healthy controls, was measured using microarrays. Sparse logistic regression was used to develop classifiers for bacterial ARI (71 probes), viral ARI (33 probes), or a noninfectious cause of illness (26 probes). Overall accuracy was 87% (238 of 273 concordant with clinical adjudication), which was more accurate than procalcitonin (78%, P < 0.03) and three published classifiers of bacterial versus viral infection (78 to 83%). The classifiers developed here externally validated in five publicly available data sets (AUC, 0.90 to 0.99). A sixth publicly available data set included 25 patients with co-identification of bacterial and viral pathogens. Applying the ARI classifiers defined four distinct groups: a host response to bacterial ARI, viral ARI, coinfection, and neither a bacterial nor a viral response. These findings create an opportunity to develop and use host gene expression classifiers as diagnostic platforms to combat inappropriate antibiotic use and emerging antibiotic resistance.

Tie2(+) bone marrow endothelial cells regulate hematopoietic stem cell regeneration following radiation injury.

Hematopoietic stem cells (HSCs) reside in proximity to bone marrow endothelial cells (BM ECs) and maintenance of the HSC pool is dependent upon EC‐mediated c‐kit signaling. Here, we used genetic models to determine whether radioprotection of BM ECs could facilitate hematopoietic regeneration following radiation‐induced myelosuppression. We developed mice bearing deletion of the proapoptotic proteins, BAK and BAX, in Tie2+ ECs and HSCs (Tie2Bak/BaxFl/− mice) and compared their hematopoietic recovery following total body irradiation (TBI) with mice which retained Bax in Tie2+ cells. Mice bearing deletion of Bak and Bax in Tie2+ cells demonstrated protection of BM HSCs, preserved BM vasculature, and 100% survival following lethal dose TBI. In contrast, mice that retained Bax expression in Tie2+ cells demonstrated depletion of BM HSCs, disrupted BM vasculature, and 10% survival post‐TBI. In a complementary study, VEcadherinBak/BaxFl/− mice, which lack Bak and Bax in VEcadherin+ ECs, also demonstrated increased recovery of BM stem/progenitor cells following TBI compared to mice which retained Bax in VEcadherin+ ECs. Importantly, chimeric mice that lacked Bak and Bax in HSCs but retained Bak and Bax in BM ECs displayed significantly decreased HSC content and survival following TBI compared to mice lacking Bak and Bax in both HSCs and BM ECs. These data suggest that the hematopoietic response to ionizing radiation is dependent upon HSC‐autonomous responses but is regulated by BM EC‐mediated mechanisms. Therefore, BM ECs may be therapeutically targeted as a means to augment hematopoietic reconstitution following myelosuppression. STEM CELLS2013;31:327–337

A comparison of models for predicting early hospital readmissions

Risk sharing arrangements between hospitals and payers together with penalties imposed by the Centers for Medicare and Medicaid (CMS) are driving an interest in decreasing early readmissions. There are a number of published risk models predicting 30day readmissions for particular patient populations, however they often exhibit poor predictive performance and would be unsuitable for use in a clinical setting. In this work we describe and compare several predictive models, some of which have never been applied to this task and which outperform the regression methods that are typically applied in the healthcare literature. In addition, we apply methods from deep learning to the five conditions CMS is using to penalize hospitals, and offer a simple framework for determining which conditions are most cost effective to target.

Blood Gene Expression Signatures Predict Invasive Candidiasis

Development and validation of a blood gene expression signature to diagnose C. albicans bloodstream infection. Bloodstream infection with the fungus Candida is the fourth most common bloodstream infection in the United States, primarily acquired during hospital stays. However, thousands of hospital patients who acquire such fungal infections are mistakenly diagnosed with a bacterial infection and subsequently treated with antibiotics when they should be treated with antifungals. In patients whose immune systems are weakened because of cancer treatments, steroids, or diseases such as AIDS, Candida infections can cause life-threatening problems. Yet current tests for Candida infections are often unreliable and take several days for results to be available. Now, Zaas and colleagues use a mouse model to develop a diagnostic tool that is able to rapidly identify global changes in the gene expression of peripheral blood immune effector cells that can distinguish between major infectious etiologies. This gene signature was able to distinguish murine candidemia versus bacteremia versus no infection, as well as the progression of illness related to candidemia. Whether or not this signature will prove effective in stratifying recently infected patients is currently under investigation, but the technique holds considerable promise for such time-sensitive clinical needs. Candidemia is the fourth most common bloodstream infection, with Candida albicans being the most common causative species. Success in reducing the associated morbidity and mortality has been limited by the inadequacy and time delay of currently available diagnostic modalities. Focusing on host response to infection, we used a murine model to develop a blood gene expression signature that accurately classified mice with candidemia and distinguished candidemia from Staphylococcus aureus bacteremia. Validation of the signature was achieved in an independent cohort of mice. Genes represented in the signature have known associations with host defense against Candida and other microorganisms. Our results demonstrate a temporal pattern of host molecular responses that distinguish candidemia from S. aureus–induced bacteremia and establish a novel paradigm for infectious disease diagnosis.

Diagnosis of Partial Body Radiation Exposure in Mice Using Peripheral Blood Gene Expression Profiles

In the event of a terrorist-mediated attack in the United States using radiological or improvised nuclear weapons, it is expected that hundreds of thousands of people could be exposed to life-threatening levels of ionizing radiation. We have recently shown that genome-wide expression analysis of the peripheral blood (PB) can generate gene expression profiles that can predict radiation exposure and distinguish the dose level of exposure following total body irradiation (TBI). However, in the event a radiation-mass casualty scenario, many victims will have heterogeneous exposure due to partial shielding and it is unknown whether PB gene expression profiles would be useful in predicting the status of partially irradiated individuals. Here, we identified gene expression profiles in the PB that were characteristic of anterior hemibody-, posterior hemibody- and single limb-irradiation at 0.5 Gy, 2 Gy and 10 Gy in C57Bl6 mice. These PB signatures predicted the radiation status of partially irradiated mice with a high level of accuracy (range 79–100%) compared to non-irradiated mice. Interestingly, PB signatures of partial body irradiation were poorly predictive of radiation status by site of injury (range 16–43%), suggesting that the PB molecular response to partial body irradiation was anatomic site specific. Importantly, PB gene signatures generated from TBI-treated mice failed completely to predict the radiation status of partially irradiated animals or non-irradiated controls. These data demonstrate that partial body irradiation, even to a single limb, generates a characteristic PB signature of radiation injury and thus may necessitate the use of multiple signatures, both partial body and total body, to accurately assess the status of an individual exposed to radiation.

A Bayesian Analysis Strategy for Cross-Study Translation of Gene Expression Biomarkers

We describe a strategy for the analysis of experimentally derived gene expression signatures and their translation to human observational data. Sparse multivariate regression models are used to identify expression signature gene sets representing downstream biological pathway events following interventions in designed experiments. When translated into in vivo human observational data, analysis using sparse latent factor models can yield multiple quantitative factors characterizing expression patterns that are often more complex than in the controlled, in vitro setting. The estimation of common patterns in expression that reflect all aspects of covariation evident in vivo offers an enhanced, modular view of the complexity of biological associations of signature genes. This can identify substructure in the biological process under experimental investigation and improved biomarkers of clinical outcomes. We illustrate the approach in a detailed study from an oncogene intervention experiment where in vivo factor profiling of an in vitro signature generates biological insights related to underlying pathway activities and chromosomal structure, and leads to refinements of cancer recurrence risk stratification across several cancer studies.

Researcher Practices on Returning Genetic Research Results

BACKGROUND/AIMS as genetic and genomic research proliferates, debate has ensued about returning results to participants. In addition to consideration of the benefits and harms to participants, researchers must also consider the logistical and financial feasibility of returning research results. However, little data exist of actual researcher practices. METHODS we conducted an online survey of 446 corresponding authors of genetic/genomic studies conducted in the United States and published in 2006-2007 to assess the frequency with which they considered, offered to, or actually returned research results, what factors influenced these decisions, and the method of communicating results. RESULTS the response rate was 24% (105/446). Fifty-four percent of respondents considered the issue of returning research results to participants, 28% offered to return individual research results, and 24% actually returned individual research results. Of those who considered the issue of returning research results during the study planning phase, the most common factors considered were whether research results were deemed clinically useful (18%) and respect for participants (13%). Researchers who had a medical degree and conducted studies on children were significantly more likely to offer to return or actually return individual results compared to those with a Ph.D. only. CONCLUSIONS we speculate that issues associated with clinical validity and respect for participants dominated concerns of time and expense given the prominent and continuing ethical debates surrounding genetics and genomics research. The substantial number of researchers who did not consider returning research results suggests that researchers and institutional review boards need to devote more attention to a topic about which research participants are interested.

Gene expression-based classifiers identify Staphylococcus aureus infection in mice and humans.

Staphylococcus aureus causes a spectrum of human infection. Diagnostic delays and uncertainty lead to treatment delays and inappropriate antibiotic use. A growing literature suggests the host’s inflammatory response to the pathogen represents a potential tool to improve upon current diagnostics. The hypothesis of this study is that the host responds differently to S. aureus than to E. coli infection in a quantifiable way, providing a new diagnostic avenue. This study uses Bayesian sparse factor modeling and penalized binary regression to define peripheral blood gene-expression classifiers of murine and human S. aureus infection. The murine-derived classifier distinguished S. aureus infection from healthy controls and Escherichia coli-infected mice across a range of conditions (mouse and bacterial strain, time post infection) and was validated in outbred mice (AUC>0.97). A S. aureus classifier derived from a cohort of 94 human subjects distinguished S. aureus blood stream infection (BSI) from healthy subjects (AUC 0.99) and E. coli BSI (AUC 0.84). Murine and human responses to S. aureus infection share common biological pathways, allowing the murine model to classify S. aureus BSI in humans (AUC 0.84). Both murine and human S. aureus classifiers were validated in an independent human cohort (AUC 0.95 and 0.92, respectively). The approach described here lends insight into the conserved and disparate pathways utilized by mice and humans in response to these infections. Furthermore, this study advances our understanding of S. aureus infection; the host response to it; and identifies new diagnostic and therapeutic avenues.

Predicting Viral Infection From High-Dimensional Biomarker Trajectories

There is often interest in predicting an individual’s latent health status based on high-dimensional biomarkers that vary over time. Motivated by time-course gene expression array data that we have collected in two influenza challenge studies performed with healthy human volunteers, we develop a novel time-aligned Bayesian dynamic factor analysis methodology. The time course trajectories in the gene expressions are related to a relatively low-dimensional vector of latent factors, which vary dynamically starting at the latent initiation time of infection. Using a nonparametric cure rate model for the latent initiation times, we allow selection of the genes in the viral response pathway, variability among individuals in infection times, and a subset of individuals who are not infected. As we demonstrate using held-out data, this statistical framework allows accurate predictions of infected individuals in advance of the development of clinical symptoms, without labeled data and even when the number of biomarkers vastly exceeds the number of individuals under study. Biological interpretation of several of the inferred pathways (factors) is provided.