Browsing by Author "Lucas, Joseph"

Now showing 1 - 7 of 7
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    A translatable predictor of human radiation exposure.
    (PLoS One, 2014) Lucas, Joseph; Dressman, Holly K; Suchindran, Sunil; Nakamura, Mai; Chao, Nelson J; Himburg, Heather; Minor, Kerry; Phillips, Gary; Ross, Joel; Abedi, Majid; Terbrueggen, Robert; Chute, John P
    Terrorism using radiological dirty bombs or improvised nuclear devices is recognized as a major threat to both public health and national security. In the event of a radiological or nuclear disaster, rapid and accurate biodosimetry of thousands of potentially affected individuals will be essential for effective medical management to occur. Currently, health care providers lack an accurate, high-throughput biodosimetric assay which is suitable for the triage of large numbers of radiation injury victims. Here, we describe the development of a biodosimetric assay based on the analysis of irradiated mice, ex vivo-irradiated human peripheral blood (PB) and humans treated with total body irradiation (TBI). Interestingly, a gene expression profile developed via analysis of murine PB radiation response alone was inaccurate in predicting human radiation injury. In contrast, generation of a gene expression profile which incorporated data from ex vivo irradiated human PB and human TBI patients yielded an 18-gene radiation classifier which was highly accurate at predicting human radiation status and discriminating medically relevant radiation dose levels in human samples. Although the patient population was relatively small, the accuracy of this classifier in discriminating radiation dose levels in human TBI patients was not substantially confounded by gender, diagnosis or prior exposure to chemotherapy. We have further incorporated genes from this human radiation signature into a rapid and high-throughput chemical ligation-dependent probe amplification assay (CLPA) which was able to discriminate radiation dose levels in a pilot study of ex vivo irradiated human blood and samples from human TBI patients. Our results illustrate the potential for translation of a human genetic signature for the diagnosis of human radiation exposure and suggest the basis for further testing of CLPA as a candidate biodosimetric assay.
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    ACLY and ACC1 Regulate Hypoxia-Induced Apoptosis by Modulating ETV4 via α-ketoglutarate.
    (PLoS Genet, 2015-10) Keenan, Melissa M; Liu, Beiyu; Tang, Xiaohu; Wu, Jianli; Cyr, Derek; Stevens, Robert D; Ilkayeva, Olga; Huang, Zhiqing; Tollini, Laura A; Murphy, Susan K; Lucas, Joseph; Muoio, Deborah M; Kim, So Young; Chi, Jen-Tsan
    In order to propagate a solid tumor, cancer cells must adapt to and survive under various tumor microenvironment (TME) stresses, such as hypoxia or lactic acidosis. To systematically identify genes that modulate cancer cell survival under stresses, we performed genome-wide shRNA screens under hypoxia or lactic acidosis. We discovered that genetic depletion of acetyl-CoA carboxylase (ACACA or ACC1) or ATP citrate lyase (ACLY) protected cancer cells from hypoxia-induced apoptosis. Additionally, the loss of ACLY or ACC1 reduced levels and activities of the oncogenic transcription factor ETV4. Silencing ETV4 also protected cells from hypoxia-induced apoptosis and led to remarkably similar transcriptional responses as with silenced ACLY or ACC1, including an anti-apoptotic program. Metabolomic analysis found that while α-ketoglutarate levels decrease under hypoxia in control cells, α-ketoglutarate is paradoxically increased under hypoxia when ACC1 or ACLY are depleted. Supplementation with α-ketoglutarate rescued the hypoxia-induced apoptosis and recapitulated the decreased expression and activity of ETV4, likely via an epigenetic mechanism. Therefore, ACC1 and ACLY regulate the levels of ETV4 under hypoxia via increased α-ketoglutarate. These results reveal that the ACC1/ACLY-α-ketoglutarate-ETV4 axis is a novel means by which metabolic states regulate transcriptional output for life vs. death decisions under hypoxia. Since many lipogenic inhibitors are under investigation as cancer therapeutics, our findings suggest that the use of these inhibitors will need to be carefully considered with respect to oncogenic drivers, tumor hypoxia, progression and dormancy. More broadly, our screen provides a framework for studying additional tumor cell stress-adaption mechanisms in the future.
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    Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies
    (BMC BIOINFORMATICS, 2010-11-09) Chen, Bo; Chen, Minhua; Paisley, John; Zaas, Aimee; Woods, Christopher; Ginsburg, Geoffrey S; Hero, Alfred; Lucas, Joseph; Dunson, David; Carin, Lawrence
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    Diagnosis of partial body radiation exposure in mice using peripheral blood gene expression profiles.
    (PLoS One, 2010-07-12) Meadows, Sarah K; Dressman, Holly K; Daher, Pamela; Himburg, Heather; Russell, J Lauren; Doan, Phuong; Chao, Nelson J; Lucas, Joseph; Nevins, Joseph R; Chute, John P
    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.
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    Dusp3 and Psme3 are associated with murine susceptibility to Staphylococcus aureus infection and human sepsis.
    (PLoS Pathog, 2014-06) Yan, Qin; Sharma-Kuinkel, Batu K; Deshmukh, Hitesh; Tsalik, Ephraim L; Cyr, Derek D; Lucas, Joseph; Woods, Christopher W; Scott, William K; Sempowski, Gregory D; Thaden, Joshua T; Rude, Thomas H; Ahn, Sun Hee; Fowler, Vance G
    Using A/J mice, which are susceptible to Staphylococcus aureus, we sought to identify genetic determinants of susceptibility to S. aureus, and evaluate their function with regard to S. aureus infection. One QTL region on chromosome 11 containing 422 genes was found to be significantly associated with susceptibility to S. aureus infection. Of these 422 genes, whole genome transcription profiling identified five genes (Dcaf7, Dusp3, Fam134c, Psme3, and Slc4a1) that were significantly differentially expressed in a) S. aureus -infected susceptible (A/J) vs. resistant (C57BL/6J) mice and b) humans with S. aureus blood stream infection vs. healthy subjects. Three of these genes (Dcaf7, Dusp3, and Psme3) were down-regulated in susceptible vs. resistant mice at both pre- and post-infection time points by qPCR. siRNA-mediated knockdown of Dusp3 and Psme3 induced significant increases of cytokine production in S. aureus-challenged RAW264.7 macrophages and bone marrow derived macrophages (BMDMs) through enhancing NF-κB signaling activity. Similar increases in cytokine production and NF-κB activity were also seen in BMDMs from CSS11 (C57BL/6J background with chromosome 11 from A/J), but not C57BL/6J. These findings suggest that Dusp3 and Psme3 contribute to S. aureus infection susceptibility in A/J mice and play a role in human S. aureus infection.
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    Gene expression-based classifiers identify Staphylococcus aureus infection in mice and humans.
    (PLoS One, 2013) Ahn, Sun Hee; Tsalik, Ephraim L; Cyr, Derek D; Zhang, Yurong; van Velkinburgh, Jennifer C; Langley, Raymond J; Glickman, Seth W; Cairns, Charles B; Zaas, Aimee K; Rivers, Emanuel P; Otero, Ronny M; Veldman, Tim; Kingsmore, Stephen F; Kingsmore, Stephen F; Lucas, Joseph; Woods, Christopher W; Ginsburg, Geoffrey S; Fowler, Vance G
    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.
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    Host gene expression classifiers diagnose acute respiratory illness etiology.
    (Sci Transl Med, 2016-01-20) Tsalik, Ephraim L; Henao, Ricardo; Nichols, Marshall; Burke, Thomas; Ko, Emily R; McClain, Micah T; Hudson, Lori L; Mazur, Anna; Freeman, Debra H; Veldman, Tim; Langley, Raymond J; Quackenbush, Eugenia B; Glickman, Seth W; Cairns, Charles B; Jaehne, Anja K; Rivers, Emanuel P; Otero, Ronny M; Zaas, Aimee K; Kingsmore, Stephen F; Lucas, Joseph; Fowler, Vance G; Carin, Lawrence; Ginsburg, Geoffrey S; Woods, Christopher W
    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.