Ephraim L. Tsalik

LIM homeobox gene-dependent expression of biogenic amine receptors in restricted regions of the C. elegans nervous system

Biogenic amines regulate a variety of behaviors. Their functions are predominantly mediated through G-protein-coupled 7-transmembrane domain receptors (GPCR), 16 of which are predicted to exist in the genome sequence of the nematode Caenorhabditis elegans. We describe here the expression pattern of several of these aminergic receptors, including two serotonin receptors (ser-1 and ser-4), one tyramine receptor (ser-2), and two dopamine receptors (dop-1 and dop-2). Moreover, we describe distinct but partially overlapping expression patterns of different splice forms of the ser-2 tyramine receptor locus. We find that each of the aminergic receptor genes is expressed in restricted regions of the nervous system and that many of them reveal significant overlap with the expression of regulatory factors of the LIM homeobox (Lhx) gene family. We demonstrate that the expression of several of the biogenic amine receptors is abrogated in specific cell types in Lhx gene mutants, thus establishing a role for these Lhx genes in regulating aspects of neurotransmission. We extend these findings with other cell fate markers and show that the lim-4 Lhx gene is required for several but not all aspects of RID motor neuron differentiation and that the lim-6 Lhx gene is required for specific aspects of RIS interneuron differentiation. We also use aminergic receptor gfp reporter fusions as tools to visualize the anatomy of specific neurons in Lhx mutant backgrounds and find that the development of the elaborate dendritic branching pattern of the PVD harsh touch sensory neuron requires the mec-3 Lhx gene. Lastly, we analyze a mutant allele of the ser-2 tyramine receptor, a target of the ttx-3 Lhx gene in the AIY interneuron class. ser-2 mutants display none of the defects previously shown to be associated with loss of AIY function.

Sepsis: An integrated clinico-metabolomic model improves prediction of death in sepsis

A molecular signature, derived from integrated analysis of clinical data, the metabolome, and the proteome in prospective human studies, improved the prediction of death in patients with sepsis, potentially identifying a subset of patients who merit intensive treatment. Understanding Survival of the Fittest in Sepsis Differentiating mild infections from life-threatening ones is a complex decision that is made millions of times a year in U.S. emergency rooms. Should a patient be sent home with antibiotics and chicken soup? Or should he or she be hospitalized for intensive treatment? Sepsis—a serious infection that is associated with a generalized inflammatory response—is one of the leading causes of death. In two prospective clinical studies reported by Langley et al., patients arriving at four urban emergency departments with symptoms of sepsis were evaluated clinically and by analysis of their plasma proteome and metabolome. Survivors and nonsurvivors at 28 days were compared, and a molecular signature was detected that appeared to differentiate these outcomes—even as early as the time of hospital arrival. The signature was part of a large set of differences between these groups, showing that better energy-producing fatty acid catabolism was associated with survival of the fittest in sepsis. A test developed from the signature was able to predict sepsis survival and nonsurvival reproducibly and better than current methods. This test could help to make all important decisions in the emergency room more accurate. Sepsis is a common cause of death, but outcomes in individual patients are difficult to predict. Elucidating the molecular processes that differ between sepsis patients who survive and those who die may permit more appropriate treatments to be deployed. We examined the clinical features and the plasma metabolome and proteome of patients with and without community-acquired sepsis, upon their arrival at hospital emergency departments and 24 hours later. The metabolomes and proteomes of patients at hospital admittance who would ultimately die differed markedly from those of patients who would survive. The different profiles of proteins and metabolites clustered into the following groups: fatty acid transport and β-oxidation, gluconeogenesis, and the citric acid cycle. They differed consistently among several sets of patients, and diverged more as death approached. In contrast, the metabolomes and proteomes of surviving patients with mild sepsis did not differ from survivors with severe sepsis or septic shock. An algorithm derived from clinical features together with measurements of five metabolites predicted patient survival. This algorithm may help to guide the treatment of individual patients with sepsis.

Multiplex PCR to diagnose bloodstream infections in patients admitted from the emergency department with sepsis.

ABSTRACT Sepsis is caused by a heterogeneous group of infectious etiologies. Early diagnosis and the provision of appropriate antimicrobial therapy correlate with positive clinical outcomes. Current microbiological techniques are limited in their diagnostic capacities and timeliness. Multiplex PCR has the potential to rapidly identify bloodstream infections and fill this diagnostic gap. We identified patients from two large academic hospital emergency departments with suspected sepsis. The results of a multiplex PCR that could detect 25 bacterial and fungal pathogens were compared to those of blood culture. The results were analyzed with respect to the likelihood of infection, sepsis severity, the site of infection, and the effect of prior antibiotic therapy. We enrolled 306 subjects with suspected sepsis. Of these, 43 were later determined not to have infectious etiologies. Of the remaining 263 subjects, 70% had sepsis, 16% had severe sepsis, and 14% had septic shock. The majority had a definite infection (41.5%) or a probable infection (30.7%). Blood culture and PCR performed similarly with samples from patients with clinically defined infections (areas under the receiver operating characteristic curves, 0.64 and 0.60, respectively). However, blood culture identified more cases of septicemia than PCR among patients with an identified infectious etiology (66 and 46, respectively; P = 0.0004). The two tests performed similarly when the results were stratified by sepsis severity or infection site. Blood culture tended to detect infections more frequently among patients who had previously received antibiotics (P = 0.06). Conversely, PCR identified an additional 24 organisms that blood culture failed to detect. Real-time multiplex PCR has the potential to serve as an adjunct to conventional blood culture, adding diagnostic yield and shortening the time to pathogen identification.

Disease Progression in Hemodynamically Stable Patients Presenting to the Emergency Department With Sepsis

BACKGROUND Aggressive diagnosis and treatment of patients presenting to the emergency department (ED) with septic shock has been shown to reduce mortality. To enhance the ability to intervene in patients with lesser illness severity, a better understanding of the natural history of the early progression from simple infection to more severe illness is needed. OBJECTIVES The objectives were to 1) describe the clinical presentation of ED sepsis, including types of infection and causative microorganisms, and 2) determine the incidence, patient characteristics, and mortality associated with early progression to septic shock among ED patients with infection. METHODS This was a multicenter study of adult ED patients with sepsis but no evidence of shock. Multivariable logistic regression was used to identify patient factors for early progression to shock and its association with 30-day mortality. RESULTS Of 472 patients not in shock at ED presentation (systolic blood pressure > 90 mm Hg and lactate < 4 mmol/L), 84 (17.8%) progressed to shock within 72 hours. Independent factors associated with early progression to shock included older age, female sex, hyperthermia, anemia, comorbid lung disease, and vascular access device infection. Early progression to shock (vs. no progression) was associated with higher 30-day mortality (13.1% vs. 3.1%, odds ratio [OR] = 4.72, 95% confidence interval [CI] = 2.01 to 11.1; p < or = 0.001). Among 379 patients with uncomplicated sepsis (i.e., no evidence of shock or any end-organ dysfunction), 86 (22.7%) progressed to severe sepsis or shock within 72 hours of hospital admission. CONCLUSIONS A significant portion of ED patients with less severe sepsis progress to severe sepsis or shock within 72 hours. Additional diagnostic approaches are needed to risk stratify and more effectively treat ED patients with sepsis.

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.

Plasma microRNA signature as a noninvasive biomarker for acute graft-versus-host disease

Acute graft-versus-host disease (aGVHD) is the leading cause of morbidity and mortality after allogeneic hematopoietic cell transplantation (HCT). Approximately 35% to 50% of HCT recipients develop aGVHD; however, there are no validated diagnostic and predictive blood biomarkers for aGVHD in clinical use. Here, we show that plasma samples from aGVHD patients have a distinct microRNA (miRNA) expression profile. We found that 6 miRNAs (miR-423, miR-199a-3p, miR-93*, miR-377, miR-155, and miR-30a) were significantly upregulated in the plasma of aGVHD patients (n = 116) when compared with non-GVHD patients (n = 52) in training and validation phases. We have developed a model including 4 miRNAs (miR-423, miR-199a-3p, miR-93*, and miR-377) that can predict the probability of aGVHD with an area under the curve of 0.80. Moreover, these elevated miRNAs were detected before the onset of aGVHD (median = 16 days before diagnosis). In addition, the levels of these miRNAs were positively associated with aGVHD severity, and high expression of the miRNA panel was associated with poor overall survival. Furthermore, the miRNA signature for aGVHD was not detected in the plasma of lung transplant or nontransplant sepsis patients. Our results have identified a specific plasma miRNA signature that may serve as an independent biomarker for the prediction, diagnosis, and prognosis of aGVHD.

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.

Integrative "omic" analysis of experimental bacteremia identifies a metabolic signature that distinguishes human sepsis from systemic inflammatory response syndromes.

RATIONALE Sepsis is a leading cause of morbidity and mortality. Currently, early diagnosis and the progression of the disease are difficult to make. The integration of metabolomic and transcriptomic data in a primate model of sepsis may provide a novel molecular signature of clinical sepsis. OBJECTIVES To develop a biomarker panel to characterize sepsis in primates and ascertain its relevance to early diagnosis and progression of human sepsis. METHODS Intravenous inoculation of Macaca fascicularis with Escherichia coli produced mild to severe sepsis, lung injury, and death. Plasma samples were obtained before and after 1, 3, and 5 days of E. coli challenge and at the time of killing. At necropsy, blood, lung, kidney, and spleen samples were collected. An integrative analysis of the metabolomic and transcriptomic datasets was performed to identify a panel of sepsis biomarkers. MEASUREMENTS AND MAIN RESULTS The extent of E. coli invasion, respiratory distress, lethargy, and mortality was dependent on the bacterial dose. Metabolomic and transcriptomic changes characterized severe infections and death, and indicated impaired mitochondrial, peroxisomal, and liver functions. Analysis of the pulmonary transcriptome and plasma metabolome suggested impaired fatty acid catabolism regulated by peroxisome-proliferator activated receptor signaling. A representative four-metabolite model effectively diagnosed sepsis in primates (area under the curve, 0.966) and in two human sepsis cohorts (area under the curve, 0.78 and 0.82). CONCLUSIONS A model of sepsis based on reciprocal metabolomic and transcriptomic data was developed in primates and validated in two human patient cohorts. It is anticipated that the identified parameters will facilitate early diagnosis and management of sepsis.

CURING SACCHAROMYCES CEREVISIAE OF THE 2 MICRON PLASMID BY TARGETED DNA DAMAGE

Powerful mutagenic screens of yeast Saccharomyces cerevisiae have recently been developed which require strains that lack the endogenous 2 micron plasmid (Burns et al., 1994). Here, we describe a simple and reliable method for curing yeast of the highly stable genetic element. The approach employs heterologous expression of a ‘step‐arrest’ mutant of the Flp recombinase. The mutant, Flp H305L (Parsons et al., 1988), forms long‐lived covalent protein–DNA complexes exclusively at 2 micron‐borne recombinase target sites. In vivo, the complexes serve as sites of targeted DNA damage. Using Southern hybridization and a colony color assay for plasmid loss, we show that expression of the mutant enzyme results in the effective elimination of the 2 micron from cells.

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.