Thomas Burke

Analysis of Cdc6 function in the assembly of mammalian prereplication complexes

Eukaryotic DNA replication requires the previous formation of a prereplication complex containing the ATPase Cdc6 and the minichromosome maintenance (Mcm) complex. Although considerable insight has been gained from in vitro studies and yeast genetics, the functional analysis of replication proteins in intact mammalian cells has been lacking. We have made use of adenoviral vectors to express normal and mutant forms of Cdc6 in quiescent mammalian cells to assess function. We demonstrate that Cdc6 expression alone is sufficient to induce a stable association of endogenous Mcm proteins with chromatin in serum-deprived cells where cyclin-dependent kinase (cdk) activity is low. Moreover, endogenous Cdc6 is sufficient to load Mcm proteins onto chromatin in the absence of cdk activity in p21-arrested cells. Cdc6 synergizes with physiological levels of cyclin E/Cdk2 to induce semiconservative DNA replication in quiescent cells whereas cyclin A/Cdk2 is unable to collaborate with Cdc6. Cdc6 that cannot be phosphorylated by cdks is fully capable of inducing Mcm chromatin association and replication. Mutation of the Cdc6 ATP-binding site severely impairs the ability of Cdc6 to induce Mcm chromatin loading and reduces its ability to induce replication. Nevertheless, the ATPase domain of Cdc6 in the absence of the noncatalytic amino terminus is not sufficient for either Mcm chromatin loading or DNA replication, indicating a requirement for this domain of Cdc6.

Human biospecimen research: Experimental protocol and quality control tools

Among the different types of variability (interindividual, intra-individual, analytical, and preanalytical) that can influence the results of any biological assay, preanalytical variations are the most difficult to manage. Preanalytical variations are defined as any variation taking place between

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.

Comparison of Moxifloxacin and Cefuroxime Axetil in the Treatment of Acute Maxillary Sinusitis

The aim of this prospective, multicenter, randomized, double-masked clinical trial was to compare the efficacy and safety of moxifloxacin with those of cefuroxime axetil for the treatment of community-acquired acute sinusitis. Five hundred forty-two adult patients with symptoms and radiographic evidence of acute maxillary sinusitis received a 10-day oral regimen of either moxifloxacin (400 mg once daily) or cefuroxime axetil (250 mg twice daily). Acute signs and symptoms at presentation had lasted >7 days but <4 weeks. Clinical response at the end of therapy (7 to 14 days after treatment) was the primary efficacy variable. Four hundred fifty-seven of the patients (223 moxifloxacin, 234 cefuroxime axetil) were included in the clinical efficacy analysis. Moxifloxacin was found to be similar in effectiveness to cefuroxime axetil at the end-of-therapy visit (90% vs. 89%, respectively; 95% confidence interval, -5.1% to 6.2%). Clinical relapse at the follow-up visit was reported for only 8 patients (3 moxifloxacin, 5 cefuroxime axetil). No clinically significant differences were observed with respect to the number of patients experiencing a successful clinical response based on demographic or infection characteristics. Five of the 542 enrolled patients were lost to follow-up. Of the 537 patients in the intent-to-treat population, drug-related adverse events were reported in 37% of moxifloxacin-treated patients and in 26% of cefuroxime axetil-treated patients (P = 0.006). Adverse-event profiles were comparable in the 2 treatment groups, with the exception of nausea, which was reported by 11% of moxifloxacin-treated patients compared with 4% of cef uroxime axetil-treated patients (P = 0.003). In this study, moxifloxacin was as effective as cefuroxime axetil in the treatment of community-acquired acute sinusitis.

Centralized Biorepositories for Genetic and Genomic Research

MEDICAL PRACTICE IS BEING TRANSFORMED BY MOlecular analyses of biological samples that provide prognostic and diagnostic information for clinical care. The analytical methods and technologies that have accompanied the sequencing of the human genome have been the subject of research on their accuracy, reliability, and precision, but far less attention has been paid to the storage methods and archiving of the specimens required for analysis. Indeed, well-annotated biospecimen collections have enabled the recent identification of genes and genetic loci thought to contribute to susceptibility for several complex diseases. Some of these genomewide association studies were based on governmentsponsored, centralized collections. However, biorepositories with standardized procedures, informatics, and embedded regulatory compliance remain rare and the state of storage of human biospecimens is often decentralized and poorly organized at many of the US medical centers. Human tissue has been collected and stored at medical and research institutions in the United States for more than 100 years. By 2000, more than 300 million human biospecimens had been stored in freezers across the United States with an estimated accrual rate of an additional 20 million specimens annually. In 2004, the National Cancer Institute estimated that it spends more than

Multiplex detection of disease biomarkers using SERS molecular sentinel-on-chip

50 million yearly on banking samples from cancer patients as part of 125 funded research programs and projects. Academic medical centers historically have discharged the responsibility of biospecimen sample and data storage to individual investigators who have developed “home remedies” to meet specific research objectives, often with limited resources and inconsistent funding. The result is a decentralized system of ad hoc solutions that, despite facilitating great advances, often lack standards, are inefficient, and create a liability for the institutions and their researchers. Most institutions today cannot readily access a list of samples stored on institutional premises, let alone ascertain the conditions under which they are stored or who donated them. With a greater reliance on sample-derived data for genetic and genomic research and clinical care, improved standards and informatics for sample procurement, storage, and analysis are required to maximize the value of tissue collection for research participants, investigators, medical centers, and funding agencies.

The current epidemiology and clinical decisions surrounding acute respiratory infections

AbstractDeveloping techniques for multiplex detection of disease biomarkers is important for clinical diagnosis. In this work, we have demonstrated for the first time the feasibility of multiplex detection of genetic disease biomarkers using the surface-enhanced Raman scattering (SERS)-based molecular sentinel-on-chip (MSC) diagnostic technology. The molecular sentinel (MS) sensing mechanism is based upon the decrease of SERS intensity when Raman labels tagged at 3′-ends of MS nanoprobes are physically displaced from the nanowave chip’s surface upon DNA hybridization. The use of bimetallic layer (silver and gold) for the nanowave fabrication was investigated. SERS measurements were performed immediately following a single hybridization reaction between the target single-stranded DNA sequences and the complementary MS nanoprobes immobilized on the nanowave chip without requiring target labeling (i.e., label-free), secondary hybridization, or post-hybridization washing, thus shortening the assay time and reducing cost. Two nucleic acid transcripts, interferon alpha-inducible protein 27 and interferon-induced protein 44-like, are used as model systems for the multiplex detection concept demonstration. These two genes are well known for their critical role in host immune response to viral infection and can be used as molecular signature for viral infection diagnosis. The results indicate the potential of the MSC technology for nucleic acid biomarker multiplex detection. FigureScheme of two-multiplex detection of complementary target ssDNA sequences using SERS-based molecular sentinel-on-chip diagnostic technology

Comparing reference-based RNA-Seq mapping methods for non-human primate data

Acute respiratory infection (ARI) is a common diagnosis in outpatient and emergent care settings. Currently available diagnostics are limited, creating uncertainty in the use of antibacterial, antiviral, or supportive care. Up to 72% of ambulatory care patients with ARI are treated with an antibacterial, despite only a small fraction actually needing one. Antibiotic overuse is not restricted to ambulatory care: ARI accounts for approximately 5 million emergency department (ED) visits annually in the USA, where 52-61% of such patients receive antibiotics. Thus, an accurate test for the presence or absence of viral or bacterial infection is needed. In this review, we focus on recent research showing that the host-response (genomic, proteomic, or miRNA) can accomplish this task.

An individualized predictor of health and disease using paired reference and target samples

BackgroundThe application of next-generation sequencing technology to gene expression quantification analysis, namely, RNA-Sequencing, has transformed the way in which gene expression studies are conducted and analyzed. These advances are of particular interest to researchers studying organisms with missing or incomplete genomes, as the need for knowledge of sequence information is overcome. De novo assembly methods have gained widespread acceptance in the RNA-Seq community for organisms with no true reference genome or transcriptome. While such methods have tremendous utility, computational cost is still a significant challenge for organisms with large and complex genomes.ResultsIn this manuscript, we present a comparison of four reference-based mapping methods for non-human primate data. We utilize TopHat2 and GSNAP for mapping to the human genome, and Bowtie2 and Stampy for mapping to the human genome and transcriptome for a total of six mapping approaches. For each of these methods, we explore mapping rates and locations, number of detected genes, correlations between computed expression values, and the utility of the resulting data for differential expression analysis.ConclusionsWe show that reference-based mapping methods indeed have utility in RNA-Seq analysis of mammalian data with no true reference, and the details of mapping methods should be carefully considered when doing so. Critical algorithm features include short seed sequences, the allowance of mismatches, and the allowance of gapped alignments in addition to splice junction gaps. Such features facilitate sensitive alignment of non-human primate RNA-Seq data to a human reference.