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A Computational Pipeline for the Development of Multi-Marker Bio-Signature Panels and Ensemble Classifiers

BACKGROUND:Biomarker panels derived separately from genomic and proteomic data and with a variety of computational methods have demonstrated promising classification performance in various diseases. An open question is how to create effective proteo-genomic panels. The framework of ensemble classifiers has been applied successfully in various analytical domains to combine classifiers so that the performance of the ensemble exceeds the performance of individual classifiers. Using blood-based diagnosis of acute renal allograft rejection as a case study, we address the following question in this paper: Can acute rejection classification performance be improved by combining individual genomic and proteomic classifiers in an ensemble?RESULTS:The first part of the paper presents a computational biomarker development pipeline for genomic and proteomic data. The pipeline begins with data acquisition (e.g., from bio-samples to microarray data), quality control, statistical analysis and mining of the data, and finally various forms of validation. The pipeline ensures that the various classifiers to be combined later in an ensemble are diverse and adequate for clinical use. Five mRNA genomic and five proteomic classifiers were developed independently using single time-point blood samples from 11 acute-rejection and 22 non-rejection renal transplant patients. The second part of the paper examines five ensembles ranging in size from two to 10 individual classifiers. Performance of ensembles is characterized by area under the curve (AUC), sensitivity, and specificity, as derived from the probability of acute rejection for individual classifiers in the ensemble in combination with one of two aggregation methods: (1) Average Probability or (2) Vote Threshold. One ensemble demonstrated superior performance and was able to improve sensitivity and AUC beyond the best values observed for any of the individual classifiers in the ensemble, while staying within the range of observed specificity. The Vote Threshold aggregation method achieved improved sensitivity for all 5 ensembles, but typically at the cost of decreased specificity.CONCLUSION:Proteo-genomic biomarker ensemble classifiers show promise in the diagnosis of acute renal allograft rejection and can improve classification performance beyond that of individual genomic or proteomic classifiers alone. Validation of our results in an international multicenter study is currently underway

White Blood Cell Differentials Enrich Whole Blood Expression Data in the Context of Acute Cardiac Allograft Rejection

Acute cardiac allograft rejection is a serious complication of heart transplantation. Investigating molecular processes in whole blood via microarrays is a promising avenue of research in transplantation, particularly due to the non-invasive nature of blood sampling. However, whole blood is a complex tissue and the consequent heterogeneity in composition amongst samples is ignored in traditional microarray analysis. This complicates the biological interpretation of microarray data. Here we have applied a statistical deconvolution approach, cell-specific significance analysis of microarrays (csSAM), to whole blood samples from subjects either undergoing acute heart allograft rejection (AR) or not (NR). We identified eight differentially expressed probe-sets significantly correlated to monocytes (mapping to 6 genes, all down-regulated in ARs versus NRs) at a false discovery rate (FDR) ≤ 15%. None of the genes identified are present in a biomarker panel of acute heart rejection previously published by our group and discovered in the same data***

MDQC: a new quality assessment method for microarrays based on quality control reports

Motivation: The process of producing microarray data involves multiple steps, some of which may suffer from technical problems and seriously damage the quality of the data. Thus, it is essential to identify those arrays with low quality. This article addresses two questions: (1) how to assess the quality of a microarray dataset using the measures provided in quality control (QC) reports; (2) how to identify possible sources of the quality problems. Results: We propose a novel multivariate approach to evaluate the quality of an array that examines the ‘Mahalanobis distance' of its quality attributes from those of other arrays. Thus, we call it Mahalanobis Distance Quality Control (MDQC) and examine different approaches of this method. MDQC flags problematic arrays based on the idea of outlier detection, i.e. it flags those arrays whose quality attributes jointly depart from those of the bulk of the data. Using two case studies, we show that a multivariate analysis gives substantially richer information than analyzing each parameter of the QC report in isolation. Moreover, once the QC report is produced, our quality assessment method is computationally inexpensive and the results can be easily visualized and interpreted. Finally, we show that computing these distances on subsets of the quality measures in the report may increase the method's ability to detect unusual arrays and helps to identify possible reasons of the quality problems. Availability: The library to implement MDQC will soon be available from Bioconductor Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Randomized Controlled Ferret Study to Assess the Direct Impact of 2008–09 Trivalent Inactivated Influenza Vaccine on A(H1N1)pdm09 Disease Risk

During spring-summer 2009, several observational studies from Canada showed increased risk of medically-attended, laboratory-confirmed A(H1N1)pdm09 illness among prior recipients of 2008–09 trivalent inactivated influenza vaccine (TIV). Explanatory hypotheses included direct and indirect vaccine effects. In a randomized placebo-controlled ferret study, we tested whether prior receipt of 2008–09 TIV may have directly influenced A(H1N1)pdm09 illness. Thirty-two ferrets (16/group) received 0.5 mL intra-muscular injections of the Canadian-manufactured, commercially-available, non-adjuvanted, split 2008–09 Fluviral or PBS placebo on days 0 and 28. On day 49 all animals were challenged (Ch0) with A(H1N1)pdm09. Four ferrets per group were randomly selected for sacrifice at day 5 post-challenge (Ch+5) and the rest followed until Ch+14. Sera were tested for antibody to vaccine antigens and A(H1N1)pdm09 by hemagglutination inhibition (HI), microneutralization (MN), nucleoprotein-based ELISA and HA1-based microarray assays. Clinical characteristics and nasal virus titers were recorded pre-challenge then post-challenge until sacrifice when lung virus titers, cytokines and inflammatory scores were determined. Baseline characteristics were similar between the two groups of influenza-naïve animals. Antibody rise to vaccine antigens was evident by ELISA and HA1-based microarray but not by HI or MN assays; virus challenge raised antibody to A(H1N1)pdm09 by all assays in both groups. Beginning at Ch+2, vaccinated animals experienced greater loss of appetite and weight than placebo animals, reaching the greatest between-group difference in weight loss relative to baseline at Ch+5 (7.4% vs. 5.2%; p = 0.01). At Ch+5 vaccinated animals had higher lung virus titers (log-mean 4.96 vs. 4.23pfu/mL, respectively; p = 0.01), lung inflammatory scores (5.8 vs. 2.1, respectively; p = 0.051) and cytokine levels (p>0.05). At Ch+14, both groups had recovered. Findings in influenza-naïve, systematically-infected ferrets may not replicate the human experience. While they cannot be considered conclusive to explain human observations, these ferret findings are consistent with direct, adverse effect of prior 2008–09 TIV receipt on A(H1N1)pdm09 illness. As such, they warrant further in-depth investigation and search for possible mechanistic explanations

Mitochondria, Energetics, Epigenetics, and Cellular Responses to Stress

Background: Cells respond to environmental stressors through several key pathways, including response to reactive oxygen species (ROS), nutrient and ATP sensing, DNA damage response (DDR), and epigenetic alterations. Mitochondria play a central role in these pathways not only through energetics and ATP production but also through metabolites generated in the tricarboxylic acid cycle, as well as mitochondria–nuclear signaling related to mitochondria morphology, biogenesis, fission/fusion, mitophagy, apoptosis, and epigenetic regulation. Objectives: We investigated the concept of bidirectional interactions between mitochondria and cellular pathways in response to environmental stress with a focus on epigenetic regulation, and we examined DNA repair and DDR pathways as examples of biological processes that respond to exogenous insults through changes in homeostasis and altered mitochondrial function. Methods: The National Institute of Environmental Health Sciences sponsored the Workshop on Mitochondria, Energetics, Epigenetics, Environment, and DNA Damage Response on 25–26 March 2013. Here, we summarize key points and ideas emerging from this meeting. Discussion: A more comprehensive understanding of signaling mechanisms (cross-talk) between the mitochondria and nucleus is central to elucidating the integration of mitochondrial functions with other cellular response pathways in modulating the effects of environmental agents. Recent studies have highlighted the importance of mitochondrial functions in epigenetic regulation and DDR with environmental stress. Development and application of novel technologies, enhanced experimental models, and a systems-type research approach will help to discern how environmentally induced mitochondrial dysfunction affects key mechanistic pathways. Conclusions: Understanding mitochondria–cell signaling will provide insight into individual responses to environmental hazards, improving prediction of hazard and susceptibility to environmental stressors. Citation: Shaughnessy DT, McAllister K, Worth L, Haugen AC, Meyer JN, Domann FE, Van Houten B, Mostoslavsky R, Bultman SJ, Baccarelli AA, Begley TJ, Sobol RW, Hirschey MD, Ideker T, Santos JH, Copeland WC, Tice RR, Balshaw DM, Tyson FL. 2014. Mitochondria, energetics, epigenetics, and cellular responses to stress. Environ Health Perspect 122:1271–1278; http://dx.doi.org/10.1289/ehp.140841

Randomized controlled ferret study to assess the direct impact of 2008-09 trivalent inactivated influenza vaccine on A(H1N1)pdm09 disease risk

During spring-summer 2009, several observational studies from Canada showed increased risk of medically-attended, laboratory-confirmed A(H1N1)pdm09 illness among prior recipients of 2008-09 trivalent inactivated influenza vaccine (TIV). Explanatory hypotheses included direct and indirect vaccine effects. In a randomized placebo-controlled ferret study, we tested whether prior receipt of 2008-09 TIV may have directly influenced A(H1N1)pdm09 illness. Thirty-two ferrets (16/group) received 0.5 mL intra-muscular injections of the Canadian-manufactured, commercially-available, non-adjuvanted, split 2008-09 Fluviral or PBS placebo on days 0 and 28. On day 49 all animals were challenged (Ch0) with A(H1N1)pdm09. Four ferrets per group were randomly selected for sacrifice at day 5 post-challenge (Ch+5) and the rest followed until Ch+14. Sera were tested for antibody to vaccine antigens and A(H1N1)pdm09 by hemagglutination inhibition (HI), microneutralization (MN), nucleoprotein-based ELISA and HA1-based microarray assays. Clinical characteristics and nasal virus titers were recorded pre-challenge then post-challenge until sacrifice when lung virus titers, cytokines and inflammatory scores were determined. Baseline characteristics were similar between the two groups of influenza-naïve animals. Antibody rise to vaccine antigens was evident by ELISA and HA1-based microarray but not by HI or MN assays; virus challenge raised antibody to A(H1N1)pdm09 by all assays in both groups. Beginning at Ch+2, vaccinated animals experienced greater loss of appetite and weight than placebo animals, reaching the greatest between-group difference in weight loss relative to baseline at Ch+5 (7.4% vs. 5.2%; p = 0.01). At Ch+ 5 vaccinated animals had higher lung virus titers (log-mean 4.96 vs. 4.23pfu/mL, respectively; p = 0.01), lung inflammatory scores (5.8 vs. 2.1, respectively; p = 0.051) and cytokine levels (p.0.05). At Ch+14, both groups had recovered. Findings in influenza-naïve, systematically-infected ferrets may not replicate the human experience. While they cannot be considered conclusive to explain human observations, these ferret findings are consistent with direct, adverse effect of prior 2008-09 TIV receipt on A(H1N1)pdm09 illness. As such, they warrant further in-depth investigation and search for possible mechanistic explanations

Relaxation of social distancing restrictions: Model estimated impact on COVID-19 epidemic in Manitoba, Canada.

ObjectivesThe unprecedented worldwide social distancing response to COVID-19 resulted in a quick reversal of escalating case numbers. Recently, local governments globally have begun to relax social distancing regulations. Using the situation in Manitoba, Canada as an example, we estimated the impact that social distancing relaxation may have on the pandemic.MethodsWe fit a mathematical model to empirically estimated numbers of people infected, recovered, and died from COVID-19 in Manitoba. We then explored the impact of social distancing relaxation on: (a) time until near elimination of COVID-19 (ResultsAssuming a closed population, near elimination of COVID-19 in Manitoba could have been achieved in 4-6 months (by July or August) if there were no relaxation of social distancing. Relaxing to 15% of pre-COVID effective contacts may extend the local epidemic for more than two years (median 2.1). Relaxation to 50% of pre-COVID effective contacts may result in a peak prevalence of 31-38% of the population, within 3-4 months of initial relaxation.ConclusionSlight relaxation of social distancing may immensely impact the pandemic duration and expected peak prevalence. Only holding the course with respect to social distancing may have resulted in near elimination before Fall of 2020; relaxing social distancing to 15% of pre-COVID-19 contacts will flatten the epidemic curve but greatly extend the duration of the pandemic