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The recent Ebola and Zika epidemics demonstrate the need for the continuous surveillance, rapid diagnosis and real-time tracking of emerging infectious diseases. Fast, affordable sequencing of pathogen genomes -- now a staple of the public health microbiology laboratory in well-resourced settings -- can affect each of these areas. Coupling genomic diagnostics and epidemiology to innovative digital disease detection platforms raises the possibility of an open, global, digital pathogen surveillance system. When informed by a One Health approach, in which human, animal and environmental health are considered together, such a genomics-based system has profound potential to improve public health in settings lacking robust laboratory capacity.

Genomic epidemiology is routinely used worldwide to interrogate infectious disease dynamics. Multiple computational tools exist that reconstruct transmission networks by coupling genomic data with epidemiological models. Resulting inferences can improve our understanding of pathogen transmission dynamics, and yet the performance of these tools has not been evaluated for tuberculosis (TB), a disease process with complex epidemiology including variable latency and within-host heterogeneity. Here, we performed a systematic comparison of six publicly available transmission reconstruction models, evaluating their accuracy when predicting transmission events in simulated and real-world Mycobacterium tuberculosis outbreaks. We observed variability in the number of transmission links that were predicted with high probability (P ≥ 0.5) and low accuracy of these predictions against known transmission in simulated outbreaks. We also found a low proportion of epidemiologically supported case–contact pairs were identified in our real-world TB clusters. The specificity of all models was high, and a relatively high proportion of the total transmission events predicted by some models were true links, notably with TransPhylo, Outbreaker2, and Phybreak. Our findings may inform the choice of tools in TB transmission analyses and underscore the need for caution when interpreting transmission networks produced using probabilistic approaches.

An outbreak of tuberculosis occurred over a 3-year period in a medium-size community in British Columbia, Canada. The results of mycobacterial interspersed repetitive unit–variable-number tandem-repeat (MIRU-VNTR) genotyping suggested the outbreak was clonal. Traditional contact tracing did not identify a source. We used whole-genome sequencing and social-network analysis in an effort to describe the outbreak dynamics at a higher resolution. Mycobacterium tuberculosis is an important infectious disease even in developed countries with extensive control programs. This is the case in British Columbia, Canada, where the 2007 incidence rate of 6.4 cases per 100,000 population exceeded the national average of 4.7 cases per 100,000 population. 1 In May 2006, a case of smear-negative pleural tuberculosis was diagnosed in an adult in a medium-size community in British Columbia. A second case, manifested as disseminated tuberculosis, was reported in an infant in July 2006. Reverse contact tracing identified nine additional cases between August and October 2006, when the British Columbia Centre for Disease Control . . .

Myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) is a syndrome of unknown etiology characterized by profound fatigue exacerbated by physical activity, also known as post-exertional malaise (PEM). Previously, we did not detect evidence of immune dysregulation or virus reactivation outside of PEM periods. Here we sought to determine whether cardiopulmonary exercise stress testing of ME/CFS patients could trigger such changes. ME/CFS patients (n = 14) and matched sedentary controls (n = 11) were subjected to cardiopulmonary exercise on 2 consecutive days and followed up to 7 days post-exercise, and longitudinal whole blood samples analyzed by RNA-seq. Although ME/CFS patients showed significant worsening of symptoms following exercise versus controls, with 8 of 14 ME/CFS patients showing reduced oxygen consumption ([Formula: see text]) on day 2, transcriptome analysis yielded only 6 differentially expressed gene (DEG) candidates when comparing ME/CFS patients to controls across all time points. None of the DEGs were related to immune signaling, and no DEGs were found in ME/CFS patients before and after exercise. Virome composition (P = 0.746 by chi-square test) and number of viral reads (P = 0.098 by paired t-test) were not significantly associated with PEM. These observations do not support transcriptionally-mediated immune cell dysregulation or viral reactivation in ME/CFS patients during symptomatic PEM episodes.

In late spring 2009, concern was raised in Canada that prior vaccination with the 2008-09 trivalent inactivated influenza vaccine (TIV) was associated with increased risk of pandemic influenza A (H1N1) (pH1N1) illness. Several epidemiologic investigations were conducted through the summer to assess this putative association. (1) test-negative case-control design based on Canada's sentinel vaccine effectiveness monitoring system in British Columbia, Alberta, Ontario, and Quebec; (2) conventional case-control design using population controls in Quebec; (3) test-negative case-control design in Ontario; and (4) prospective household transmission (cohort) study in Quebec. Logistic regression was used to estimate odds ratios for TIV effect on community- or hospital-based laboratory-confirmed seasonal or pH1N1 influenza cases compared to controls with restriction, stratification, and adjustment for covariates including combinations of age, sex, comorbidity, timeliness of medical visit, prior physician visits, and/or health care worker (HCW) status. For the prospective study risk ratios were computed. Based on the sentinel study of 672 cases and 857 controls, 2008-09 TIV was associated with statistically significant protection against seasonal influenza (odds ratio 0.44, 95% CI 0.33-0.59). In contrast, estimates from the sentinel and three other observational studies, involving a total of 1,226 laboratory-confirmed pH1N1 cases and 1,505 controls, indicated that prior receipt of 2008-09 TIV was associated with increased risk of medically attended pH1N1 illness during the spring-summer 2009, with estimated risk or odds ratios ranging from 1.4 to 2.5. Risk of pH1N1 hospitalization was not further increased among vaccinated people when comparing hospitalized to community cases. Prior receipt of 2008-09 TIV was associated with increased risk of medically attended pH1N1 illness during the spring-summer 2009 in Canada. The occurrence of bias (selection, information) or confounding cannot be ruled out. Further experimental and epidemiological assessment is warranted. Possible biological mechanisms and immunoepidemiologic implications are considered.

Microbial genome sequencing is now being routinely used in many clinical and public health laboratories. Understanding how to report complex genomic test results to stakeholders who may have varying familiarity with genomics-including clinicians, laboratorians, epidemiologists, and researchers-is critical to the successful and sustainable implementation of this new technology; however, there are no evidence-based guidelines for designing such a report in the pathogen genomics domain. Here, we describe an iterative, human-centered approach to creating a report template for communicating tuberculosis (TB) genomic test results. We used Design Study Methodology-a human centered approach drawn from the information visualization domain-to redesign an existing clinical report. We used expert consults and an online questionnaire to discover various stakeholders' needs around the types of data and tasks related to TB that they encounter in their daily workflow. We also evaluated their perceptions of and familiarity with genomic data, as well as its utility at various clinical decision points. These data shaped the design of multiple prototype reports that were compared against the existing report through a second online survey, with the resulting qualitative and quantitative data informing the final, redesigned, report. We recruited 78 participants, 65 of whom were clinicians, nurses, laboratorians, researchers, and epidemiologists involved in TB diagnosis, treatment, and/or surveillance. Our first survey indicated that participants were largely enthusiastic about genomic data, with the majority agreeing on its utility for certain TB diagnosis and treatment tasks and many reporting some confidence in their ability to interpret this type of data (between 58.8% and 94.1%, depending on the specific data type). When we compared our four prototype reports against the existing design, we found that for the majority (86.7%) of design comparisons, participants preferred the alternative prototype designs over the existing version, and that both clinicians and non-clinicians expressed similar design preferences. Participants showed clearer design preferences when asked to compare individual design elements versus entire reports. Both the quantitative and qualitative data informed the design of a revised report, available online as a LaTeX template. We show how a human-centered design approach integrating quantitative and qualitative feedback can be used to design an alternative report for representing complex microbial genomic data. We suggest experimental and design guidelines to inform future design studies in the bioinformatics and microbial genomics domains, and suggest that this type of mixed-methods study is important to facilitate the successful translation of pathogen genomics in the clinic, not only for clinical reports but also more complex bioinformatics data visualization software.

To examine how stratifying persons born outside Canada according to tuberculosis (TB) incidence in their birth country and other demographic factors refines our understanding of TB epidemiology and local TB transmission. Population-level TB surveillance programs and research studies in low incidence settings often report all persons born outside the country in which the study is conducted as \"foreign-born\"-a single label for a highly diverse population with variable TB risks. This may mask important TB epidemiologic trends and not accurately reflect local transmission patterns. We used population-level data from two large cohorts in British Columbia (BC), Canada: an immigration cohort (n = 337,492 permanent residents to BC) and a genotyping cohort (n = 2290 culture-confirmed active TB cases). We stratified active TB case counts, incidence rates, and genotypic clustering (an indicator of TB transmission) in BC by birth country TB incidence, age at immigration, and years since arrival. Persons from high-incidence countries had a 12-fold higher TB incidence than those emigrating from low-incidence settings. Estimates of local transmission, as captured by genotyping, versus reactivation of latent TB infection acquired outside Canada varied when data were stratified by birthplace TB incidence, as did patient-level characteristics of individuals in each group, such as age and years between immigration and diagnosis. Categorizing persons beyond simply \"foreign-born\", particularly in the context of TB epidemiologic and molecular data, is needed for a more accurate understanding of TB rates and patterns of transmission.

Background. Cases of infection due to a novel swine-origin variant of influenza A virus subtype H3N2 (H3N2v) have recently been identified in the United States, primarily among children. We estimate levels of cross-reactive antibody to H3N2v by age and assess whether seasonal trivalent inactivated influenza vaccine (TIV), with or without adjuvant, may increase seroprotection. Methods. Antibody to H3N2v was assessed by hemagglutination inhibition (HI) assay and, for a subset, also by microneutralization assay. Seroprevalence and seroprotection were defined as an HI titer of ≥40, and levels were compared with those for ancestral and contemporary human strains. The analysis included 1116 sera collected during fall 2010, corresponding to approximately 100 sera per decade of life. Vaccine-induced antibody levels were also assessed in sera from 136 children aged <10 years and 65 adults aged 20-59 years before and after receipt of 2010-2011 split TIV and in sera from 182 elderly individuals aged > 65 years before and after receipt of 2011-2012 split TIV (for 31 individuals), MF59-adjuvanted TIV (for 72), or unadjuvanted subunit TIV (for 79). Results. The overall prevalence of HI titers of ≥40 against A(H3N2) v was 25%. No children aged <5 years and <20% of individuals aged ≤14 years or ≥40 years had an HI titer of ≥40. Conversely, among individuals aged 15-39 years, half of teens and adults showed H3N2v seroprotection. Following TIV receipt, <15% of individuals in any vaccine group developed a 4-fold increase in antibody level. Conclusions. A substantial proportion of adolescents and young adults have cross-reactive antibody against emerging H3N2v, whereas children and older adults show broad susceptibility. Recent formulations of TIV do not substantially increase seroprotection. A specific vaccine would be needed if H3N2v establishes epidemic spread.

While seasonal influenza vaccination is recommended for individuals with asthma, uptake in this population is low. We examined how self-rated health impacts reasons for not being immunized against influenza in Canadian adults with asthma, focusing on those who have never been immunized. We pooled four cycles of the Canadian Community Health Survey (cycles 3.1(2005), 2007/08, 2009/10 and 2011/12), grouping individuals by whether their reasons for not having been vaccinated were perceptual or technical. We used a multivariable logistic regression model, adjusted for confounders, to quantify the relationship between self-rated health and their reported reasons for not vaccinating. Among the 9,836 respondents, 84.4% cited perceptual barriers as a reason for not being vaccinated. After adjusting for socio-demographic characteristics and province of residence, we determined that reporting perceptual barriers was associated with self-rated health status, with the adjusted odds ratios ranging from 1.42 (95%CI: 0.97, 2.09) to 2.64 (95%CI: 1.74, 3.99) for fair and excellent health versus poor health, respectively. Each increase in self-rated health category was associated with greater odds of citing a perceptual rather than technical barrier as a reason for non-vaccination. Self-reported health influences people's perception of the need for influenza vaccination. Viewing the results through the lens of the precaution adoption process model suggests that personalizing communication around both the risk of influenza and the effectiveness of the vaccine may improve uptake amongst adults with asthma.

Although considerable progress has been made in dissecting the signaling pathways involved in the innate immune response, it is now apparent that this response can no longer be productively thought of in terms of simple linear pathways. InnateDB ( www.innatedb.ca ) has been developed to facilitate systems‐level analyses that will provide better insight into the complex networks of pathways and interactions that govern the innate immune response. InnateDB is a publicly available, manually curated, integrative biology database of the human and mouse molecules, experimentally verified interactions and pathways involved in innate immunity, along with centralized annotation on the broader human and mouse interactomes. To date, more than 3500 innate immunity‐relevant interactions have been contextually annotated through the review of 1000 plus publications. Integrated into InnateDB are novel bioinformatics resources, including network visualization software, pathway analysis, orthologous interaction network construction and the ability to overlay user‐supplied gene expression data in an intuitively displayed molecular interaction network and pathway context, which will enable biologists without a computational background to explore their data in a more systems‐oriented manner. Synopsis The importance of the innate immune response has long been recognized in the front line of defense against invading pathogens. If not tightly regulated, however, an overwhelming immune response can lead to what is sometimes called a cytokine storm. One such out‐of‐control response, sepsis, results in more than 200 000 deaths a year in the United States alone (Angus et al , 2001 ). Over the course of the last decade, significant progress has been made in understanding the innate immune response, including the detailed dissection of some of the critical signaling pathways involved (Lang and Mansell, 2007 ; Matsukawa, 2007 ). Despite these efforts, many questions remain unanswered including how the innate immune system initiates distinct responses toward particular pathogens. It is becoming increasingly clear that the innate immune response does not involve simple linear pathways but rather complex networks of pathways and interactions, positive and negative feedback loops and multifaceted transcriptional responses (Tegner et al , 2006 ; Lee and Kim, 2007 ). To better understand the complexities of the innate immune response and the cross‐talk between its components, complementary systems‐level analyses and more focused follow‐up experimental approaches are now needed. Recently, researchers have started to apply systems biology approaches to the study of the immune system (Gilchrist et al , 2006 ; Tegner et al , 2006 ; Andersen et al , 2008 ) and bioinformatics resources are now emerging to aid these types of analyses. Despite the enormous efforts of the major publicly available interaction and pathway databases to provide as wide‐ranging cover as possible (Salwinski et al , 2004 ; Alfarano et al , 2005 ; Joshi‐Tope et al , 2005 ; Breitkreutz et al , 2007 ; Chatr‐aryamontri et al , 2007 ; Kanehisa et al , 2007 ; Kerrien et al , 2007 ), it was quickly apparent to us that currently available bioinformatics resources provided poor coverage and detail of the molecular interactions and pathways relevant to innate immunity, information that is essential for the systems‐orientated interpretation of large‐scale genomics data. To overcome these problems and to provide a resource that will enable biologists without a computational background to explore their data in a more systems‐oriented manner, we have developed InnateDB. InnateDB ( www.innatedb.ca ) is a publicly available database and analysis platform for the genes, proteins, experimentally verified interactions and pathways involved in the human and murine innate immune responses. One of the primary goals of InnateDB is to provide a manually curated centralized resource for experimentally verified human and mouse protein, gene and RNA molecular interactions involved in the innate immune system. To do this, a dedicated full‐time team of curators has been assembled to review the relevant biomedical literature and to submit detailed annotation on these interactions and pathways to InnateDB through customized submission system software. To date, more than 3500 innate immunity‐relevant interactions, involving around 1000 genes, have been manually curated through the review of approximately 1000 publications. Only interactions with published direct experimental evidence of a physical or biochemical interaction are submitted to InnateDB. The importance of manual curation is clear, as we are often able to double the number of interactions for a given gene or protein compared to the number currently present in the other interaction databases combined. Furthermore, this detailed manual curation permitted us to richly annotate these interactions and to place them in their relevant context. Interaction data in InnateDB are also curated, stored and downloadable in the Proteomics Standards Initiative Molecular Interaction (PSI‐MI) 2.5‐compliant XML format (Hermjakob et al , 2004 ). In addition to the detailed manual curation of the genes, proteins and their interactions and pathways that are specifically known to have a role in the innate immune response, InnateDB also incorporates data on the entire human and mouse interactomes. To do this, annotation on more than 100 000 human and mouse interactions was integrated from several of the major publicly available interaction databases into InnateDB (Figure 1 ). To enable the investigation of genes, proteins and their molecular interactions that are relevant to particular pathways, InnateDB also includes cross‐references of genes not only to innate immunity‐relevant pathways but also to more than 2500 pathways from several of the major publicly available pathway databases (Figure 1 ). Detailed gene and protein annotation has also been extracted from a variety of other data sources. Specific interactions, pathways and genes or proteins of interest can be interactively searched for in InnateDB through the flexible web‐based search interface of the database, providing a knowledge base for the community, whereas the bioinformatics and network visualization tools incorporated into InnateDB elevate the system from database to robust analysis platform. InnateDB allows one to integrate quantitative data (such as differential gene expression) into a molecular interaction network and pathway context, enabling the interrogation of such data in novel and insightful ways. Investigating differentially expressed molecular interaction networks may identify subnetworks or as‐yet unidentified pathways as being significantly involved in the response to a particular stimulus. By incorporating Cytoscape into InnateDB, investigators are able to take a closer look at the interactions involved in these pathways or subnetworks, potentially identifying cross‐talk between key pathways, and highlighting the molecules that are the hubs of these networks. Our Cerebral plugin allows one to further extend this experience, visually interrogating quantitative data across multiple conditions in more biologically intuitive pathway‐like layouts of networks, which are generated using subcellular localization information. Integrated pathway over‐representation analysis can identify those pathways that are significantly associated with differentially regulated genes, highlighting those pathways that are significantly altered in their gene expression. Through such pathway analysis, it is possible to identify common pathways that are involved in the innate immune response to particular infections, and to identify the common central regulators of these pathways as attractive targets for immune modulation. (Figure 4 ). InnateDB, along with other emerging resources for bioinformatics and systems‐level analysis of immunology (Kelley et al , 2005 ; Ortutay and Vihinen, 2006 ; Hijikata et al , 2007 ; Korb et al , 2008 ), will undoubtedly lead to novel and much deeper insights into the innate immune response to particular pathogens. InnateDB is a molecular interaction and pathway database and analysis platform that has been developed to facilitate systems level analyses of the complex networks of pathways and interactions that govern the innate immune response, the wider immune system and the entire mammalian interactome. To date, more than 3,500 innate immunity relevant interactions have been contextually annotated through the review of 1,000 plus publications. Integrated into InnateDB are novel bioinformatics resources including, network visualization software, pathway analysis, orthologous interaction network construction and the ability to overlay user‐supplied gene expression data in an intuitively displayed molecular interaction network and pathway context, that will enable biologists without a computational background to explore their data in a more systems‐oriented, yet user‐friendly, manner.