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Background: In recent years, researchers have had an increased focus on multiplex microarray assays, in which antibodies are measured against multiple related antigens, for use in seroepidemiological studies to infer past transmission.Methods: We assess the performance of a flavivirus microarray assay for determining past dengue virus (DENV) infection history in a dengue-endemic setting, Vietnam. We tested the microarray on samples from 1 and 6 months postinfection from DENV-infected patients (infecting serotype was determined using reverse-transcription polymerase chain reaction during acute, past primary, and secondary infection assessed using plaque reduction neutralization tests 6 months postinfection).Results: Binomial models developed to discriminate past primary from secondary infection using the protein microarray (PMA) titers had high area under the curve (0.90-0.97) and accuracy (0.84-0.86). Multinomial models developed to identify most recent past infecting serotype using PMA titers performed well in those with past primary infection (average test set: κ = 0.85, accuracy of 0.92) but not those with past secondary infection (κ = 0.24, accuracy of 0.45).Conclusions: Our results suggest that the microarray will be useful in seroepidemiological studies aimed at classifying the past infection history of individuals (past primary vs secondary and serotype of past primary infections) and thus inferring past transmission intensity of DENV in dengue-endemic settings. Future work to validate these models should be undertaken in different transmission settings and with samples later after infection. [ABSTRACT FROM AUTHOR]

With the advent of high-throughput proteomics, the type and amount of data pose a significant challenge to statistical approaches used to validate current quantitative analysis. Whereas many studies focus on the analysis at the protein level, the analysis of peptide-level data provides insight into changes at the sub-protein level, including splice variants, isoforms and a range of post-translational modifications. Statistical evaluation of liquid chromatography–mass spectrometry/mass spectrometry peptide-based label-free differential data is most commonly performed using a t -test or analysis of variance, often after the application of data imputation to reduce the number of missing values. In high-throughput proteomics, statistical analysis methods and imputation techniques are difficult to evaluate, given the lack of gold standard data sets. Here, we use experimental and resampled data to evaluate the performance of four statistical analysis methods and the added value of imputation, for different numbers of biological replicates. We find that three or four replicates are the minimum requirement for high-throughput data analysis and confident assignment of significant changes. Data imputation does increase sensitivity in some cases, but leads to a much higher actual false discovery rate. Additionally, we find that empirical Bayes method (limma) achieves the highest sensitivity, and we thus recommend its use for performing differential expression analysis at the peptide level. [ABSTRACT FROM AUTHOR]

Objectives: To characterize the host response to dendritic cell-based immunotherapy and subsequent combined antiretroviral therapy (cART) interruption in HIV-1-infected individuals at the plasma protein level. Design: An autologous dendritic cell (DC) therapeutic vaccine was administered to HIV-infected individuals, stable on cART. The effect of vaccination was evaluated at the plasma protein level during the period preceding cART interruption, during analytical therapy interruption and at viral reactivation. Healthy controls and post-exposure prophylactically treated healthy individuals were included as controls. Methods: Plasma marker (‘analyte’) levels including cytokines, chemokines, growth factors, and hormones were measured in trial participants and control plasma samples using a multiplex immunoassay. Analyte levels were analysed using principle component analysis, cluster analysis and limma. Blood neutrophil counts were analysed using linear regression. Results: Plasma analyte levels of HIV-infected individuals are markedly different from those of healthy controls and HIV-negative individuals receiving post-exposure prophylaxis. Viral reactivation following cART interruption also affects multiple analytes, but cART interruption itself only has only a minor effect. We find that Thyroxine-Binding Globulin (TBG) levels and late-stage neutrophil numbers correlate with the time off cART after DC vaccination. Furthermore, analysis shows that cART alters several regulators of blood glucose levels, including C-peptide, chromogranin-A and leptin. HIV reactivation is associated with the upregulation of CXCR3 ligands. Conclusions: Chronic HIV infection leads to a change in multiple plasma analyte levels, as does virus reactivation after cART interruption. Furthermore, we find evidence for the involvement of TBG and neutrophils in the response to DC-vaccination in the setting of HIV-infection. [ABSTRACT FROM AUTHOR]

Background: Infection with Mycobacterium tuberculosis (Mtb) results in different clinical outcomes ranging from asymptomatic containment to rapidly progressing tuberculosis (TB). The mechanisms controlling TB progression in immunologically-competent hosts remain unclear. Methodology/Principal Findings: To address these mechanisms, we analyzed TB progression in a panel of genetically heterogeneous (A/SnxI/St) F2 mice, originating from TB-highly-susceptible I/St and more resistant A/Sn mice. In F2 mice the rates of TB progression differed. In mice that did not reach terminal stage of infection, TB progression did not correlate with lung Mtb loads. Nor was TB progression correlated with lung expression of factors involved in antibacterial immunity, such as iNOS, IFN-γ, or IL-12p40. The major characteristics of progressing TB was high lung expression of the inflammationrelated factors IL-1β, IL-6, IL-11 (p<0.0003); CCL3, CCL4, CXCL2 (p<0.002); MMP-8 (p<0.0001). The major predictors of TB progression were high expressions of IL-1β and IL-11. TNF-α had both protective and harmful effects. Factors associated with TB progression were expressed mainly by macrophages (F4-80+ cells) and granulocytes (Gr-1hi/Ly-6Ghi cells). Macrophages and granulocytes from I/St and A/Sn parental strains exhibited intrinsic differences in the expression of inflammatory factors, suggesting that genetically determined peculiarities of phagocytes transcriptional response could account for the peculiarities of gene expression in the infected lungs. Another characteristic feature of progressing TB was the accumulation in the infected lungs of Gr-1dim cells that could contribute to TB progression. Conclusions/Significance: In a population of immunocompetent hosts, the outcome of TB depends on quantitatively- and genetically-controlled differences in the intensity of inflammatory responses, rather than being a direct consequence of mycobacterial colonization. Local accumulation of Gr-1dim cells is a newly identified feature of progressing TB. High expression of IL-1β and IL-11 are potential risk factors for TB progression and possible targets for TB immunomodulation. [ABSTRACT FROM AUTHOR]

Respiratory syncytial virus (RSV) is the leading cause of lower respiratory tract infections in infants. Despite over 50 years of research, to date no safe and efficacious RSV vaccine has been licensed. Many experimental vaccination strategies failed to induce balanced T-helper (Th) responses and were associated with adverse effects such as hypersensitivity and immunopathology upon challenge. In this study, we explored the well-established recombinant vaccinia virus (rVV) RSV-F/RSV-G vaccination-challenge mouse model to study phenotypically distinct vaccine-mediated host immune responses at the proteome level. In this model, rVV-G priming and not rVV-F priming results in the induction of Th2 skewed host responses upon RSV challenge. Mass spectrometry- based spectral count comparisons enabled us to identify seven host proteins for which expression in lung tissue is associated with an aberrant Th2 skewed response characterized by the influx of eosinophils and neutrophils. These proteins are involved in processes related to the direct influx of eosinophils (eosinophil peroxidase [Epx]) and to chemotaxis and extravasation processes (Chil3 [chitinase-like-protein 3]) as well as to eosinophil and neutrophil homing signals to the lung (Itgam). In addition, the increased levels of Arg1 and Chil3 proteins point to a functional and regulatory role for alternatively activated macrophages and type 2 innate lymphoid cells in Th2 cytokine-driven RSV vaccine-mediated enhanced disease. [ABSTRACT FROM AUTHOR]

With the advent of high-throughput proteomics, the type and amount of data pose a significant challenge to statistical approaches used to validate current quantitative analysis. Whereas many studies focus on the analysis at the protein level, the analysis of peptide-level data provides insight into changes at the sub-protein level, including splice variants, isoforms and a range of post-translational modifications. Statistical evaluation of liquid chromatography-mass spectrometry/mass spectrometry peptide-based label-free differential data is most commonly performed using a t-test or analysis of variance, often after the application of data imputation to reduce the number of missing values. In high-throughput proteomics, statistical analysis methods and imputation techniques are difficult to evaluate, given the lack of gold standard data sets. Here, we use experimental and resampled data to evaluate the performance of four statistical analysis methods and the added value of imputation, for different numbers of biological replicates. We find that three or four replicates are the minimum requirement for high-throughput data analysis and confident assignment of significant changes. Data imputation does increase sensitivity in some cases, but leads to a much higher actual false discovery rate. Additionally, we find that empirical Bayes method (limma) achieves the highest sensitivity, and we thus recommend its use for performing differential expression analysis at the peptide level.