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PDF file - 2331K, Supplementary Figure S1: TAA peptides bind to MHC-I.

Most tumor-associated antigens (TAA) are self-molecules that are abnormally expressed in cancer cells and become targets of antitumor immune responses. Antibodies and T cells specific for some TAAs have been found in healthy individuals and are associated with lowered lifetime risk for developing cancer. Lower risk for cancer has also been associated with a history of febrile viral diseases. We hypothesized that virus infections could lead to transient expression of abnormal forms of self-molecules, some of which are TAAs; facilitated by the adjuvant effects of infection and inflammation, these molecules could elicit specific antibodies, T cells, and lasting immune memory simultaneously with immunity against viral antigens. Such infection-induced immune memory for TAA would be expected to provide life-long immune surveillance of cancer. Using influenza virus infection in mice as a model system, we tested this hypothesis and demonstrated that influenza-experienced mice control 3LL mouse lung tumor challenge better than infection-naive control mice. Using 2D-difference gel electrophoresis and mass spectrometry, we identified numerous molecules, some of which are known TAAs, on the 3LL tumor cells recognized by antibodies elicited by two successive influenza infections. We studied in detail immune responses against glyceraldehyde-3-phosphate dehydrogenase (GAPDH), histone H4, HSP90, malate dehydrogenase 2, and annexin A2, all of which were overexpressed in influenza-infected lungs and in tumor cells. Finally, we show that immune responses generated through vaccination against peptides derived from these antigens correlated with improved tumor control. Cancer Immunol Res; 2(3); 263–73. ©2013 AACR.

PDF file - 1448K, Supplementary Figure S2: Example gating strategy of antigen specific CD3+CD8+Dimer-X+ T cells.

After extensive exposure to Mycobacterium tuberculosis (Mtb), most individuals acquire latent Mtb infection (LTBI) defined by a positive tuberculin skin test (TST) or interferon-γ release assay (IGRA). To identify mechanisms of resistance to Mtb infection, we compared transcriptional profiles from highly exposed contacts who resist TST/IGRA conversion (resisters, RSTRs) and controls with LTBI using RNAseq. Gene sets related to carbon metabolism and free fatty acid (FFA) transcriptional responses enriched across 2 independent cohorts suggesting RSTR and LTBI monocytes have distinct activation states. We compared intracellular Mtb replication in macrophages treated with FFAs and found that palmitic acid (PA), but not oleic acid (OA), enhanced Mtb intracellular growth. This PA activity correlated with its inhibition of proinflammatory cytokines in Mtb-infected cells. Mtb growth restriction in PA-treated macrophages was restored by activation of AMP kinase (AMPK), a central host metabolic regulator known to be inhibited by PA. Finally, we genotyped AMPK variants and found 7 SNPs in PRKAG2, which encodes the AMPK-γ subunit, that strongly associated with RSTR status. Taken together, RSTR and LTBI phenotypes are distinguished by FFA transcriptional programs and by genetic variation in a central metabolic regulator, which suggests immunometabolic pathways regulate TST/IGRA conversion.

In a phase 3 trial in African infants/children, the RTS,S/AS01 (GSK) vaccine showed moderate efficacy against clinical malaria. We aimed to identify RTS,S/AS01-induced signatures associated with clinical malaria by analyzing antigen-stimulated peripheral blood mononuclear cells sampled from a subset of trial participants at baseline and month 3 (one month post-third dose). RTS,S/AS01 vaccination was associated with downregulation of B-cell and monocyte-related blood transcriptional modules (BTMs) and upregulation of T-cell related BTMs, as well as higher month 3 (vs baseline) circumsporozoite protein (CSP)-specific CD4+T-cell responses. There were few RTS,S/AS01-associated BTMs whose month 3 levels correlated with malaria risk. In contrast, baseline levels of BTMs associated with dendritic cells and with monocytes (among others) correlated with malaria risk. A cross-study analysis supported generalizability of the baseline dendritic cell- and monocyte-related BTM correlations with malaria risk to healthy, malaria-naïve adults, suggesting inflammatory monocytes may inhibit protective RTS,S/AS01-induced responses.

Two-dimensional difference gel electrophoresis (2D DIGE) is a modified form of 2D electrophoresis (2D E) that allows one to compare two or three protein samples simultaneously on the same gel. The proteins in each sample are covalently tagged with different color fluorescent dyes that are designed to have no effect on the relative migration of proteins during electrophoresis. Proteins that are common to the samples appear as "spots" with a fixed ratio of fluorescent signals, whereas proteins that differ between the samples have different fluorescence ratios. With conventional imaging systems, DIGE is capable of reliably detecting as little as 0.2 fmol of protein, and protein differences down to ± 15%, over a ~10,000-fold protein concentration range. DIGE combined with digital image analysis therefore greatly improves the statistical assessment of proteome variation. Here we describe a protocol for conducting DIGE experiments, which takes 2-3 days to complete. We have further improved upon 2D DIGE by introducing in-gel equilibration to improve protein retention during transfer between the first and second dimensions of electrophoresis and by developing a fluorescent gel imaging system with a millionfold dynamic range.

Phase contrast time-lapse microscopy is a non-destructive technique that generates large volumes of image-based information to quantify the behaviour of individual cells or cell populations. To guide the development of algorithms for computer-aided cell tracking and analysis, 48 time-lapse image sequences, each spanning approximately 3.5 days, were generated with accompanying ground truths for C2C12 myoblast cells cultured under 4 different media conditions, including with fibroblast growth factor 2 (FGF2), bone morphogenetic protein 2 (BMP2), FGF2 + BMP2, and control (no growth factor). The ground truths generated contain information for tracking at least 3 parent cells and their descendants within these datasets and were validated using a two-tier system of manual curation. This comprehensive, validated dataset will be useful in advancing the development of computer-aided cell tracking algorithms and function as a benchmark, providing an invaluable opportunity to deepen our understanding of individual and population-based cell dynamics for biomedical research. Machine-accessible metadata file describing the reported data (ISA-Tab format)

Motivation Open source software for computational cytometry has gained in popularity over the past few years. Efforts such as FlowCAP, the Lyoplate and Euroflow projects have highlighted the importance of efforts to standardize both experimental and computational aspects of cytometry data analysis. The R/BioConductor platform hosts the largest collection of open source cytometry software covering all aspects of data analysis and providing infrastructure to represent and analyze cytometry data with all relevant experimental, gating and cell population annotations enabling fully reproducible data analysis. Data visualization frameworks to support this infrastructure have lagged behind. Results ggCyto is a new open-source BioConductor software package for cytometry data visualization built on ggplot2 that enables ggplot-like functionality with the core BioConductor flow cytometry data structures. Amongst its features are the ability to transform data and axes on-the-fly using cytometry-specific transformations, plot faceting by experimental meta-data variables and partial matching of channel, marker and cell populations names to the contents of the BioConductor cytometry data structures. We demonstrate the salient features of the package using publicly available cytometry data with complete reproducible examples in a Supplementary Material. Availability and implementation https://bioconductor.org/packages/devel/bioc/html/ggcyto.html Supplementary information Supplementary data are available at Bioinformatics online.

The 2DE is a powerful proteomic technique, with excellent protein separation capabilities where intact proteins are spatially separated by pI and molecular weight. 2DE is commonly used in conjunction with MS to identify proteins of interest. Current 2DE workflow requires several manual processing steps that can lead to experimental variability and sample loss. One such step is the transition between first dimension IEF and second-dimension SDS-PAGE, which requires exchanging denaturants and the reduction and alkylation of proteins. This in-solution-based equilibration step has been shown to be rather inefficient, losing up to 30% of the original starting material through diffusion effects. We have developed a refinement of this equilibration step using agarose stacking gels poured on top of the second-dimension SDS-PAGE gel, referred to as in-gel equilibration. We show that in-gel equilibration is effective at reduction and alkylation in SDS-PAGE gels. Quantification of whole-cell extracts separated on 2DE gels shows that in-gel equilibration increases protein retention, decreased intergel variability, and simplifies 2DE workflow.

A current challenge for proteomics is detecting proteins over the large concentration ranges found in complex biological samples such as whole-cell extracts. Currently, no unbiased, whole-proteome analysis scheme is capable of detecting the full range of cellular proteins. This is due in part to the limited dynamic range of the detectors used to sense proteins or peptides. We present a new technology, structured illumination (SI) gel imager, which detects fluorescently labeled proteins in electrophoretic gels over a 1 000 000-fold concentration range. SI uses computer-generated masks to attenuate the illumination of highly abundant proteins, allowing for long exposures of low-abundance proteins, thus avoiding detector saturation. A series of progressively masked gel images are assembled into a single, very high dynamic range image. We demonstrate that the SI imager can detect proteins over a concentration range of approximately 1 000 000-fold, making it a useful tool for comprehensive, unbiased proteome-wide surveys.

Adjustment of physiology in response to changes in oxygen availability is critical for the survival of all organisms. However, the chronology of events and the regulatory processes that determine how and when changes in environmental oxygen tension result in an appropriate cellular response is not well understood at a systems level. Therefore, transcriptome, proteome, ATP, and growth changes were analyzed in a halophilic archaeon to generate a temporal model that describes the cellular events that drive the transition between the organism’s two opposing cell states of anoxic quiescence and aerobic growth. According to this model, upon oxygen influx, an initial burst of protein synthesis precedes ATP and transcription induction, rapidly driving the cell out of anoxic quiescence, culminating in the resumption of growth. This model also suggests that quiescent cells appear to remain actively poised for energy production from a variety of different sources. Dynamic temporal analysis of relationships between transcription and translation of key genes suggests several important mechanisms for cellular sustenance under anoxia as well as specific instances of post-transcriptional regulation.

The 2DE is a powerful proteomic technique, with excellent protein separation capabilities where intact proteins are spatially separated by pI and molecular weight. 2DE is commonly used in conjunction with MS to identify proteins of interest. Current 2DE workflow requires several manual processing steps that can lead to experimental variability and sample loss. One such step is the transition between first dimension IEF and second-dimension SDS-PAGE, which requires exchanging denaturants and the reduction and alkylation of proteins. This in-solution-based equilibration step has been shown to be rather inefficient, losing up to 30% of the original starting material through diffusion effects. We have developed a refinement of this equilibration step using agarose stacking gels poured on top of the second-dimension SDS-PAGE gel, referred to as in-gel equilibration. We show that in-gel equilibration is effective at reduction and alkylation in SDS-PAGE gels. Quantification of whole-cell extracts separated on 2DE gels shows that in-gel equilibration increases protein retention, decreased intergel variability, and simplifies 2DE workflow.

Most tumor-associated antigens (TAA) are self-molecules that are abnormally expressed in cancer cells and become targets of antitumor immune responses. Antibodies and T cells specific for some TAAs have been found in healthy individuals and are associated with lowered lifetime risk for developing cancer. Lower risk for cancer has also been associated with a history of febrile viral diseases. We hypothesized that virus infections could lead to transient expression of abnormal forms of self-molecules, some of which are TAAs; facilitated by the adjuvant effects of infection and inflammation, these molecules could elicit specific antibodies, T cells, and lasting immune memory simultaneously with immunity against viral antigens. Such infection-induced immune memory for TAA would be expected to provide life-long immune surveillance of cancer. Using influenza virus infection in mice as a model system, we tested this hypothesis and demonstrated that influenza-experienced mice control 3LL mouse lung tumor challenge better than infection-naive control mice. Using 2D-difference gel electrophoresis and mass spectrometry, we identified numerous molecules, some of which are known TAAs, on the 3LL tumor cells recognized by antibodies elicited by two successive influenza infections. We studied in detail immune responses against glyceraldehyde-3-phosphate dehydrogenase (GAPDH), histone H4, HSP90, malate dehydrogenase 2, and annexin A2, all of which were overexpressed in influenza-infected lungs and in tumor cells. Finally, we show that immune responses generated through vaccination against peptides derived from these antigens correlated with improved tumor control. Cancer Immunol Res; 2(3); 263–73. ©2013 AACR.

A current challenge for proteomics is detecting proteins over the large concentration ranges found in complex biological samples such as whole-cell extracts. Currently, no unbiased, whole-proteome analysis scheme is capable of detecting the full range of cellular proteins. This is due in part to the limited dynamic range of the detectors used to sense proteins or peptides. We present a new technology, structured illumination (SI) gel imager, which detects fluorescently labeled proteins in electrophoretic gels over a 1 000 000-fold concentration range. SI uses computer-generated masks to attenuate the illumination of highly abundant proteins, allowing for long exposures of low-abundance proteins, thus avoiding detector saturation. A series of progressively masked gel images are assembled into a single, very high dynamic range image. We demonstrate that the SI imager can detect proteins over a concentration range of approximately 1 000 000-fold, making it a useful tool for comprehensive, unbiased proteome-wide surveys.

Inference of an environmental and gene regulatory influence network (EGRINOS) by integrating transcriptional responses to H2O2 and paraquat (PQ) has revealed a multi-tiered oxidative stress (OS)-management program to transcriptionally coordinate three peroxidase/catalase enzymes, two superoxide dismutases, production of rhodopsins, carotenoids and gas vesicles, metal trafficking, and various other aspects of metabolism. ChIP-chip, microarray, and survival assays have validated important architectural aspects of this network, identified novel defense mechanisms (including two evolutionarily distant peroxidase enxymes), and showed that general transcription factors of the transcription factor B family have an important function in coordinating the OS response (OSR) despite their inability to directly sense ROS. A comparison of transcriptional responses to sub-lethal doses of H2O2 and PQ with predictions of these responses made by an EGRIN model generated earlier from responses to other environmental factors has confirmed that a significant fraction of the OSR is made up of a generalized component that is also observed in response to other stressors. Analysis of active regulons within environment and gene regulatory influence network for OS (EGRINOS) across diverse environmental conditions has identified the specialized component of oxidative stress response (OSR) that is triggered by sub-lethal OS, but not by other stressors, including sub-inhibitory levels of redox-active metals, extreme changes in oxygen tension, and a sub-lethal dose of γ rays., Reactive oxygen species (ROS), such as hydrogen peroxide (H2O2), superoxide (O2−), and hydroxyl (OH−) radicals, are normal by-products of aerobic metabolism. Evolutionarily conserved mechanisms including detoxification enzymes (peroxidase/catalase and superoxide dismutase (SOD)) and free radical scavengers manage this endogenous production of ROS. OS is a condition reached when certain environmental stresses or genetic defects cause the production of ROS to exceed the management capacity. The damage to diverse cellular components including DNA, proteins, lipids, and carbohydrates resulting from OS (Imlay, 2003; Apel and Hirt, 2004; Perrone et al, 2008) is recognized as an important player in many diseases and in the aging process (Finkel, 2005). We have applied a systems approach to characterize the OSR of an archaeal model organism, Halobacterium salinarum NRC-1. This haloarchaeon grows aerobically at 4.3 M salt concentration in which it routinely faces cycles of desiccation and rehydration, and increased ultraviolet radiation—both of which can increase the production of ROS (Farr and Kogoma, 1991; Oliver et al, 2001). We have reconstructed the physiological adjustments associated with management of excessive OS through the analysis of global transcriptional changes elicited by step exposure to growth sub-inhibitory and sub-lethal levels of H2O2 and PQ (a redox-cycling drug that produces O2−; Hassan and Fridovich, 1979) as well as during subsequent recovery from these stresses. We have integrated all of these data into a unified model for OSR to discover conditional functional links between protective mechanisms and normal aspects of metabolism. Subsequent phenotypic analysis of gene deletion strains has verified the conditional detoxification functions of three putative peroxidase/catalase enzymes, two SODs, and the protective function of rhodopsins under increased levels of H2O2 and PQ. Similarly, we have also validated ROS scavenging by carotenoids and flotation by gas vesicles as secondary mechanisms that may minimize OS. Given the ubiquitous nature of OS, it is not entirely surprising that most organisms have evolved similar multiple lines of defense—both passive and active. Although such mechanisms have been extensively characterized using other model organisms, our integrated systems approach has uncovered additional protective mechanisms in H. salinarum (e.g. two evolutionarily distant peroxidase/catalase enzymes) and revealed a structure and hierarchy to the OSR through conditional regulatory associations among various components of the response. We have validated some aspects of the architecture of the regulatory network for managing OS by confirming physical protein–DNA interactions of six transcription factors (TFs) with promoters of genes they were predicted to influence in EGRINOS. Furthermore, we have also shown the consequence of deleting two of these TFs on transcript levels of genes they control and survival rate under OS. It is notable that these TFs are not directly associated with sensing ROS, but, rather, they have a general function in coordinating the overall response. This insight would not have been possible without constructing EGRINOS through systems integration of diverse datasets. Although it has been known that OS is a component of diverse environmental stress conditions, we quantitatively show for the first time that much of the transcriptional responses induced by the two treatments could indeed have been predicted using a model constructed from the analysis of transcriptional responses to changes in other environmental factors (UV and γ-radiation, light, oxygen, and six metals). However, using specific examples we also reveal the specific components of the OSR that are triggered only under severe OS. Notably, this model of OSR gives a unified perspective of the interconnections among all of these generalized and OS-specific regulatory mechanisms., Complexity of cellular response to oxidative stress (OS) stems from its wide-ranging damage to nucleic acids, proteins, carbohydrates, and lipids. We have constructed a systems model of OS response (OSR) for Halobacterium salinarum NRC-1 in an attempt to understand the architecture of its regulatory network that coordinates this complex response. This has revealed a multi-tiered OS-management program to transcriptionally coordinate three peroxidase/catalase enzymes, two superoxide dismutases, production of rhodopsins, carotenoids and gas vesicles, metal trafficking, and various other aspects of metabolism. Through experimental validation of interactions within the OSR regulatory network, we show that despite their inability to directly sense reactive oxygen species, general transcription factors have an important function in coordinating this response. Remarkably, a significant fraction of this OSR was accurately recapitulated by a model that was earlier constructed from cellular responses to diverse environmental perturbations—this constitutes the general stress response component. Notwithstanding this observation, comparison of the two models has identified the coordination of frontline defense and repair systems by regulatory mechanisms that are triggered uniquely by severe OS and not by other environmental stressors, including sub-inhibitory levels of redox-active metals, extreme changes in oxygen tension, and a sub-lethal dose of γ rays.

The relatively small numbers of proteins and fewer possible post-translational modifications in microbes provide a unique opportunity to comprehensively characterize their dynamic proteomes. We have constructed a PeptideAtlas (PA) covering 62.7% of the predicted proteome of the extremely halophilic archaeon Halobacterium salinarum NRC-1 by compiling approximately 636 000 tandem mass spectra from 497 mass spectrometry runs in 88 experiments. Analysis of the PA with respect to biophysical properties of constituent peptides, functional properties of parent proteins of detected peptides, and performance of different mass spectrometry approaches has highlighted plausible strategies for improving proteome coverage and selecting signature peptides for targeted proteomics. Notably, discovery of a significant correlation between absolute abundances of mRNAs and proteins has helped identify low abundance of proteins as the major limitation in peptide detection. Furthermore, we have discovered that iTRAQ labeling for quantitative proteomic analysis introduces a significant bias in peptide detection by mass spectrometry. Therefore, despite identifying at least one proteotypic peptide for almost all proteins in the PA, a context-dependent selection of proteotypic peptides appears to be the most effective approach for targeted proteomics.