Characterization of the systemic loss of dendritic cells in murine lymph nodes during polymicrobial sepsis

Dendritic cells (DCs) play a key role in critical illness and are depleted in spleens from septic patients and mice. To date, few studies have characterized the systemic effect of sepsis on DC populations in lymphoid tissues. We analyzed the phenotype of DCs and Th cells present in the local (mesenteric) and distant (inguinal and popliteal) lymph nodes of mice with induced polymicrobial sepsis (cecal ligation and puncture). Flow cytometry and immunohistochemical staining demonstrated that there was a significant local (mesenteric nodes) and partial systemic (inguinal, but not popliteal nodes) loss of DCs from lymph nodes in septic mice, and that this process was associated with increased apoptosis. This sepsis-induced loss of DCs occurred after CD3(+)CD4(+) T cell activation and loss in the lymph nodes, and the loss of DCs was not preceded by any sustained increase in their maturation status. In addition, there was no preferential loss of either mature/activated (MHCII(high)/CD86(high)) or immature (MHCII(low)/CD86(low)) DCs during sepsis. However, there was a preferential loss of CD8(+) DCs in the local and distant lymph nodes. The loss of DCs in lymphoid tissue, particularly CD8(+) lymphoid-derived DCs, may contribute to the alterations in acquired immune status that frequently accompany sepsis.     

Markov regression models for time series: a quasi-likelihood approach

This paper discusses a quasi-likelihood (QL) approach to regression analysis with time series data. We consider a class of Markov models, referred to by Cox (1981, Scandinavian Journal of Statistics 8, 93-115) as "observation-driven" models in which the conditional means and variances given the past are explicit functions of past outcomes. The class includes autoregressive and Markov chain models for continuous and categorical observations as well as models for counts (e.g., Poisson) and continuous outcomes with constant coefficient of variation (e.g., gamma). We focus on Poisson and gamma data for illustration. Analogous to QL for independent observations, large-sample properties of the regression coefficients depend only on correct specification of the first conditional moment.     

Reversible Inactivation and Desiccation Tolerance of Silicified Viruses

Long-distance host-independent virus dispersal is poorly understood, especially for viruses found in isolated ecosystems. To demonstrate a possible dispersal mechanism, we show that bacteriophage T4, archaeal virus Sulfolobus spindle-shaped virus Kamchatka, and vaccinia virus are reversibly inactivated by mineralization in silica under conditions similar to volcanic hot springs. In contrast, bacteriophage PRD1 is not silicified. Moreover, silicification provides viruses with remarkable desiccation resistance, which could allow extensive aerial dispersal.     

CD8+ T Cell Priming by Dendritic Cell Vaccines Requires Antigen Transfer to Endogenous Antigen Presenting Cells

Background

Immunotherapeutic strategies to stimulate anti-tumor immunity are promising approaches for cancer treatment. A major barrier to their success is the immunosuppressive microenvironment of tumors, which inhibits the functions of endogenous dendritic cells (DCs) that are necessary for the generation of anti-tumor CD8+ T cells. To overcome this problem, autologous DCs are generated ex vivo, loaded with tumor antigens, and activated in this non-suppressive environment before administration to patients. However, DC-based vaccines rarely induce tumor regression.

Methodology/Principal Findings

We examined the fate and function of these DCs following their injection using murine models, in order to better understand their interaction with the host immune system. Contrary to previous assumptions, we show that DC vaccines have an insignificant role in directly priming CD8+ T cells, but instead function primarily as vehicles for transferring antigens to endogenous antigen presenting cells, which are responsible for the subsequent activation of T cells.

Conclusions/Significance

This reliance on endogenous immune cells may explain the limited success of current DC vaccines to treat cancer and offers new insight into how these therapies can be improved. Future approaches should focus on creating DC vaccines that are more effective at directly priming T cells, or abrogating the tumor induced suppression of endogenous DCs.     

Horizontal distribution affects the vertical distribution of native and invasive container‐inhabiting Aedes mosquitoes within an urban landscape

The vertical dimension constitutes an important niche axis along which mosquitoes may adjust their distribution. Here, we evaluated whether the vertical distribution of container‐inhabiting Aedes mosquitoes differs along a gradient of anthropogenic land‐use intensity within an urban landscape. Using a pulley system, we hung oviposition cups at three heights (ground level, 4.5, and 9 m) and in three habitats: forest, park, and a built environment. We hypothesized that mosquito abundance and diversity would be highest in the least disturbed forest habitat, decrease in the park, and be lowest at the UNC‐Greensboro campus. We also expected Aedes albopictus (Skuse) and Ae. triseriatus (Say) to mainly oviposit at ground level and Ae. hendersoni (Cockerell) at canopy height. Aedes albopictus was the most common species (68.8%) collected in all three habitat types and was the only species found in the built environment. In that habitat, Ae. albopictus exhibited a bimodal distribution with the lowest activity at the intermediate height (4.5 m). Aedes triseriatus (28.9%) did not differ in egg abundance between the forest and park habitats but did exhibit diverse vertical habitat use while avoiding the canopy in the park habitat. Aedes hendersoni (2.3%) was the most sylvatic species and oviposited only at ground level. Our results indicate that the vertical distribution of mosquitoes is affected by the type of habitat in which they occur, and that this variation could be driven via local‐scale modification of microclimatic factors.     

QuantFusion: Novel Unified Methodology for Enhanced Coverage and Precision in Quantifying Global Proteomic Changes in Whole Tissues

Single quantitative platforms such as label-based or label-free quantitation (LFQ) present compromises in accuracy, precision, protein sequence coverage, and speed of quantifiable proteomic measurements. To maximize the quantitative precision and the number of quantifiable proteins or the quantifiable coverage of tissue proteomes, we have developed a unified approach, termed QuantFusion, that combines the quantitative ratios of all peptides measured by both LFQ and label-based methodologies. Here, we demonstrate the use of QuantFusion in determining the proteins differentially expressed in a pair of patient-derived tumor xenografts (PDXs) representing two major breast cancer (BC) subtypes, basal and luminal. Label-based in-spectra quantitative peptides derived from amino acid-coded tagging (AACT, also known as SILAC) of a non-malignant mammary cell line were uniformly added to each xenograft with a constant predefined ratio, from which Ratio-of-Ratio estimates were obtained for the label-free peptides paired with AACT peptides in each PDX tumor. A mixed model statistical analysis was used to determine global differential protein expression by combining complementary quantifiable peptide ratios measured by LFQ and Ratio-of-Ratios, respectively. With minimum number of replicates required for obtaining the statistically significant ratios, QuantFusion uses the distinct mechanisms to “rescue” the missing data inherent to both LFQ and label-based quantitation. Combined quantifiable peptide data from both quantitative schemes increased the overall number of peptide level measurements and protein level estimates. In our analysis of the PDX tumor proteomes, QuantFusion increased the number of distinct peptide ratios by 65%, representing differentially expressed proteins between the BC subtypes. This quantifiable coverage improvement, in turn, not only increased the number of measurable protein fold-changes by 8% but also increased the average precision of quantitative estimates by 181% so that some BC subtypically expressed proteins were rescued by QuantFusion. Thus, incorporating data from multiple quantitative approaches while accounting for measurement variability at both the peptide and global protein levels make QuantFusion unique for obtaining increased coverage and quantitative precision for tissue proteomes.The past decade has witnessed rapid progress in mass spectrometry (MS)-based quantitative proteomics with the development of software and data analysis tools to interrogate large amounts of MS data. Quantitative proteomic technologies have shown great potential in delineating dysregulated proteomes in diseases such as cancer (1–4). Quantitative schemes via either stable isotope labeling or label-free quantitation (LFQ)¹ are used widely to assist MS for quantitative assessments of the changes in protein expression, post-translational modifications (5), and protein-protein interactions (6) in many biological systems, including tumor samples (7–11). However, the integration of accuracy, sensitivity, and totality in the analysis of tumor-specific proteoforms from individual patients still remains challenging with the current quantitative platforms. For example, strategies to increase analytical throughput (12) for tumor analysis have utilized the multiplexing advantage of isobaric mass tags such as tandem mass tags or isotope tagging for relative and absolute quantitation (13, 14). However, for routine quantitative analysis of large scale peptides/proteins, tandem mass tags and isotope tagging for relative and absolute quantitation reagents are prohibitively expensive due to the requirement of large amounts of protein as input. The use of added internal peptide standards, derived from isotope-labeled cell lines, or ¹⁸O labeling to quantify peptides (15) allows for quantitation of proteome expression changes; however, these methods require high resolution in both LC separation and MS acquisition for accurate quantitation of overlapping isotopes. The metabolic incorporation of in-spectra quantitative markers through cell culture (16, 17), in vivo quantitation strategies involving amino acid-coded tags (AACT, also known as SILAC or stable isotope labeling by amino acids in cell culture (18)), is still considered the gold standard for accurate quantitation of relative changes in protein abundance across different biological states. However, for tissue proteomics, neither a single cell line as an add-in SILAC standard (19) nor a library of cell lines (a super-SILAC mix (20)) is close to being a universal standard due to peptides that are either missing or present at low levels. The missing internal standards that fail to cover tissue-peptide counterparts, referred to as orphan peptides, preclude quantitative estimation of tissue proteome differences, an issue that has been addressed recently by the addition of peptide standards (21). A more universal labeling strategy such as complete labeling of the equivalent tissue of the organism of interest via stable isotope labeling of mammals (SILAM) has found limited utility (22, 23). The relatively high cost and laborious procedures associated with animal feeding and labeling prevent widespread use of SILAM.Conversely, quantitation of tissue and tumor proteins is very amenable to LFQ and has gained traction recently as an alternative to spiked-in labeled standards (24, 25). Despite the inherent low precision and low throughput of LFQ methods (i.e. multiple separate or independent LC-MS runs as opposed to interdependent, multiplexed LC-MS runs), LFQ does offer some advantages. Running each sample separately provides a higher number of peptide identifications, whereas LFQ avoids issues inherent to multiplexing, such as low or discrepant labeling efficiencies, inaccuracies in sample mixing, and the need for scrambling/switching the isotope-labeled samples to test whether conversion of isotopically labeled arginine to proline impacts results (26). Also LFQ using MS1 peak intensities can significantly improve the sensitivity as much as 60% compared with label-based quantitative methods such as AACT that rely on the MS1 peak intensities (18).We therefore reasoned that the integration of multiple quantitative schemes would provide synergy, higher throughput, and effectiveness to more precisely determine the changes of protein expressions with a larger coverage of given tissue proteome across different tumor subtypes. Specifically, combining peptide abundance differences using both LFQ and AACT label-based, Ratio-of-Ratio (RoR) estimations would greatly increase the overall number of quantifiable peptide/protein changes to distinguish various tumors. When a common set of peptide features cannot be matched and quantitated between two independent LFQ LC-MS runs due to the frequently occurring issues of retention-time misalignment, the labeled-based quantification strategy could provide complementary peptide ratio estimation. Conversely, when LFQ provides quantitation ratios between samples after retention-time alignment of features, a situation may also exist wherein, at minimum, one of the samples lacks a labeled peptide counterpart, making the label-based estimate impossible. To achieve a complementary quantitative scheme, here we report our development of a unified quantitative approach, termed QuantFusion, that uses a multivariate mixed model to interrogate quantifiable peptide data derived from both LFQ and label-based AACT methods from a single MS experimental run. As stated above, LFQ and RoR measurements share complementary information and therefore can be integrated to reduce the number of replicates required for generating the statistically significant LFQ ratios.The complexity of combining dependent outcomes with heterogeneous error structures and varying sample sizes within each protein necessitated the use of a statistical model. We demonstrate the merit of the mixed model-based approach on the integration of the global-scale proteome characteristics implicated in two major breast cancer (BC) subtypes. QuantFusion increased by 65% the number of distinct peptide ratios to highlight BC-subtypic proteome differences. This increase of quantifiable peptide coverage, in turn, increased the number of measurable protein fold-changes by 8% and increased the average precision of quantitative peptide estimates by 181%. The Statistical Analysis Software code used to implement the statistical model along with a test data set used in this study are available to investigators who wish to perform QuantFusion experiments.     

A comparison of two bias-corrected covariance estimators for generalized estimating equations

Mancl and DeRouen (2001, Biometrics57, 126-134) and Kauermann and Carroll (2001, JASA96, 1387-1398) proposed alternative bias-corrected covariance estimators for generalized estimating equations parameter estimates of regression models for marginal means. The finite sample properties of these estimators are compared to those of the uncorrected sandwich estimator that underestimates variances in small samples. Although the formula of Mancl and DeRouen generally overestimates variances, it often leads to coverage of 95% confidence intervals near the nominal level even in some situations with as few as 10 clusters. An explanation for these seemingly contradictory results is that the tendency to undercoverage resulting from the substantial variability of sandwich estimators counteracts the impact of overcorrecting the bias. However, these positive results do not generally hold; for small cluster sizes (e.g., <10) their estimator often results in overcoverage, and the bias-corrected covariance estimator of Kauermann and Carroll may be preferred. The methods are illustrated using data from a nested cross-sectional cluster intervention trial on reducing underage drinking.     

Adenoviral delivery of human and viral IL-10 in murine sepsis.

Adenovirus (Ad) gene therapy has been proposed as a drug-delivery system for the targeted administration of protein-based therapies, including growth factors and biological response modifiers. However, inflammation associated with Ad transduction has raised concern about its safety and efficacy in acute inflammatory diseases. In the present report, intratracheal and i.v. administration of a first-generation adenoviral recombinant (E1,E3 deleted) either containing an empty cassette or expressing the anti-inflammatory cytokines viral or human IL-10 (IL-10) was administered to mice subjected to zymosan-induced multisystem organ failure or to acute necrotizing pancreatitis. Pretreatment of mice with the intratracheal instillation of Ad expressing human IL-10 or viral IL-10 reduced weight loss, attenuated the proinflammatory cytokine response, and reduced mortality in the zymosan-induced model, whereas pretreatment with a control adenoviral recombinant did not significantly exacerbate the response. Pretreatment of mice with pancreatitis using adenoviral vectors expressing IL-10 significantly reduced the degree of pancreatic and liver injury and liver inflammation when administered systemically, but not intratracheally. We conclude that adenoviral vectors can be administered prophylactically in acute inflammatory syndromes, and expression of the anti-inflammatory protein IL-10 can be used to suppress the underlying inflammatory process.     

Sampling strategy for prostate tissue microarrays for Ki-67 and androgen receptor biomarkers

OBJECTIVE: To develop an optimal sampling strategy for tissue microarrays using automated digital analysis for androgen receptor (heterogeneous expression) and the cellular proliferation marker Ki-67 (homogeneous expression and evaluated by others using nonautomated methods). STUDY DESIGN: Tissue microarrays were constructed from 23 radical prostatectomy specimens and immunostained for androgen receptor expression and cellular proliferation. Automated digital image analysis was used, and the minimum number of cores necessary to capture variance change <3% was determined. Androgen receptor immunostaining was described by percent positive nuclei (PPN) and mean optical density (MOD). RESULTS: Androgen receptor PPN variance measurements showed that 5 cores should be obtained when a single block of a radical prostatectomy specimen contained cancer. If all of 15 blocks contained cancer, 2 cores should be obtained from each of 6 blocks. An optimal sampling strategy was developed for androgen receptor PPN, androgen receptor MOD and Ki-67 PPN. CONCLUSION: The selection of the number of cores to sample is a tradeoff between the number of cores available that contain cancer and the amount of work involved in the analysis. Sampling no fewer than 5 but no more than 12 cores per radical prostatectomy specimen can capture tissue heterogeneity.