Secondary memory CD8+ T cells are more protective but slower to acquire a central–memory phenotype

The formation of memory CD8 T cells is an important goal of vaccination. However, although widespread use of booster immunizations in humans generates secondary and tertiary CD8 T cell memory, experimental data are limited to primary CD8 T cell memory. Here, we show that, compared with primary memory CD8 T cells, secondary memory CD8 T cells exhibit substantially delayed conversion to a central–memory phenotype, as determined by CD62L expression and interleukin (IL)-2 production. This delayed conversion to a central–memory phenotype correlates with reduced basal proliferation and responsiveness to IL-15, although in vitro coculture with a high concentration of IL-15 is capable of inducing proliferation and CD62L upregulation. Functionally, secondary memory CD8 T cells are more protective in vivo on a per cell basis, and this may be explained by sustained lytic ability. Additionally, secondary memory CD8 T cells are more permissive than primary memory CD8 T cells for new T cell priming in lymph nodes, possibly suggesting a mechanism of replacement for memory T cells. Thus, primary and secondary memory CD8 T cells are functionally distinct, and the number of encounters with antigen influences memory CD8 T cell function

Viral Infection Results in Massive CD8+ T Cell Expansion and Mortality in Vaccinated Perforin-Deficient Mice

AbstractPerforin-mediated cytotoxicity is essential for clearance of primary LCMV infection. BALB/c-perforin-deficient (PKO) mice survived LCMV infection by deleting NP118-specific CD8+ T cells whereas vaccination of PKO mice with Listeria expressing NP118 generated a stable memory CD8+ T cell population. However, >85% of vaccinated BALB/c-PKO mice died after LCMV infection. Mortality was associated with enormous expansion of NP118-specific CD8+ T cells in both lymphoid and nonlymphoid tissues and aberrant CD8+ T cell cytokine production. Depletion of CD8+ T cells or treatment with anti-IFNγ antibody rescued vaccinated mice from mortality. Thus, perforin was essential for resistance to secondary LCMV infection, and, in the absence of perforin, vaccination resulted in lethal disease mediated by dysregulated CD8+ T cell expansion and cytokine production

Differential Role of “Signal 3” Inflammatory Cytokines in Regulating CD8 T Cell Expansion and Differentiation in vivo

Following an infection, naïve CD8 T cells are stimulated by dendritic cells (DC) displaying pathogen-derived peptides on MHC class I molecules (signal 1) and costimulatory molecules (signal 2). Additionally, pathogen-induced inflammatory cytokines also act directly on the responding CD8 T cells to regulate their expansion and differentiation. In particular, both type I interferons (IFNs) and IL-12 have been described as critical survival signals (signal 3) for optimal CD8 T cell accumulation during the expansion phase. Furthermore, expansion in numbers of antigen-specific CD8 T cells is coupled with their acquisition of effector functions to combat the infection. However, it still remains unclear whether these same cytokines also regulate the effector/memory differentiation program of the CD8 T cell response in vivo. Here, we demonstrate that defective signaling by either type I IFNs or IL-12 to the responding CD8 T cells impairs maximal expansion in response to DC immunization + CpG ODN, but neither of these cytokines is essential to regulate the effector/memory differentiation program. In addition, lack of direct IL-12 signaling to CD8 T cells accelerates the development of central memory phenotype in both primary and secondary antigen-specific memory CD8 T cells

Tissue Microenvironments Define and Get Reinforced by Macrophage Phenotypes in Homeostasis or during Inflammation, Repair and Fibrosis

Current macrophage phenotype classifications are based on distinct in vitro culture conditions that do not adequately mirror complex tissue environments. In vivo monocyte progenitors populate all tissues for immune surveillance which supports the maintenance of homeostasis as well as regaining homeostasis after injury. Here we propose to classify macrophage phenotypes according to prototypical tissue environments, e.g. as they occur during homeostasis as well as during the different phases of (dermal) wound healing. In tissue necrosis and/or infection, damage- and/or pathogen-associated molecular patterns induce proinflammatory macrophages by Toll-like receptors or inflammasomes. Such classically activated macrophages contribute to further tissue inflammation and damage. Apoptotic cells and antiinflammatory cytokines dominate in postinflammatory tissues which induce macrophages to produce more antiinflammatory mediators. Similarly, tumor-associated macrophages also confer immunosuppression in tumor stroma. Insufficient parenchymal healing despite abundant growth factors pushes macrophages to gain a profibrotic phenotype and promote fibrocyte recruitment which both enforce tissue scarring. Ischemic scars are largely devoid of cytokines and growth factors so that fibrolytic macrophages that predominantly secrete proteases digest the excess extracellular matrix. Together, macrophages stabilize their surrounding tissue microenvironments by adapting different phenotypes as feed-forward mechanisms to maintain tissue homeostasis or regain it following injury. Furthermore, macrophage heterogeneity in healthy or injured tissues mirrors spatial and temporal differences in microenvironments during the various stages of tissue injury and repair. Copyright (C) 2012 S. Karger AG, Base

Dynamic Imaging of the Effector Immune Response to Listeria Infection In Vivo

Host defense against the intracellular pathogen Listeria monocytogenes (Lm) requires innate and adaptive immunity. Here, we directly imaged immune cell dynamics at Lm foci established by dendritic cells in the subcapsular red pulp (scDC) using intravital microscopy. Blood borne Lm rapidly associated with scDC. Myelomonocytic cells (MMC) swarmed around non-motile scDC forming foci from which blood flow was excluded. The depletion of scDC after foci were established resulted in a 10-fold reduction in viable Lm, while graded depletion of MMC resulted in 30–1000 fold increase in viable Lm in foci with enhanced blood flow. Effector CD8+ [CD8 superscript +] T cells at sites of infection displayed a two-tiered reduction in motility with antigen independent and antigen dependent components, including stable interactions with infected and non-infected scDC. Thus, swarming MMC contribute to control of Lm prior to development of T cell immunity by direct killing and sequestration from blood flow, while scDC appear to promote Lm survival while preferentially interacting with CD8+ [CD8 superscript +] T cells in effector sites.National Institutes of Health (U.S.) (Grant P01AI-071195

A Comprehensive Genetic Analysis of Candidate Genes Regulating Response to Trypanosoma congolense Infection in Mice

About one-third of cattle in sub-Saharan Africa are at risk of contracting “Nagana”—a disease caused by Trypanosoma parasites similar to those that cause human “Sleeping Sickness.” Laboratory mice can also be infected by trypanosomes, and different mouse breeds show varying levels of susceptibility to infection, similar to what is seen between different breeds of cattle. Survival time after infection is controlled by the underlying genetics of the mouse breed, and previous studies have localised three genomic regions that regulate this trait. These three “Quantitative Trait Loci” (QTL), which have been called Tir1, Tir2 and Tir3 (for Trypanosoma Infection Response 1–3) are well defined, but nevertheless still contain over one thousand genes, any number of which may be influencing survival. This study has aimed to identify the specific differences associated with genes that are controlling mouse survival after T. congolense infection. We have applied a series of analyses to existing datasets, and combined them with novel sequencing, and other genetic data to create short lists of genes that share polymorphisms across susceptible mouse breeds, including two promising “candidate genes”: Pram1 at Tir1 and Cd244 at Tir3. These genes can now be tested to confirm their effect on response to trypanosome infection

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

Risk Prediction for Acute Kidney Injury in Acute Medical Admissions in the UK

Background Acute Kidney Injury (AKI) is associated with adverse outcomes; identifying patients who are at risk of developing AKI in hospital may lead to targeted prevention. This approach is advocated in national guidelines but is not well studied in acutely unwell medical patients. We therefore aimed to undertake a UK-wide study in acute medical units (AMUs) with the following aims: to define the proportion of acutely unwell medical patients who develop hospital-acquired AKI (hAKI); to determine risk factors associated with the development of hAKI; and to assess the feasibility of using these risk factors to develop an AKI risk prediction score. Methods In September 2016, a prospective multicentre cohort study across 72 UK AMUs was undertaken. Data were collected from all patients who presented over a 24-hour period. Chronic dialysis, community-acquired AKI (cAKI) and those with fewer than two creatinine measurements were subsequently excluded. The primary outcome was the development of h-AKI. Results 2,446 individuals were admitted to the AMUs of the 72 participating centres. 384 patients (16%) sustained AKI of whom 287 (75%) were cAKI and 97 (25%) were hAKI. After exclusions, 1,235 participants remained in whom chronic kidney disease (OR 3.08, 95% CI 1.96-4.83), diuretic prescription (OR 2.33, 95% CI 1.5-3.65), a lower haemoglobin concentration and an elevated serum bilirubin were independently associated with development of hAKI. Multivariable model discrimination was moderate (c-statistic 0.75), and this did not support the development of a robust clinical risk prediction score. Mortality was higher in those with hAKI (adjusted OR 5.22; 95% CI 2.23-12.20). Conclusion AKI in AMUs is common and associated with worse outcomes, with the majority of cases community acquired. The smaller proportion of hAKI cases, only moderate discrimination of prognostic risk factor modelling and the resource implications of widespread application of an AKI clinical risk score across all AMU admissions suggests that this approach is not currently justified. More targeted risk assessment or automated methods of calculating individual risk may be more appropriate alternatives