B-lymphocyte effector functions in health and disease.

B-lymphocytes have traditionally been thought to contribute to immunity and autoimmune disease through terminal differentiation into plasma cells that secrete antibody. However, studies in mice and recent clinical studies have demonstrated that genetically altered B-cell function and B-cell-targeted therapies can significantly affect autoimmune diseases that were predominantly thought to be T-cell-mediated. B-cell depletion in mouse models of disease has also led to the identification of alternative B-cell effector functions that regulate normal immune responses and autoimmune disease. This review highlights multiple B-cell effector mechanisms, including the promotion of cellular immunity, the negative regulation of immune responses, and the production of pathogenic antibodies.

Porcine endothelial cells cocultured with smooth muscle cells became procoagulant in vitro.

Endothelial cell (EC) seeding represents a promising approach to provide a nonthrombogenic surface on vascular grafts. In this study, we used a porcine EC/smooth muscle cell (SMC) coculture model that was previously developed to examine the efficacy of EC seeding. Expression of tissue factor (TF), a primary initiator in the coagulation cascade, and TF activity were used as indicators of thrombogenicity. Using immunostaining, primary cultures of porcine EC showed a low level of TF expression, but a highly heterogeneous distribution pattern with 14% of ECs expressing TF. Quiescent primary cultures of porcine SMCs displayed a high level of TF expression and a uniform pattern of staining. When we used a two-stage amidolytic assay, TF activity of ECs cultured alone was very low, whereas that of SMCs was high. ECs cocultured with SMCs initially showed low TF activity, but TF activity of cocultures increased significantly 7-8 days after EC seeding. The increased TF activity was not due to the activation of nuclear factor kappa-B on ECs and SMCs, as immunostaining for p65 indicated that nuclear factor kappa-B was localized in the cytoplasm in an inactive form in both ECs and SMCs. Rather, increased TF activity appeared to be due to the elevated reactive oxygen species levels and contraction of the coculture, thereby compromising the integrity of EC monolayer and exposing TF on SMCs. The incubation of cocultures with N-acetyl-cysteine (2 mM), an antioxidant, inhibited contraction, suggesting involvement of reactive oxygen species in regulating the contraction. The results obtained from this study provide useful information for understanding thrombosis in tissue-engineered vascular grafts.

Invasive fungal infections among organ transplant recipients: results of the Transplant-Associated Infection Surveillance Network (TRANSNET).

BACKGROUND: Invasive fungal infections (IFIs) are a major cause of morbidity and mortality among organ transplant recipients. Multicenter prospective surveillance data to determine disease burden and secular trends are lacking. METHODS: The Transplant-Associated Infection Surveillance Network (TRANSNET) is a consortium of 23 US transplant centers, including 15 that contributed to the organ transplant recipient dataset. We prospectively identified IFIs among organ transplant recipients from March, 2001 through March, 2006 at these sites. To explore trends, we calculated the 12-month cumulative incidence among 9 sequential cohorts. RESULTS: During the surveillance period, 1208 IFIs were identified among 1063 organ transplant recipients. The most common IFIs were invasive candidiasis (53%), invasive aspergillosis (19%), cryptococcosis (8%), non-Aspergillus molds (8%), endemic fungi (5%), and zygomycosis (2%). Median time to onset of candidiasis, aspergillosis, and cryptococcosis was 103, 184, and 575 days, respectively. Among a cohort of 16,808 patients who underwent transplantation between March 2001 and September 2005 and were followed through March 2006, a total of 729 IFIs were reported among 633 persons. One-year cumulative incidences of the first IFI were 11.6%, 8.6%, 4.7%, 4.0%, 3.4%, and 1.3% for small bowel, lung, liver, heart, pancreas, and kidney transplant recipients, respectively. One-year incidence was highest for invasive candidiasis (1.95%) and aspergillosis (0.65%). Trend analysis showed a slight increase in cumulative incidence from 2002 to 2005. CONCLUSIONS: We detected a slight increase in IFIs during the surveillance period. These data provide important insights into the timing and incidence of IFIs among organ transplant recipients, which can help to focus effective prevention and treatment strategies.

Prospective surveillance for invasive fungal infections in hematopoietic stem cell transplant recipients, 2001-2006: overview of the Transplant-Associated Infection Surveillance Network (TRANSNET) Database.

BACKGROUND: The incidence and epidemiology of invasive fungal infections (IFIs), a leading cause of death among hematopoeitic stem cell transplant (HSCT) recipients, are derived mainly from single-institution retrospective studies. METHODS: The Transplant Associated Infections Surveillance Network, a network of 23 US transplant centers, prospectively enrolled HSCT recipients with proven and probable IFIs occurring between March 2001 and March 2006. We collected denominator data on all HSCTs preformed at each site and clinical, diagnostic, and outcome information for each IFI case. To estimate trends in IFI, we calculated the 12-month cumulative incidence among 9 sequential subcohorts. RESULTS: We identified 983 IFIs among 875 HSCT recipients. The median age of the patients was 49 years; 60% were male. Invasive aspergillosis (43%), invasive candidiasis (28%), and zygomycosis (8%) were the most common IFIs. Fifty-nine percent and 61% of IFIs were recognized within 60 days of neutropenia and graft-versus-host disease, respectively. Median onset of candidiasis and aspergillosis after HSCT was 61 days and 99 days, respectively. Within a cohort of 16,200 HSCT recipients who received their first transplants between March 2001 and September 2005 and were followed up through March 2006, we identified 718 IFIs in 639 persons. Twelve-month cumulative incidences, based on the first IFI, were 7.7 cases per 100 transplants for matched unrelated allogeneic, 8.1 cases per 100 transplants for mismatched-related allogeneic, 5.8 cases per 100 transplants for matched-related allogeneic, and 1.2 cases per 100 transplants for autologous HSCT. CONCLUSIONS: In this national prospective surveillance study of IFIs in HSCT recipients, the cumulative incidence was highest for aspergillosis, followed by candidiasis. Understanding the epidemiologic trends and burden of IFIs may lead to improved management strategies and study design.

Factors associated with mortality in transplant patients with invasive aspergillosis.

BACKGROUND: Invasive aspergillosis (IA) is an important cause of morbidity and mortality in hematopoietic stem cell transplant (HSCT) and solid organ transplant (SOT) recipients. The purpose of this study was to evaluate factors associated with mortality in transplant patients with IA. METHODS: Transplant patients from 23 US centers were enrolled from March 2001 to October 2005 as part of the Transplant Associated Infection Surveillance Network. IA cases were identified prospectively in this cohort through March 2006, and data were collected. Factors associated with 12-week all-cause mortality were determined by logistic regression analysis and Cox proportional hazards regression. RESULTS: Six-hundred forty-two cases of proven or probable IA were evaluated, of which 317 (49.4%) died by the study endpoint. All-cause mortality was greater in HSCT patients (239 [57.5%] of 415) than in SOT patients (78 [34.4%] of 227; P<.001). Independent poor prognostic factors in HSCT patients were neutropenia, renal insufficiency, hepatic insufficiency, early-onset IA, proven IA, and methylprednisolone use. In contrast, white race was associated with decreased risk of death. Among SOT patients, hepatic insufficiency, malnutrition, and central nervous system disease were poor prognostic indicators, whereas prednisone use was associated with decreased risk of death. Among HSCT or SOT patients who received antifungal therapy, use of an amphotericin B preparation as part of initial therapy was associated with increased risk of death. CONCLUSIONS: There are multiple variables associated with survival in transplant patients with IA. Understanding these prognostic factors may assist in the development of treatment algorithms and clinical trials.

A low-cost, portable, and quantitative spectral imaging system for application to biological tissues.

The ability of diffuse reflectance spectroscopy to extract quantitative biological composition of tissues has been used to discern tissue types in both pre-clinical and clinical cancer studies. Typically, diffuse reflectance spectroscopy systems are designed for single-point measurements. Clinically, an imaging system would provide valuable spatial information on tissue composition. While it is feasible to build a multiplexed fiber-optic probe based spectral imaging system, these systems suffer from drawbacks with respect to cost and size. To address these we developed a compact and low cost system using a broadband light source with an 8-slot filter wheel for illumination and silicon photodiodes for detection. The spectral imaging system was tested on a set of tissue mimicking liquid phantoms which yielded an optical property extraction accuracy of 6.40 +/- 7.78% for the absorption coefficient (micro(a)) and 11.37 +/- 19.62% for the wavelength-averaged reduced scattering coefficient (micro(s)').

CD20 deficiency in humans results in impaired T cell-independent antibody responses.

CD20 was the first B cell differentiation antigen identified, and CD20-specific mAbs are commonly used for the treatment of B cell malignancies and autoantibody-mediated autoimmune diseases. Despite this the role of CD20 in human B cell physiology has remained elusive. We describe here a juvenile patient with CD20 deficiency due to a homozygous mutation in a splice junction of the CD20 gene (also known as MS4A1) that results in "cryptic" splicing and nonfunctional mRNA species. Analysis of this patient has led us to conclude that CD20 has a central role in the generation of T cell-independent (TI) antibody responses. Key evidence to support this conclusion was provided by the observation that although antigen-independent B cells developed normally in the absence of CD20 expression, antibody formation, particularly after vaccination with TI antigens, was strongly impaired in the patient. Consistent with this, TI antipolysaccharide B cell responses were severely impeded in CD20-deficient mice. Our study therefore identifies what we believe to be a novel type of humoral immunodeficiency caused by CD20 deficiency and characterized by normal development of antigen-independent B cells, along with a reduced capacity to mount proper antibody responses.

permGPU: Using graphics processing units in RNA microarray association studies.

BACKGROUND: Many analyses of microarray association studies involve permutation, bootstrap resampling and cross-validation, that are ideally formulated as embarrassingly parallel computing problems. Given that these analyses are computationally intensive, scalable approaches that can take advantage of multi-core processor systems need to be developed. RESULTS: We have developed a CUDA based implementation, permGPU, that employs graphics processing units in microarray association studies. We illustrate the performance and applicability of permGPU within the context of permutation resampling for a number of test statistics. An extensive simulation study demonstrates a dramatic increase in performance when using permGPU on an NVIDIA GTX 280 card compared to an optimized C/C++ solution running on a conventional Linux server. CONCLUSIONS: permGPU is available as an open-source stand-alone application and as an extension package for the R statistical environment. It provides a dramatic increase in performance for permutation resampling analysis in the context of microarray association studies. The current version offers six test statistics for carrying out permutation resampling analyses for binary, quantitative and censored time-to-event traits.

The life of leaders: an intensive health program for clergy.

Clergy suffer from chronic disease rates that are higher than those of non-clergy. Health interventions for clergy are needed, and some exist, although none to date have been described in the literature. Life of Leaders is a clergy health intervention designed with particular attention to the lifestyle and beliefs of United Methodist clergy, directed by Methodist LeBonheur Healthcare Center of Excellence in Faith and Health. It consists of a two-day retreat of a comprehensive executive physical and leadership development process. Its guiding principles include a focus on personal assets, multi-disciplinary, integrated care, and an emphasis on the contexts of ministry for the poor and community leadership. Consistent with calls to intervene on clergy health across multiple ecological levels, Life of Leaders intervenes at the individual and interpersonal levels, with potential for congregational and religious denominational change. Persons wishing to improve the health of clergy may wish to implement Life of Leaders or borrow from its guiding principles.

Finding our way through phenotypes.

Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility.

Explicit DNase sequence bias modeling enables high-resolution transcription factor footprint detection.

DNaseI footprinting is an established assay for identifying transcription factor (TF)-DNA interactions with single base pair resolution. High-throughput DNase-seq assays have recently been used to detect in vivo DNase footprints across the genome. Multiple computational approaches have been developed to identify DNase-seq footprints as predictors of TF binding. However, recent studies have pointed to a substantial cleavage bias of DNase and its negative impact on predictive performance of footprinting. To assess the potential for using DNase-seq to identify individual binding sites, we performed DNase-seq on deproteinized genomic DNA and determined sequence cleavage bias. This allowed us to build bias corrected and TF-specific footprint models. The predictive performance of these models demonstrated that predicted footprints corresponded to high-confidence TF-DNA interactions. DNase-seq footprints were absent under a fraction of ChIP-seq peaks, which we show to be indicative of weaker binding, indirect TF-DNA interactions or possible ChIP artifacts. The modeling approach was also able to detect variation in the consensus motifs that TFs bind to. Finally, cell type specific footprints were detected within DNase hypersensitive sites that are present in multiple cell types, further supporting that footprints can identify changes in TF binding that are not detectable using other strategies.

Cumulative stress in childhood is associated with blunted reward-related brain activity in adulthood.

Early life stress (ELS) is strongly associated with negative outcomes in adulthood, including reduced motivation and increased negative mood. The mechanisms mediating these relations, however, are poorly understood. We examined the relation between exposure to ELS and reward-related brain activity, which is known to predict motivation and mood, at age 26, in a sample followed since kindergarten with annual assessments. Using functional neuroimaging, we assayed individual differences in the activity of the ventral striatum (VS) during the processing of monetary rewards associated with a simple card-guessing task, in a sample of 72 male participants. We examined associations between a cumulative measure of ELS exposure and VS activity in adulthood. We found that greater levels of cumulative stress during childhood and adolescence predicted lower reward-related VS activity in adulthood. Extending this general developmental pattern, we found that exposure to stress early in development (between kindergarten and grade 3) was significantly associated with variability in adult VS activity. Our results provide an important demonstration that cumulative life stress, especially during this childhood period, is associated with blunted reward-related VS activity in adulthood. These differences suggest neurobiological pathways through which a history of ELS may contribute to reduced motivation and increased negative mood.

Thinking Outside the Box: Treating Acute Heart Failure Outside the Hospital to Improve Care and Reduce Admissions.

The management of acute heart failure is shifting toward treatment approaches outside of a traditional hospital setting. Many heart failure providers are now treating patients in less familiar health care settings, such as acute care clinics, emergency departments, and skilled nursing facilities. In this review we describe the current pressures driving change in the delivery of acute heart failure and summarize the evidence regarding treatments for acute heart failure outside of the inpatient setting. We also provide considerations for the design of future treatment strategies to be implemented in alternative care settings.

Computational Inference of Genome-Wide Protein-DNA Interactions Using High-Throughput Genomic Data

Transcriptional regulation has been studied intensively in recent decades. One important aspect of this regulation is the interaction between regulatory proteins, such as transcription factors (TF) and nucleosomes, and the genome. Different high-throughput techniques have been invented to map these interactions genome-wide, including ChIP-based methods (ChIP-chip, ChIP-seq, etc.), nuclease digestion methods (DNase-seq, MNase-seq, etc.), and others. However, a single experimental technique often only provides partial and noisy information about the whole picture of protein-DNA interactions. Therefore, the overarching goal of this dissertation is to provide computational developments for jointly modeling different experimental datasets to achieve a holistic inference on the protein-DNA interaction landscape.

We first present a computational framework that can incorporate the protein binding information in MNase-seq data into a thermodynamic model of protein-DNA interaction. We use a correlation-based objective function to model the MNase-seq data and a Markov chain Monte Carlo method to maximize the function. Our results show that the inferred protein-DNA interaction landscape is concordant with the MNase-seq data and provides a mechanistic explanation for the experimentally collected MNase-seq fragments. Our framework is flexible and can easily incorporate other data sources. To demonstrate this flexibility, we use prior distributions to integrate experimentally measured protein concentrations.

We also study the ability of DNase-seq data to position nucleosomes. Traditionally, DNase-seq has only been widely used to identify DNase hypersensitive sites, which tend to be open chromatin regulatory regions devoid of nucleosomes. We reveal for the first time that DNase-seq datasets also contain substantial information about nucleosome translational positioning, and that existing DNase-seq data can be used to infer nucleosome positions with high accuracy. We develop a Bayes-factor-based nucleosome scoring method to position nucleosomes using DNase-seq data. Our approach utilizes several effective strategies to extract nucleosome positioning signals from the noisy DNase-seq data, including jointly modeling data points across the nucleosome body and explicitly modeling the quadratic and oscillatory DNase I digestion pattern on nucleosomes. We show that our DNase-seq-based nucleosome map is highly consistent with previous high-resolution maps. We also show that the oscillatory DNase I digestion pattern is useful in revealing the nucleosome rotational context around TF binding sites.

Finally, we present a state-space model (SSM) for jointly modeling different kinds of genomic data to provide an accurate view of the protein-DNA interaction landscape. We also provide an efficient expectation-maximization algorithm to learn model parameters from data. We first show in simulation studies that the SSM can effectively recover underlying true protein binding configurations. We then apply the SSM to model real genomic data (both DNase-seq and MNase-seq data). Through incrementally increasing the types of genomic data in the SSM, we show that different data types can contribute complementary information for the inference of protein binding landscape and that the most accurate inference comes from modeling all available datasets.

This dissertation provides a foundation for future research by taking a step toward the genome-wide inference of protein-DNA interaction landscape through data integration.