TCR affinity associated with functional differences between dominant and subdominant SIV epitope-specific CD8+ T cells in Mamu-A*01+ rhesus monkeys.

Many of the factors that contribute to CD8+ T cell immunodominance hierarchies during viral infection are known. However, the functional differences that exist between dominant and subdominant epitope-specific CD8+ T cells remain poorly understood. In this study, we characterized the phenotypic and functional differences between dominant and subdominant simian immunodeficiency virus (SIV) epitope-specific CD8+ T cells restricted by the major histocompatibility complex (MHC) class I allele Mamu-A*01 during acute and chronic SIV infection. Whole genome expression analyses during acute infection revealed that dominant SIV epitope-specific CD8+ T cells had a gene expression profile consistent with greater maturity and higher cytotoxic potential than subdominant epitope-specific CD8+ T cells. Flow-cytometric measurements of protein expression and anti-viral functionality during chronic infection confirmed these phenotypic and functional differences. Expression analyses of exhaustion-associated genes indicated that LAG-3 and CTLA-4 were more highly expressed in the dominant epitope-specific cells during acute SIV infection. Interestingly, only LAG-3 expression remained high during chronic infection in dominant epitope-specific cells. We also explored the binding interaction between peptide:MHC (pMHC) complexes and their cognate TCRs to determine their role in the establishment of immunodominance hierarchies. We found that epitope dominance was associated with higher TCR:pMHC affinity. These studies demonstrate that significant functional differences exist between dominant and subdominant epitope-specific CD8+ T cells within MHC-restricted immunodominance hierarchies and suggest that TCR:pMHC affinity may play an important role in determining the frequency and functionality of these cell populations. These findings advance our understanding of the regulation of T cell immunodominance and will aid HIV vaccine design.

CGBayesNets: conditional Gaussian Bayesian network learning and inference with mixed discrete and continuous data.

Bayesian Networks (BN) have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous variables, which can lead to information loss, or do not include inference routines, which makes prediction with the BN impossible. We present CGBayesNets, a BN package focused around prediction of a clinical phenotype from mixed discrete and continuous variables, which fills these gaps. CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs) formalism, one appropriate for predicting an outcome of interest from, e.g., multimodal genomic data. We provide four different network learning algorithms, each making a different tradeoff between computational cost and network likelihood. CGBayesNets provides a full suite of functions for model exploration and verification, including cross validation, bootstrapping, and AUC manipulation. We highlight several results obtained previously with CGBayesNets, including predictive models of wood properties from tree genomics, leukemia subtype classification from mixed genomic data, and robust prediction of intensive care unit mortality outcomes from metabolomic profiles. We also provide detailed example analysis on public metabolomic and gene expression datasets. CGBayesNets is implemented in MATLAB and available as MATLAB source code, under an Open Source license and anonymous download at http://www.cgbayesnets.com.

Mapping transcription mechanisms from multimodal genomic data.

BACKGROUND: Identification of expression quantitative trait loci (eQTLs) is an emerging area in genomic study. The task requires an integrated analysis of genome-wide single nucleotide polymorphism (SNP) data and gene expression data, raising a new computational challenge due to the tremendous size of data. RESULTS: We develop a method to identify eQTLs. The method represents eQTLs as information flux between genetic variants and transcripts. We use information theory to simultaneously interrogate SNP and gene expression data, resulting in a Transcriptional Information Map (TIM) which captures the network of transcriptional information that links genetic variations, gene expression and regulatory mechanisms. These maps are able to identify both cis- and trans- regulating eQTLs. The application on a dataset of leukemia patients identifies eQTLs in the regions of the GART, PCP4, DSCAM, and RIPK4 genes that regulate ADAMTS1, a known leukemia correlate. CONCLUSIONS: The information theory approach presented in this paper is able to infer the dependence networks between SNPs and transcripts, which in turn can identify cis- and trans-eQTLs. The application of our method to the leukemia study explains how genetic variants and gene expression are linked to leukemia.

Transcriptional network classifiers.

BACKGROUND: Gene interactions play a central role in transcriptional networks. Many studies have performed genome-wide expression analysis to reconstruct regulatory networks to investigate disease processes. Since biological processes are outcomes of regulatory gene interactions, this paper develops a system biology approach to infer function-dependent transcriptional networks modulating phenotypic traits, which serve as a classifier to identify tissue states. Due to gene interactions taken into account in the analysis, we can achieve higher classification accuracy than existing methods. RESULTS: Our system biology approach is carried out by the Bayesian networks framework. The algorithm consists of two steps: gene filtering by Bayes factor followed by collinearity elimination via network learning. We validate our approach with two clinical data. In the study of lung cancer subtypes discrimination, we obtain a 25-gene classifier from 111 training samples, and the test on 422 independent samples achieves 95% classification accuracy. In the study of thoracic aortic aneurysm (TAA) diagnosis, 61 samples determine a 34-gene classifier, whose diagnosis accuracy on 33 independent samples achieves 82%. The performance comparisons with three other popular methods, PCA/LDA, PAM, and Weighted Voting, confirm that our approach yields superior classification accuracy and a more compact signature. CONCLUSIONS: The system biology approach presented in this paper is able to infer function-dependent transcriptional networks, which in turn can classify biological samples with high accuracy. The validation of our classifier using clinical data demonstrates the promising value of our proposed approach for disease diagnosis.

Lutein Induces Autophagy via Beclin-1 Upregulation in IEC-6 Rat Intestinal Epithelial Cells.

Lutein is a carotenoid with anti-oxidant properties. Autophagy, an evolutionarily conserved catabolic cellular pathway for coping with stress conditions, is responsive to reactive oxygen species (ROS) and degrades damaged organelles. We previously demonstrated that lutein can induce anti-oxidant enzymes to relieve methotrexate-induced ROS stress. We therefore hypothesized that lutein, which activates ROS-scavenging enzymes, can also induce autophagy for cell survival. In this study, we demonstrated that lutein treatment attenuated the reduction in cell viability caused by H2O2. Lutein dose-dependently induced the processing of microtubule-associated protein light chain 3 (LC3)-II, an autophagy marker protein, and accumulation of LC3-positive puncta in rat intestinal IEC-6 cells. Furthermore, (a) direct observation of autophagosome formation through transmission electron microscopy, (b) upregulation of autophagy-related genes including ATG4A, ATG5, ATG7, ATG12, and beclin-1 (BENC1), and (c) increased BECN1/Bcl-2 ratio confirmed the induction of autophagy by lutein. The results revealed that bafilomycin-A1-induced inhibition of autophagy reduced cell viability and increased apoptosis in lutein-treated cells, indicating a protective role of lutein-induced autophagy. Lutein treatment also activated adenosine monophosphate-activated protein kinase (AMPK), c-Jun N-terminal kinase (JNK), and p-38, but had no effects on the induction of extracellular signal-related kinase or inhibition of mTOR; however, the inhibition of activated AMPK, JNK, or p-38 did not attenuate lutein-induced autophagy. Finally, increased BECN1 expression levels were detected in lutein-treated cells, and BECN1 knockdown abolished autophagy induction. These results suggest that lutein-induced autophagy was mediated by the upregulation of BECN1 in IEC-6 cells. We are the first to demonstrate that lutein induces autophagy. Elevated autophagy in lutein-treated IEC-6 cells may have a protective role against various stresses, and this warrants further investigation.

18ß-glycyrrhetinic acid derivative promotes proliferation, migration and aquaporin-3 expression in human dermal fibroblasts.

Licorice (Glycyrrhiza) species have been widely used as a traditional medicine and a natural sweetener in foods. The 18ß-glycyrrhetinic acid (18ß-GA) is a bioactive compound in licorice that exhibits potential anti-cancer, anti-inflammatory, and anti-microbial activities. Many synthesized derivatives of 18ß-GA have been reported to be cytotoxic and suggested for the treatment of malignant diseases. In this study, we explored the possible pharmacological roles of an 18ß-GA derivative in skin biology using primary human dermal fibroblasts and HaCaT keratinocytes as cell models. We found that this 18ß-GA derivative did not cause cell death, but significantly enhanced the proliferation of dermal fibroblasts and HaCaT keratinocytes. A scratch wound healing assay revealed that the 18ß-GA derivative promoted the migration of fibroblasts. Due to the important role of aquaporin-3 in cell migration and proliferation, we also investigated the expression of aquaporin-3 and found this compound up-regulated the expression of aquaporin-3 in dermal fibroblasts and HaCaT keratinocytes. In dermal fibroblasts, the 18ß-GA derivative induced the phosphorylation of Akt, ERK, and p38. The inhibitor of Akt predominantly suppressed the 18ß-GA derivative-induced expression of aquaporin-3. Collectively, this compound had a positive effect on the proliferation, migration, and aquaporin-3 expression of skin cells, implying its potential role in the treatment of skin diseases characterized by impaired wound healing or dermal defects.

Photoprotective Effects of Cycloheterophyllin against UVA-Induced Damage and Oxidative Stress in Human Dermal Fibroblasts.

Ultraviolet (UV) radiation, particularly ultraviolet A (UVA), is known to play a major role in photoaging of the human skin. Many studies have demonstrated that UV exposure causes the skin cells to generate reactive oxygen species and activates the mitogen-activated protein kinase (MAPK) pathway. Previous studies have also demonstrated that cycloheterophyllin has an antioxidant effect and can effectively scavenge free radicals. Extending the aforementioned investigations, in this study, human dermal fibroblasts were used to investigate the protective effect of cycloheterophyllin against UV-induced damage. We found that cycloheterophyllin not only significantly increased cell viability, but also attenuated the phosphorylation of MAPK after UVA exposure. Furthermore, cycloheterophyllin could reduce hydrogen peroxide (H2O2) generation and down-regulate H2O2-induced MAPK phosphorylation. In the in vivo studies, the topical application of cycloheterophyllin before UVA irradiation significantly decreased trans-epidermal water loss (TEWL), erythema, and blood flow rate. These results indicate that cycloheterophyllin is a photoprotective agent that inhibits UVA-induced oxidative stress and damage, and could be used in the research on and prevention of skin photoaging.

Inhibitory effects of resveratrol on PDGF-BB-induced retinal pigment epithelial cell migration via PDGFRß, PI3K/Akt and MAPK pathways.

PURPOSE: In diseases such as proliferative vitreoretinopathy (PVR), proliferative diabetic retinopathy, and age-related macular degeneration, retinal pigment epithelial (RPE) cells proliferate and migrate. Moreover, platelet-derived growth factor (PDGF) has been shown to enhance proliferation and migration of RPE cells in PVR. Even resveratrol can suppress the migration and adhesion of many cell types, its effects on RPE cell migration and adhesion remain unknown. In this study, we investigated the inhibitory effects of resveratrol on RPE cell migration induced by PDGF-BB, an isoform of PDGF, and adhesion to fibronectin, a major ECM component of PVR tissue. METHODS: The migration of RPE cells was assessed by an electric cell-substrate impedance sensing migration assay and a Transwell migration assay. A cell viability assay was used to determine the viability of resveratrol treated-cells. The cell adhesion to fibronectin was examined by an adhesion assay. The interactions of resveratrol with PDGF-BB were analyzed by a dot binding assay. The PDGF-BB-induced signaling pathways were determined by western blotting and scratch wound healing assay. RESULTS: Resveratrol inhibited PDGF-BB-induced RPE cell migration in a dose-dependent manner, but showed no effects on ARPE19 cell adhesion to fibronectin. The cell viability assay showed no cytotoxicity of resveratrol on RPE cells and the dot binding assay revealed no direct interactions of resveratrol with PDGF-BB. Inhibitory effects of resveratrol on PDGF-BB-induced platelet-derived growth factor receptor ß (PDGFRß) and tyrosine phosphorylation and the underlying pathways of PI3K/Akt, ERK and p38 activation were found; however, resveratrol and PDGF-BB showed no effects on PDGFRα and JNK activation. Scratch wound healing assay demonstrated resveratrol and the specific inhibitors of PDGFR, PI3K, MEK or p38 suppressed PDGF-BB-induced cell migration. CONCLUSIONS: These results indicate that resveratrol is an effective inhibitor of PDGF-BB-induced RPE cell migration via PDGFRß, PI3K/Akt and MAPK pathways, but has no effects on the RPE cell adhesion to fibronectin.

Lutein protects against methotrexate-induced and reactive oxygen species-mediated apoptotic cell injury of IEC-6 cells.

PURPOSE: High-dose chemotherapy using methotrexate (MTX) frequently induces side effects such as mucositis that leads to intestinal damage and diarrhea. Several natural compounds have been demonstrated of their effectiveness in protecting intestinal epithelial cells from these adverse effects. In this paper, we investigated the protection mechanism of lutein against MTX-induced damage in IEC-6 cells originating from the rat jejunum crypt. METHODS: The cell viability, induced-apoptosis, reactive oxygen species (ROS) generation, and mitochondrial membrane potential in IEC-6 cells under MTX treatment were examined in the presence or absence of lutein. Expression level of Bcl2, Bad and ROS scavenging enzymes (including SOD, catalase and Prdx1) were detected by quantitative RT-PCR. RESULTS: The cell viability of IEC-6 cells exposed to MTX was decreased in a dose- and time-dependent manner. MTX induces mitochondrial membrane potential loss, ROS generation and caspase 3 activation in IEC-6 cells. The cytotoxicity of MTX was reduced in IEC-6 cells by the 24 h pre-treatment of lutein. We found that pre-treatment of lutein significantly reduces MTX-induced ROS and apoptosis. The expression of SOD was up-regulated by the pre-treatment of lutein in the MTX-treated IEC-6 cells. These results indicated that lutein can protect IEC-6 cells from the chemo-drugs induced damage through increasing ROS scavenging ability. CONCLUSION: The MTX-induced apoptosis of IEC-6 cells was shown to be repressed by the pre-treatment of lutein, which may represent a promising adjunct to conventional chemotherapy for preventing intestinal damages.

Transcriptional network predicts viral set point during acute HIV-1 infection.

BACKGROUND: HIV-1-infected individuals with higher viral set points progress to AIDS more rapidly than those with lower set points. Predicting viral set point early following infection can contribute to our understanding of early control of HIV-1 replication, to predicting long-term clinical outcomes, and to the choice of optimal therapeutic regimens. METHODS: In a longitudinal study of 10 untreated HIV-1-infected patients, we used gene expression profiling of peripheral blood mononuclear cells to identify transcriptional networks for viral set point prediction. At each sampling time, a statistical analysis inferred the optimal transcriptional network that best predicted viral set point. We then assessed the accuracy of this transcriptional model by predicting viral set point in an independent cohort of 10 untreated HIV-1-infected patients from Malawi. RESULTS: The gene network inferred at time of enrollment predicted viral set point 24 weeks later in the independent Malawian cohort with an accuracy of 87.5%. As expected, the predictive accuracy of the networks inferred at later time points was even greater, exceeding 90% after week 4. The composition of the inferred networks was largely conserved between time points. The 12 genes comprising this dynamic signature of viral set point implicated the involvement of two major canonical pathways: interferon signaling (p<0.0003) and membrane fraction (p<0.02). A silico knockout study showed that HLA-DRB1 and C4BPA may contribute to restricting HIV-1 replication. CONCLUSIONS: Longitudinal gene expression profiling of peripheral blood mononuclear cells from patients with acute HIV-1 infection can be used to create transcriptional network models to early predict viral set point with a high degree of accuracy.

Phenotype prediction by integrative network analysis of SNP and gene expression microarrays.

A long-term goal of biomedical research is to decipher how genetic processes influence disease formation. Ubiquitous and advancing microarray technology can measure millions of DNA structural variants (single-nucleotide polymorphisms, or SNPs) and thousands of gene transcripts (RNA expression microarrays) in cells. Both of these information modalities can be brought to bear on disease etiology. This paper develops a Bayesian network-based approach to integrate SNP and expression microarray data. The network models SNP-gene interactions using a phenotype-centric network. Inferring the network consists of two steps: variable selection and network learning. The learned network illustrates how functionally dependent SNPs and genes influence each other, and also serves as a predictor of the phenotype. The application of the proposed method to a pediatric acute lymphoblastic leukemia dataset demonstrates the feasibility of our approach and its impact on biological investigation and clinical practice.

A transcriptional network signature characterizes lung cancer subtypes.

BACKGROUND: Transcriptional networks play a central role in cancer development. The authors described a systems biology approach to cancer classification based on the reverse engineering of the transcriptional network surrounding the 2 most common types of lung cancer: adenocarcinoma (AC) and squamous cell carcinoma (SCC). METHODS: A transcriptional network classifier was inferred from the molecular profiles of 111 human lung carcinomas. The authors tested its classification accuracy in 7 independent cohorts, for a total of 422 subjects of Caucasian, African, and Asian descent. RESULTS: The model for distinguishing AC from SCC was a 25-gene network signature. Its performance on the 7 independent cohorts achieved 95.2% classification accuracy. Even more surprisingly, 95% of this accuracy was explained by the interplay of 3 genes (KRT6A, KRT6B, KRT6C) on a narrow cytoband of chromosome 12. The role of this chromosomal region in distinguishing AC and SCC was further confirmed by the analysis of another group of 28 independent subjects assayed by DNA copy number changes. The copy number variations of bands 12q12, 12q13, and 12q12-13 discriminated these samples with 84% accuracy. CONCLUSIONS: These results suggest the existence of a robust signature localized in a relatively small area of the genome, and show the clinical potential of reverse engineering transcriptional networks from molecular profiles.

Automatic detection of regional heart rejection in USPIO-enhanced MRI.

Contrast-enhanced magnetic resonance imaging (MRI) is useful to study the infiltration of cells in vivo. This research adopts ultrasmall superparamagnetic iron oxide (USPIO) particles as contrast agents. USPIO particles administered intravenously can be endocytosed by circulating immune cells, in particular, macrophages. Hence, macrophages are labeled with USPIO particles. When a transplanted heart undergoes rejection, immune cells will infiltrate the allograft. Imaged by T(2)(*)-weighted MRI, USPIO-labeled macrophages display dark pixel intensities. Detecting these labeled cells in the image facilitates the identification of acute heart rejection. This paper develops a classifier to detect the presence of USPIO-labeled macrophages in the myocardium in the framework of spectral graph theory. First, we describe a USPIO-enhanced heart image with a graph. Classification becomes equivalent to partitioning the graph into two disjoint subgraphs. We use the Cheeger constant of the graph as an objective functional to derive the classifier. We represent the classifier as a linear combination of basis functions given from the spectral analysis of the graph Laplacian. Minimization of the Cheeger constant based functional leads to the optimal classifier. Experimental results and comparisons with other methods suggest the feasibility of our approach to study the rejection of hearts imaged by USPIO-enhanced MRI.

Immune cells detection of the in vivo rejecting heart in USPIO-enhanced magnetic resonance imaging.

Contrast-enhanced magnetic resonance imaging (MRI) is useful to study the infiltration of immune cells, in particular macrophages. Contrast agents, for example ultra-small superparamagnetic iron oxide (USPIO) particles, administered intravenously into the blood stream will be engulfed by macrophages circulating in the circulation system. When a transplanted heart rejects, macrophages and other immune cells will infiltrate the rejecting tissue. Imaged by T*2 weighted MRI, USPIO-labeled macrophages will display dark pixel intensities. Detecting the presence of USPIO particles in the images facilitates the study of heart rejection. We cast the problem of detecting the presence of USPIO-labeled myocardium in the framework of spectral graph theory, and treat our decision function as a level set function on the image. The pixels with positive level set values correspond to the presence of immune cells, and negative to the absence. When the image is modeled by a graph, the spectral analysis of the graph Laplacian provides a basis to represent the level set function. We develop from the Cheeger constant of the graph an objective functional of the level set function. The minimization of the objective leads to the optimal level set function. Experimental results suggest the feasibility of our approach in the study of rejecting hearts.