Unsupervised Bayesian linear unmixing of gene expression microarrays

Background: This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for uBLU is a Bayesian model for the data samples which are represented as an additive mixture of random positive gene signatures, called factors, with random positive mixing coefficients, called factor scores, that specify the relative contribution of each signature to a specific sample. The particularity of the proposed method is that uBLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Furthermore, it also provides estimates of the number of factors. A Gibbs sampling strategy is adopted here to generate random samples according to the posterior distribution of the factors, factor scores, and number of factors. These samples are then used to estimate all the unknown parameters. Results: Firstly, the proposed uBLU method is applied to several simulated datasets with known ground truth and compared with previous factor decomposition methods, such as principal component analysis (PCA), non negative matrix factorization (NMF), Bayesian factor regression modeling (BFRM), and the gradient-based algorithm for general matrix factorization (GB-GMF). Secondly, we illustrate the application of uBLU on a real time-evolving gene expression dataset from a recent viral challenge study in which individuals have been inoculated with influenza A/H3N2/Wisconsin. We show that the uBLU method significantly outperforms the other methods on the simulated and real data sets considered here. Conclusions: The results obtained on synthetic and real data illustrate the accuracy of the proposed uBLU method when compared to other factor decomposition methods from the literature (PCA, NMF, BFRM, and GB-GMF). The uBLU method identifies an inflammatory component closely associated with clinical symptom scores collected during the study. Using a constrained model allows recovery of all the inflammatory genes in a single factor

A Randomised Controlled Trial Assessing the Effect of Oral Diazepam on F-18-FDG Uptake in the Neck and Upper Chest Region

A distinctive pattern of physiological symmetrical uptake of F-18-fluorodeoxyglucose (F-18-FDG) in the neck and upper chest region is a phenomenon that is sometimes observed on positron emission tomography (PET) scans of some oncologic patients. Initially, it was assumed to be muscle uptake secondary to patient anxiety or tension, which could be prevented by diazepam treatment. However, PET-computed tomography data have shown that F-18-FDG uptake is not restricted to the musculature but is also localised within the non-muscular soft tissue, such as brown adipose tissue. The efficacy of benzodiazepine treatment to reduce this uptake has not been well established. Therefore, a randomised controlled trial was conducted to decide whether diazepam would decrease physiological F-18-FDG uptake in the neck and upper chest region (FDG-NUC). A randomised, double-blind, placebo-controlled trial was conducted to assess the effect on FDG-NUC of 5 mg diazepam, given orally 1 h before F-18-FDG injection. Patients younger than 40 years, having or suspected to have a malignancy, were eligible for inclusion. The primary endpoint was FDG-NUC, as assessed by visual analysis of whole-body PET scans by two independent observers. The secondary endpoint was clinical relevance of FDG-NUC. Fifty-two patients were included between September 2003 and January 2005. Twenty-eight patients (54%) received placebo; 24 (46%) received diazepam. FDG-NUC was seen in 25% of the patients in the diazepam group versus 29% in the placebo group. This difference was not statistically significant. No beneficial effect of administration of diazepam could be established. Pre-medication with benzodiazepines to diminish physiological uptake of F-18-FDG in the neck and upper chest region is not indicate

Early rheumatoid arthritis is characterized by a distinct and transient synovial fluid cytokine profile of T cell and stromal cell origin

Pathological processes involved in the initiation of rheumatoid synovitis remain unclear. We undertook the present study to identify immune and stromal processes that are present soon after the clinical onset of rheumatoid arthritis ( RA) by assessing a panel of T cell, macrophage, and stromal cell related cytokines and chemokines in the synovial fluid of patients with early synovitis. Synovial fluid was aspirated from inflamed joints of patients with inflammatory arthritis of duration 3 months or less, whose outcomes were subsequently determined by follow up. For comparison, synovial fluid was aspirated from patients with acute crystal arthritis, established RA and osteoarthritis. Rheumatoid factor activity was blocked in the synovial fluid samples, and a panel of 23 cytokines and chemokines measured using a multiplex based system. Patients with early inflammatory arthritis who subsequently developed RA had a distinct but transient synovial fluid cytokine profile. The levels of a range of T cell, macrophage and stromal cell related cytokines ( e. g. IL-2, IL-4, IL-13, IL-17, IL-15, basic fibroblast growth factor and epidermal growth factor) were significantly elevated in these patients within 3 months after symptom onset, as compared with early arthritis patients who did not develop RA. In addition, this profile was no longer present in established RA. In contrast, patients with non-rheumatoid persistent synovitis exhibited elevated levels of interferon-gamma at initiation. Early synovitis destined to develop into RA is thus characterized by a distinct and transient synovial fluid cytokine profile. The cytokines present in the early rheumatoid lesion suggest that this response is likely to influence the microenvironment required for persistent RA

Robust Detection of Hierarchical Communities from Escherichia coli Gene Expression Data

Determining the functional structure of biological networks is a central goal of systems biology. One approach is to analyze gene expression data to infer a network of gene interactions on the basis of their correlated responses to environmental and genetic perturbations. The inferred network can then be analyzed to identify functional communities. However, commonly used algorithms can yield unreliable results due to experimental noise, algorithmic stochasticity, and the influence of arbitrarily chosen parameter values. Furthermore, the results obtained typically provide only a simplistic view of the network partitioned into disjoint communities and provide no information of the relationship between communities. Here, we present methods to robustly detect coregulated and functionally enriched gene communities and demonstrate their application and validity for Escherichia coli gene expression data. Applying a recently developed community detection algorithm to the network of interactions identified with the context likelihood of relatedness (CLR) method, we show that a hierarchy of network communities can be identified. These communities significantly enrich for gene ontology (GO) terms, consistent with them representing biologically meaningful groups. Further, analysis of the most significantly enriched communities identified several candidate new regulatory interactions. The robustness of our methods is demonstrated by showing that a core set of functional communities is reliably found when artificial noise, modeling experimental noise, is added to the data. We find that noise mainly acts conservatively, increasing the relatedness required for a network link to be reliably assigned and decreasing the size of the core communities, rather than causing association of genes into new communities.Comment: Due to appear in PLoS Computational Biology. Supplementary Figure S1 was not uploaded but is available by contacting the author. 27 pages, 5 figures, 15 supplementary file

A patient with hypereosinophilic syndrome that manifested with acquired hemophilia and elevated IgG4: a case report

<p>Abstract</p> <p>Introduction</p> <p>Hypereosinophilic syndrome is defined as a prolonged state (more than six months) of eosinophilia (greater than 1500 cells/μL), without an apparent etiology and with end-organ damage. Hypereosinophilic syndrome can cause coagulation abnormalities. Among hypereosinophilic syndrome types, the lymphocytic variant (lymphocytic hypereosinophilic syndrome) is derived from a monoclonal proliferation of T lymphocytes. Here, we describe the case of a patient with lymphocytic hypereosinophilic syndrome who presented with a coagulation abnormality. To the best of our knowledge, this is the first such report including a detailed clinical picture and temporal cytokine profile.</p> <p>Case presentation</p> <p>A 77-year-old Japanese man presented to our facility with massive hematuria and hypereosinophilia (greater than 2600 cells/μl). His eosinophilia first appeared five years earlier when he developed femoral artery occlusion. He manifested with multiple hematomas and prolonged activated partial thromboplastin time. His IgG4 level was remarkably elevated (greater than 2000 mg/dL). Polymerase chain reaction tests of peripheral blood and bone marrow identified lymphocytic hypereosinophilic syndrome. His prolonged activated partial thromboplastin time was found to be due to acquired hemophilia. Glucocorticoids suppressed both the hypereosinophilia and coagulation abnormality. However, tapering of glucocorticoids led to a relapse of the coagulation abnormality alone, without eosinophilia. Tumor necrosis factor α, interleukin-5, and/or eotaxin-3 may have caused the hypereosinophilia, and interleukin-10 was correlated with the coagulation abnormality.</p> <p>Conclusions</p> <p>To the best of our knowledge, this is the first case in which lymphocytic hypereosinophilic syndrome and IgG4-related disease have overlapped. In addition, our patient is only the second case of hypereosinophilic disease that manifested with acquired hemophilia. Our patient relapsed with the coagulation abnormality alone, without eosinophilia. This report shows that the link between eosinophilia, IgG4, and clinical manifestations is not simple and provides useful insight into the immunopathology of hypereosinophilic syndrome and IgG4-related disease.</p

Integrin-Linked Kinase Overexpression and Its Oncogenic Role in Promoting Tumorigenicity of Hepatocellular Carcinoma

Background: Integrin-linked kinase (ILK) was first discovered as an integrin β1-subunit binding protein. It localizes at the focal adhesions and is involved in cytoskeleton remodeling. ILK overexpression and its dysregulated signaling cascades have been reported in many human cancers. Aberrant expression of ILK influenced a wide range of signaling pathways and cellular functions. Although ILK has been well characterized in many malignancies, its role in hepatocellular carcinoma (HCC) is still largely unknown. Methodology/Principal Findings: Quantitative PCR analysis was used to examine ILK mRNA expression in HCC clinical samples. It was shown that ILK was overexpressed in 36.9% (21/57) of HCC tissues when compared to the corresponding non-tumorous livers. The overall ILK expression level was significantly higher in tumorous tissues (P = 0.004), with a significant stepwise increase in expression level along tumor progression from tumor stage I to IV (P = 0.045). ILK knockdown stable clones were established in two HCC cell lines, BEL7402 and HLE, and were subjected to different functional assays. Knockdown of ILK significantly suppressed HCC cell growth, motility and invasion in vitro and inhibited tumorigenicity in vivo. Western blot analysis revealed a reduced phosphorylated-Akt (pAkt) at Serine-473 expression in ILK knockdown stable clones when compared to control clones. Conclusion/Significance: This study provides evidence about the clinical relevance of ILK in hepatocarcinogenesis. ILK was found to be progressively elevated along HCC progression. Here our findings also provide the first validation about the oncogenic capacity of ILK in vivo by suppressing its expression in HCC cells. The oncogenic role of ILK is implicated to be mediated by Akt pathway. © 2011 Chan et al.published_or_final_versio

De novo identification of viral pathogens from cell culture hologenomes

<p>Abstract</p> <p>Background</p> <p>Fast, specific identification and surveillance of pathogens is the cornerstone of any outbreak response system, especially in the case of emerging infectious diseases and viral epidemics. This process is generally tedious and time-consuming thus making it ineffective in traditional settings. The added complexity in these situations is the non-availability of pure isolates of pathogens as they are present as mixed genomes or hologenomes. Next-generation sequencing approaches offer an attractive solution in this scenario as it provides adequate depth of sequencing at fast and affordable costs, apart from making it possible to decipher complex interactions between genomes at a scale that was not possible before. The widespread application of next-generation sequencing in this field has been limited by the non-availability of an efficient computational pipeline to systematically analyze data to delineate pathogen genomes from mixed population of genomes or hologenomes.</p> <p>Findings</p> <p>We applied next-generation sequencing on a sample containing mixed population of genomes from an epidemic with appropriate processing and enrichment. The data was analyzed using an extensive computational pipeline involving mapping to reference genome sets and <it>de-novo </it>assembly. In depth analysis of the data generated revealed the presence of sequences corresponding to <it>Japanese encephalitis </it>virus. The genome of the virus was also independently <it>de-novo </it>assembled. The presence of the virus was in addition, verified using standard molecular biology techniques.</p> <p>Conclusions</p> <p>Our approach can accurately identify causative pathogens from cell culture hologenome samples containing mixed population of genomes and in principle can be applied to patient hologenome samples without any background information. This methodology could be widely applied to identify and isolate pathogen genomes and understand their genomic variability during outbreaks.</p

Myeloid Sirtuin 2 expression does not impact long-term Mycobacterium tuberculosis control

Sirtuins (Sirts) regulate several cellular mechanisms through deacetylation of several transcription factors and enzymes. Recently, Sirt2 was shown to prevent the development of inflammatory processes and its expression favors acute Listeria monocytogenes infection. The impact of this molecule in the context of chronic infections remains unknown. We found that specific Sirt2 deletion in the myeloid lineage transiently increased Mycobacterium tuberculosis load in the lungs and liver of conditional mice. Sirt2 did not affect long-term infection since no significant differences were observed in the bacterial burden at days 60 and 120 post-infection. The initial increase in M. tuberculosis growth was not due to differences in inflammatory cell infiltrates in the lung, myeloid or CD4+ T cells. The transcription levels of IFN-?, IL-17, TNF, IL-6 and NOS2 were also not affected in the lungs by Sirt2-myeloid specific deletion. Overall, our results demonstrate that Sirt2 expression has a transitory effect in M. tuberculosis infection. Thus, modulation of Sirt2 activity in vivo is not expected to affect chronic infection with M. tuberculosis.Fundação para a Ciência e Tecnologia, Portugal and cofunded by Programa Operacional Regional do Norte (ON.2–O Novo Norte), Quadro de Referência Estratégico Nacional (QREN), through the Fundo Europeu de Desenvolvimento Regional (FEDER). Project grants: PTDC/SAU-MII/101977/2008 (to AGC) and PTDC/BIA-BCM/102776/2008 (to MS). LMT was supported by FCT Grant SFRH/BPD/77399/20

Query Large Scale Microarray Compendium Datasets Using a Model-Based Bayesian Approach with Variable Selection

In microarray gene expression data analysis, it is often of interest to identify genes that share similar expression profiles with a particular gene such as a key regulatory protein. Multiple studies have been conducted using various correlation measures to identify co-expressed genes. While working well for small datasets, the heterogeneity introduced from increased sample size inevitably reduces the sensitivity and specificity of these approaches. This is because most co-expression relationships do not extend to all experimental conditions. With the rapid increase in the size of microarray datasets, identifying functionally related genes from large and diverse microarray gene expression datasets is a key challenge. We develop a model-based gene expression query algorithm built under the Bayesian model selection framework. It is capable of detecting co-expression profiles under a subset of samples/experimental conditions. In addition, it allows linearly transformed expression patterns to be recognized and is robust against sporadic outliers in the data. Both features are critically important for increasing the power of identifying co-expressed genes in large scale gene expression datasets. Our simulation studies suggest that this method outperforms existing correlation coefficients or mutual information-based query tools. When we apply this new method to the Escherichia coli microarray compendium data, it identifies a majority of known regulons as well as novel potential target genes of numerous key transcription factors