Analyzing M-CSF dependent monocyte/macrophage differentiation: expression modes and meta-modes derived from an independent component analysis

Is There a Need for a More Precise Description of Biomolecule Interactions to Understand Cell Function?

An important goal of biological research is to explain and hopefully predict cell behavior from the molecular properties of cellular components. Accordingly, much work was done to build extensive “omic” datasets and develop theoretical methods, including computer simulation and network analysis to process as quantitatively as possible the parameters contained in these resources. Furthermore, substantial effort was made to standardize data presentation and make experimental results accessible to data scientists. However, the power and complexity of current experimental and theoretical tools make it more and more difficult to assess the capacity of gathered parameters to support optimal progress in our understanding of cell function. The purpose of this review is to focus on biomolecule interactions, the interactome, as a specific and important example, and examine the limitations of the explanatory and predictive power of parameters that are considered as suitable descriptors of molecular interactions. Recent experimental studies on important cell functions, such as adhesion and processing of environmental cues for decision-making, support the suggestion that it should be rewarding to complement standard binding properties such as affinity and kinetic constants, or even force dependence, with less frequently used parameters such as conformational flexibility or size of binding molecules.

Monocytes in the Peripheral Clearance of Amyloid-β and Alzheimer's Disease

Aging societies have high incidence rates of Alzheimer's disease (AD). AD is diagnosed at later disease stages and has a poor prognosis, and effective drugs and treatments for AD are lacking. The molecular mechanism of AD is not clear, and current research focuses primarily on amyloid-β (Aβ) deposition and tau protein hyperphosphorylation. Aβ deposition is the most frequently hypothesized initiating factor of AD, and Aβ clearance during the pathogenesis of AD may be an optional strategy to suppress AD development. Monocytes play important roles in the peripheral clearance of Aβ. Therefore, the present review summarizes our current knowledge of the potential roles of infiltrating macrophages, circulating monocytes, and Kupffer cells in the peripheral clearance of Aβ in AD.

Immune response characterization and vaccine potential of a recombinant chimera comprising B-cell epitope of Aeromonas hydrophila outer membrane protein C and LTB

Aeromonas hydrophila is one of the most virulent fish pathogens, causing colossal economic losses to the aquaculture industry annually. The absence of a safe and effective vaccine makes it very difficult to control this infection. Outer membrane proteins have been widely illustrated to confer protective immunity against a broad spectrum of gram negative bacteria. In the current study, we have analyzed the ability of B-cell epitopes of A. hydrophila's outer membrane protein C (OmpC) to confer protection against bacterial virulence. Bioinformatic algorithms were used to predict linear B-cell epitopes of OmpC and the corresponding nucleotide sequences were cloned in translational fusion with heat labile enterotoxin B subunit (LTB) of E. coli. Of the three recombinant LTB.epitope fusion proteins evaluated, antisera against the fusion protein comprising the epitope stretch of 143-175 amino acids gave maximum cross reactivity with the parent protein OmpC. The anti-fusion protein antisera contained both OmpC- and LTB-specific antibodies. The fusion proteins' LTB moiety retained its ability to bind to the GM₁ ganglioside receptor, an essential requirement for its adjuvanicity. Antibody isotyping, cytokine ELISA, and cytokine array analysis revealed a Th2 skewed type immune response along with the presence of some relevant Th17 and Th1 cytokines involved in conferring protective immunity. Surface exposure of the epitope_143-175 on live A. hydrophila membrane was investigated and validated using bacterial agglutination and flow cytometry analysis using anti-fusion protein antisera. Our results strongly support the potential of B-cell epitope_143-175 of OmpC of A. hydrophila, in fusion with the LTB, as an effective and promising vaccine candidate against this bacterium.

Linking Activation of Microglia and Peripheral Monocytic Cells to the Pathophysiology of Psychiatric Disorders

A wide variety of studies have identified microglial activation in psychiatric disorders, such as schizophrenia, bipolar disorder, and major depressive disorder. Relatively fewer, but robust, studies have detected activation of peripheral monocytic cells in psychiatric disorders. Considering the origin of microglia, as well as neuropsychoimmune interactions in the context of the pathophysiology of psychiatric disorders, it is reasonable to speculate that microglia interact with peripheral monocytic cells in relevance with the pathogenesis of psychiatric disorders; however, these interactions have drawn little attention. In this review, we summarize findings relevant to activation of microglia and monocytic cells in psychiatric disorders, discuss the potential association between these cell types and disease pathogenesis, and propose perspectives for future research on these processes.

K1 and K15 of Kaposi's Sarcoma-Associated Herpesvirus Are Partial Functional Homologues of Latent Membrane Protein 2A of Epstein-Barr Virus

The human herpesviruses Epstein-Barr virus (EBV) and Kaposi's sarcoma-associated herpesvirus (KSHV) are associated with Hodgkin's lymphoma (HL) and Primary effusion lymphomas (PEL), respectively, which are B cell malignancies that originate from germinal center B cells. PEL cells but also a quarter of EBV-positive HL tumor cells do not express the genuine B cell receptor (BCR), a situation incompatible with survival of normal B cells. EBV encodes LMP2A, one of EBV's viral latent membrane proteins, which likely replaces the BCR's survival signaling in HL. Whether KSHV encodes a viral BCR mimic that contributes to oncogenesis is not known because an experimental model of KSHV-mediated B cell transformation is lacking. We addressed this uncertainty with mutant EBVs encoding the KSHV genes K1 or K15 in lieu of LMP2A and infected primary BCR-negative (BCR(-)) human B cells with them. We confirmed that the survival of BCR(-) B cells and their proliferation depended on an active LMP2A signal. Like LMP2A, the expression of K1 and K15 led to the survival of BCR(-) B cells prone to apoptosis, supported their proliferation, and regulated a similar set of cellular target genes. K1 and K15 encoded proteins appear to have noncomplementing, redundant functions in this model, but our findings suggest that both KSHV proteins can replace LMP2A's key activities contributing to the survival, activation and proliferation of BCR(-) PEL cells in vivo.

IMPORTANCE

Several herpesviruses encode oncogenes that are receptor-like proteins. Often, they are constitutively active providing important functions to the latently infected cells. LMP2A of Epstein-Barr virus (EBV) is such a receptor that mimics an activated B cell receptor, BCR. K1 and K15, related receptors of Kaposi's sarcoma-associated herpesvirus (KSHV) expressed in virus-associated tumors, have less obvious functions. We found in infection experiments that both viral receptors of KSHV can replace LMP2A and deliver functions similar to the endogenous BCR. K1, K15, and LMP2A also control the expression of a related set of cellular genes in primary human B cells, the target cells of EBV and KSHV. The observed phenotypes, as well as the known characteristics of these genes, argue for their contributions to cellular survival, B cell activation, and proliferation. Our findings provide one possible explanation for the tumorigenicity of KSHV, which poses a severe problem in immunocompromised patients.

Aging enhances release of exosomal cytokine mRNAs by Aβ1-42-stimulated macrophages

Amyloid-β1-42 (Aβ) peptide effects on human models of central nervous system (CNS)-patrolling macrophages (Ms) and CD4 memory T-cells (CD4-Tms) were investigated to examine immune responses to Aβ in Alzheimer's disease. Aβ and lipopolysaccharide (LPS) elicited similar M cytokine and exosomal mRNA (ex-mRNA) responses. Aβ- and LPS-stimulated Ms from 20 ≥65-yr-old subjects generated significantly more IL-1, TNF-α, and IL-6, but not IL-8 or IL-12, and significantly more ex-mRNAs for IL-6, TNF-α, and IL-12, but not for IL-8 or IL-1, than Ms from 20 matched 21- to 45-yr-old subjects. CD4-Tm generation of IL-2, IL-4, and IFN-γ and, for young subjects, IL-10, but not IL-6, evoked by Aβ was significantly lower than with anti-T-cell antigen receptor antibodies (Abs). Abs significantly increased all CD4-Tm ex-mRNAs, but only IL-2 and IL-6 ex-mRNAs were increased by Aβ. There were no significant differences between cytokine and ex-mRNA responses of CD4-Tms from the old compared to the young subjects. M-derived serum exosomes from the old subjects had significantly higher IL-6 and IL-12 ex-mRNA levels than those from the young subjects, whereas there were no differences for CD4-Tm-derived serum exosomes. An Aβ level relevant to neurodegeneration elicited broad M cytokine and ex-mRNA responses that were significantly greater in the old subjects, but only narrow and age-independent CD4-Tm responses.

Statistical methods for the analysis of high-throughput metabolomics data

Metabolomics is a relatively new high-throughput technology that aims at measuring all endogenous metabolites within a biological sample in an unbiased fashion. The resulting metabolic profiles may be regarded as functional signatures of the physiological state, and have been shown to comprise effects of genetic regulation as well as environmental factors. This potential to connect genotypic to phenotypic information promises new insights and biomarkers for different research fields, including biomedical and pharmaceutical research. In the statistical analysis of metabolomics data, many techniques from other omics fields can be reused. However recently, a number of tools specific for metabolomics data have been developed as well. The focus of this mini review will be on recent advancements in the analysis of metabolomics data especially by utilizing Gaussian graphical models and independent component analysis.

Quality Visualization of Microarray Datasets Using Circos

Quality control and normalization is considered the most important step in the analysis of microarray data. At present there are various methods available for quality assessments of microarray datasets. However there seems to be no standard visualization routine, which also depicts individual microarray quality. Here we present a convenient method for visualizing the results of standard quality control tests using Circos plots. In these plots various quality measurements are drawn in a circular fashion, thus allowing for visualization of the quality and all outliers of each distinct array within a microarray dataset. The proposed method is intended for use with the Affymetrix Human Genome platform (i.e., GPL 96, GPL570 and GPL571). Circos quality measurement plots are a convenient way for the initial quality estimate of Affymetrix datasets that are stored in publicly available databases.

Bayesian independent component analysis recovers pathway signatures from blood metabolomics data

Interpreting the complex interplay of metabolites in heterogeneous biosamples still poses a challenging task. In this study, we propose independent component analysis (ICA) as a multivariate analysis tool for the interpretation of large-scale metabolomics data. In particular, we employ a Bayesian ICA method based on a mean-field approach, which allows us to statistically infer the number of independent components to be reconstructed. The advantage of ICA over correlation-based methods like principal component analysis (PCA) is the utilization of higher order statistical dependencies, which not only yield additional information but also allow a more meaningful representation of the data with fewer components. We performed the described ICA approach on a large-scale metabolomics data set of human serum samples, comprising a total of 1764 study probands with 218 measured metabolites. Inspecting the source matrix of statistically independent metabolite profiles using a weighted enrichment algorithm, we observe strong enrichment of specific metabolic pathways in all components. This includes signatures from amino acid metabolism, energy-related processes, carbohydrate metabolism, and lipid metabolism. Our results imply that the human blood metabolome is composed of a distinct set of overlaying, statistically independent signals. ICA furthermore produces a mixing matrix, describing the strength of each independent component for each of the study probands. Correlating these values with plasma high-density lipoprotein (HDL) levels, we establish a novel association between HDL plasma levels and the branched-chain amino acid pathway. We conclude that the Bayesian ICA methodology has the power and flexibility to replace many of the nowadays common PCA and clustering-based analyses common in the research field.

A mixture model with a reference-based automatic selection of components for disease classification from protein and/or gene expression levels

BACKGROUND

Bioinformatics data analysis is often using linear mixture model representing samples as additive mixture of components. Properly constrained blind matrix factorization methods extract those components using mixture samples only. However, automatic selection of extracted components to be retained for classification analysis remains an open issue.

RESULTS

The method proposed here is applied to well-studied protein and genomic datasets of ovarian, prostate and colon cancers to extract components for disease prediction. It achieves average sensitivities of: 96.2 (sd = 2.7%), 97.6% (sd = 2.8%) and 90.8% (sd = 5.5%) and average specificities of: 93.6% (sd = 4.1%), 99% (sd = 2.2%) and 79.4% (sd = 9.8%) in 100 independent two-fold cross-validations.

CONCLUSIONS

We propose an additive mixture model of a sample for feature extraction using, in principle, sparseness constrained factorization on a sample-by-sample basis. As opposed to that, existing methods factorize complete dataset simultaneously. The sample model is composed of a reference sample representing control and/or case (disease) groups and a test sample. Each sample is decomposed into two or more components that are selected automatically (without using label information) as control specific, case specific and not differentially expressed (neutral). The number of components is determined by cross-validation. Automatic assignment of features (m/z ratios or genes) to particular component is based on thresholds estimated from each sample directly. Due to the locality of decomposition, the strength of the expression of each feature across the samples can vary. Yet, they will still be allocated to the related disease and/or control specific component. Since label information is not used in the selection process, case and control specific components can be used for classification. That is not the case with standard factorization methods. Moreover, the component selected by proposed method as disease specific can be interpreted as a sub-mode and retained for further analysis to identify potential biomarkers. As opposed to standard matrix factorization methods this can be achieved on a sample (experiment)-by-sample basis. Postulating one or more components with indifferent features enables their removal from disease and control specific components on a sample-by-sample basis. This yields selected components with reduced complexity and generally, it increases prediction accuracy.

Integrating genome-wide genetic variations and monocyte expression data reveals trans-regulated gene modules in humans

One major expectation from the transcriptome in humans is to characterize the biological basis of associations identified by genome-wide association studies. So far, few cis expression quantitative trait loci (eQTLs) have been reliably related to disease susceptibility. Trans-regulating mechanisms may play a more prominent role in disease susceptibility. We analyzed 12,808 genes detected in at least 5% of circulating monocyte samples from a population-based sample of 1,490 European unrelated subjects. We applied a method of extraction of expression patterns-independent component analysis-to identify sets of co-regulated genes. These patterns were then related to 675,350 SNPs to identify major trans-acting regulators. We detected three genomic regions significantly associated with co-regulated gene modules. Association of these loci with multiple expression traits was replicated in Cardiogenics, an independent study in which expression profiles of monocytes were available in 758 subjects. The locus 12q13 (lead SNP rs11171739), previously identified as a type 1 diabetes locus, was associated with a pattern including two cis eQTLs, RPS26 and SUOX, and 5 trans eQTLs, one of which (MADCAM1) is a potential candidate for mediating T1D susceptibility. The locus 12q24 (lead SNP rs653178), which has demonstrated extensive disease pleiotropy, including type 1 diabetes, hypertension, and celiac disease, was associated to a pattern strongly correlating to blood pressure level. The strongest trans eQTL in this pattern was CRIP1, a known marker of cellular proliferation in cancer. The locus 12q15 (lead SNP rs11177644) was associated with a pattern driven by two cis eQTLs, LYZ and YEATS4, and including 34 trans eQTLs, several of them tumor-related genes. This study shows that a method exploiting the structure of co-expressions among genes can help identify genomic regions involved in trans regulation of sets of genes and can provide clues for understanding the mechanisms linking genome-wide association loci to disease.

Monocytes and Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disease characterized by extracellular amyloid beta (Aβ) deposition and intracellular neurofibrillary tangle formation. Monocyte is part of the innate immune system and can effectively remove dead cells and debris. It has been suggested that Aβ can recruit monocytes into brain in AD mice, resulting in restriction of cerebral amyloidosis. However, monocyte may act as a double-edged sword, either beneficial (e.g., clearance of Aβ) or detrimental (e.g., secretion of neurotoxic factors). In addition, recent studies indicate that in AD patients, Aβ phagocytosis by monocytes is ineffective. The present review mainly summarized the current knowledge on monocytes and their potential roles in AD.

Independent component and pathway-based analysis of miRNA-regulated gene expression in a model of type 1 diabetes

Background

Several approaches have been developed for miRNA target prediction, including methods that incorporate expression profiling. However the methods are still in need of improvements due to a high false discovery rate. So far, none of the methods have used independent component analysis (ICA). Here, we developed a novel target prediction method based on ICA that incorporates both seed matching and expression profiling of miRNA and mRNA expressions. The method was applied on a cellular model of type 1 diabetes.

Results

Microrray profiling identified eight miRNAs (miR-124/128/192/194/204/375/672/708) with differential expression. Applying ICA on the mRNA profiling data revealed five significant independent components (ICs) correlating to the experimental conditions. The five ICs also captured the miRNA expressions by explaining >97% of their variance. By using ICA, seven of the eight miRNAs showed significant enrichment of sequence predicted targets, compared to only four miRNAs when using simple negative correlation. The ICs were enriched for miRNA targets that function in diabetes-relevant pathways e.g. type 1 and type 2 diabetes and maturity onset diabetes of the young (MODY).

Conclusions

In this study, ICA was applied as an attempt to separate the various factors that influence the mRNA expression in order to identify miRNA targets. The results suggest that ICA is better at identifying miRNA targets than negative correlation. Additionally, combining ICA and pathway analysis constitutes a means for prioritizing between the predicted miRNA targets. Applying the method on a model of type 1 diabetes resulted in identification of eight miRNAs that appear to affect pathways of relevance to disease mechanisms in diabetes.

Knowledge-based gene expression classification via matrix factorization

Motivation: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considered indicative of underlying regulatory processes. They can as well be applied to the classification of gene expression datasets by grouping samples into different categories for diagnostic purposes or group genes into functional categories for further investigation of related metabolic pathways and regulatory networks.

Results: In this study we focus on unsupervised matrix factorization techniques and apply ICA and sparse NMF to microarray datasets. The latter monitor the gene expression levels of human peripheral blood cells during differentiation from monocytes to macrophages. We show that these tools are able to identify relevant signatures in the deduced component matrices and extract informative sets of marker genes from these gene expression profiles. The methods rely on the joint discriminative power of a set of marker genes rather than on single marker genes. With these sets of marker genes, corroborated by leave-one-out or random forest cross-validation, the datasets could easily be classified into related diagnostic categories. The latter correspond to either monocytes versus macrophages or healthy vs Niemann Pick C disease patients.

Supplementary information:Supplementary data are available at Bioinformatics online.

Contact:elmar.lang@biologie.uni-regensburg.de