Mark Burton

Evolution of the acyl-CoA binding protein (ACBP)

Acyl-CoA-binding protein (ACBP) is a 10 kDa protein that binds C12-C22 acyl-CoA esters with high affinity. In vitro and in vivo experiments suggest that it is involved in multiple cellular tasks including modulation of fatty acid biosynthesis, enzyme regulation, regulation of the intracellular acyl-CoA pool size, donation of acyl-CoA esters for beta-oxidation, vesicular trafficking, complex lipid synthesis and gene regulation. In the present study, we delineate the evolutionary history of ACBP to get a complete picture of its evolution and distribution among species. ACBP homologues were identified in all four eukaryotic kingdoms, Animalia, Plantae, Fungi and Protista, and eleven eubacterial species. ACBP homologues were not detected in any other known bacterial species, or in archaea. Nearly all of the ACBP-containing bacteria are pathogenic to plants or animals, suggesting that an ACBP gene could have been acquired from a eukaryotic host by horizontal gene transfer. Many bacterial, fungal and higher eukaryotic species only harbour a single ACBP homologue. However, a number of species, ranging from protozoa to vertebrates, have evolved two to six lineage-specific paralogues through gene duplication and/or retrotransposition events. The ACBP protein is highly conserved across phylums, and the majority of ACBP genes are subjected to strong purifying selection. Experimental evidence indicates that the function of ACBP has been conserved from yeast to humans and that the multiple lineage-specific paralogues have evolved altered functions. The appearance of ACBP very early on in evolution points towards a fundamental role of ACBP in acyl-CoA metabolism, including ceramide synthesis and in signalling.

Acyl-CoA binding proteins; structural and functional conservation over 2000 MYA

Besides serving as essential substrates for β-oxidation and synthesis of triacylglycerols and more complex lipids like sphingolipids and sterol esters, long-chain fatty acyl-CoA esters are increasingly being recognized as important regulators of enzyme activities and gene transcription. Acyl-CoA binding protein, ACBP, has been proposed to play a pivotal role in the intracellular trafficking and utilization of long-chain fatty acyl-CoA esters. Depletion of acyl-CoA binding protein in yeast results in aberrant organelle morphology incl. fragmented vacuoles, multi-layered plasma membranes and accumulation of vesicles of variable sizes. In contrast to synthesis and turn-over of glycerolipids, the levels of very-long-chain fatty acids, long-chain bases and ceramide are severely affected by Acb1p depletion, suggesting that Acb1p, rather than playing a general role, serves specific roles in cellular lipid metabolism.

The Circulating Cell-free microRNA Profile in Systemic Sclerosis Is Distinct from Both Healthy Controls and Systemic Lupus Erythematosus

Objective. To evaluate the expression profile of cell-free circulating microRNA (miRNA) in systemic sclerosis (SSc), healthy controls (HC), and systemic lupus erythematosus (SLE). Methods. Total RNA was purified from plasma and 45 different, mature miRNA were measured using quantitative PCR assays after reverse transcription. Samples (n = 189) were from patients with SSc (n = 120), SLE (n = 29), and from HC (n = 40). Expression data were clustered by principal components analysis, and diagnostically specific miRNA profiles were developed by leave-one-out cross-validation. Diagnostic probability scores were derived from stepwise logistic regression. Results. Thirty-seven miRNA specificities were consistently detected and 26 of these were unaffected by SSc sample age and present in more than two-thirds of SSc samples. SSc cases showed a distinct expression profile with 14/26 miRNA significantly decreased (false discovery rate < 0.05) and 5/26 increased compared with HC. A 21-miRNA classifier gave optimum accuracy (80%) for discriminating SSc from both HC and SLE. The discrimination between HC and SSc (95% accuracy) was strongly driven by miRNA of the 17∼92 cluster and by miR-16, -223, and -638, while SLE and SSc differed mainly in the expression of miR-142-3p, -150, -223, and -638. Except for a weak correlation between anti-Scl-70 and miR-638 (p = 0.048), there were no correlations with other patient variables. Conclusion. Circulating miRNA profiles are characteristic for SSc compared with both HC and SLE cases. Some of the predicted targets of the differentially regulated miRNA are of relevance for transforming growth factor-β signaling and fibrosis, but need to be validated in independent studies.

Long chain acyl-CoA esters and acyl-CoA binding protein (ACBP) in cell function

Publisher Summary The physiological role of long-chain fatty acyl-CoA (LCACoA) is an intermediate in lipid metabolism. However, LCACoA esters are increasingly recognized as modulators of a wide range of cellular functions. This requires a tight regulation of the intracellular LCACoA concentration in a way that simultaneously allows large variation in the rate of lipogenesis and β-oxidation and sufficient supply of LCACoA for special purposes like protein acylation. The major players in the control of the cellular LCACoA concentration are fatty acid supply, the delicate balance between the activity of acyl-CoA synthetases (ACS) and acyl-CoA thioesterase (TE), and finally the concentration of cellular LCACoA binding proteins. A number of proteins are reported to bind LCACoA including liver fatty acid binding protein (L-FABP), sterol carrier protein 2 (SCP-2), and acyl-coenzyme A binding protein (ACBP). In contrast to FABPs and SCP-2, ACBP binds only LCACoA. ACBP is a ∼10 kDa cytosolic protein, which binds LCACoA esters with high specificity and affinity.

Establishment and Characterization of a Tumor Stem Cell-Based Glioblastoma Invasion Model

Aims Glioblastoma is the most frequent and malignant brain tumor. Recurrence is inevitable and most likely connected to tumor invasion and presence of therapy resistant stem-like tumor cells. The aim was therefore to establish and characterize a three-dimensional in vivo-like in vitro model taking invasion and tumor stemness into account. Methods Glioblastoma stem cell-like containing spheroid (GSS) cultures derived from three different patients were established and characterized. The spheroids were implanted in vitro into rat brain slice cultures grown in stem cell medium and in vivo into brains of immuno-compromised mice. Invasion was followed in the slice cultures by confocal time-lapse microscopy. Using immunohistochemistry, we compared tumor cell invasion as well as expression of proliferation and stem cell markers between the models. Results We observed a pronounced invasion into brain slice cultures both by confocal time-lapse microscopy and immunohistochemistry. This invasion closely resembled the invasion in vivo. The Ki-67 proliferation indexes in spheroids implanted into brain slices were lower than in free-floating spheroids. The expression of stem cell markers varied between free-floating spheroids, spheroids implanted into brain slices and tumors in vivo. Conclusion The established invasion model kept in stem cell medium closely mimics tumor cell invasion into the brain in vivo preserving also to some extent the expression of stem cell markers. The model is feasible and robust and we suggest the model as an in vivo-like model with a great potential in glioma studies and drug discovery.

Gene Expression Profiles for Predicting Metastasis in Breast Cancer: A Cross-Study Comparison of Classification Methods

Machine learning has increasingly been used with microarray gene expression data and for the development of classifiers using a variety of methods. However, method comparisons in cross-study datasets are very scarce. This study compares the performance of seven classification methods and the effect of voting for predicting metastasis outcome in breast cancer patients, in three situations: within the same dataset or across datasets on similar or dissimilar microarray platforms. Combining classification results from seven classifiers into one voting decision performed significantly better during internal validation as well as external validation in similar microarray platforms than the underlying classification methods. When validating between different microarray platforms, random forest, another voting-based method, proved to be the best performing method. We conclude that voting based classifiers provided an advantage with respect to classifying metastasis outcome in breast cancer patients.

The gene expression and immunohistochemical time-course of diphenylcyclopropenone induced contact allergy in healthy humans following repeated epicutaneous challenges

The gene expression time‐course of repeated challenge of contact allergy (CA) remains largely unknown. Therefore, using diphenylcyclopropenone (DPCP) as model allergen in healthy humans we set out to examine: (i) the monotonous and complex gene expression time‐course trajectories following repeated DPCP challenges to find the predominant gene expression pattern, (ii) the time‐course of cell infiltration following repeated DPCP challenges and (iii) the transcriptome of a repeated CA exposure model. We obtained punch biopsies from control and DPCP‐exposed skin from ten DPCP sensitized individuals at 5‐6 monthly elicitation challenges. Biopsies were used for microarray gene expression profiling, histopathology and immunohistochemical staining. Validation of microarray data by qRT‐PCR was performed on 15 selected genes. Early gene expression time points were also validated in an independent data set. An increasing and decreasing trend in gene expression followed by a plateau was predominantly observed during repeated DPCP challenges. Immune responses reached a plateau after two challenges histopathologically, immunohistochemically and in the time‐course gene expression analysis. Transcriptional responses over time revealed a Th1/Th17 polarization as three upstream regulators (IFN‐γ, IL‐1 and IL‐17) activated most of the top upregulated genes. Of the latter genes, 9 of 10 were the same throughout the time course. Excellent correlations between array and PCR data were observed. The transcriptional responses to DPCP over time followed a monotonous pattern. This response pattern confirms and supports the newly reported clinical time‐course observations in de novo‐sensitized individuals showing a plateau response, and thus, there is concordance between clinical response, histopathology, immunohistochemistry and microarray gene expression in volunteers de novo‐sensitized to DPCP.

A 7-Gene Signature Depicts the Biochemical Profile of Early Prefibrotic Myelofibrosis

Recent studies have shown that a large proportion of patients classified as essential thrombocythemia (ET) actually have early primary prefibrotic myelofibrosis (prePMF), which implies an inferior prognosis as compared to patients being diagnosed with so-called genuine or true ET. According to the World Health Organization (WHO) 2008 classification, bone marrow histology is a major component in the distinction between these disease entities. However, the differential diagnosis between them may be challenging and several studies have not been able to distinguish between them. Most lately, it has been argued that simple blood tests, including the leukocyte count and plasma lactate dehydrogenase (LDH) may be useful tools to separate genuine ET from prePMF, the latter disease entity more often being featured by anemia, leukocytosis and elevated LDH. Whole blood gene expression profiling was performed in 17 and 9 patients diagnosed with ET and PMF, respectively. Using elevated LDH obtained at the time of diagnosis as a marker of prePMF, a 7-gene signature was identified which correctly predicted the prePMF group with a sensitivity of 100% and a specificity of 89%. The 7 genes included MPO, CEACAM8, CRISP3, MS4A3, CEACAM6, HEMGN, and MMP8, which are genes known to be involved in inflammation, cell adhesion, differentiation and proliferation. Evaluation of bone marrow biopsies and the 7-gene signature showed a concordance rate of 71%, 79%, 62%, and 38%. Our 7-gene signature may be a useful tool to differentiate between genuine ET and prePMF but needs to be validated in a larger cohort of “ET” patients.

Plasma MicroRNA Profiles in Patients with Early Rheumatoid Arthritis Responding to Adalimumab plus Methotrexate vs Methotrexate Alone: A Placebo-controlled Clinical Trial

Objective. The aim was to identify plasma (i.e., cell-free) microRNA (miRNA) predicting antitumor necrosis and/or methotrexate (MTX) treatment response in patients enrolled in an investigator-initiated, prospective, double-blinded, placebo-controlled trial (The OPERA study, NCT00660647). Methods. We included 180 disease-modifying antirheumatic drug–naive patients with early rheumatoid arthritis (RA) randomized to adalimumab (ADA; n = 89) or placebo (n = 91) in combination with MTX. Plasma samples before and 3 months after treatment initiation were analyzed for 91 specific miRNA by quantitative reverse transcriptase-polymerase chain reaction on microfluidic dynamic arrays. A linear mixed-effects model was used to test for associations between pretreatment miRNA and changes in miRNA expression and American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) Boolean (28 joints) remission at 3 and 12 months, applying false discovery rate correction for multiple testing. Using leave-one-out cross validation, we built predictive multivariate miRNA models and estimated classification performances using receiver-operating characteristics (ROC) curves. Results. In the ADA group, a higher pretreatment level of miR-27a-3p was significantly associated with remission at 12 months. The level decreased in remitting patients between pretreatment and 3 months, and increased in nonremitting patients. No associations were found in the placebo group receiving only MTX. Two multivariate miRNA models were able to predict response to ADA treatment after 3 and 12 months, with 63% and 82% area under the ROC curves, respectively. Conclusion. We identified miR-27a-3p as a potential predictive biomarker of ACR/EULAR remission in patients with early RA treated with ADA in combination with MTX. We conclude that pretreatment plasma-miRNA profiles may be of predictive value, but the results need confirmation in independent cohorts.

Prediction of Breast Cancer Metastasis by Gene Expression Profiles: A Comparison of Metagenes and Single Genes

Background The popularity of a large number of microarray applications has in cancer research led to the development of predictive or prognostic gene expression profiles. However, the diversity of microarray platforms has made the full validation of such profiles and their related gene lists across studies difficult and, at the level of classification accuracies, rarely validated in multiple independent datasets. Frequently, while the individual genes between such lists may not match, genes with same function are included across such gene lists. Development of such lists does not take into account the fact that genes can be grouped together as metagenes (MGs) based on common characteristics such as pathways, regulation, or genomic location. Such MGs might be used as features in building a predictive model applicable for classifying independent data. It is, therefore, demanding to systematically compare independent validation of gene lists or classifiers based on metagene or individual gene (SG) features. Methods In this study we compared the performance of either metagene- or single gene-based feature sets and classifiers using random forest and two support vector machines for classifier building. The performance within the same dataset, feature set validation performance, and validation performance of entire classifiers in strictly independent datasets were assessed by 10 times repeated 10-fold cross validation, leave-one-out cross validation, and one-fold validation, respectively. To test the significance of the performance difference between MG- and SG-features/classifiers, we used a repeated down-sampled binomial test approach. Results MG- and SG-feature sets are transferable and perform well for training and testing prediction of metastasis outcome in strictly independent data sets, both between different and within similar microarray platforms, while classifiers had a poorer performance when validated in strictly independent datasets. The study showed that MG- and SG-feature sets perform equally well in classifying independent data. Furthermore, SG-classifiers significantly outperformed MG-classifier when validation is conducted between datasets using similar platforms, while no significant performance difference was found when validation was performed between different platforms. Conclusion Prediction of metastasis outcome in lymph node–negative patients by MG- and SG-classifiers showed that SG-classifiers performed significantly better than MG-classifiers when validated in independent data based on the same microarray platform as used for developing the classifier. However, the MG- and SG-classifiers had similar performance when conducting classifier validation in independent data based on a different microarray platform. The latter was also true when only validating sets of MG- and SG-features in independent datasets, both between and within similar and different platforms.