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More effort — more results
(2016)
The development of 'omics' technologies has progressed to address complex biological questions that underlie various plant functions thereby producing copious amounts of data. The need to assimilate large amounts of data into biologically meaningful interpretations has necessitated the development of statistical methods to integrate multidimensional information. Throughout this review, we provide examples of recent outcomes of 'omics' data integration together with an overview of available statistical methods and tools.
Data integration has become a useful strategy for uncovering new insights into complex biological networks. We studied whether this approach can help to delineate the signal transducer and activator of transcription 6 (STAT6)-mediated transcriptional network driving T helper (Th) 2 cell fate decisions. To this end, we performed an integrative analysis of publicly available RNA-seq data of Stat6-knockout mouse studies together with STAT6 ChIP-seq data and our own gene expression time series data during Th2 cell differentiation. We focused on transcription factors (TFs), cytokines, and cytokine receptors and delineated 59 positively and 41 negatively STAT6-regulated genes, which were used to construct a transcriptional network around STAT6. The network illustrates that important and well-known TFs for Th2 cell differentiation are positively regulated by STAT6 and act either as activators for Th2 cells (e.g., Gata3, Atf3, Satb1, Nfil3, Maf, and Pparg) or as suppressors for other Th cell subpopulations such as Th1 (e.g., Ar), Th17 (e.g., Etv6), or iTreg (e.g., Stat3 and Hifla) cells. Moreover, our approach reveals 11 TFs (e.g., Atf5, Creb3l2, and Asb2) with unknown functions in Th cell differentiation. This fact together with the observed enrichment of asthma risk genes among those regulated by STAT6 underlines the potential value of the data integration strategy used here. Thus, our results clearly support the opinion that data integration is a useful tool to delineate complex physiological processes.
Phenomic experiments are carried out in large-scale plant phenotyping facilities that acquire a large number of pictures of hundreds of plants simultaneously. With the aid of automated image processing, the data are converted into genotype-feature matrices that cover many consecutive days of development. Here, we explore the possibility of predicting the biomass of the fully grown plant from early developmental stage image-derived features. We performed phenomic experiments on 195 inbred and 382 hybrid maizes varieties and followed their progress from 16 days after sowing (DAS) to 48 DAS with 129 image-derived features. By applying sparse regression methods, we show that 73% of the variance in hybrid fresh weight of fully-grown plants is explained by about 20 features at the three-leaf-stage or earlier. Dry weight prediction explained over 90% of the variance. When phenomic features of parental inbred lines were used as predictors of hybrid biomass, the proportion of variance explained was 42 and 45%, for fresh weight and dry weight models consisting of 35 and 36 features, respectively. These models were very robust, showing only a small amount of variation in performance over the time scale of the experiment. We also examined mid-parent heterosis in phenomic features. Feature heterosis displayed a large degree of variance which resulted in prediction performance that was less robust than models of either parental or hybrid predictors. Our results show that phenomic prediction is a viable alternative to genomic and metabolic prediction of hybrid performance. In particular, the utility of early-stage parental lines is very encouraging. (C) 2016 Elsevier Ireland Ltd. All rights reserved.
analysis
(2016)
The development of ‘omics’ technologies has progressed to address complex biological questions that underlie various plant functions thereby producing copious amounts of data. The need to assimilate large amounts of data into biologically meaningful interpretations has necessitated the development of statistical methods to integrate multidimensional information. Throughout this review, we provide examples of recent outcomes of ‘omics’ data integration together with an overview of available statistical methods and tools.
Refined elasticity sampling for Monte Carlo-based identification of stabilizing network patterns
(2015)
Motivation: Structural kinetic modelling (SKM) is a framework to analyse whether a metabolic steady state remains stable under perturbation, without requiring detailed knowledge about individual rate equations. It provides a representation of the system's Jacobian matrix that depends solely on the network structure, steady state measurements, and the elasticities at the steady state. For a measured steady state, stability criteria can be derived by generating a large number of SKMs with randomly sampled elasticities and evaluating the resulting Jacobian matrices. The elasticity space can be analysed statistically in order to detect network positions that contribute significantly to the perturbation response. Here, we extend this approach by examining the kinetic feasibility of the elasticity combinations created during Monte Carlo sampling.
Results: Using a set of small example systems, we show that the majority of sampled SKMs would yield negative kinetic parameters if they were translated back into kinetic models. To overcome this problem, a simple criterion is formulated that mitigates such infeasible models. After evaluating the small example pathways, the methodology was used to study two steady states of the neuronal TCA cycle and the intrinsic mechanisms responsible for their stability or instability. The findings of the statistical elasticity analysis confirm that several elasticities are jointly coordinated to control stability and that the main source for potential instabilities are mutations in the enzyme alpha-ketoglutarate dehydrogenase.
The adaptive response of skeletal muscle to exercise training is tightly controlled and therefore requires transcriptional regulation. DNA methylation is an epigenetic mechanism known to modulate gene expression, but its contribution to exercise-induced adaptations in skeletal muscle is not well studied. Here, we describe a genome-wide analysis of DNA methylation in muscle of trained mice (n = 3). Compared with sedentary controls, 2,762 genes exhibited differentially methylated CpGs (P < 0.05, meth diff >5%, coverage > 10) in their putative promoter regions. Alignment with gene expression data (n = 6) revealed 200 genes with a negative correlation between methylation and expression changes in response to exercise training. The majority of these genes were related to muscle growth and differentiation, and a minor fraction involved in metabolic regulation. Among the candidates were genes that regulate the expression of myogenic regulatory factors (Plexin A2) as well as genes that participate in muscle hypertrophy (Igfbp4) and motor neuron innervation (Dok7). Interestingly, a transcription factor binding site enrichment study discovered significantly enriched occurrence of CpG methylation in the binding sites of the myogenic regulatory factors MyoD and myogenin. These findings suggest that DNA methylation is involved in the regulation of muscle adaptation to regular exercise training.
Glucocorticoids are indispensable anti-inflammatory and decongestant drugs with high prevalence of use at (similar to)0.9% of the adult population. Better holistic insights into glucocorticoid-induced changes are crucial for effective use as concurrent medication and management of adverse effects. The profiles of 214 metabolites from plasma of 20 male healthy volunteers were recorded prior to and after ingestion of a single dose of 4 mg dexamethasone (+20 mg pantoprazole). Samples were drawn at three predefined time points per day: seven untreated (day 1 midday - day 3 midday) and four treated (day 3 evening - day 4 evening) per volunteer. Statistical analysis revealed tremendous impact of dexamethasone on the metabolome with 150 of 214 metabolites being significantly deregulated on at least one time point after treatment (ANOVA, Benjamini-Hochberg corrected, q < 0.05). Inter-person variability was high and remained uninfluenced by treatment. The clearly visible circadian rhythm prior to treatment was almost completely suppressed and deregulated by dexamethasone. The results draw a holistic picture of the severe metabolic deregulation induced by single-dose, short-term glucocorticoid application. The observed metabolic changes suggest a potential for early detection of severe side effects, raising hope for personalized early countermeasures increasing quality of life and reducing health care costs.
A detailed knowledge of cell wall heterogeneity and complexity is crucial for understanding plant growth and development. One key challenge is to establish links between polysaccharide-rich cell walls and their phenotypic characteristics. It is of particular interest for some plant material, like cotton fibers, which are of both biological and industrial importance. To this end, we attempted to study cotton fiber characteristics together with glycan arrays using regression based approaches. Taking advantage of the comprehensive microarray polymer profiling technique (CoMPP), 32 cotton lines from different cotton species were studied. The glycan array was generated by sequential extraction of cell wall polysaccharides from mature cotton fibers and screening samples against eleven extensively characterized cell wall probes. Also, phenotypic characteristics of cotton fibers such as length, strength, elongation and micronaire were measured. The relationship between the two datasets was established in an integrative manner using linear regression methods. In the conducted analysis, we demonstrated the usefulness of regression based approaches in establishing a relationship between glycan measurements and phenotypic traits. In addition, the analysis also identified specific polysaccharides which may play a major role during fiber development for the final fiber characteristics. Three different regression methods identified a negative correlation between micronaire and the xyloglucan and homogalacturonan probes. Moreover, homogalacturonan and callose were shown to be significant predictors for fiber length. The role of these polysaccharides was already pointed out in previous cell wall elongation studies. Additional relationships were predicted for fiber strength and elongation which will need further experimental validation.
Recent advances in high-throughput omics techniques render it possible to decode the function of genes by using the "guilt-by-association" principle on biologically meaningful clusters of gene expression data. However, the existing frameworks for biological evaluation of gene clusters are hindered by two bottleneck issues: (1) the choice for the number of clusters, and (2) the external measures which do not take in consideration the structure of the analyzed data and the ontology of the existing biological knowledge. Here, we address the identified bottlenecks by developing a novel framework that allows not only for biological evaluation of gene expression clusters based on existing structured knowledge, but also for prediction of putative gene functions. The proposed framework facilitates propagation of statistical significance at each of the following steps: (1) estimating the number of clusters, (2) evaluating the clusters in terms of novel external structural measures, (3) selecting an optimal clustering algorithm, and (4) predicting gene functions. The framework also includes a method for evaluation of gene clusters based on the structure of the employed ontology. Moreover, our method for obtaining a probabilistic range for the number of clusters is demonstrated valid on synthetic data and available gene expression profiles from Saccharomyces cerevisiae. Finally, we propose a network-based approach for gene function prediction which relies on the clustering of optimal score and the employed ontology. Our approach effectively predicts gene function on the Saccharomyces cerevisiae data set and is also employed to obtain putative gene functions for an Arabidopsis thaliana data set.
Objective Wnt signalling pathways regulate proliferation, motility and survival in a variety of human cell types. Dickkopf 1 (DKK1) gene codes for a secreted Wnt inhibitory factor. It functions as tumour suppressor gene in breast cancer and as a pro-apoptotic factor in glioma cells. In this study, we aimed to demonstrate whether the different expression of DKK1 in human glioma-derived cells is dependent on microenvironmental factors like hypoxia and regulated by the intercellular crosstalk with bone-marrow-derived mesenchymal stem cells (bmMSCs).
Methods Glioma cell line U87-MG, three cell lines from human glioblastoma grade IV (glioma-derived mesenchymal stem cells) and three bmMSCs were selected for the experiment. The expression of DKK1 in cell lines under normoxic/hypoxic environment or co-culture condition was measured using real-time PCR and enzyme-linked immunoadsorbent assay. The effect of DKK1 on cell migration and proliferation was evaluated by in vitro wound healing assays and sulphorhodamine assays, respectively.
Results Glioma-derived cells U87-MG displayed lower DKK1 expression compared with bmMSCs. Hypoxia led to an overexpression of DKK1 in bmMSCs and U87-MG when compared to normoxic environment, whereas co-culture of U87-MG with bmMSCs induced the expression of DKK1 in both cell lines. Exogenous recombinant DKK1 inhibited cell migration on all cell lines, but did not have a significant effect on cell proliferation of bmMSCs and glioma cell lines.
Conclusion In this study, we showed for the first time that the expression of DKK1 was hypoxia dependent in human malignant glioma cell lines. The induction of DKK1 by intracellular crosstalk or hypoxia stimuli sheds light on the intense adaption of glial tumour cells to environmental alterations.
Natural genetic diversity provides a powerful tool to study the complex interrelationship between metabolism and growth. Profiling of metabolic traits combined with network-based and statistical analyses allow the comparison of conditions and identification of sets of traits that predict biomass. However, it often remains unclear why a particular set of metabolites is linked with biomass and to what extent the predictive model is applicable beyond a particular growth condition. A panel of 97 genetically diverse Arabidopsis (Arabidopsis thaliana) accessions was grown in near-optimal carbon and nitrogen supply, restricted carbon supply, and restricted nitrogen supply and analyzed for biomass and 54 metabolic traits. Correlation-based metabolic networks were generated from the genotype-dependent variation in each condition to reveal sets of metabolites that show coordinated changes across accessions. The networks were largely specific for a single growth condition. Partial least squares regression from metabolic traits allowed prediction of biomass within and, slightly more weakly, across conditions (cross-validated Pearson correlations in the range of 0.27-0.58 and 0.21-0.51 and P values in the range of <0.001-<0.13 and <0.001-<0.023, respectively). Metabolic traits that correlate with growth or have a high weighting in the partial least squares regression were mainly condition specific and often related to the resource that restricts growth under that condition. Linear mixed-model analysis using the combined metabolic traits from all growth conditions as an input indicated that inclusion of random effects for the conditions improves predictions of biomass. Thus, robust prediction of biomass across a range of conditions requires condition-specific measurement of metabolic traits to take account of environment-dependent changes of the underlying networks.
Many deep evolutionary divergences still remain unresolved, such as those among major taxa of the Lophotrochozoa. As alternative phylogenetic markers, the intron-exon structure of eukaryotic genomes and the patterns of absence and presence of spliceosomal introns appear to be promising. However, given the potential homoplasy of intron presence, the phylogenetic analysis of this data using standard evolutionary approaches has remained a challenge. Here, we used Mutual Information (MI) to estimate the phylogeny of Protostomia using gene structure data, and we compared these results with those obtained with Dollo Parsimony. Using full genome sequences from nine Metazoa, we identified 447 groups of orthologous sequences with 21,732 introns in 4,870 unique intron positions. We determined the shared absence and presence of introns in the corresponding sequence alignments and have made this data available in "IntronBase", a web-accessible and downloadable SQLite database. Our results obtained using Dollo Parsimony are obviously misled through systematic errors that arise from multiple intron loss events, but extensive filtering of data improved the quality of the estimated phylogenies. Mutual Information, in contrast, performs better with larger datasets, but at the same time it requires a complete data set, which is difficult to obtain for orthologs from a large number of taxa. Nevertheless, Mutual Information-based distances proved to be useful in analyzing this kind of data, also because the estimation of MI-based distances is independent of evolutionary models and therefore no pre-definitions of ancestral and derived character states are necessary.
Background
High blood glucose and diabetes are amongst the conditions causing the greatest losses in years of healthy life worldwide. Therefore, numerous studies aim to identify reliable risk markers for development of impaired glucose metabolism and type 2 diabetes. However, the molecular basis of impaired glucose metabolism is so far insufficiently understood. The development of so called 'omics' approaches in the recent years promises to identify molecular markers and to further understand the molecular basis of impaired glucose metabolism and type 2 diabetes. Although univariate statistical approaches are often applied, we demonstrate here that the application of multivariate statistical approaches is highly recommended to fully capture the complexity of data gained using high-throughput methods.
Methods
We took blood plasma samples from 172 subjects who participated in the prospective Metabolic Syndrome Berlin Potsdam follow-up study (MESY-BEPO Follow-up). We analysed these samples using Gas Chromatography coupled with Mass Spectrometry (GC-MS), and measured 286 metabolites. Furthermore, fasting glucose levels were measured using standard methods at baseline, and after an average of six years. We did correlation analysis and built linear regression models as well as Random Forest regression models to identify metabolites that predict the development of fasting glucose in our cohort.
Results
We found a metabolic pattern consisting of nine metabolites that predicted fasting glucose development with an accuracy of 0.47 in tenfold cross-validation using Random Forest regression. We also showed that adding established risk markers did not improve the model accuracy. However, external validation is eventually desirable. Although not all metabolites belonging to the final pattern are identified yet, the pattern directs attention to amino acid metabolism, energy metabolism and redox homeostasis.
Conclusions
We demonstrate that metabolites identified using a high-throughput method (GC-MS) perform well in predicting the development of fasting plasma glucose over several years. Notably, not single, but a complex pattern of metabolites propels the prediction and therefore reflects the complexity of the underlying molecular mechanisms. This result could only be captured by application of multivariate statistical approaches. Therefore, we highly recommend the usage of statistical methods that seize the complexity of the information given by high-throughput methods.
Mesenchymal stem cells and glioma cells form a structural as well as a functional syncytium in vitro
(2012)
The interaction of human mesenchymal stem cells (hMSCs) and tumor cells has been investigated in various contexts. HMSCs are considered as cellular treatment vectors based on their capacity to migrate towards a malignant lesion. However, concerns about unpredictable behavior of transplanted hMSCs are accumulating. In malignant gliomas, the recruitment mechanism is driven by glioma-secreted factors which lead to accumulation of both, tissue specific stem cells as well as bone marrow derived hMSCs within the tumor. The aim of the present work was to study specific cellular interactions between hMSCs and glioma cells in vitro. We show, that glioma cells as well as hMSCs differentially express connexins. and that they interact via gap-junctional coupling. Besides this so-called functional syncytium formation, we also provide evidence of cell fusion events (structural syncytium). These complex cellular interactions led to an enhanced migration and altered proliferation of both, tumor and mesenchymal stem cell types in vitro. The presented work shows that glioma cells display signs of functional as well as structural syncytium formation with hMSCs in vitro. The described cellular phenomena provide new insight into the complexity of interaction patterns between tumor cells and host cells. Based on these findings, further studies are warranted to define the impact of a functional or structural syncytium formation on malignant tumors and cell based therapies in vivo.
Metabolic networks are characterized by complex interactions and regulatory mechanisms between many individual components. These interactions determine whether a steady state is stable to perturbations. Structural kinetic modeling (SKM) is a framework to analyze the stability of metabolic steady states that allows the study of the system Jacobian without requiring detailed knowledge about individual rate equations. Stability criteria can be derived by generating a large number of structural kinetic models (SK-models) with randomly sampled parameter sets and evaluating the resulting Jacobian matrices. Until now, SKM experiments applied univariate tests to detect the network components with the largest influence on stability. In this work, we present an extended SKM approach relying on supervised machine learning to detect patterns of enzyme-metabolite interactions that act together in an orchestrated manner to ensure stability. We demonstrate its application on a detailed SK-model of the Calvin-Benson cycle and connected pathways. The identified stability patterns are highly complex reflecting that changes in dynamic properties depend on concerted interactions between several network components. In total, we find more patterns that reliably ensure stability than patterns ensuring instability. This shows that the design of this system is strongly targeted towards maintaining stability. We also investigate the effect of allosteric regulators revealing that the tendency to stability is significantly increased by including experimentally determined regulatory mechanisms that have not yet been integrated into existing kinetic models.
Background: Flux coupling analysis (FCA) has become a useful tool in the constraint-based analysis of genome-scale metabolic networks. FCA allows detecting dependencies between reaction fluxes of metabolic networks at steady-state. On the one hand, this can help in the curation of reconstructed metabolic networks by verifying whether the coupling between reactions is in agreement with the experimental findings. On the other hand, FCA can aid in defining intervention strategies to knock out target reactions.
Results: We present a new method F2C2 for FCA, which is orders of magnitude faster than previous approaches. As a consequence, FCA of genome-scale metabolic networks can now be performed in a routine manner.
Conclusions: We propose F2C2 as a fast tool for the computation of flux coupling in genome-scale metabolic networks. F2C2 is freely available for non-commercial use at https://sourceforge.net/projects/f2c2/files/.
To contribute to a further insight into heterosis we applied an integrative analysis to a systems biological network approach and a quantitative genetics analysis towards biomass heterosis in early Arabidopsis thaliana development. The study was performed on the parental accessions C24 and Col-0 and the reciprocal crosses. In an over-representation analysis it was tested if the overlap between the resulting gene lists of the two approaches is significantly larger than expected by chance. Top ranked genes in the results list of the systems biological analysis were significantly over-represented in the heterotic QTL candidate regions for either hybrid as well as regarding mid-parent and best-parent heterosis. This suggests that not only a few but rather several genes that influence biomass heterosis are located within each heterotic QTL region. Furthermore, the overlapping resulting genes of the two integrated approaches were particularly enriched in biomass related pathways. A chromosome-wise over-representation analysis gave rise to the hypothesis that chromosomes number 2 and 4 probably carry a majority of the genes involved in biomass heterosis in the early development of Arabidopsis thaliana.
Isolation and characterization of bone marrow-derived progenitor cells from malignant gliomas
(2012)
Background: Malignant gliomas are highly-vascularised tumours. Neoangiogenesis is a crucial factor in the malignant behaviour of tumour and prognosis of patients. Several mechanisms are suspected to lead to neoangiogenesis, one of them is the recruitment of multipotent progenitor cells towards the tumour. Factors such as Vascular endothelial growth factor-A (VEGF-A) were described to recruit bone marrow-derived endothelial progenitor cells (EPCs) to the glioma stroma and vasculature. Little is known about isolating EPCs from normal or malignant tissues. Materials and Methods: In this study, we addressed the topic of characterization of tumour-isolated EPCs and re-defined the clonal relationship between EPCs and hematopoietic stem cells (HSCs) in gliomas. We first checked public gene expression data of glioma for putative marker expression, pointing towards a prevalence of EPCs and HSCs in glioma. Immunohistochemical staining of glioma tissue confirmed the higher expression of these progenitor markers in glioma tissue. EPCs and HSCs were consequently isolated and characterized at the phenotypic and functional levels. We applied a new isolation method, for the first time, to specimen from patients with high grade glioma including seven grade IV glioblastoma, five-grade III astrocytoma, and three grade III oligoastrocytoma. Results: In all samples, we were able to isolate the tumour-derived EPCs, which were positive for characteristic markers: CD31, CD34 and VEGFR2. The EPCs formed capillary networks in vitro and had the ability to take up acetylated low-density lipoprotein. Glioma-derived HSCs were positive for CD34 and CD45, but they were unable to form a capillary network in vitro. These findings on tumour-derived EPCs/HSCs were in concordance with the results, derived from peripheral blood of healthy volunteers. Conclusion: In our study, we established a new method for EPC/HSC isolation from human gliomas, defined the contribution of EPCs and HSCs to the tumour tissue, and highlighted the intense in vivo tumour host interaction.
Background: Different immunotherapy approaches for the treatment of cancer and autoimmune diseases are being developed and tested in clinical studies worldwide. Their resulting complex experimental data should be properly evaluated, therefore reliable normal healthy control baseline values are indispensable.
Methodology/Principal Findings: To assess intra- and inter-individual variability of various biomarkers, peripheral blood of 16 age and gender equilibrated healthy volunteers was sampled on 3 different days within a period of one month. Complex "crossomics'' analyses of plasma metabolite profiles, antibody concentrations and lymphocyte subset counts as well as whole genome expression profiling in CD4(+)T and NK cells were performed. Some of the observed age, gender and BMI dependences are in agreement with the existing knowledge, like negative correlation between sex hormone levels and age or BMI related increase in lipids and soluble sugars. Thus we can assume that the distribution of all 39.743 analysed markers is well representing the normal Caucasoid population. All lymphocyte subsets, 20% of metabolites and less than 10% of genes, were identified as highly variable in our dataset.
Conclusions/Significance: Our study shows that the intra- individual variability was at least two-fold lower compared to the inter-individual one at all investigated levels, showing the importance of personalised medicine approach from yet another perspective.