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Institute
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.
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.
Structural kinetic modeling (SKM) enables the analysis of dynamical properties of metabolic networks solely based on topological information and experimental data. Current SKM-based experiments are hampered by the time-intensive process of assigning model parameters and choosing appropriate sampling intervals for MonteCarlo experiments. We introduce a toolbox for the automatic and efficient construction and evaluation of structural kinetic models (SK models). Quantitative and qualitative analyses of network stability properties are performed in an automated manner. We illustrate the model building and analysis process in detailed example scripts that provide toolbox implementations of previously published literature models.
Complex networks have been successfully employed to represent different levels of biological systems, ranging from gene regulation to protein-protein interactions and metabolism. Network-based research has mainly focused on identifying unifying structural properties, such as small average path length, large clustering coefficient, heavy-tail degree distribution and hierarchical organization, viewed as requirements for efficient and robust system architectures. However, for biological networks, it is unclear to what extent these properties reflect the evolutionary history of the represented systems. Here, we show that the salient structural properties of six metabolic networks from all kingdoms of life may be inherently related to the evolution and functional organization of metabolism by employing network randomization under mass balance constraints. Contrary to the results from the common Markov-chain switching algorithm, our findings suggest the evolutionary importance of the small-world hypothesis as a fundamental design principle of complex networks. The approach may help us to determine the biologically meaningful properties that result from evolutionary pressure imposed on metabolism, such as the global impact of local reaction knockouts. Moreover, the approach can be applied to test to what extent novel structural properties can be used to draw biologically meaningful hypothesis or predictions from structure alone.
Background: In trying to understand the evolutionary relationships of organisms, the current flood of sequence data offers great opportunities, but also reveals new challenges with regard to data quality, the selection of data for subsequent analysis, and the automation of steps that were once done manually for single-gene analyses. Even though genome or transcriptome data is available for representatives of most bilaterian phyla, some enigmatic taxa still have an uncertain position in the animal tree of life. This is especially true for myzostomids, a group of symbiotic ( or parasitic) protostomes that are either placed with annelids or flatworms.
Methodology: Based on similarity criteria, Illumina-based transcriptome sequences of one myzostomid were compared to protein sequences of one additional myzostomid and 29 reference metazoa and clustered into gene families. These families were then used to investigate the phylogenetic position of Myzostomida using different approaches: Alignments of 989 sequence families were concatenated, and the resulting superalignment was analyzed under a Maximum Likelihood criterion. We also used all 1,878 gene trees with at least one myzostomid sequence for a supertree approach: the individual gene trees were computed and then reconciled into a species tree using gene tree parsimony.
Conclusions: Superalignments require strictly orthologous genes, and both the gene selection and the widely varying amount of data available for different taxa in our dataset may cause anomalous placements and low bootstrap support. In contrast, gene tree parsimony is designed to accommodate multilocus gene families and therefore allows a much more comprehensive data set to be analyzed. Results of this supertree approach showed a well-resolved phylogeny, in which myzostomids were part of the annelid radiation, and major bilaterian taxa were found to be monophyletic.
F2C2
(2012)
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/.
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/.
Heterosis-associated cellular and molecular processes were analyzed in seeds and seedlings of Arabidopsis thaliana accessions Col-0 and C24 and their heterotic hybrids. Microscopic examination revealed no advantages in terms of hybrid mature embryo organ sizes or cell numbers. Increased cotyledon sizes were detectable 4 days after sowing. Growth heterosis results from elevated cell sizes and numbers, and is well established at 10 days after sowing. The relative growth rates of hybrid seedlings were most enhanced between 3 and 4 days after sowing. Global metabolite profiling and targeted fatty acid analysis revealed maternal inheritance patterns for a large proportion of metabolites in the very early stages. During developmental progression, the distribution shifts to dominant, intermediate and heterotic patterns, with most changes occurring between 4 and 6 days after sowing. The highest incidence of heterotic patterns coincides with establishment of size differences at 4 days after sowing. In contrast, overall transcript patterns at 4, 6 and 10 days after sowing are characterized by intermediate to dominant patterns, with parental transcript levels showing the largest differences. Overall, the results suggest that, during early developmental stages, intermediate gene expression and higher metabolic activity in the hybrids compared to the parents lead to better resource efficiency, and therefore enhanced performance in the hybrids.
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.