Heinaniemi M.,University of Luxembourg |
Heinaniemi M.,Luxembourg Center for Systems Biomedicine |
Heinaniemi M.,University of Eastern Finland |
Nykter M.,Tampere University of Technology |
And 14 more authors.
Nature Methods | Year: 2013
The distinct cell types of multicellular organisms arise owing to constraints imposed by gene regulatory networks on the collective change of gene expression across the genome, creating self-stabilizing expression states, or attractors. We curated human expression data comprising 166 cell types and 2,602 transcription-regulating genes and developed a data-driven method for identifying putative determinants of cell fate built around the concept of expression reversal of gene pairs, such as those participating in toggle-switch circuits. This approach allows us to organize the cell types into their ontogenic lineage relationships. Our method identifies genes in regulatory circuits that control neuronal fate, pluripotency and blood cell differentiation, and it may be useful for prioritizing candidate factors for direct conversion of cell fate. © 2013 Nature America, Inc. All rights reserved.
Schneider J.G.,Luxembourg Center for Systems Biomedicine |
Schneider J.G.,Saarland University |
Nadeau J.H.,Pacific Northwest Research Institute
Cell Metabolism | Year: 2015
Serotonin acts as neurotransmitter in the brain and as a multifaceted signaling molecule coordinating many physiological processes in the periphery. In a recent issue of Nature Medicine, Crane et al. (2014) find that peripheral serotonin controls thermogenesis in adipose tissue by modulating β-adrenergic stimulation of UCP-1, thereby affecting glucose homeostasis and weight gain. © 2015 Elsevier Inc.
Thurley K.,Max Delbrück Center for Molecular Medicine |
Thurley K.,University of Cambridge |
Thurley K.,Charité - Medical University of Berlin |
Tovey S.C.,University of Cambridge |
And 9 more authors.
Science Signaling | Year: 2014
Ca2+ is a ubiquitous intracellular messenger that regulates diverse cellular activities. Extracellular stimuli often evoke sequences of intracellular Ca2+ spikes, and spike frequency may encode stimulus intensity. However, the timing of spikes within a cell is random because each interspike interval has a large stochastic component. In human embryonic kidney (HEK) 293 cells and rat primary hepatocytes, we found that the average interspike interval also varied between individual cells. To evaluate how individual cells reliably encoded stimuli when Ca2+ spikes exhibited such unpredictability, we combined Ca2+ imaging of single cells with mathematical analyses of the Ca2+ spikes evoked by receptors that stimulate formation of inositol 1,4,5-trisphosphate (IP3). This analysis revealed that signal-to-noise ratios were improved by slow recovery from feedback inhibition of Ca2+ spiking operating at the whole-cell level and that they were robust against perturbations of the signaling pathway. Despite variability in the frequency of Ca2+ spikes between cells, steps in stimulus intensity caused the stochastic period of the interspike interval to change by the same factor in all cells. These fold changes reliably encoded changes in stimulus intensity, and they resulted in an exponential dependence of average interspike interval on stimulation strength.We conclude that Ca2+ spikes enable reliable signaling in a cell population despite randomness and cell-to-cell variability, because global feedback reduces noise, and changes in stimulus intensity are represented by fold changes in the stochastic period of the interspike interval.
Diederich N.J.,Center Hospitalier Of Luxembourg |
Diederich N.J.,Luxembourg Center for Systems Biomedicine |
Parent A.,Laval University
Journal of the Neurological Sciences | Year: 2012
In Parkinson's disease (PD) many motor and non-motor symptoms are difficult to explain in terms of a purely ascending degeneration process as described by Braak. This essay proposes phylogenetic considerations for consolidating the multidimensional elements of PD. Subtle clinical analysis paired with ethological comparisons as well as patho-anatomical data suggests that disrupted automatic gait control, olfactory deficits, selected visual deficits, impaired emotional face recognition, and REM sleep behavior disorder could be due to dysfunction of phylogenetically ancient networks. Neuroanatomical and behavioral findings lead to a reconsideration of the basal ganglia, to be viewed as the nuclear core of a widely distributed neural network that arborizes throughout the primordial core of the neuraxis, including the brainstem. Fragility of the resulting multiple, closed, ancillary loops that link brainstem and forebrain components of the basal ganglia may be a nodal point, pivotal to the pathogenesis of PD. Other primitive neural networks, such as those located at cardiac or gastro-intestinal levels, may share the same vulnerability. Such a network-based hypothesis overrides the need of a fixed temporal ordering of symptoms based on putative caudal-cephalic propagation patterns of pathological lesions. It also creates testable, secondary hypotheses such as differential gene expression in different neural networks, potential early epigenetic influences, concepts of "overuse" or maladaptation of primitive networks to the constraints of adult life, and system frailty due to irreparable mitochondrial "exhaustion" in highly energy consuming postmitotic cells. © 2011 Elsevier B.V. All rights reserved.
Sudhakar P.,TU Hamburg - Harburg |
Reck M.,Helmholtz Center for Infection Research |
Wang W.,TU Hamburg - Harburg |
He F.Q.,Luxembourg Center for Systems Biomedicine |
And 2 more authors.
BMC Genomics | Year: 2014
Background: Carolacton is a newly identified secondary metabolite causing altered cell morphology and death of Streptococcus mutans biofilm cells. To unravel key regulators mediating these effects, the transcriptional regulatory response network of S. mutans biofilms upon carolacton treatment was constructed and analyzed. A systems biological approach integrating time-resolved transcriptomic data, reverse engineering, transcription factor binding sites, and experimental validation was carried out.Results: The co-expression response network constructed from transcriptomic data using the reverse engineering algorithm called the Trend Correlation method consisted of 8284 gene pairs. The regulatory response network inferred by superimposing transcription factor binding site information into the co-expression network comprised 329 putative transcriptional regulatory interactions and could be classified into 27 sub-networks each co-regulated by a transcription factor. These sub-networks were significantly enriched with genes sharing common functions. The regulatory response network displayed global hierarchy and network motifs as observed in model organisms. The sub-networks modulated by the pyrimidine biosynthesis regulator PyrR, the glutamine synthetase repressor GlnR, the cysteine metabolism regulator CysR, global regulators CcpA and CodY and the two component system response regulators VicR and MbrC among others could putatively be related to the physiological effect of carolacton. The predicted interactions from the regulatory network between MbrC, known to be involved in cell envelope stress response, and the murMN-SMU_718c genes encoding peptidoglycan biosynthetic enzymes were experimentally confirmed using Electro Mobility Shift Assays. Furthermore, gene deletion mutants of five predicted key regulators from the response networks were constructed and their sensitivities towards carolacton were investigated. Deletion of cysR, the node having the highest connectivity among the regulators chosen from the regulatory network, resulted in a mutant which was insensitive to carolacton thus demonstrating not only the essentiality of cysR for the response of S. mutans biofilms to carolacton but also the relevance of the predicted network.Conclusion: The network approach used in this study revealed important regulators and interactions as part of the response mechanisms of S. mutans biofilm cells to carolacton. It also opens a door for further studies into novel drug targets against streptococci. © 2014 Sudhakar et al.; licensee BioMed Central Ltd.
Mojtahedi M.,University of Calgary |
D'Herouel A.F.,Institute for Systems Biology |
D'Herouel A.F.,Luxembourg Center for Systems Biomedicine |
Huang S.,University of Calgary |
Huang S.,Institute for Systems Biology
Nucleic Acids Research | Year: 2014
Digital PCR (dPCR) exploits limiting dilution of a template into an array of PCR reactions. From this array the number of reactions that contain at least one (as opposed to zero) initial template is determined, allowing inferring the original template concentration. Here we present a novel protocol to efficiently infer the concentration of a sample and its optimal dilution for dPCR from few targeted qPCR assays. By taking advantage of the real-time amplification feature of qPCR as opposed to relying on endpoint PCR assessment as in standard dPCR prior knowledge of template concentration is not necessary. This eliminates the need for serial dilutions in a separate titration and reduces the number of necessary reactions. We describe the theory underlying our approach and discuss experimental moments that contribute to uncertainty. We present data from a controlled experiment where the initial template concentration is known as proof of principle and apply our method on directly monitoring transcript level change during cell differentiation as well as gauging amplicon numbers in cDNA samples after pre-amplification. © The Author(s) 2014.
Del Sol I.C.A.,Luxembourg Center for Systems Biomedicine |
Del Sol I.C.A.,University of Luxembourg
Stem Cells | Year: 2013
Transcription factor cross-repression is an important concept in cellular differentiation. A bistable toggle switch constitutes a molecular mechanism that determines cellular commitment and provides stability to transcriptional programs of binary cell fate choices. Experiments support that perturbations of these toggle switches can interconvert these binary cell fate choices, suggesting potential reprogramming strategies. However, more complex types of cellular transitions could involve perturbations of combinations of different types of multistable motifs. Here, we introduce a method that generalizes the concept of transcription factor cross-repression to systematically predict sets of genes, whose perturbations induce cellular transitions between any given pair of cell types. Furthermore, to our knowledge, this is the first method that systematically makes these predictions without prior knowledge of potential candidate genes and pathways involved, providing guidance on systems where little is known. Given the increasing interest of cellular reprogramming in medicine and basic research, our method represents a useful computational methodology to assist researchers in the field in designing experimental strategies. © AlphaMed Press.
Ostaszewski M.,Luxembourg Center for Systems Biomedicine |
Eifes S.,Luxembourg Center for Systems Biomedicine |
del Sol A.,Luxembourg Center for Systems Biomedicine
PLoS ONE | Year: 2012
The impact of gene silencing on cellular phenotypes is difficult to establish due to the complexity of interactions in the associated biological processes and pathways. A recent genome-wide RNA knock-down study both identified and phenotypically characterized a set of important genes for the cell cycle in HeLa cells. Here, we combine a molecular interaction network analysis, based on physical and functional protein interactions, in conjunction with evolutionary information, to elucidate the common biological and topological properties of these key genes. Our results show that these genes tend to be conserved with their corresponding protein interactions across several species and are key constituents of the evolutionary conserved molecular interaction network. Moreover, a group of bistable network motifs is found to be conserved within this network, which are likely to influence the network stability and therefore the robustness of cellular functioning. They form a cluster, which displays functional homogeneity and is significantly enriched in genes phenotypically relevant for mitosis. Additional results reveal a relationship between specific cellular processes and the phenotypic outcomes induced by gene silencing. This study introduces new ideas regarding the relationship between genotype and phenotype in the context of the cell cycle. We show that the analysis of molecular interaction networks can result in the identification of genes relevant to cellular processes, which is a promising avenue for future research. © 2012 Ostaszewski et al.
Yuan Y.,University of Cambridge |
Glover K.,University of Cambridge |
Goncalves J.,University of Cambridge |
Goncalves J.,Luxembourg Center for Systems Biomedicine
Automatica | Year: 2015
Motivated by the fact that transfer functions do not contain structural information about networks (dependency of state variables), dynamical structure functions were introduced to capture causal relationships between measured nodes in networks. From the dynamical structure functions, (a) we show that the actual number of hidden states can be larger than the number of hidden states estimated from the corresponding transfer function; (b) we can obtain partial information about the true state-space equation, which cannot in general be obtained from the transfer function. Based on these properties, this paper proposes algorithms to find minimal realisations for a given dynamical structure function. This helps to estimate the minimal number of hidden states, to better understand the complexity of the network, and to identify potential targets for new measurements. © 2015 Elsevier Ltd.
Wegner A.,Luxembourg Center for Systems Biomedicine |
Sapcariu S.C.,Luxembourg Center for Systems Biomedicine |
Weindl D.,Luxembourg Center for Systems Biomedicine |
Hiller K.,Luxembourg Center for Systems Biomedicine
Analytical Chemistry | Year: 2013
Gas chromatography coupled to mass spectrometry (GC/MS) has emerged as a powerful tool in metabolomics studies. A major bottleneck in current data analysis of GC/MS-based metabolomics studies is compound matching and identification, as current methods generate high rates of false positive and false -negative identifications. This is especially true for data sets containing a high amount of noise. In this work, a novel spectral similarity measure based on the specific fragmentation patterns of electron impact mass spectra is proposed. An important aspect of these algorithmic methods is the handling of noisy data. The performance of the proposed method compared to the dot product, the current gold standard, was evaluated on a complex biological data set. The analysis results showed significant improvements of the proposed method in compound matching and chromatogram alignment compared to the dot product. © 2013 American Chemical Society.