Measuring specialization in species interaction networks

BACKGROUND: Network analyses of plant-animal interactions hold valuable biological information. They are often used to quantify the degree of specialization between partners, but usually based on qualitative indices such as 'connectance' or number of links. These measures ignore interaction frequencies or sampling intensity, and strongly depend on network size. RESULTS: Here we introduce two quantitative indices using interaction frequencies to describe the degree of specialization, based on information theory. The first measure (d') describes the degree of interaction specialization at the species level, while the second measure (H(2)') characterizes the degree of specialization or partitioning among two parties in the entire network. Both indices are mathematically related and derived from Shannon entropy. The species-level index d' can be used to analyze variation within networks, while H(2)' as a network-level index is useful for comparisons across different interaction webs. Analyses of two published pollinator networks identified differences and features that have not been detected with previous approaches. For instance, plants and pollinators within a network differed in their average degree of specialization (weighted mean d'), and the correlation between specialization of pollinators and their relative abundance also differed between the webs. Rarefied sampling effort in both networks and null model simulations suggest that H(2)' is not affected by network size or sampling intensity. CONCLUSION: Quantitative analyses reflect properties of interaction networks more appropriately than previous qualitative attempts, and are robust against variation in sampling intensity, network size and symmetry. These measures will improve our understanding of patterns of specialization within and across networks from a broad spectrum of biological interactions

Mathematical models in mammalian cell biology

A report on the Conference on Systems Biology of Mammalian Cells, Dresden, Germany, 22-24 May 2008

Mathematical Modeling Identifies Inhibitors of Apoptosis as Mediators of Positive Feedback and Bistability

The intrinsic, or mitochondrial, pathway of caspase activation is essential for apoptosis induction by various stimuli including cytotoxic stress. It depends on the cellular context, whether cytochrome c released from mitochondria induces caspase activation gradually or in an all-or-none fashion, and whether caspase activation irreversibly commits cells to apoptosis. By analyzing a quantitative kinetic model, we show that inhibition of caspase-3 (Casp3) and Casp9 by inhibitors of apoptosis (IAPs) results in an implicit positive feedback, since cleaved Casp3 augments its own activation by sequestering IAPs away from Casp9. We demonstrate that this positive feedback brings about bistability (i.e., all-or-none behaviour), and that it cooperates with Casp3-mediated feedback cleavage of Casp9 to generate irreversibility in caspase activation. Our calculations also unravel how cell-specific protein expression brings about the observed qualitative differences in caspase activation (gradual versus all-or-none and reversible versus irreversible). Finally, known regulators of the pathway are shown to efficiently shift the apoptotic threshold stimulus, suggesting that the bistable caspase cascade computes multiple inputs into an all-or-none caspase output. As cellular inhibitory proteins (e.g., IAPs) frequently inhibit consecutive intermediates in cellular signaling cascades (e.g., Casp3 and Casp9), the feedback mechanism described in this paper is likely to be a widespread principle on how cells achieve ultrasensitivity, bistability, and irreversibility

Discovering Causal Signaling Pathways Through Gene-Expression Patterns

High-throughput gene-expression studies result in lists of differentially expressed genes. Most current meta-analyses of these gene lists include searching for significant membership of the translated proteins in various signaling pathways. However, such membership enrichment algorithms do not provide insight into which pathways caused the genes to be differentially expressed in the first place. Here, we present an intuitive approach for discovering upstream signaling pathways responsible for regulating these differentially expressed genes. We identify consistently regulated signature genes specific for signal transduction pathways from a panel of single-pathway perturbation experiments. An algorithm that detects overrepresentation of these signature genes in a gene group of interest is used to infer the signaling pathway responsible for regulation. We expose our novel resource and algorithm through a web server called SPEED: Signaling Pathway Enrichment using Experimental Data sets. SPEED can be freely accessed at http://speed.sys-bio.net/

Recurrent design patterns in the feedback regulation of the mammalian signalling network

Biochemical networks are characterized by recurrent patterns and motifs, but the design principles underlying the dynamics of the mammalian intracellular signalling network remain unclear. We systematically analysed decay rates of 134 signalling proteins and investigated their gene expression profiles in response to stimulation to get insights into transcriptional feedback regulation. We found a clear separation of the signalling pathways into flexible and static parts: for each pathway a subgroup of unstable signal inhibitors is transcriptionally induced upon stimulation, while the other constitutively expressed signalling proteins are long-lived. Kinetic modelling suggests that this design principle allows for swift feedback regulation and establishes latency phases after signalling, and that it might be an optimal design due to a trade-off between energy efficiency and flexibility

Neuronal activity regulates alternative exon usage

Neuronal activity-regulated gene transcription underlies plasticity-dependent changes in the molecular composition and structure of neurons. A large number of genes regulated by different neuronal plasticity inducing pathways have been identified, but altered gene expression levels represent only part of the complexity of the activity-regulated transcriptional program. Alternative splicing, the differential inclusion and exclusion of exonic sequence in mRNA, is an additional mechanism that is thought to define the activity-dependent transcriptome. Here, we present a genome wide microarray-based survey to identify exons with increased expression levels at 1, 4 or 8 h following neuronal activity in the murine hippocampus provoked by generalized seizures. We used two different bioinformatics approaches to identify alternative activity-induced exon usage and to predict alternative splicing, ANOSVA (ANalysis Of Splicing VAriation) which we here adjusted to accommodate data from different time points and FIRMA (Finding Isoforms using Robust Multichip Analysis). RNA sequencing, in situ hybridization and reverse transcription PCR validate selected activity-dependent splicing events of previously described and so far undescribed activity-regulated transcripts, including Homer1a, Homer1d, Ania3, Errfi1, Inhba, Dclk1, Rcan1, Cda, Tpm1 and Krt75. Taken together, our survey significantly adds to the comprehensive understanding of the complex activity-dependent neuronal transcriptomic signature. In addition, we provide data sets that will serve as rich resources for future comparative expression analyses.Projekt DEALPeer Reviewe

Systems level expression correlation of Ras GTPase regulators

Background: Proteins of the ubiquitously expressed core proteome are quantitatively correlated across multiple eukaryotic species. In addition, it was found that many protein paralogues exhibit expression anticorrelation, suggesting that the total level of protein with a given functionality must be kept constant. Methods: We performed Spearman’s rank correlation analyses of gene expression levels for the RAS GTPase subfamily and their regulatory GEF and GAP proteins across tissues and across individuals for each tissue. A large set of published data for normal tissues from a wide range of species, human cancer tissues and human cell lines was analysed. Results: We show that although the multidomain regulatory proteins of Ras GTPases exhibit considerable tissue and individual gene expression variability, their total amounts are balanced in normal tissues. In a given tissue, the sum of activating (GEFs) and deactivating (GAPs) domains of Ras GTPases can vary considerably, but each person has balanced GEF and GAP levels. This balance is impaired in cell lines and in cancer tissues for some individuals. Conclusions: Our results are relevant for critical considerations of knock out experiments, where functionally related homologs may compensate for the down regulation of a protein

Kinetic modeling of stem cell transcriptome dynamics to identify regulatory modules of normal and disturbed neuroectodermal differentiation

Thousands of transcriptome data sets are available, but approaches for their use in dynamic cell response modelling are few, especially for processes affected simultaneously by two orthogonal influencing variables. We approached this problem for neuroepithelial development of human pluripotent stem cells (differentiation variable), in the presence or absence of valproic acid (signaling variable). Using few basic assumptions (sequential differentiation states of cells; discrete on/off states for individual genes in these states), and time-resolved transcriptome data, a comprehensive model of spontaneous and perturbed gene expression dynamics was developed. The model made reliable predictions (average correlation of 0.85 between predicted and subsequently tested expression values). Even regulations predicted to be non-monotonic were successfully validated by PCR in new sets of experiments. Transient patterns of gene regulation were identified from model predictions. They pointed towards activation of Wnt signaling as a candidate pathway leading to a redirection of differentiation away from neuroepithelial cells towards neural crest. Intervention experiments, using a Wnt/beta-catenin antagonist, led to a phenotypic rescue of this disturbed differentiation. Thus, our broadly applicable model allows the analysis of transcriptome changes in complex time/perturbation matrices