In JoVE (1)

Other Publications (80)

Articles by Jesper Tegnér in JoVE

Other articles by Jesper Tegnér on PubMed

Reverse Engineering Gene Networks Using Singular Value Decomposition and Robust Regression

Proceedings of the National Academy of Sciences of the United States of America. Apr, 2002  |  Pubmed ID: 11983907

We propose a scheme to reverse-engineer gene networks on a genome-wide scale using a relatively small amount of gene expression data from microarray experiments. Our method is based on the empirical observation that such networks are typically large and sparse. It uses singular value decomposition to construct a family of candidate solutions and then uses robust regression to identify the solution with the smallest number of connections as the most likely solution. Our algorithm has O(log N) sampling complexity and O(N(4)) computational complexity. We test and validate our approach in a series of in numero experiments on model gene networks.

Spike-timing-dependent Plasticity: Common Themes and Divergent Vistas

Biological Cybernetics. Dec, 2002  |  Pubmed ID: 12461634

Recent experimental observations of spike-timing-dependent synaptic plasticity (STDP) have revitalized the study of synaptic learning rules. The most surprising aspect of these experiments lies in the observation that synapses activated shortly after the occurrence of a postsynaptic spike are weakened. Thus, synaptic plasticity is sensitive to the temporal ordering of pre- and postsynaptic activation. This temporal asymmetry has been suggested to underlie a range of learning tasks. In the first part of this review we highlight some of the common themes from a range of findings in the framework of predictive coding. As an example of how this principle can be used in a learning task, we discuss a recent model of cortical map formation. In the second part of the review, we point out some of the differences in STDP models and their functional consequences. We discuss how differences in the weight-dependence, the time-constants and the non-linear properties of learning rules give rise to distinct computational functions. In light of these computational issues raised, we review current experimental findings and suggest further experiments to resolve some controversies.

The Dynamical Stability of Reverberatory Neural Circuits

Biological Cybernetics. Dec, 2002  |  Pubmed ID: 12461636

The concept of reverberation proposed by Lorente de Nó and Hebb is key to understanding strongly recurrent cortical networks. In particular, synaptic reverberation is now viewed as a likely mechanism for the active maintenance of working memory in the prefrontal cortex. Theoretically, this has spurred a debate as to how such a potentially explosive mechanism can provide stable working-memory function given the synaptic and cellular mechanisms at play in the cerebral cortex. We present here new evidence for the participation of NMDA receptors in the stabilization of persistent delay activity in a biophysical network model of conductance-based neurons. We show that the stability of working-memory function, and the required NMDA/AMPA ratio at recurrent excitatory synapses, depend on physiological properties of neurons and synaptic interactions, such as the time constants of excitation and inhibition, mutual inhibition between interneurons, differential NMDA receptor participation at excitatory projections to pyramidal neurons and interneurons, or the presence of slow intrinsic ion currents in pyramidal neurons. We review other mechanisms proposed to enhance the dynamical stability of synaptically generated attractor states of a reverberatory circuit. This recent work represents a necessary and significant step towards testing attractor network models by cortical electrophysiology.

Reverse Engineering Gene Networks: Integrating Genetic Perturbations with Dynamical Modeling

Proceedings of the National Academy of Sciences of the United States of America. May, 2003  |  Pubmed ID: 12730377

While the fundamental building blocks of biology are being tabulated by the various genome projects, microarray technology is setting the stage for the task of deducing the connectivity of large-scale gene networks. We show how the perturbation of carefully chosen genes in a microarray experiment can be used in conjunction with a reverse engineering algorithm to reveal the architecture of an underlying gene regulatory network. Our iterative scheme identifies the network topology by analyzing the steady-state changes in gene expression resulting from the systematic perturbation of a particular node in the network. We highlight the validity of our reverse engineering approach through the successful deduction of the topology of a linear in numero gene network and a recently reported model for the segmentation polarity network in Drosophila melanogaster. Our method may prove useful in identifying and validating specific drug targets and in deconvolving the effects of chemical compounds.

Temporally Irregular Mnemonic Persistent Activity in Prefrontal Neurons of Monkeys During a Delayed Response Task

Journal of Neurophysiology. Nov, 2003  |  Pubmed ID: 12773500

An important question in neuroscience is whether and how temporal patterns and fluctuations in neuronal spike trains contribute to information processing in the cortex. We have addressed this issue in the memory-related circuits of the prefrontal cortex by analyzing spike trains from a database of 229 neurons recorded in the dorsolateral prefrontal cortex of 4 macaque monkeys during the performance of an oculomotor delayed-response task. For each task epoch, we have estimated their power spectrum together with interspike interval histograms and autocorrelograms. We find that 1). the properties of most (about 60%) neurons approximated the characteristics of a Poisson process. For about 25% of cells, with characteristics typical of interneurons, the power spectrum showed a trough at low frequencies (<20 Hz) and the autocorrelogram a dip near zero time lag. About 15% of neurons had a peak at <20 Hz in the power spectrum, associated with the burstiness of the spike train; 2). a small but significant task dependency of spike-train temporal structure: delay responses to preferred locations were characterized not only by elevated firing, but also by suppressed power at low (<20 Hz) frequencies; and 3). the variability of interspike intervals is typically higher during the mnemonic delay period than during the fixation period, regardless of the remembered cue. The high irregularity of neural persistent activity during the delay period is likely to be a characteristic signature of recurrent prefrontal network dynamics underlying working memory.

Systems Biology is Taking Off

Genome Research. Nov, 2003  |  Pubmed ID: 14597651

Detection of Compound Mode of Action by Computational Integration of Whole-genome Measurements and Genetic Perturbations

BMC Bioinformatics. Feb, 2006  |  Pubmed ID: 16451737

A key problem of drug development is to decide which compounds to evaluate further in expensive clinical trials (Phase I- III). This decision is primarily based on the primary targets and mechanisms of action of the chemical compounds under consideration. Whole-genome expression measurements have shown to be useful for this process but current approaches suffer from requiring either a large number of mutant experiments or a detailed understanding of the regulatory networks.

Transcriptional Network Dynamics in Macrophage Activation

Genomics. Aug, 2006  |  Pubmed ID: 16698233

Transcriptional regulatory networks govern cell differentiation and the cellular response to external stimuli. However, mammalian model systems have not yet been accessible for network analysis. Here, we present a genome-wide network analysis of the transcriptional regulation underlying the mouse macrophage response to bacterial lipopolysaccharide (LPS). Key to uncovering the network structure is our combination of time-series cap analysis of gene expression with in silico prediction of transcription factor binding sites. By integrating microarray and qPCR time-series expression data with a promoter analysis, we find dynamic subnetworks that describe how signaling pathways change dynamically during the progress of the macrophage LPS response, thus defining regulatory modules characteristic of the inflammatory response. In particular, our integrative analysis enabled us to suggest novel roles for the transcription factors ATF-3 and NRF-2 during the inflammatory response. We believe that our system approach presented here is applicable to understanding cellular differentiation in higher eukaryotes.

Systems Biology of Innate Immunity

Cellular Immunology. Dec, 2006  |  Pubmed ID: 17433274

Systems Biology has emerged as an exciting research approach in molecular biology and functional genomics that involves a systematic use of genomic, proteomic, and metabolomic technologies for the construction of network-based models of biological processes. These endeavors, collectively referred to as systems biology establish a paradigm by which to systematically interrogate, model, and iteratively refine our knowledge of the regulatory events within a cell. Here, we present a new systems approach, integrating DNA and transcript expression information, specifically designed to identify transcriptional networks governing the macrophage immune response to lipopolysaccharide (LPS). Using this approach, we are not only able to infer a global macrophage transcriptional network, but also time-specific sub-networks that are dynamically active across the LPS response. We believe that our system biological approach could be useful for identifying other complex networks mediating immunological responses.

Brain Activity Related to Working Memory and Distraction in Children and Adults

Cerebral Cortex (New York, N.Y. : 1991). May, 2007  |  Pubmed ID: 16801377

In order to retain information in working memory (WM) during a delay, distracting stimuli must be ignored. This important ability improves during childhood, but the neural basis for this development is not known. We measured brain activity with functional magnetic resonance imaging in adults and 13-year-old children. Data were analyzed with an event-related design to isolate activity during cue, delay, distraction, and response selection. Adults were more accurate and less distractible than children. Activity in the middle frontal gyrus and intraparietal cortex was stronger in adults than in children during the delay, when information was maintained in WM. Distraction during the delay evoked activation in parietal and occipital cortices in both adults and children. However, distraction activated frontal cortex only in children. The larger frontal activation in response to distracters presented during the delay may explain why children are more susceptible to interfering stimuli.

Perturbations to Uncover Gene Networks

Trends in Genetics : TIG. Jan, 2007  |  Pubmed ID: 17098324

After the major achievements of the DNA sequencing projects, an equally important challenge now is to uncover the functional relationships among genes (i.e. gene networks). It has become increasingly clear that computational algorithms are crucial for extracting meaningful information from the massive amount of data generated by high-throughput genome-wide technologies. Here, we summarise how systems identification algorithms, originating from physics and control theory, have been adapted for use in biology. We also explain how experimental perturbations combined with genome-wide measurements are being used to uncover gene networks. Perturbation techniques could pave the way for identifying gene networks in more complex settings such as multifactorial diseases and for improving the efficacy of drug evaluation.

Thematic Review Series: Systems Biology Approaches to Metabolic and Cardiovascular Disorders. Multi-organ Whole-genome Measurements and Reverse Engineering to Uncover Gene Networks Underlying Complex Traits

Journal of Lipid Research. Feb, 2007  |  Pubmed ID: 17142807

Together with computational analysis and modeling, the development of whole-genome measurement technologies holds the potential to fundamentally change research on complex disorders such as coronary artery disease. With these tools, the stage has been set to reveal the full repertoire of biological components (genes, proteins, and metabolites) in complex diseases and their interplay in modules and networks. Here we review how network identification based on reverse engineering, as applied to whole-genome datasets from simpler organisms, is now being adapted to more complex settings such as datasets from human cell lines and organs in relation to physiological and pathological states. Our focus is on the use of a systems biological approach to identify gene networks in coronary atherosclerosis. We also address how gene networks will probably play a key role in the development of early diagnostics and treatments for complex disorders in the coming era of individualized medicine.

Neuronal Firing Rates Account for Distractor Effects on Mnemonic Accuracy in a Visuo-spatial Working Memory Task

Biological Cybernetics. Apr, 2007  |  Pubmed ID: 17260154

Persistent neural activity constitutes one neuronal correlate of working memory, the ability to hold and manipulate information across time, a prerequisite for cognition. Yet, the underlying neuronal mechanisms are still elusive. Here, we design a visuo- spatial delayed-response task to identify the relationship between the cue-distractor spatial distance and mnemonic accuracy. Using a shared experimental and computational test protocol, we probe human subjects in computer experiments, and subsequently we evaluate different neural mechanisms underlying persistent activity using an in silico prefrontal network model. Five modes of action of the network were tested: weak or strong synaptic interactions, wide synaptic arborization, cellular bistability and reduced synaptic NMDA component. The five neural mechanisms and the human behavioral data, all exhibited a significant deterioration of the mnemonic accuracy with decreased spatial distance between the distractor and the cue. A subsequent computational analysis revealed that the firing rate and not the neural mechanism per se, accounted for the positive correlation between mnemonic accuracy and spatial distance. Moreover, the computational modeling predicts an inverse correlation between accuracy and distractibility. In conclusion, any pharmacological modulation, pathological condition or memory training paradigm targeting the underlying neural circuitry and altering the net population firing rate during the delay is predicted to determine the amount of influence of a visual distraction.

Stronger Synaptic Connectivity As a Mechanism Behind Development of Working Memory-related Brain Activity During Childhood

Journal of Cognitive Neuroscience. May, 2007  |  Pubmed ID: 17488202

The cellular maturational processes behind cognitive development during childhood, including the development of working memory capacity, are still unknown. By using the most standard computational model of visuospatial working memory, we investigated the consequences of cellular maturational processes, including myelination, synaptic strengthening, and synaptic pruning, on working memory-related brain activity and performance. We implemented five structural developmental changes occurring as a result of the cellular maturational processes in the biophysically based computational network model. The developmental changes in memory activity predicted from the simulations of the model were then compared to brain activity measured with functional magnetic resonance imaging in children and adults. We found that networks with stronger fronto-parietal synaptic connectivity between cells coding for similar stimuli, but not those with faster conduction, stronger connectivity within a region, or increased coding specificity, predict measured developmental increases in both working memory-related brain activity and in correlations of activity between regions. Stronger fronto-parietal synaptic connectivity between cells coding for similar stimuli was thus the only developmental process that accounted for the observed changes in brain activity associated with development of working memory during childhood.

Detecting Multivariate Differentially Expressed Genes

BMC Bioinformatics. May, 2007  |  Pubmed ID: 17490475

Gene expression is governed by complex networks, and differences in expression patterns between distinct biological conditions may therefore be complex and multivariate in nature. Yet, current statistical methods for detecting differential expression merely consider the univariate difference in expression level of each gene in isolation, thus potentially neglecting many genes of biological importance.

Human C-reactive Protein Slows Atherosclerosis Development in a Mouse Model with Human-like Hypercholesterolemia

Proceedings of the National Academy of Sciences of the United States of America. Aug, 2007  |  Pubmed ID: 17702862

Increased baseline values of the acute-phase reactant C-reactive protein (CRP) are significantly associated with future cardiovascular disease, and some in vitro studies have claimed that human CRP (hCRP) has proatherogenic effects. in vivo studies in apolipoprotein E-deficient mouse models, however, have given conflicting results. We bred atherosclerosis-prone mice (Apob(100/100)Ldlr(-/-)), which have human-like hypercholesterolemia, with hCRP transgenic mice (hCRP(+/0)) and studied lesion development at 15, 30, 40, and 50 weeks of age. Atherosclerotic lesions were smaller in hCRP(+/0)Apob(100/100)Ldlr(-/-) mice than in hCRP(0/0)Apob(100/100)Ldlr(-/-) controls, as judged from the lesion surface areas of pinned-out aortas from mice at 40 and 50 weeks of age. In lesions from 40-week-old mice, mRNA expression levels of several genes in the proteasome degradation pathway were higher in hCRP(+/0)Apob(100/100)Ldlr(-/-) mice than in littermate controls, as shown by global gene expression profiles. These results were confirmed by real-time PCR, which also indicated that the activities of those genes were the same at 30 and 40 weeks in hCRP(+/0)Apob(100/100)Ldlr(-/-) mice but were significantly lower at 40 weeks than at 30 weeks in controls. Our results show that hCRP is not proatherogenic but instead slows atherogenesis, possibly through proteasome-mediated protein degradation.

[Systems Biology Makes Detailed Understanding of Complex Diseases Possible. Arteriosclerosis is an Example]

Lakartidningen. Oct 17-23, 2007  |  Pubmed ID: 17985711

Fronto-parietal Connection Asymmetry Regulates Working Memory Distractibility

Journal of Integrative Neuroscience. Dec, 2007  |  Pubmed ID: 18181269

Recent functional magnetic resonance imaging studies demonstrate that increased task-related neural activity in parietal and frontal cortex during development and training is positively correlated with improved visuospatial working memory (vsWM) performance. Yet, the analysis of the corresponding underlying functional reorganization of the fronto-parietal network has received little attention. Here, we perform an integrative experimental and computational analysis to determine the effective balance between the superior frontal sulcus (SFS) and intraparietal sulcus (IPS) and their putative role(s) in protecting against distracters. To this end, we performed electroencephalographic (EEG) recordings during a vsWM task. We utilized a biophysically based computational cortical network model to analyze the effects of different neural changes in the underlying cortical networks on the directed transfer function (DTF) and spiking activity. Combining a DTF analysis of our EEG data with the DTF analysis of the computational model, a directed strong SFS --> IPS network was revealed. Such a configuration offers protection against distracters, whereas the opposite is true for strong IPS --> SFS connections. Our results therefore suggest that the previously demonstrated improvement of vsWM performance during development could be due to a shift in the control of the effective balance between the SFS-IPS networks.

Integrated Approaches to Uncovering Transcription Regulatory Networks in Mammalian Cells

Genomics. Mar, 2008  |  Pubmed ID: 18191937

Integrative systems biology has emerged as an exciting research approach in molecular biology and functional genomics that involves the integration of genomics, proteomics, and metabolomics datasets. These endeavors establish a systematic paradigm by which to interrogate, model, and iteratively refine our knowledge of the regulatory events within a cell. Here we review the latest technologies available to collect high-throughput measurements of a cellular state as well as the most successful methods for the integration and interrogation of these measurements. In particular we will focus on methods available to infer transcription regulatory networks in mammals.

Electrotonic Signals Along Intracellular Membranes May Interconnect Dendritic Spines and Nucleus

PLoS Computational Biology. Mar, 2008  |  Pubmed ID: 18369427

Synapses on dendritic spines of pyramidal neurons show a remarkable ability to induce phosphorylation of transcription factors at the nuclear level with a short latency, incompatible with a diffusion process from the dendritic spines to the nucleus. To account for these findings, we formulated a novel extension of the classical cable theory by considering the fact that the endoplasmic reticulum (ER) is an effective charge separator, forming an intrinsic compartment that extends from the spine to the nuclear membrane. We use realistic parameters to show that an electrotonic signal may be transmitted along the ER from the dendritic spines to the nucleus. We found that this type of signal transduction can additionally account for the remarkable ability of the cell nucleus to differentiate between depolarizing synaptic signals that originate from the dendritic spines and back-propagating action potentials. This study considers a novel computational role for dendritic spines, and sheds new light on how spines and ER may jointly create an additional level of processing within the single neuron.

Transcriptional Profiling Uncovers a Network of Cholesterol-responsive Atherosclerosis Target Genes

PLoS Genetics. Mar, 2008  |  Pubmed ID: 18369455

Despite the well-documented effects of plasma lipid lowering regimes halting atherosclerosis lesion development and reducing morbidity and mortality of coronary artery disease and stroke, the transcriptional response in the atherosclerotic lesion mediating these beneficial effects has not yet been carefully investigated. We performed transcriptional profiling at 10-week intervals in atherosclerosis-prone mice with human-like hypercholesterolemia and a genetic switch to lower plasma lipoproteins (Ldlr(-/-)Apo(100/100)Mttp(flox/flox) Mx1-Cre). Atherosclerotic lesions progressed slowly at first, then expanded rapidly, and plateaued after advanced lesions formed. Analysis of lesion expression profiles indicated that accumulation of lipid-poor macrophages reached a point that led to the rapid expansion phase with accelerated foam-cell formation and inflammation, an interpretation supported by lesion histology. Genetic lowering of plasma cholesterol (e.g., lipoproteins) at this point all together prevented the formation of advanced plaques and parallel transcriptional profiling of the atherosclerotic arterial wall identified 37 cholesterol-responsive genes mediating this effect. Validation by siRNA-inhibition in macrophages incubated with acetylated-LDL revealed a network of eight cholesterol-responsive atherosclerosis genes regulating cholesterol-ester accumulation. Taken together, we have identified a network of atherosclerosis genes that in response to plasma cholesterol-lowering prevents the formation of advanced plaques. This network should be of interest for the development of novel atherosclerosis therapies.

ApoB100-LDL Acts As a Metabolic Signal from Liver to Peripheral Fat Causing Inhibition of Lipolysis in Adipocytes

PloS One. 2008  |  Pubmed ID: 19020660

Free fatty acids released from adipose tissue affect the synthesis of apolipoprotein B-containing lipoproteins and glucose metabolism in the liver. Whether there also exists a reciprocal metabolic arm affecting energy metabolism in white adipose tissue is unknown.

Evidence of Highly Regulated Genes (in-Hubs) in Gene Networks of Saccharomyces Cerevisiae

Bioinformatics and Biology Insights. Jul, 2008  |  Pubmed ID: 19812784

Uncovering interactions between genes, gene networks, is important to increase our understanding of intrinsic cellular processes and responses to external stimuli such as drugs. Gene networks can be computationally inferred from repeated measurements of gene expression, using algorithms, which assume that each gene is controlled by only a small number of other proteins. Here, by extending the transcription network with cofactors (defined from protein-protein binding data) as active regulators, we identified the effective gene network, providing evidence of in-hubs in the gene regulatory networks of yeast. Then, using the notion that in-hub genes will be differentially expressed over several experimental conditions, we designed an algorithm, the HubDetector, enabling identification of in-hubs directly from gene expression data. Applying the HubDetector to 488 genome-wide expression profiles from two independent datasets, we identified putative in-hubs overlapping significantly with in-hubs in the effective gene network.

On Reliable Discovery of Molecular Signatures

BMC Bioinformatics. Jan, 2009  |  Pubmed ID: 19178740

Molecular signatures are sets of genes, proteins, genetic variants or other variables that can be used as markers for a particular phenotype. Reliable signature discovery methods could yield valuable insight into cell biology and mechanisms of human disease. However, it is currently not clear how to control error rates such as the false discovery rate (FDR) in signature discovery. Moreover, signatures for cancer gene expression have been shown to be unstable, that is, difficult to replicate in independent studies, casting doubts on their reliability.

Mechanism for Top-down Control of Working Memory Capacity

Proceedings of the National Academy of Sciences of the United States of America. Apr, 2009  |  Pubmed ID: 19339493

Working memory capacity, the maximum number of items that we can transiently store in working memory, is a good predictor of our general cognitive abilities. Neural activity in both dorsolateral prefrontal cortex and posterior parietal cortex has been associated with memory retention during visuospatial working memory tasks. The parietal cortex is thought to store the memories. However, the role of the dorsolateral prefrontal cortex, a top-down control area, during pure information retention is debated, and the mechanisms regulating capacity are unknown. Here, we propose that a major role of the dorsolateral prefrontal cortex in working memory is to boost parietal memory capacity. Furthermore, we formulate the boosting mechanism computationally in a biophysical cortical microcircuit model and derive a simple, explicit mathematical formula relating memory capacity to prefrontal and parietal model parameters. For physiologically realistic parameter values, lateral inhibition in the parietal cortex limits mnemonic capacity to a maximum of 2-7 items. However, at high loads inhibition can be counteracted by excitatory prefrontal input, thus boosting parietal capacity. Predictions from the model were confirmed in an fMRI study. Our results show that although memories are stored in the parietal cortex, interindividual differences in memory capacity are partly determined by the strength of prefrontal top-down control. The model provides a mechanistic framework for understanding top-down control of working memory and specifies two different contributions of prefrontal and parietal cortex to working memory capacity.

Reverse Engineering of Gene Networks with LASSO and Nonlinear Basis Functions

Annals of the New York Academy of Sciences. Mar, 2009  |  Pubmed ID: 19348648

The quest to determine cause from effect is often referred to as reverse engineering in the context of cellular networks. Here we propose and evaluate an algorithm for reverse engineering a gene regulatory network from time-series and steady-state data. Our algorithmic pipeline, which is rather standard in its parts but not in its integrative composition, combines ordinary differential equations, parameter estimations by least angle regression, and cross-validation procedures for determining the in-degrees and selection of nonlinear transfer functions. The result of the algorithm is a complete directed network, in which each edge has been assigned a score from a bootstrap procedure. To evaluate the performance, we submitted the outcome of the algorithm to the reverse engineering assessment competition DREAM2, where we used the data corresponding to the InSilico1 and InSilico2 networks as input. Our algorithm outperformed all other algorithms when inferring one of the directed gene-to-gene networks.

The Transcriptional Network That Controls Growth Arrest and Differentiation in a Human Myeloid Leukemia Cell Line

Nature Genetics. May, 2009  |  Pubmed ID: 19377474

Using deep sequencing (deepCAGE), the FANTOM4 study measured the genome-wide dynamics of transcription-start-site usage in the human monocytic cell line THP-1 throughout a time course of growth arrest and differentiation. Modeling the expression dynamics in terms of predicted cis-regulatory sites, we identified the key transcription regulators, their time-dependent activities and target genes. Systematic siRNA knockdown of 52 transcription factors confirmed the roles of individual factors in the regulatory network. Our results indicate that cellular states are constrained by complex networks involving both positive and negative regulatory interactions among substantial numbers of transcription factors and that no single transcription factor is both necessary and sufficient to drive the differentiation process.

Computational Disease Modeling - Fact or Fiction?

BMC Systems Biology. Jun, 2009  |  Pubmed ID: 19497118

Biomedical research is changing due to the rapid accumulation of experimental data at an unprecedented scale, revealing increasing degrees of complexity of biological processes. Life Sciences are facing a transition from a descriptive to a mechanistic approach that reveals principles of cells, cellular networks, organs, and their interactions across several spatial and temporal scales. There are two conceptual traditions in biological computational-modeling. The bottom-up approach emphasizes complex intracellular molecular models and is well represented within the systems biology community. On the other hand, the physics-inspired top-down modeling strategy identifies and selects features of (presumably) essential relevance to the phenomena of interest and combines available data in models of modest complexity.

Can Modular Analysis Identify Disease-associated Candidate Genes for Therapeutics?

Journal of Biology. 2009  |  Pubmed ID: 19519937

Complex diseases such as allergy change gene expression in several cell types and tissues. Benson and colleagues have now shown, in a paper in BMC Systems Biology, that this complexity can be studied effectively using an integrated experimental and computational modular analysis. Their strategy revealed a core of allergy-associated genes of potential therapeutic value.

Bridging the Gap Between Systems Biology and Medicine

Genome Medicine. Sep, 2009  |  Pubmed ID: 19754960

Systems biology has matured considerably as a discipline over the last decade, yet some of the key challenges separating current research efforts in systems biology and clinically useful results are only now becoming apparent. As these gaps are better defined, the new discipline of systems medicine is emerging as a translational extension of systems biology. How is systems medicine defined? What are relevant ontologies for systems medicine? What are the key theoretic and methodologic challenges facing computational disease modeling? How are inaccurate and incomplete data, and uncertain biologic knowledge best synthesized in useful computational models? Does network analysis provide clinically useful insight? We discuss the outstanding difficulties in translating a rapidly growing body of data into knowledge usable at the bedside. Although core-specific challenges are best met by specialized groups, it appears fundamental that such efforts should be guided by a roadmap for systems medicine drafted by a coalition of scientists from the clinical, experimental, computational, and theoretic domains.

Multi-organ Expression Profiling Uncovers a Gene Module in Coronary Artery Disease Involving Transendothelial Migration of Leukocytes and LIM Domain Binding 2: the Stockholm Atherosclerosis Gene Expression (STAGE) Study

PLoS Genetics. Dec, 2009  |  Pubmed ID: 19997623

Environmental exposures filtered through the genetic make-up of each individual alter the transcriptional repertoire in organs central to metabolic homeostasis, thereby affecting arterial lipid accumulation, inflammation, and the development of coronary artery disease (CAD). The primary aim of the Stockholm Atherosclerosis Gene Expression (STAGE) study was to determine whether there are functionally associated genes (rather than individual genes) important for CAD development. To this end, two-way clustering was used on 278 transcriptional profiles of liver, skeletal muscle, and visceral fat (n = 66/tissue) and atherosclerotic and unaffected arterial wall (n = 40/tissue) isolated from CAD patients during coronary artery bypass surgery. The first step, across all mRNA signals (n = 15,042/12,621 RefSeqs/genes) in each tissue, resulted in a total of 60 tissue clusters (n = 3958 genes). In the second step (performed within tissue clusters), one atherosclerotic lesion (n = 49/48) and one visceral fat (n = 59) cluster segregated the patients into two groups that differed in the extent of coronary stenosis (P = 0.008 and P = 0.00015). The associations of these clusters with coronary atherosclerosis were validated by analyzing carotid atherosclerosis expression profiles. Remarkably, in one cluster (n = 55/54) relating to carotid stenosis (P = 0.04), 27 genes in the two clusters relating to coronary stenosis were confirmed (n = 16/17, P<10(-27 and-30)). Genes in the transendothelial migration of leukocytes (TEML) pathway were overrepresented in all three clusters, referred to as the atherosclerosis module (A-module). In a second validation step, using three independent cohorts, the A-module was found to be genetically enriched with CAD risk by 1.8-fold (P<0.004). The transcription co-factor LIM domain binding 2 (LDB2) was identified as a potential high-hierarchy regulator of the A-module, a notion supported by subnetwork analysis, by cellular and lesion expression of LDB2, and by the expression of 13 TEML genes in Ldb2-deficient arterial wall. Thus, the A-module appears to be important for atherosclerosis development and, together with LDB2, merits further attention in CAD research.

DGAT1 Participates in the Effect of HNF4A on Hepatic Secretion of Triglyceride-rich Lipoproteins

Arteriosclerosis, Thrombosis, and Vascular Biology. May, 2010  |  Pubmed ID: 20167659

Hepatocyte nuclear factor-4alpha (HNF4A) is a transcription factor that influences plasma triglyceride metabolism via an as of yet unknown mechanism. In this study, we searched for the critical protein that mediates this effect using different human model systems.

An Atlas of Combinatorial Transcriptional Regulation in Mouse and Man

Cell. Mar, 2010  |  Pubmed ID: 20211142

Combinatorial interactions among transcription factors are critical to directing tissue-specific gene expression. To build a global atlas of these combinations, we have screened for physical interactions among the majority of human and mouse DNA-binding transcription factors (TFs). The complete networks contain 762 human and 877 mouse interactions. Analysis of the networks reveals that highly connected TFs are broadly expressed across tissues, and that roughly half of the measured interactions are conserved between mouse and human. The data highlight the importance of TF combinations for determining cell fate, and they lead to the identification of a SMAD3/FLI1 complex expressed during development of immunity. The availability of large TF combinatorial networks in both human and mouse will provide many opportunities to study gene regulation, tissue differentiation, and mammalian evolution.

A Vision and Strategy for the Virtual Physiological Human in 2010 and Beyond

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences. Jun, 2010  |  Pubmed ID: 20439264

European funding under framework 7 (FP7) for the virtual physiological human (VPH) project has been in place now for nearly 2 years. The VPH network of excellence (NoE) is helping in the development of common standards, open-source software, freely accessible data and model repositories, and various training and dissemination activities for the project. It is also helping to coordinate the many clinically targeted projects that have been funded under the FP7 calls. An initial vision for the VPH was defined by framework 6 strategy for a European physiome (STEP) project in 2006. It is now time to assess the accomplishments of the last 2 years and update the STEP vision for the VPH. We consider the biomedical science, healthcare and information and communications technology challenges facing the project and we propose the VPH Institute as a means of sustaining the vision of VPH beyond the time frame of the NoE.

Blood Levels of Dual-specificity Phosphatase-1 Independently Predict Risk for Post-operative Morbidities Causing Prolonged Hospitalization After Coronary Artery Bypass Grafting

International Journal of Molecular Medicine. Jun, 2011  |  Pubmed ID: 21424112

New technologies to generate high-dimensional data provide unprecedented opportunities for unbiased identification of biomarkers that can be used to optimize pre-operative planning, with the goal of avoiding costly post-operative complications and prolonged hospitalization. To identify such markers, we studied the global gene expression profiles of three organs central to the metabolic and inflammatory homeostasis isolated from coronary artery disease (CAD) patients during coronary artery bypass grafting (CABG) surgery. A total of 198 whole-genome expression profiles of liver, skeletal muscle and visceral fat from 66 CAD patients of the Stockholm Atherosclerosis Gene Expression (STAGE) cohort were analyzed. Of ~50,000 mRNAs measured in each patient, the mRNA levels of the anti-inflammatory gene, dual-specificity phosphatase-1 (DUSP1) correlated independently with post-operative stay, discriminating patients with normal (≤8 days) from those with prolonged (>8 days) hospitalization (p<0.004). To validate DUSP1 as a marker of risk for post-operative complications, we prospectively analyzed 181 patients undergoing CABG at Tartu University Hospital for DUSP1 protein levels in pre-operative blood samples. The pre-operative plasma levels of DUSP1 clearly discriminated patients with normal from those with prolonged hospitalization (p=2x10-13; odds ratio = 5.1, p<0.0001; receiver operating characteristic area under the curve = 0.80). Taken together, these results indicate that blood levels of the anti-inflammatory protein DUSP1 can be used as a biomarker for post-operative complications leading to prolonged hospitalization after CABG and therefore merit further testing in longitudinal studies of patients eligible for CABG.

Carotid Plaque Age is a Feature of Plaque Stability Inversely Related to Levels of Plasma Insulin

PloS One. 2011  |  Pubmed ID: 21490968

The stability of atherosclerotic plaques determines the risk for rupture, which may lead to thrombus formation and potentially severe clinical complications such as myocardial infarction and stroke. Although the rate of plaque formation may be important for plaque stability, this process is not well understood. We took advantage of the atmospheric (14)C-declination curve (a result of the atomic bomb tests in the 1950s and 1960s) to determine the average biological age of carotid plaques.

ParkDB: a Parkinson's Disease Gene Expression Database

Database : the Journal of Biological Databases and Curation. 2011  |  Pubmed ID: 21593080

Parkinson's disease (PD) is a common, adult-onset, neuro-degenerative disorder characterized by the degeneration of cardinal motor signs mainly due to the loss of dopaminergic neurons in the substantia nigra. To date, researchers still have limited understanding of the key molecular events that provoke neurodegeneration in this disease. Here, we present ParkDB, the first queryable database dedicated to gene expression in PD. ParkDB contains a complete set of re-analyzed, curated and annotated microarray datasets. This resource enables scientists to identify and compare expression signatures involved in PD and dopaminergic neuron differentiation under different biological conditions and across species. Database URL:

Systems Medicine and Integrated Care to Combat Chronic Noncommunicable Diseases

Genome Medicine. 2011  |  Pubmed ID: 21745417

We propose an innovative, integrated, cost-effective health system to combat major non-communicable diseases (NCDs), including cardiovascular, chronic respiratory, metabolic, rheumatologic and neurologic disorders and cancers, which together are the predominant health problem of the 21st century. This proposed holistic strategy involves comprehensive patient-centered integrated care and multi-scale, multi-modal and multi-level systems approaches to tackle NCDs as a common group of diseases. Rather than studying each disease individually, it will take into account their intertwined gene-environment, socio-economic interactions and co-morbidities that lead to individual-specific complex phenotypes. It will implement a road map for predictive, preventive, personalized and participatory (P4) medicine based on a robust and extensive knowledge management infrastructure that contains individual patient information. It will be supported by strategic partnerships involving all stakeholders, including general practitioners associated with patient-centered care. This systems medicine strategy, which will take a holistic approach to disease, is designed to allow the results to be used globally, taking into account the needs and specificities of local economies and health systems.

Decoding Complex Biological Networks - Tracing Essential and Modulatory Parameters in Complex and Simplified Models of the Cell Cycle

BMC Systems Biology. Aug, 2011  |  Pubmed ID: 21819620

One of the most well described cellular processes is the cell cycle, governing cell division. Mathematical models of this gene-protein network are therefore a good test case for assessing to what extent we can dissect the relationship between model parameters and system dynamics. Here we combine two strategies to enable an exploration of parameter space in relation to model output. A simplified, piecewise linear approximation of the original model is combined with a sensitivity analysis of the same system, to obtain and validate analytical expressions describing the dynamical role of different model parameters.

Workflow for Generating Competing Hypothesis from Models with Parameter Uncertainty

Interface Focus. Jun, 2011  |  Pubmed ID: 22670212

Mathematical models are increasingly used in life sciences. However, contrary to other disciplines, biological models are typically over-parametrized and loosely constrained by scarce experimental data and prior knowledge. Recent efforts on analysis of complex models have focused on isolated aspects without considering an integrated approach-ranging from model building to derivation of predictive experiments and refutation or validation of robust model behaviours. Here, we develop such an integrative workflow, a sequence of actions expanding upon current efforts with the purpose of setting the stage for a methodology facilitating an extraction of core behaviours and competing mechanistic hypothesis residing within underdetermined models. To this end, we make use of optimization search algorithms, statistical (machine-learning) classification techniques and cluster-based analysis of the state variables' dynamics and their corresponding parameter sets. We apply the workflow to a mathematical model of fat accumulation in the arterial wall (atherogenesis), a complex phenomena with limited quantitative understanding, thus leading to a model plagued with inherent uncertainty. We find that the mathematical atherogenesis model can still be understood in terms of a few key behaviours despite the large number of parameters. This result enabled us to derive distinct mechanistic predictions from the model despite the lack of confidence in the model parameters. We conclude that building integrative workflows enable investigators to embrace modelling of complex biological processes despite uncertainty in parameters.

Pre-B Cell to Macrophage Transdifferentiation Without Significant Promoter DNA Methylation Changes

Nucleic Acids Research. Mar, 2012  |  Pubmed ID: 22086955

Transcription factor-induced lineage reprogramming or transdifferentiation experiments are essential for understanding the plasticity of differentiated cells. These experiments helped to define the specific role of transcription factors in conferring cell identity and played a key role in the development of the regenerative medicine field. We here investigated the acquisition of DNA methylation changes during C/EBPα-induced pre-B cell to macrophage transdifferentiation. Unexpectedly, cell lineage conversion occurred without significant changes in DNA methylation not only in key B cell- and macrophage-specific genes but also throughout the entire set of genes differentially methylated between the two parental cell types. In contrast, active and repressive histone modification marks changed according to the expression levels of these genes. We also demonstrated that C/EBPα and RNA Pol II are associated with the methylated promoters of macrophage-specific genes in reprogrammed macrophages without inducing methylation changes. Our findings not only provide insights about the extent and hierarchy of epigenetic events in pre-B cell to macrophage transdifferentiation but also show an important difference to reprogramming towards pluripotency where promoter DNA demethylation plays a pivotal role.

A Beta-mixture Quantile Normalization Method for Correcting Probe Design Bias in Illumina Infinium 450 K DNA Methylation Data

Bioinformatics (Oxford, England). Jan, 2013  |  Pubmed ID: 23175756

The Illumina Infinium 450 k DNA Methylation Beadchip is a prime candidate technology for Epigenome-Wide Association Studies (EWAS). However, a difficulty associated with these beadarrays is that probes come in two different designs, characterized by widely different DNA methylation distributions and dynamic range, which may bias downstream analyses. A key statistical issue is therefore how best to adjust for the two different probe designs.

Identification of Novel Markers in Rheumatoid Arthritis Through Integrated Analysis of DNA Methylation and MicroRNA Expression

Journal of Autoimmunity. Mar, 2013  |  Pubmed ID: 23306098

Autoimmune rheumatic diseases are complex disorders, whose etiopathology is attributed to a crosstalk between genetic predisposition and environmental factors. Both variants of autoimmune susceptibility genes and environment are involved in the generation of aberrant epigenetic profiles in a cell-specific manner, which ultimately result in dysregulation of expression. Furthermore, changes in miRNA expression profiles also cause gene dysregulation associated with aberrant phenotypes. In rheumatoid arthritis, several cell types are involved in the destruction of the joints, synovial fibroblasts being among the most important. In this study we performed DNA methylation and miRNA expression screening of a set of rheumatoid arthritis synovial fibroblasts and compared the results with those obtained from osteoarthritis patients with a normal phenotype. DNA methylation screening allowed us to identify changes in novel key target genes like IL6R, CAPN8 and DPP4, as well as several HOX genes. A significant proportion of genes undergoing DNA methylation changes were inversely correlated with expression. miRNA screening revealed the existence of subsets of miRNAs that underwent changes in expression. Integrated analysis highlighted sets of miRNAs that are controlled by DNA methylation, and genes that are regulated by DNA methylation and are targeted by miRNAs with a potential use as clinical markers. Our study enabled the identification of novel dysregulated targets in rheumatoid arthritis synovial fibroblasts and generated a new workflow for the integrated analysis of miRNA and epigenetic control.

Pediatric Systems Medicine: Evaluating Needs and Opportunities Using Congenital Heart Block As a Case Study

Pediatric Research. Apr, 2013  |  Pubmed ID: 23370412

Medicine and pediatrics are changing and health care is moving from being reactive to becoming preventive. Despite rapid developments of new technologies for molecular profiling and systems analysis of diseases, significant hurdles remain. Here, we use the clinical setting of congenital heart block (CHB) to uncover and illustrate key informatics challenges impeding the development of a systems medicine approach emphasizing the prevention and prediction of disease. We find that there is a paucity of useful bioinformatics tools enabling the integrative analysis of different databases of molecular information and clinical sources in a disease context such as CHB, contrasting with the current emphasis on developing bioinformatics tools for the analysis of individual data types. Moreover, informatics solutions for managing data, such as the Integrating Biology and the Bedside (i2b2) or Stanford Translational Research Integrated Database Environment, require serious software engineering support for the maintenance and import of data beyond the capabilities of clinicians working with CHB. Hence, there is an urgent unmet need for user-friendly tools facilitating the integrative analysis and management of omics data and clinical information. Pediatrics represents an untapped potential to execute such a systems medicine program in close collaboration with clinicians and families who are keen to do what is needed for their children to prevent and predict diseases and nurture wellness.

An Evaluation of Analysis Pipelines for DNA Methylation Profiling Using the Illumina HumanMethylation450 BeadChip Platform

Epigenetics. Mar, 2013  |  Pubmed ID: 23422812

The proper identification of differentially methylated CpGs is central in most epigenetic studies. The Illumina HumanMethylation450 BeadChip is widely used to quantify DNA methylation; nevertheless, the design of an appropriate analysis pipeline faces severe challenges due to the convolution of biological and technical variability and the presence of a signal bias between Infinium I and II probe design types. Despite recent attempts to investigate how to analyze DNA methylation data with such an array design, it has not been possible to perform a comprehensive comparison between different bioinformatics pipelines due to the lack of appropriate data sets having both large sample size and sufficient number of technical replicates. Here we perform such a comparative analysis, targeting the problems of reducing the technical variability, eliminating the probe design bias and reducing the batch effect by exploiting two unpublished data sets, which included technical replicates and were profiled for DNA methylation either on peripheral blood, monocytes or muscle biopsies. We evaluated the performance of different analysis pipelines and demonstrated that: (1) it is critical to correct for the probe design type, since the amplitude of the measured methylation change depends on the underlying chemistry; (2) the effect of different normalization schemes is mixed, and the most effective method in our hands were quantile normalization and Beta Mixture Quantile dilation (BMIQ); (3) it is beneficial to correct for batch effects. In conclusion, our comparative analysis using a comprehensive data set suggests an efficient pipeline for proper identification of differentially methylated CpGs using the Illumina 450K arrays.

Implementation of the CDC Translational Informatics Platform - from Genetic Variants to the National Swedish Rheumatology Quality Register

Journal of Translational Medicine. 2013  |  Pubmed ID: 23548156

Sequencing of the human genome and the subsequent analyses have produced immense volumes of data. The technological advances have opened new windows into genomics beyond the DNA sequence. In parallel, clinical practice generate large amounts of data. This represents an underused data source that has much greater potential in translational research than is currently realized. This research aims at implementing a translational medicine informatics platform to integrate clinical data (disease diagnosis, diseases activity and treatment) of Rheumatoid Arthritis (RA) patients from Karolinska University Hospital and their research database (biobanks, genotype variants and serology) at the Center for Molecular Medicine, Karolinska Institutet.

A Vision and Strategy for the Virtual Physiological Human: 2012 Update

Interface Focus. Apr, 2013  |  Pubmed ID: 24427536

European funding under Framework 7 (FP7) for the virtual physiological human (VPH) project has been in place now for 5 years. The VPH Network of Excellence (NoE) has been set up to help develop common standards, open source software, freely accessible data and model repositories, and various training and dissemination activities for the project. It is also working to coordinate the many clinically targeted projects that have been funded under the FP7 calls. An initial vision for the VPH was defined by the FP6 STEP project in 2006. In 2010, we wrote an assessment of the accomplishments of the first two years of the VPH in which we considered the biomedical science, healthcare and information and communications technology challenges facing the project (Hunter et al. 2010 Phil. Trans. R. Soc. A 368, 2595-2614 (doi:10.1098/rsta.2010.0048)). We proposed that a not-for-profit professional umbrella organization, the VPH Institute, should be established as a means of sustaining the VPH vision beyond the time-frame of the NoE. Here, we update and extend this assessment and in particular address the following issues raised in response to Hunter et al.: (i) a vision for the VPH updated in the light of progress made so far, (ii) biomedical science and healthcare challenges that the VPH initiative can address while also providing innovation opportunities for the European industry, and (iii) external changes needed in regulatory policy and business models to realize the full potential that the VPH has to offer to industry, clinics and society generally.

Data Integration in the Era of Omics: Current and Future Challenges

BMC Systems Biology. 2014  |  Pubmed ID: 25032990

To integrate heterogeneous and large omics data constitutes not only a conceptual challenge but a practical hurdle in the daily analysis of omics data. With the rise of novel omics technologies and through large-scale consortia projects, biological systems are being further investigated at an unprecedented scale generating heterogeneous and often large data sets. These data-sets encourage researchers to develop novel data integration methodologies. In this introduction we review the definition and characterize current efforts on data integration in the life sciences. We have used a web-survey to assess current research projects on data-integration to tap into the views, needs and challenges as currently perceived by parts of the research community.

STATegra EMS: an Experiment Management System for Complex Next-generation Omics Experiments

BMC Systems Biology. 2014  |  Pubmed ID: 25033091

High-throughput sequencing assays are now routinely used to study different aspects of genome organization. As decreasing costs and widespread availability of sequencing enable more laboratories to use sequencing assays in their research projects, the number of samples and replicates in these experiments can quickly grow to several dozens of samples and thus require standardized annotation, storage and management of preprocessing steps. As a part of the STATegra project, we have developed an Experiment Management System (EMS) for high throughput omics data that supports different types of sequencing-based assays such as RNA-seq, ChIP-seq, Methyl-seq, etc, as well as proteomics and metabolomics data. The STATegra EMS provides metadata annotation of experimental design, samples and processing pipelines, as well as storage of different types of data files, from raw data to ready-to-use measurements. The system has been developed to provide research laboratories with a freely-available, integrated system that offers a simple and effective way for experiment annotation and tracking of analysis procedures.

Non-HLA Genes PTPN22, CDK6 and PADI4 Are Associated with Specific Autoantibodies in HLA-defined Subgroups of Rheumatoid Arthritis

Arthritis Research & Therapy. Aug, 2014  |  Pubmed ID: 25138370

Genetic susceptibility to complex diseases has been intensively studied during the last decade, yet only signals with small effect have been found leaving open the possibility that subgroups within complex traits show stronger association signals. In rheumatoid arthritis (RA), autoantibody production serves as a helpful discriminator in genetic studies and today anti-citrullinated cyclic peptide (anti-CCP) antibody positivity is employed for diagnosis of disease. The HLA-DRB1 locus is known as the most important genetic contributor for the risk of RA, but is not sufficient to drive autoimmunity and additional genetic and environmental factors are involved. Hence, we addressed the association of previously discovered RA loci with disease-specific autoantibody responses in RA patients stratified by HLA-DRB1*04.

Accelerating Translational Research by Clinically Driven Development of an Informatics Platform--a Case Study

PloS One. 2014  |  Pubmed ID: 25203647

Translational medicine is becoming increasingly dependent upon data generated from health care, clinical research, and molecular investigations. This increasing rate of production and diversity in data has brought about several challenges, including the need to integrate fragmented databases, enable secondary use of patient clinical data from health care in clinical research, and to create information systems that clinicians and biomedical researchers can readily use. Our case study effectively integrates requirements from the clinical and biomedical researcher perspectives in a translational medicine setting. Our three principal achievements are (a) a design of a user-friendly web-based system for management and integration of clinical and molecular databases, while adhering to proper de-identification and security measures; (b) providing a real-world test of the system functionalities using clinical cohorts; and (c) system integration with a clinical decision support system to demonstrate system interoperability. We engaged two active clinical cohorts, 747 psoriasis patients and 2001 rheumatoid arthritis patients, to demonstrate efficient query possibilities across the data sources, enable cohort stratification, extract variation in antibody patterns, study biomarker predictors of treatment response in RA patients, and to explore metabolic profiles of psoriasis patients. Finally, we demonstrated system interoperability by enabling integration with an established clinical decision support system in health care. To assure the usefulness and usability of the system, we followed two approaches. First, we created a graphical user interface supporting all user interactions. Secondly we carried out a system performance evaluation study where we measured the average response time in seconds for active users, http errors, and kilobits per second received and sent. The maximum response time was found to be 0.12 seconds; no server or client errors of any kind were detected. In conclusion, the system can readily be used by clinicians and biomedical researchers in a translational medicine setting.

Oxygen Pathway Modeling Estimates High Reactive Oxygen Species Production Above the Highest Permanent Human Habitation

PloS One. 2014  |  Pubmed ID: 25375931

The production of reactive oxygen species (ROS) from the inner mitochondrial membrane is one of many fundamental processes governing the balance between health and disease. It is well known that ROS are necessary signaling molecules in gene expression, yet when expressed at high levels, ROS may cause oxidative stress and cell damage. Both hypoxia and hyperoxia may alter ROS production by changing mitochondrial Po2 (PmO2). Because PmO2 depends on the balance between O2 transport and utilization, we formulated an integrative mathematical model of O2 transport and utilization in skeletal muscle to predict conditions to cause abnormally high ROS generation. Simulations using data from healthy subjects during maximal exercise at sea level reveal little mitochondrial ROS production. However, altitude triggers high mitochondrial ROS production in muscle regions with high metabolic capacity but limited O2 delivery. This altitude roughly coincides with the highest location of permanent human habitation. Above 25,000 ft., more than 90% of exercising muscle is predicted to produce abnormally high levels of ROS, corresponding to the "death zone" in mountaineering.

Systems Medicine: from Molecular Features and Models to the Clinic in COPD

Journal of Translational Medicine. Nov, 2014  |  Pubmed ID: 25471042

Chronic Obstructive Pulmonary Disease (COPD) patients are characterized by heterogeneous clinical manifestations and patterns of disease progression. Two major factors that can be used to identify COPD subtypes are muscle dysfunction/wasting and co-morbidity patterns. We hypothesized that COPD heterogeneity is in part the result of complex interactions between several genes and pathways. We explored the possibility of using a Systems Medicine approach to identify such pathways, as well as to generate predictive computational models that may be used in clinic practice.

Biomedical Research in a Digital Health Framework

Journal of Translational Medicine. Nov, 2014  |  Pubmed ID: 25472554

This article describes a Digital Health Framework (DHF), benefitting from the lessons learnt during the three-year life span of the FP7 Synergy-COPD project. The DHF aims to embrace the emerging requirements--data and tools--of applying systems medicine into healthcare with a three-tier strategy articulating formal healthcare, informal care and biomedical research. Accordingly, it has been constructed based on three key building blocks, namely, novel integrated care services with the support of information and communication technologies, a personal health folder (PHF) and a biomedical research environment (DHF-research). Details on the functional requirements and necessary components of the DHF-research are extensively presented. Finally, the specifics of the building blocks strategy for deployment of the DHF, as well as the steps toward adoption are analyzed. The proposed architectural solutions and implementation steps constitute a pivotal strategy to foster and enable 4P medicine (Predictive, Preventive, Personalized and Participatory) in practice and should provide a head start to any community and institution currently considering to implement a biomedical research platform.

Predictive Medicine: Outcomes, Challenges and Opportunities in the Synergy-COPD Project

Journal of Translational Medicine. Nov, 2014  |  Pubmed ID: 25472742

Chronic Obstructive Pulmonary Disease (COPD) is a major challenge for healthcare. Heterogeneities in clinical manifestations and in disease progression are relevant traits in COPD with impact on patient management and prognosis. It is hypothesized that COPD heterogeneity results from the interplay of mechanisms governing three conceptually different phenomena: 1) pulmonary disease, 2) systemic effects of COPD and 3) co-morbidity clustering.

Synergy-COPD: a Systems Approach for Understanding and Managing Chronic Diseases

Journal of Translational Medicine. Nov, 2014  |  Pubmed ID: 25472826

Chronic diseases (CD) are generating a dramatic societal burden worldwide that is expected to persist over the next decades. The challenges posed by the epidemics of CD have triggered a novel health paradigm with major consequences on the traditional concept of disease and with a profound impact on key aspects of healthcare systems. We hypothesized that the development of a systems approach to understand CD together with the generation of an ecosystem to transfer the acquired knowledge into the novel healthcare scenario may contribute to a cost-effective enhancement of health outcomes. To this end, we designed the Synergy-COPD project wherein the heterogeneity of chronic obstructive pulmonary disease (COPD) was addressed as a use case representative of CD. The current manuscript describes main features of the project design and the strategies put in place for its development, as well the expected outcomes during the project life-span. Moreover, the manuscript serves as introductory and unifying chapter of the different papers associated to the Supplement describing the characteristics, tools and the objectives of Synergy-COPD.

Chronic Obstructive Pulmonary Disease Heterogeneity: Challenges for Health Risk Assessment, Stratification and Management

Journal of Translational Medicine. Nov, 2014  |  Pubmed ID: 25472887

Heterogeneity in clinical manifestations and disease progression in Chronic Obstructive Pulmonary Disease (COPD) lead to consequences for patient health risk assessment, stratification and management. Implicit with the classical "spill over" hypothesis is that COPD heterogeneity is driven by the pulmonary events of the disease. Alternatively, we hypothesized that COPD heterogeneities result from the interplay of mechanisms governing three conceptually different phenomena: 1) pulmonary disease, 2) systemic effects of COPD and 3) co-morbidity clustering, each of them with their own dynamics.

An Integrative Analysis Reveals Coordinated Reprogramming of the Epigenome and the Transcriptome in Human Skeletal Muscle After Training

Epigenetics. Dec, 2014  |  Pubmed ID: 25484259

Regular endurance exercise training induces beneficial functional and health effects in human skeletal muscle. The putative contribution to the training response of the epigenome as a mediator between genes and environment has not been clarified. Here we investigated the contribution of DNA methylation and associated transcriptomic changes in a well-controlled human intervention study. Training effects were mirrored by significant alterations in DNA methylation and gene expression in regions with a homogeneous muscle energetics and remodeling ontology. Moreover, a signature of DNA methylation and gene expression separated the samples based on training and gender. Differential DNA methylation was predominantly observed in enhancers, gene bodies and intergenic regions and less in CpG islands or promoters. We identified transcriptional regulator binding motifs of MRF, MEF2 and ETS proteins in the proximity of the changing sites. A transcriptional network analysis revealed modules harboring distinct ontologies and, interestingly, the overall direction of the changes of methylation within each module was inversely correlated to expression changes. In conclusion, we show that highly consistent and associated modifications in methylation and expression, concordant with observed health-enhancing phenotypic adaptations, are induced by a physiological stimulus.

Signaling Networks in MS: a Systems-based Approach to Developing New Pharmacological Therapies

Multiple Sclerosis (Houndmills, Basingstoke, England). Feb, 2015  |  Pubmed ID: 25112814

The pathogenesis of multiple sclerosis (MS) involves alterations to multiple pathways and processes, which represent a significant challenge for developing more-effective therapies. Systems biology approaches that study pathway dysregulation should offer benefits by integrating molecular networks and dynamic models with current biological knowledge for understanding disease heterogeneity and response to therapy. In MS, abnormalities have been identified in several cytokine-signaling pathways, as well as those of other immune receptors. Among the downstream molecules implicated are Jak/Stat, NF-Kb, ERK1/3, p38 or Jun/Fos. Together, these data suggest that MS is likely to be associated with abnormalities in apoptosis/cell death, microglia activation, blood-brain barrier functioning, immune responses, cytokine production, and/or oxidative stress, although which pathways contribute to the cascade of damage and can be modulated remains an open question. While current MS drugs target some of these pathways, others remain untouched. Here, we propose a pragmatic systems analysis approach that involves the large-scale extraction of processes and pathways relevant to MS. These data serve as a scaffold on which computational modeling can be performed to identify disease subgroups based on the contribution of different processes. Such an analysis, targeting these relevant MS-signaling pathways, offers the opportunity to accelerate the development of novel individual or combination therapies.

VEGF-B Promotes Cancer Metastasis Through a VEGF-A-independent Mechanism and Serves As a Marker of Poor Prognosis for Cancer Patients

Proceedings of the National Academy of Sciences of the United States of America. Jun, 2015  |  Pubmed ID: 25991856

The biological functions of VEGF-B in cancer progression remain poorly understood. Here, we report that VEGF-B promotes cancer metastasis through the remodeling of tumor microvasculature. Knockdown of VEGF-B in tumors resulted in increased perivascular cell coverage and impaired pulmonary metastasis of human melanomas. In contrast, the gain of VEGF-B function in tumors led to pseudonormalized tumor vasculatures that were highly leaky and poorly perfused. Tumors expressing high levels of VEGF-B were more metastatic, although primary tumor growth was largely impaired. Similarly, VEGF-B in a VEGF-A-null tumor resulted in attenuated primary tumor growth but substantial pulmonary metastases. VEGF-B also led to highly metastatic phenotypes in Vegfr1 tk(-/-) mice and mice treated with anti-VEGF-A. These data indicate that VEGF-B promotes cancer metastasis through a VEGF-A-independent mechanism. High expression levels of VEGF-B in two large-cohort studies of human patients with lung squamous cell carcinoma and melanoma correlated with poor survival. Taken together, our findings demonstrate that VEGF-B is a vascular remodeling factor promoting cancer metastasis and that targeting VEGF-B may be an important therapeutic approach for cancer metastasis.

Laboratory Biomarkers and Frailty: Presentation of the FRAILOMIC Initiative

Clinical Chemistry and Laboratory Medicine. Sep, 2015  |  Pubmed ID: 25993734

Monozygotic Twins Discordant for Common Variable Immunodeficiency Reveal Impaired DNA Demethylation During Naïve-to-memory B-cell Transition

Nature Communications. Jun, 2015  |  Pubmed ID: 26081581

Common variable immunodeficiency (CVID), the most frequent primary immunodeficiency characterized by loss of B-cell function, depends partly on genetic defects, and epigenetic changes are thought to contribute to its aetiology. Here we perform a high-throughput DNA methylation analysis of this disorder using a pair of CVID-discordant MZ twins and show predominant gain of DNA methylation in CVID B cells with respect to those from the healthy sibling in critical B lymphocyte genes, such as PIK3CD, BCL2L1, RPS6KB2, TCF3 and KCNN4. Individual analysis confirms hypermethylation of these genes. Analysis in naive, unswitched and switched memory B cells in a CVID patient cohort shows impaired ability to demethylate and upregulate these genes in transitioning from naive to memory cells in CVID. Our results not only indicate a role for epigenetic alterations in CVID but also identify relevant DNA methylation changes in B cells that could explain the clinical manifestations of CVID individuals.

IL-1β Promotes Th17 Differentiation by Inducing Alternative Splicing of FOXP3

Scientific Reports. Oct, 2015  |  Pubmed ID: 26441347

CD4(+)FOXP3(+) regulatory T (Treg) cells are essential for maintaining immunological self-tolerance. Treg cell development and function depend on the transcription factor FOXP3, which is present in several distinct isoforms due to alternative splicing. Despite the importance of FOXP3 in the proper maintenance of Treg cells, the regulation and functional consequences of FOXP3 isoform expression remains poorly understood. Here, we show that in human Treg cells IL-1β promotes excision of FOXP3 exon 7. FOXP3 is not only expressed by Treg cells but is also transiently expressed when naïve T cells differentiate into Th17 cells. Forced splicing of FOXP3 into FOXP3Δ2Δ7 strongly favored Th17 differentiation in vitro. We also found that patients with Crohn's disease express increased levels of FOXP3 transcripts lacking exon 7, which correlate with disease severity and IL-17 production. Our results demonstrate that alternative splicing of FOXP3 modulates T cell differentiation. These results highlight the importance of characterizing FOXP3 expression on an isoform basis and suggest that immune responses may be manipulated by modulating the expression of FOXP3 isoforms, which has broad implications for the treatment of autoimmune diseases.

The Folate-coupled Enzyme MTHFD2 is a Nuclear Protein and Promotes Cell Proliferation

Scientific Reports. Oct, 2015  |  Pubmed ID: 26461067

Folate metabolism is central to cell proliferation and a target of commonly used cancer chemotherapeutics. In particular, the mitochondrial folate-coupled metabolism is thought to be important for proliferating cancer cells. The enzyme MTHFD2 in this pathway is highly expressed in human tumors and broadly required for survival of cancer cells. Although the enzymatic activity of the MTHFD2 protein is well understood, little is known about its larger role in cancer cell biology. We here report that MTHFD2 is co-expressed with two distinct gene sets, representing amino acid metabolism and cell proliferation, respectively. Consistent with a role for MTHFD2 in cell proliferation, MTHFD2 expression was repressed in cells rendered quiescent by deprivation of growth signals (serum) and rapidly re-induced by serum stimulation. Overexpression of MTHFD2 alone was sufficient to promote cell proliferation independent of its dehydrogenase activity, even during growth restriction. In addition to its known mitochondrial localization, we found MTHFD2 to have a nuclear localization and co-localize with DNA replication sites. These findings suggest a previously unknown role for MTHFD2 in cancer cell proliferation, adding to its known function in mitochondrial folate metabolism.

Bioinformatics Mining and Modeling Methods for the Identification of Disease Mechanisms in Neurodegenerative Disorders

International Journal of Molecular Sciences. Dec, 2015  |  Pubmed ID: 26690135

Since the decoding of the Human Genome, techniques from bioinformatics, statistics, and machine learning have been instrumental in uncovering patterns in increasing amounts and types of different data produced by technical profiling technologies applied to clinical samples, animal models, and cellular systems. Yet, progress on unravelling biological mechanisms, causally driving diseases, has been limited, in part due to the inherent complexity of biological systems. Whereas we have witnessed progress in the areas of cancer, cardiovascular and metabolic diseases, the area of neurodegenerative diseases has proved to be very challenging. This is in part because the aetiology of neurodegenerative diseases such as Alzheimer´s disease or Parkinson´s disease is unknown, rendering it very difficult to discern early causal events. Here we describe a panel of bioinformatics and modeling approaches that have recently been developed to identify candidate mechanisms of neurodegenerative diseases based on publicly available data and knowledge. We identify two complementary strategies-data mining techniques using genetic data as a starting point to be further enriched using other data-types, or alternatively to encode prior knowledge about disease mechanisms in a model based framework supporting reasoning and enrichment analysis. Our review illustrates the challenges entailed in integrating heterogeneous, multiscale and multimodal information in the area of neurology in general and neurodegeneration in particular. We conclude, that progress would be accelerated by increasing efforts on performing systematic collection of multiple data-types over time from each individual suffering from neurodegenerative disease. The work presented here has been driven by project AETIONOMY; a project funded in the course of the Innovative Medicines Initiative (IMI); which is a public-private partnership of the European Federation of Pharmaceutical Industry Associations (EFPIA) and the European Commission (EC).

In Search of 'Omics'-Based Biomarkers to Predict Risk of Frailty and Its Consequences in Older Individuals: The FRAILOMIC Initiative

Gerontology. 2016  |  Pubmed ID: 26227153

An increase in the number of older people experiencing disability and dependence is a critical aspect of the demographic change that will emerge within Europe due to the rise in life expectancy. In this scenario, prevention of these conditions is crucial for the well-being of older citizens and for the sustainability of our healthcare systems. Thus, the diagnosis and management of conditions like frailty, which identifies the people at the highest risk for developing those adverse outcomes, is of critical relevance. Currently, assessment of frailty relies primarily on measuring functional parameters, which have limited clinical utility. In this viewpoint article, we describe the FRAILOMIC Initiative, an international, large-scale, multi-endpoint, community- and clinic-based research study funded by the European Commission. The aim of the study is to develop validated measures, comprising both classic and 'omics-based' laboratory biomarkers, which can predict the risk of frailty, improve the accuracy of its diagnosis in clinical practice and provide a prognostic forecast on the evolution from frailty to disability. The initiative includes eight established cohorts of older adults, encompassing >75,000 subjects, most of whom (∼70%) are aged >65 years. Data on function, nutritional status and exercise habits have been collected, and cardiovascular health has been evaluated at baseline. Subjects will be stratified as 'non-frail' or 'frail' using Fried's definition, all adverse outcomes of interest will be recorded and differentially expressed biomarkers associated with the risk of frailty will be identified. Genomic, proteomic and transcriptomic investigations will be carried out using array-based systems. As circulating microRNAs in plasma have been identified in the context of senescence, ageing and age-associated diseases, a miRNome-wide analysis will also be undertaken to identify a miRNA-based signature of frailty. Blood concentrations of secreted proteins known to be upregulated significantly in senescent endothelial cells and other hypothesis-driven biomarkers will be measured using ELISAs. The FRAILOMIC Initiative aims to issue a series of interim scientific reports as key results emerge. Ultimately, it is hoped that this study will contribute to the development of new clinical tools, which may help individuals to enjoy an old age that is healthier and free from disability.

Normalization of Circulating MicroRNA Expression Data Obtained by Quantitative Real-time RT-PCR

Briefings in Bioinformatics. Mar, 2016  |  Pubmed ID: 26238539

The high-throughput analysis of microRNAs (miRNAs) circulating within the blood of healthy and diseased individuals is an active area of biomarker research. Whereas quantitative real-time reverse transcription polymerase chain reaction (qPCR)-based methods are widely used, it is yet unresolved how the data should be normalized. Here, we show that a combination of different algorithms results in the identification of candidate reference miRNAs that can be exploited as normalizers, in both discovery and validation phases. Using the methodology considered here, we identify normalizers that are able to reduce nonbiological variation in the data and we present several case studies, to illustrate the relevance in the context of physiological or pathological scenarios. In conclusion, the discovery of stable reference miRNAs from high-throughput studies allows appropriate normalization of focused qPCR assays.

From Systems Understanding to Personalized Medicine: Lessons and Recommendations Based on a Multidisciplinary and Translational Analysis of COPD

Methods in Molecular Biology (Clifton, N.J.). 2016  |  Pubmed ID: 26677188

Systems medicine, using and adapting methods and approaches as developed within systems biology, promises to be essential in ongoing efforts of realizing and implementing personalized medicine in clinical practice and research. Here we review and critically assess these opportunities and challenges using our work on COPD as a case study. We find that there are significant unresolved biomedical challenges in how to unravel complex multifactorial components in disease initiation and progression producing different clinical phenotypes. Yet, while such a systems understanding of COPD is necessary, there are other auxiliary challenges that need to be addressed in concert with a systems analysis of COPD. These include information and communication technology (ICT)-related issues such as data harmonization, systematic handling of knowledge, computational modeling, and importantly their translation and support of clinical practice. For example, clinical decision-support systems need a seamless integration with new models and knowledge as systems analysis of COPD continues to develop. Our experience with clinical implementation of systems medicine targeting COPD highlights the need for a change of management including design of appropriate business models and adoption of ICT providing and supporting organizational interoperability among professional teams across healthcare tiers, working around the patient. In conclusion, in our hands the scope and efforts of systems medicine need to concurrently consider these aspects of clinical implementation, which inherently drives the selection of the most relevant and urgent issues and methods that need further development in a systems analysis of disease.

Methods of Information Theory and Algorithmic Complexity for Network Biology

Seminars in Cell & Developmental Biology. Mar, 2016  |  Pubmed ID: 26802516

We survey and introduce concepts and tools located at the intersection of information theory and network biology. We show that Shannon's information entropy, compressibility and algorithmic complexity quantify different local and global aspects of synthetic and biological data. We show examples such as the emergence of giant components in Erdös-Rényi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression data. We provide exact theoretical calculations, numerical approximations and error estimations of entropy, algorithmic probability and Kolmogorov complexity for different types of graphs, characterizing their variant and invariant properties. We introduce formal definitions of complexity for both labeled and unlabeled graphs and prove that the Kolmogorov complexity of a labeled graph is a good approximation of its unlabeled Kolmogorov complexity and thus a robust definition of graph complexity.

Evaluating Network Inference Methods in Terms of Their Ability to Preserve the Topology and Complexity of Genetic Networks

Seminars in Cell & Developmental Biology. Mar, 2016  |  Pubmed ID: 26851626

Network inference is a rapidly advancing field, with new methods being proposed on a regular basis. Understanding the advantages and limitations of different network inference methods is key to their effective application in different circumstances. The common structural properties shared by diverse networks naturally pose a challenge when it comes to devising accurate inference methods, but surprisingly, there is a paucity of comparison and evaluation methods. Historically, every new methodology has only been tested against gold standard (true values) purpose-designed synthetic and real-world (validated) biological networks. In this paper we aim to assess the impact of taking into consideration aspects of topological and information content in the evaluation of the final accuracy of an inference procedure. Specifically, we will compare the best inference methods, in both graph-theoretic and information-theoretic terms, for preserving topological properties and the original information content of synthetic and biological networks. New methods for performance comparison are introduced by borrowing ideas from gene set enrichment analysis and by applying concepts from algorithmic complexity. Experimental results show that no individual algorithm outperforms all others in all cases, and that the challenging and non-trivial nature of network inference is evident in the struggle of some of the algorithms to turn in a performance that is superior to random guesswork. Therefore special care should be taken to suit the method to the purpose at hand. Finally, we show that evaluations from data generated using different underlying topologies have different signatures that can be used to better choose a network reconstruction method.

Comparative Analysis of Protocols to Induce Human CD4+Foxp3+ Regulatory T Cells by Combinations of IL-2, TGF-beta, Retinoic Acid, Rapamycin and Butyrate

PloS One. 2016  |  Pubmed ID: 26886923

Regulatory T cells (Tregs) suppress other immune cells and are critical mediators of peripheral tolerance. Therapeutic manipulation of Tregs is subject to numerous clinical investigations including trials for adoptive Treg transfer. Since the number of naturally occurring Tregs (nTregs) is minute, it is highly desirable to develop a complementary approach of inducing Tregs (iTregs) from naïve T cells. Mouse studies exemplify the importance of peripherally induced Tregs as well as the applicability of iTreg transfer in different disease models. Yet, procedures to generate iTregs are currently controversial, particularly for human cells. Here we therefore comprehensively compare different established and define novel protocols of human iTreg generation using TGF-β in combination with other compounds. We found that human iTregs expressed several Treg signature molecules, such as Foxp3, CTLA-4 and EOS, while exhibiting low expression of the cytokines Interferon-γ, IL-10 and IL-17. Importantly, we identified a novel combination of TGF-β, retinoic acid and rapamycin as a robust protocol to induce human iTregs with superior suppressive activity in vitro compared to currently established induction protocols. However, iTregs generated by these protocols did not stably retain Foxp3 expression and did not suppress in vivo in a humanized graft-versus-host-disease mouse model, highlighting the need for further research to attain stable, suppressive iTregs. These results advance our understanding of the conditions enabling human iTreg generation and may have important implications for the development of adoptive transfer strategies targeting autoimmune and inflammatory diseases.

Human Macrophages Induce CD4(+)Foxp3(+) Regulatory T Cells Via Binding and Re-release of TGF-β

Immunology and Cell Biology. Sep, 2016  |  Pubmed ID: 27075967

While pro-inflammatory immune responses are a requirement to combat microbes, uncontrolled self-directed inflammatory immune responses are the hallmark of autoimmune diseases. Restoration of immunological tolerance involves both suppression of ongoing tissue-destructive immune responses and re-education of the host immune system. Both functionally immunosuppressive macrophages (M2) and regulatory T cells (Tregs) are implicated in these processes. Their mutual interaction is synergistic in this context and adoptive transfer of each cell type has been functioning as immunotherapy in experimental models, being particularly effective when using M2 macrophages generated with an optimized interleukin-4 (IL-4)/interleukin-10 (IL-10)/transforming growth factor-β (TGF-β) combination. As a prerequisite for eventual translation of M2 therapy into clinical settings we herein studied the induction, stability and mechanism of generation of human induced Tregs (iTregs) by M2 macrophages generated with IL-4/IL-10/TGF-β. The supernatants of monocyte-derived human M2 macrophages robustly induced FOXP3 and other Treg signature molecules such as CTLA-4 and IKZF4 in human naïve CD4 T cells. M2-induced iTregs displayed enhanced FOXP3 stability and low expression of pro-inflammatory cytokines interferon-γ and IL-17, as well as functional immunosuppressive activity compared with control T cells. The FOXP3-inducing activity was dependent on TGF-β, which was both expressed and captured with re-release by M2 macrophages into the soluble supernatant fraction, in which the TGF-β was not confined to extracellular vesicles such as exosomes. We propose that adoptive transfer of human M2 macrophages may be exploited in the future to induce Tregs in situ by delivering TGF-β, which could be developed as a therapeutic strategy to target autoimmune and other inflammatory diseases.

Proposals for Enhanced Health Risk Assessment and Stratification in an Integrated Care Scenario

BMJ Open. Apr, 2016  |  Pubmed ID: 27084274

Population-based health risk assessment and stratification are considered highly relevant for large-scale implementation of integrated care by facilitating services design and case identification. The principal objective of the study was to analyse five health-risk assessment strategies and health indicators used in the five regions participating in the Advancing Care Coordination and Telehealth Deployment (ACT) programme ( The second purpose was to elaborate on strategies toward enhanced health risk predictive modelling in the clinical scenario.

Conditional Disease Development Extracted from Longitudinal Health Care Cohort Data Using Layered Network Construction

Scientific Reports. May, 2016  |  Pubmed ID: 27211115

Health care data holds great promise to be used in clinical decision support systems. However, frequent near-synonymous diagnoses recorded separately, as well as the sheer magnitude and complexity of the disease data makes it challenging to extract non-trivial conclusions beyond confirmatory associations from such a web of interactions. Here we present a systematic methodology to derive statistically valid conditional development of diseases. To this end we utilize a cohort of 5,512,469 individuals followed over 13 years at inpatient care, including data on disability pension and cause of death. By introducing a causal information fraction measure and taking advantage of the composite structure in the ICD codes, we extract an effective directed lower dimensional network representation (100 nodes and 130 edges) of our cohort. Unpacking composite nodes into bipartite graphs retrieves, for example, that individuals with behavioral disorders are more likely to be followed by prescription drug poisoning episodes, whereas women with leiomyoma were more likely to subsequently experience endometriosis. The conditional disease development represent putative causal relations, indicating possible novel clinical relationships and pathophysiological associations that have not been explored yet.

Human Cytomegalovirus May Promote Tumour Progression by Upregulating Arginase-2

Oncotarget. Jul, 2016  |  Pubmed ID: 27363017

Both arginase (ARG2) and human cytomegalovirus (HCMV) have been implicated in tumorigenesis. However, the role of ARG2 in the pathogenesis of glioblastoma (GBM) and the HCMV effects on ARG2 are unknown. We hypothesize that HCMV may contribute to tumorigenesis by increasing ARG2 expression.

Systems Toxicology: Systematic Approach to Predict Toxicity

Current Pharmaceutical Design. Oct, 2016  |  Pubmed ID: 27697024

Drug discovery is complex and expensive. Numerous drug candidates fail rather late in clinical trials or even after released to the market. This is due to not only commercial considerations and less optimal drug efficacies, but adverse reactions originating from toxic effects constitute a major challenge. During the last two decades significant advances has been made enabling early prediction of toxic effects using in silico techniques. Yet, these essentially statistical techniques have not by design taken the disease driving pathophysiological mechanisms into account. The complexity of such mechanisms in combination with their interactions with drug-specific properties and environmental and life-style related factors renders the task of predicting toxicity on a purely statistical basis insurmountable challenging. In response to this situation, an interdisciplinary field has developed, referred to as systems toxicology, where the notion of a network is used to integrate and model different types of information to better predict drug toxicity. Here, we briefly review merits and limitations of such recent promising predictive approaches integrating molecular networks, chemical compound networks, and protein drug association networks.

A Perspective on Bridging Scales and Design of Models Using Low-dimensional Manifolds and Data-driven Model Inference

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences. Nov, 2016  |  Pubmed ID: 27698038

Systems in nature capable of collective behaviour are nonlinear, operating across several scales. Yet our ability to account for their collective dynamics differs in physics, chemistry and biology. Here, we briefly review the similarities and differences between mathematical modelling of adaptive living systems versus physico-chemical systems. We find that physics-based chemistry modelling and computational neuroscience have a shared interest in developing techniques for model reductions aiming at the identification of a reduced subsystem or slow manifold, capturing the effective dynamics. By contrast, as relations and kinetics between biological molecules are less characterized, current quantitative analysis under the umbrella of bioinformatics focuses on signal extraction, correlation, regression and machine-learning analysis. We argue that model reduction analysis and the ensuing identification of manifolds bridges physics and biology. Furthermore, modelling living systems presents deep challenges as how to reconcile rich molecular data with inherent modelling uncertainties (formalism, variables selection and model parameters). We anticipate a new generative data-driven modelling paradigm constrained by identified governing principles extracted from low-dimensional manifold analysis. The rise of a new generation of models will ultimately connect biology to quantitative mechanistic descriptions, thereby setting the stage for investigating the character of the model language and principles driving living systems.This article is part of the themed issue 'Multiscale modelling at the physics-chemistry-biology interface'.

Adaptive Input Data Transformation for Improved Network Reconstruction with Information Theoretic Algorithms

Statistical Applications in Genetics and Molecular Biology. Dec, 2016  |  Pubmed ID: 27875324

We propose a novel systematic procedure of non-linear data transformation for an adaptive algorithm in the context of network reverse-engineering using information theoretic methods. Our methodology is rooted in elucidating and correcting for the specific biases in the estimation techniques for mutual information (MI) given a finite sample of data. These are, in turn, tied to lack of well-defined bounds for numerical estimation of MI for continuous probability distributions from finite data. The nature and properties of the inevitable bias is described, complemented by several examples illustrating their form and variation. We propose an adaptive partitioning scheme for MI estimation that effectively transforms the sample data using parameters determined from its local and global distribution guaranteeing a more robust and reliable reconstruction algorithm. Together with a normalized measure (Shared Information Metric) we report considerably enhanced performance both for in silico and real-world biological networks. We also find that the recovery of true interactions is in particular better for intermediate range of false positive rates, suggesting that our algorithm is less vulnerable to spurious signals of association.

High-specificity Bioinformatics Framework for Epigenomic Profiling of Discordant Twins Reveals Specific and Shared Markers for ACPA and ACPA-positive Rheumatoid Arthritis

Genome Medicine. Nov, 2016  |  Pubmed ID: 27876072

Twin studies are powerful models to elucidate epigenetic modifications resulting from gene-environment interactions. Yet, commonly a limited number of clinical twin samples are available, leading to an underpowered situation afflicted with false positives and hampered by low sensitivity. We investigated genome-wide DNA methylation data from two small sets of monozygotic twins representing different phases during the progression of rheumatoid arthritis (RA) to find novel genes for further research.

Corrigendum to "Methods of Information Theory and Algorithmic Complexity for Network Biology" [Semin. Cell Dev. Biol. 51 (2016) 32-43]

Seminars in Cell & Developmental Biology. Dec, 2016  |  Pubmed ID: 27979328

simple hit counter