Novel Aspects in Pattern Formation Arise from Coupling Turing Reaction-Diffusion and Chemotaxis

Recent experimental studies on primary hair follicle formation and feather bud morphogenesis indicate a coupling between Turing-type diffusion driven instability and chemotactic patterning. Inspired by these findings we develop and analyse a mathematical model that couples chemotaxis to a reaction-diffusion system exhibiting diffusion-driven (Turing) instability. While both systems, reaction-diffusion systems and chemotaxis, can independently generate spatial patterns, we were interested in how the coupling impacts the stability of the system, parameter region for patterning, pattern geometry, as well as the dynamics of pattern formation. We conduct a classical linear stability analysis for different model structures, and confirm our results by numerical analysis of the system. Our results show that the coupling generally increases the robustness of the patterning process by enlarging the pattern region in the parameter space. Concerning time scale and pattern regularity, we find that an increase in the chemosensitivity can speed up the patterning process for parameters inside and outside of the Turing space, but generally reduces spatial regularity of the pattern. Interestingly, our analysis indicates that pattern formation can also occur when neither the Turing nor the chemotaxis system can independently generate pattern. On the other hand, for some parameter settings, the coupling of the two processes can extinguish the pattern formation, rather than reinforce it. These theoretical findings can be used to corroborate the biological findings on morphogenesis and guide future experimental studies. From a mathematical point of view, this work sheds a light on coupling classical pattern formation systems from the parameter space perspective.

QuickPIV: Efficient 3D particle image velocimetry software applied to quantifying cellular migration during embryogenesis

BACKGROUND

The technical development of imaging techniques in life sciences has enabled the three-dimensional recording of living samples at increasing temporal resolutions. Dynamic 3D data sets of developing organisms allow for time-resolved quantitative analyses of morphogenetic changes in three dimensions, but require efficient and automatable analysis pipelines to tackle the resulting Terabytes of image data. Particle image velocimetry (PIV) is a robust and segmentation-free technique that is suitable for quantifying collective cellular migration on data sets with different labeling schemes. This paper presents the implementation of an efficient 3D PIV package using the Julia programming language-quickPIV. Our software is focused on optimizing CPU performance and ensuring the robustness of the PIV analyses on biological data.

RESULTS

QuickPIV is three times faster than the Python implementation hosted in openPIV, both in 2D and 3D. Our software is also faster than the fastest 2D PIV package in openPIV, written in C++. The accuracy evaluation of our software on synthetic data agrees with the expected accuracies described in the literature. Additionally, by applying quickPIV to three data sets of the embryogenesis of Tribolium castaneum, we obtained vector fields that recapitulate the migration movements of gastrulation, both in nuclear and actin-labeled embryos. We show normalized squared error cross-correlation to be especially accurate in detecting translations in non-segmentable biological image data.

CONCLUSIONS

The presented software addresses the need for a fast and open-source 3D PIV package in biological research. Currently, quickPIV offers efficient 2D and 3D PIV analyses featuring zero-normalized and normalized squared error cross-correlations, sub-pixel/voxel approximation, and multi-pass. Post-processing options include filtering and averaging of the resulting vector fields, extraction of velocity, divergence and collectiveness maps, simulation of pseudo-trajectories, and unit conversion. In addition, our software includes functions to visualize the 3D vector fields in Paraview.

Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis

BACKGROUND

Organoids are morphologically heterogeneous three-dimensional cell culture systems and serve as an ideal model for understanding the principles of collective cell behaviour in mammalian organs during development, homeostasis, regeneration, and pathogenesis. To investigate the underlying cell organisation principles of organoids, we imaged hundreds of pancreas and cholangiocarcinoma organoids in parallel using light sheet and bright-field microscopy for up to 7 days.

RESULTS

We quantified organoid behaviour at single-cell (microscale), individual-organoid (mesoscale), and entire-culture (macroscale) levels. At single-cell resolution, we monitored formation, monolayer polarisation, and degeneration and identified diverse behaviours, including lumen expansion and decline (size oscillation), migration, rotation, and multi-organoid fusion. Detailed individual organoid quantifications lead to a mechanical 3D agent-based model. A derived scaling law and simulations support the hypotheses that size oscillations depend on organoid properties and cell division dynamics, which is confirmed by bright-field microscopy analysis of entire cultures.

CONCLUSION

Our multiscale analysis provides a systematic picture of the diversity of cell organisation in organoids by identifying and quantifying the core regulatory principles of organoid morphogenesis.

Cell fate clusters in ICM organoids arise from cell fate heredity and division: a modelling approach

During the mammalian preimplantation phase, cells undergo two subsequent cell fate decisions. During the first decision, the trophectoderm and the inner cell mass are formed. Subsequently, the inner cell mass segregates into the epiblast and the primitive endoderm. Inner cell mass organoids represent an experimental model system, mimicking the second cell fate decision. It has been shown that cells of the same fate tend to cluster stronger than expected for random cell fate decisions. Three major processes are hypothesised to contribute to the cell fate arrangements: (1) chemical signalling; (2) cell sorting; and (3) cell proliferation. In order to quantify the influence of cell proliferation on the observed cell lineage type clustering, we developed an agent-based model accounting for mechanical cell-cell interaction, i.e. adhesion and repulsion, cell division, stochastic cell fate decision and cell fate heredity. The model supports the hypothesis that initial cell fate acquisition is a stochastically driven process, taking place in the early development of inner cell mass organoids. Further, we show that the observed neighbourhood structures can emerge solely due to cell fate heredity during cell division.

A survey of within-host and between-hosts modelling for antibiotic resistance

Antibiotic resistance is a global public health problem which has the attention of many stakeholders including clinicians, the pharmaceutical industry, researchers and policy makers. Despite the existence of many studies, control of resistance transmission has become a rather daunting task as the mechanisms underlying resistance evolution and development are not fully known. Here, we discuss the mechanisms underlying antibiotic resistance development, explore some treatment strategies used in the fight against antibiotic resistance and consider recent findings on collateral susceptibilities amongst antibiotic classes. Mathematical models have proved valuable for unravelling complex mechanisms in biology and such models have been used in the quest of understanding the development and spread of antibiotic resistance. While assessing the importance of such mathematical models, previous systematic reviews were interested in investigating whether these models follow good modelling practice. We focus on theoretical approaches used for resistance modelling considering both within and between host models as well as some pharmacodynamic and pharmakokinetic approaches and further examine the interaction between drugs and host immune response during treatment with antibiotics. Finally, we provide an outlook for future research aimed at modelling approaches for combating antibiotic resistance.

Feather arrays are patterned by interacting signalling and cell density waves

Feathers are arranged in a precise pattern in avian skin. They first arise during development in a row along the dorsal midline, with rows of new feather buds added sequentially in a spreading wave. We show that the patterning of feathers relies on coupled fibroblast growth factor (FGF) and bone morphogenetic protein (BMP) signalling together with mesenchymal cell movement, acting in a coordinated reaction-diffusion-taxis system. This periodic patterning system is partly mechanochemical, with mechanical-chemical integration occurring through a positive feedback loop centred on FGF20, which induces cell aggregation, mechanically compressing the epidermis to rapidly intensify FGF20 expression. The travelling wave of feather formation is imposed by expanding expression of Ectodysplasin A (EDA), which initiates the expression of FGF20. The EDA wave spreads across a mesenchymal cell density gradient, triggering pattern formation by lowering the threshold of mesenchymal cells required to begin to form a feather bud. These waves, and the precise arrangement of feather primordia, are lost in the flightless emu and ostrich, though via different developmental routes. The ostrich retains the tract arrangement characteristic of birds in general but lays down feather primordia without a wave, akin to the process of hair follicle formation in mammalian embryos. The embryonic emu skin lacks sufficient cells to enact feather formation, causing failure of tract formation, and instead the entire skin gains feather primordia through a later process. This work shows that a reaction-diffusion-taxis system, integrated with mechanical processes, generates the feather array. In flighted birds, the key role of the EDA/Ectodysplasin A receptor (EDAR) pathway in vertebrate skin patterning has been recast to activate this process in a quasi-1-dimensional manner, imposing highly ordered pattern formation.

Molecular signatures associated with tumor-specific immune response in melanoma patients treated with dendritic cell-based immunotherapy

Purpose

We previously showed that autologous dendritic cells (DCs) loaded with an allogeneic heat shock (HS)-conditioned melanoma cell-derived lysate, called TRIMEL, induce T-cell-mediated immune responses in stage IV melanoma patients. Importantly, a positive delayed-type hypersensitivity (DTH) reaction against TRIMEL after vaccination, correlated with patients prolonged survival. Furthermore, we observed that DTH reaction was associated with a differential response pattern reflected in the presence of distinct cell subpopulations in peripheral blood. Detected variations in patient responses encouraged molecular studies aimed to identify gene expression profiles induced after vaccination in treated patients, allowing the identification of new molecular predictive markers.

Methods

Gene expression patterns were analyzed by microarrays during vaccination, and some of them confirmed by quantitative real-time reverse transcriptase PCR (qRT-PCR) in the total leukocyte population of a representative group of responder and non-responder patients. New candidates for biomarkers with predictive value were identified using bioinformatics, molecular analysis, and flow cytometry.

Results

Seventeen genes overexpressed in responder patients after vaccination respect to non-responders were identified after a mathematical analysis, from which ten were linked to immune responses and five related to cell cycle control and signal transduction. In immunological responder patients, increased protein levels of the chemokine receptor CXCR4 and the Fc-receptor CD32 were observed on cell membranes of CD8+ T and B cells and the monocyte population, respectively, confirming gene expression results.

Conclusions

Our study contributes to finding new molecular markers associated with clinical outcome and better understanding of clinically relevant immunological responses induced by anti-tumor DC-vaccines.

Epigenetically Regulated Chromosome 14q32 miRNA Cluster Induces Metastasis and Predicts Poor Prognosis in Lung Adenocarcinoma Patients

Most lung cancer deaths are related to metastases, which indicates the necessity of detecting and inhibiting tumor cell dissemination. Here, we aimed to identify miRNAs involved in metastasis of lung adenocarcinoma as prognostic biomarkers and therapeutic targets. To that end, lymph node metastasis-associated miRNAs were identified in The Cancer Genome Atlas lung adenocarcinoma patient cohort (sequencing data; n = 449) and subsequently validated by qRT-PCR in an independent clinical cohort (n = 108). Overexpression of miRNAs located on chromosome 14q32 was associated with metastasis in lung adenocarcinoma patients. Importantly, Kaplan-Meier analysis and log-rank test revealed that higher expression levels of individual 14q32 miRNAs (mir-539, mir-323b, and mir-487a) associated with worse disease-free survival of never-smoker patients. Epigenetic analysis including DNA methylation microarray data and bisulfite sequencing validation demonstrated that the induction of 14q32 cluster correlated with genomic hypomethylation of the 14q32 locus. CRISPR activation technology, applied for the first time to functionally study the increase of clustered miRNA levels in a coordinated manner, showed that simultaneous overexpression of 14q32 miRNAs promoted tumor cell migratory and invasive properties. Analysis of individual miRNAs by mimic transfection further illustrated that miR-323b-3p, miR-487a-3p, and miR-539-5p significantly contributed to the invasive phenotype through the indirect regulation of different target genes. In conclusion, overexpression of 14q32 miRNAs, associated with the respective genomic hypomethylation, promotes metastasis and correlates with poor patient prognosis in lung adenocarcinoma.Implications: This study points to chromosome 14q32 miRNAs as promising targets to inhibit tumor cell dissemination and to predict patient prognosis in lung adenocarcinoma. Mol Cancer Res; 16(3); 390-402. ©2018 AACR.

Hierarchical patterning modes orchestrate hair follicle morphogenesis

Two theories address the origin of repeating patterns, such as hair follicles, limb digits, and intestinal villi, during development. The Turing reaction-diffusion system posits that interacting diffusible signals produced by static cells first define a prepattern that then induces cell rearrangements to produce an anatomical structure. The second theory, that of mesenchymal self-organisation, proposes that mobile cells can form periodic patterns of cell aggregates directly, without reference to any prepattern. Early hair follicle development is characterised by the rapid appearance of periodic arrangements of altered gene expression in the epidermis and prominent clustering of the adjacent dermal mesenchymal cells. We assess the contributions and interplay between reaction-diffusion and mesenchymal self-organisation processes in hair follicle patterning, identifying a network of fibroblast growth factor (FGF), wingless-related integration site (WNT), and bone morphogenetic protein (BMP) signalling interactions capable of spontaneously producing a periodic pattern. Using time-lapse imaging, we find that mesenchymal cell condensation at hair follicles is locally directed by an epidermal prepattern. However, imposing this prepattern's condition of high FGF and low BMP activity across the entire skin reveals a latent dermal capacity to undergo spatially patterned self-organisation in the absence of epithelial direction. This mesenchymal self-organisation relies on restricted transforming growth factor (TGF) β signalling, which serves to drive chemotactic mesenchymal patterning when reaction-diffusion patterning is suppressed, but, in normal conditions, facilitates cell movement to locally prepatterned sources of FGF. This work illustrates a hierarchy of periodic patterning modes operating in organogenesis.

An individual-based model for collective cancer cell migration explains speed dynamics and phenotype variability in response to growth factors

Collective cell migration is a common phenotype in epithelial cancers, which is associated with tumor cell metastasis and poor patient survival. However, the interplay between physiologically relevant pro-migratory stimuli and the underlying mechanical cell–cell interactions are poorly understood. We investigated the migratory behavior of different collectively migrating non-small cell lung cancer cell lines in response to motogenic growth factors (e.g. epidermal growth factor) or clinically relevant small compound inhibitors. Depending on the treatment, we observed distinct behaviors in a classical lateral migration assay involving traveling fronts, finger-shapes or the development of cellular bridges. Particle image velocimetry analysis revealed characteristic speed dynamics (evolution of the average speed of all cells in a frame) in all experiments exhibiting initial acceleration and subsequent deceleration of the cell populations. To better understand the mechanical properties of individual cells leading to the observed speed dynamics and the phenotypic differences we developed a mathematical model based on a Langevin approach. This model describes intercellular forces, random motility, and stimulation of active migration by mechanical interaction between cells. Simulations show that the model is able to reproduce the characteristic spatio-temporal speed distributions as well as most migratory phenotypes of the studied cell lines. A specific strength of the proposed model is that it identifies a small set of mechanical features necessary to explain all phenotypic and dynamical features of the migratory response of non-small cell lung cancer cells to chemical stimulation/inhibition. Furthermore, all processes included in the model can be associated with potential molecular components, and are therefore amenable to experimental validation. Thus, the presented mathematical model may help to predict which mechanical aspects involved in non-small cell lung cancer cell migration are affected by the respective therapeutic treatment.

In many cancers, spreading and the formation of metastasis involve the coordinated migration of many cells. An interdisciplinary team of researchers from Heidelberg and Frankfurt studied the collective movement of cultured lung cancer cells subject to chemical stimulation. Based on extensive data analysis a mathematical model was developed to explain the variety of migration behaviors observed under different treatments. The model describes the mechanics of compression, stretch, cell elasticity and force-regulated active motion—which in sum lead to coordination within large cell groups. Simulations demonstrate how these mechanical features affect cell coordination and collective behavior. In tests of potential medical treatment strategies, the model can be used to predict the effects of the drug on specific mechanical properties of single cells.

TTCA: an R package for the identification of differentially expressed genes in time course microarray data

BACKGROUND

The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements.

RESULTS

The method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF).

CONCLUSION

Here we describe a new, efficient method for the analysis of sparse and heterogeneous time course data with high detection sensitivity and transparency. It is implemented as R package TTCA (transcript time course analysis) and can be installed from the Comprehensive R Archive Network, CRAN. The source code is provided with the Additional file 1.

Abstract 1945: Identification of a miRNA/mRNA network driving non-small cell lung cancer (NSCLC) dissemination

Background:

Non-small cell lung cancer (NSCLC) is one of the most aggressive tumor entities and first data indicate that microRNAs (miRNAs) are central regulators of NSCLC dissemination. Since each miRNA is able to modulate the expression of several transcripts, they are promising targets for the development of drugs that cause efficient antitumor effects and low resistance. However, the relevant network of miRNA/mRNA driving NSCLC metastasis has not been identified yet.

Methods:

The differential expression of miRNAs was compared between NSCLC samples from patients with and without lymph node metastasis (N1, N2 and N3 vs. N0) in a cohort of The Cancer Genomic Atlas (TCGA) database (n = 449). The dysregulation of the miRNAs in tumors versus normal lung samples (n = 39) was also analyzed. For validation, fresh-frozen samples from an independent patient cohort (n = 108) were analyzed by qRT-PCR. The role of selected miRNAs in tumor dissemination was assessed by migration and invasion experiments after transfection of respective antagomirs and agomirs in NSCLC cells (time-lapse microscopy). The novel algorithm “miRlastic” was used to identify potential miRNA targets through the integration of miRNA-mRNA expression data by negative multiple linear regression analysis. Moreover, differential methylation of the miRNA genomic locations was studied as a possible mechanism of miRNA dysregulation by analyzing Illumina Infinium 450 k DNA methylation TCGA data (n = 29).

Results:

By using a stringent selection process, we identified 135 miRNAs differentially induced or reduced in NSCLCs with lymph node metastasis (p≤0.05). Interestingly, 22/135 (16.3%) of the selected miRNAs were located in the chromosomal cluster 14q32.31. Elevated expression of miR-323b, miR-487a and miR-539, which are located in 14q32.31, significantly correlated with poor patient survival. Time-resolved and quantitative analysis of lateral migration illustrated that these miRNAs increased tumor migration without affecting cell viability. Moreover, miRlastic identified several metastasis-related genes as potential downstream targets of these miRNAs. The connection between miRNAs encoded in 14q32.31 and candidate targets was confirmed in NSCLC cell lines (e.g. Pumilio RNA-Binding Family Member-2; PUM2). Lastly, hypomethylation of the 14q32.31 cluster in tumor tissues might explain increased expression of these miRNAs.

Conclusions: Our results demonstrate that miRNAs located in the chromosomal cluster 14q32.31 are driving NSCLC dissemination. Therefore, we hypothesize that the coordinated overexpression of these miRNAs is part of a genetic network supporting cancer progression and that they represent promising cancer biomarkers and therapeutic targets.

Citation Format: Margarita González-Vallinas, Marco Albrecht, Adriana Pitea, Manuel Rodríguez-Paredes, Damian Stichel, Steffen Sass, Julian Gutekunst, Jennifer Schmitt, Thomas Muley, Michael Meister, Arne Warth, Peter Schirmacher, Fabian J. Theis, Nikola S. Müller, Franziska Matthäus, Kai Breuhahn. Identification of a miRNA/mRNA network driving non-small cell lung cancer (NSCLC) dissemination. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1945.

Concomitant expression of far upstream element (FUSE) binding protein (FBP) interacting repressor (FIR) and its splice variants induce migration and invasion of non-small cell lung cancer (NSCLC) cells

Transcription factors integrate a variety of oncogenic input information, facilitate tumour growth and cell dissemination, and therefore represent promising therapeutic target structures. Because over-expression of DNA-interacting far upstream element binding protein (FBP) supports non-small cell lung cancer (NSCLC) migration, we asked whether its repressor, FBP-interacting repressor (FIR) is functionally inactivated and how FIR might affect NSCLC cell biology. Different FIR splice variants were highly expressed in the majority of NSCLCs, with the highest levels in tumours carrying genomic gains of chromosome 8q24.3, which contained the FIR gene locus. Nuclear FIR expression was significantly enriched at the invasion front of primary NSCLCs, but this did not correlate with tumour cell proliferation. FIR accumulation was associated with worse patient survival and tumour recurrence; in addition, FIR over-expression significantly correlated with lymph node metastasis in squamous cell carcinomas (SCCs). In vitro, we applied newly developed methods and modelling approaches for the quantitative and time-resolved description of the pro-migratory and pro-invasive capacities of SCC cells. siRNA-mediated silencing of all FIR variants significantly reduced the speed and directional movement of tumour cells in all phases of migration. Furthermore, sprouting efficiency and single cell invasiveness were diminished following FIR inhibition. Interestingly, the silencing of FIR isoforms lacking exon 2 (FIR(Δexon2)) alone was sufficient to reduce lateral migration and invasion. In summary, by using scale-spanning data derived from primary human tissues, quantitative cellular analyses and mathematical modelling, we have demonstrated that concomitant over-expression of FIR and its splice variants drives NSCLC migration and dissemination.

Does body shaping influence brain shape? Habitual physical activity is linked to brain morphology independent of age

OBJECTIVES

Physical activity (PA) was found to influence human brain morphology. However, the impact of PA on brain morphology was mainly demonstrated in seniors. We investigated healthy individuals across a broad age range for the relation between habitual PA and brain morphology.

METHODS

Ninety-five participants (19-82 years) were assessed for self-reported habitual PA with the "Baecke habitual physical activity questionnaire", and T1-weighted magnetic resonance images were evaluated with whole brain voxel based morphometry for gray and white matter volumes and densities.

RESULTS

Regression analyses revealed a positive relation between the extent of physical activity and gray matter volume bilaterally in the anterior hippocampal and parahippocampal gyrus independent of age and gender. Age as well as leisure and locomotion activities were linked to enhanced white matter volumes in the posterior cingulate gyrus and precuneus, suggesting a positive interaction especially in seniors.

CONCLUSIONS

Habitual physical activity is associated with regional volumetric gray and white matter alterations. The positive relation of hippocampal volume and physical activity seems not to be restricted to seniors. Thus, habitual physical activity should be generally considered as an influencing factor in studies investigating medial temporal lobe volume and associated cognitive functions (memory), especially in psychiatric research.

Gene expression in superior temporal cortex of schizophrenia patients

We investigated gene expression pattern obtained from microarray data of 10 schizophrenia patients and 10 control subjects. Brain tissue samples were obtained postmortem; thus, the different ages of the patients at death also allowed a study of the dynamic behavior of the expression patterns over a time frame of many years. We used statistical tests and dimensionality reduction methods to characterize the subset of genes differentially expressed in the two groups. A set of 10 genes were significantly downregulated, and a larger set of 40 genes were upregulated in the schizophrenia patients. Interestingly, the set of upregulated genes includes a large number of genes associated with gene transcription (zinc finger proteins and histone methylation) and apoptosis. We furthermore identified genes with a significant trend correlating with age in the control (MLL3) or the schizophrenia group (SOX5, CTRL). Assessments of correlations of other genes with the disorder (RRM1) or with the duration of medication could not be resolved, because all patients were medicated. This hypothesis-free approach uncovered a series of genes differentially expressed in schizophrenia that belong to a number of distinct cell functions, such as apoptosis, transcriptional regulation, cell motility, energy metabolism and hypoxia.

Effects of normal aging and SCN1A risk-gene expression on brain metabolites: evidence for an association between SCN1A and myo-inositol

Previously reported MRS findings in the aging brain include lower N-acetylaspartate (NAA) and higher myo-inositol (mI), total creatine (Cr) and choline-containing compound (Cho) concentrations. Alterations in the sodium channel voltage gated type I, alpha subunit SCN1A variant rs10930201 have been reported to be associated with several neurological disorders with cognitive deficits. MRS studies in SCN1A-related diseases have reported striking differences in the mI concentrations between patients and controls. In a study on 'healthy aging', we investigated metabolite spectra in a sample of 83 healthy volunteers and determined their age dependence. We also investigated a potential link between SCN1A and mI. We observed a significantly negative association of NAA (p = 0.004) and significantly positive associations of mI (p ≤ 0.001), Cr (p ≤ 0.001) and Cho (p = 0.034) with age in frontal white matter. The linear association of Cho ends at the age of about 50 years and is followed by an inverted 'U'-shaped curve. Further, mI was higher in C allele carriers of the SCN1A variant rs10930201. Our results corroborated the age-related changes in metabolite concentrations, and found evidence for a link between SCN1A and frontal white matter mI in healthy subjects.

New measurement criteria for studying alcohol drinking and relapse in rodents

Purpose

Relapse to alcohol use is considered as one of the central features distinguishing dependence from controlled alcohol consumption. Relapse-like drinking in rodents is a transient episode of heavy drinking that follows a period of abstinence. This behaviour is called the alcohol deprivation effect (ADE). Not all animals develop behavioural changes that resemble relapse-like drinking behaviour. The purpose of our study was to develop a generalized quantitative criterion by which animals could be separated into two groups depending on their behaviour during a relapse-like situation (ADE vs. no-ADE).

Methods

An automated drinkometer system was used for data collection. This system measures fluid consumption by means of high-precision sensors attached to the drinking bottles in the home cage of the rat. We used a four bottle free choice paradigm with water 5, 10, and 20% ethanol solutions. For data analysis we developed a new measure of alcohol intake that quantifies net alcohol intake in relation to net consumption of water. This new measure is called water-penalized net ethanol intake.

Results

The new measure is more robust than commonly used measurements, such as alcohol preference and intake. It allows the comparison of alcohol intake between different groups of animals and different setups using an arbitrary number of bottles. Based on this new measure we developed a method to automatically select the threshold for the presence of ADE in individual animals.

Conclusions

Separating animals by their behavior during relapse-like situation could be used as one of the criteria for identification of alcohol addicted and non-addicted rats. A classification into presenting ADE or not is also essential to test the effectiveness of newly developed therapeutic drugs.

A systems medicine research approach for studying alcohol addiction

According to the World Health Organization, about 2 billion people drink alcohol. Excessive alcohol consumption can result in alcohol addiction, which is one of the most prevalent neuropsychiatric diseases afflicting our society today. Prevention and intervention of alcohol binging in adolescents and treatment of alcoholism are major unmet challenges affecting our health-care system and society alike. Our newly formed German SysMedAlcoholism consortium is using a new systems medicine approach and intends (1) to define individual neurobehavioral risk profiles in adolescents that are predictive of alcohol use disorders later in life and (2) to identify new pharmacological targets and molecules for the treatment of alcoholism. To achieve these goals, we will use omics-information from epigenomics, genetics transcriptomics, neurodynamics, global neurochemical connectomes and neuroimaging (IMAGEN; Schumann et al. ) to feed mathematical prediction modules provided by two Bernstein Centers for Computational Neurosciences (Berlin and Heidelberg/Mannheim), the results of which will subsequently be functionally validated in independent clinical samples and appropriate animal models. This approach will lead to new early intervention strategies and identify innovative molecules for relapse prevention that will be tested in experimental human studies. This research program will ultimately help in consolidating addiction research clusters in Germany that can effectively conduct large clinical trials, implement early intervention strategies and impact political and healthcare decision makers.

Stathmin regulates keratinocyte proliferation and migration during cutaneous regeneration

Cutaneous regeneration utilizes paracrine feedback mechanisms to fine-tune the regulation of epidermal keratinocyte proliferation and migration. However, it is unknown how fibroblast-derived hepatocyte growth factor (HGF) affects these mutually exclusive processes in distinct cell populations. We here show that HGF stimulates the expression and phosphorylation of the microtubule-destabilizing factor stathmin in primary human keratinocytes. Quantitative single cell- and cell population-based analyses revealed that basal stathmin levels are important for the migratory ability of keratinocytes in vitro; however, its expression is moderately induced in the migration tongue of mouse skin or organotypic multi-layered keratinocyte 3D cultures after full-thickness wounding. In contrast, clearly elevated stathmin expression is detectable in hyperproliferative epidermal areas. In vitro, stathmin silencing significantly reduced keratinocyte proliferation. Automated quantitative and time-resolved analyses in organotypic cocultures demonstrated a high correlation between Stathmin/phospho-Stathmin and Ki67 positivity in epidermal regions with proliferative activity. Thus, activation of stathmin may stimulate keratinocyte proliferation, while basal stathmin levels are sufficient for keratinocyte migration during cutaneous regeneration.

Circadian rhythms investigated on the cellular and molecular levels

Investigations on circadian rhythms have markedly advanced our understanding of health and disease with the advent of high-throughput technologies like microarrays and epigenetic profiling. They elucidated the multi-level behaviour of interactive oscillations from molecules to neuronal networks and eventually to processes of learning and memory in an impressive manner. The small-world topology of synchronized firing through neuron-neuron and neuron-glia gap junctions is discussed as a mathematical approach to these intensively studied issues. It has become evident that, apart from some disorders caused by gene mutations, the majority of disorders originating from disturbances of rhythms arise from environmental influences and epigenetic changes. In this context, it was mandatory to think of and devise experiments on temporary scales, which exponentially increased the volumes of data obtained from time-series and rapidly became prohibitive of manual inspection. Therefore, more and more sophisticated mathematical algorithms have been developed to identify rhythmic expression of genes and to find coexpression by their clustering. It is expected that disturbed rhythmic behaviour in mental disorders is reflected in altered oscillatory behaviour of gene expression.

Contextual social cognition impairments in schizophrenia and bipolar disorder

Background

The ability to integrate contextual information with social cues to generate social meaning is a key aspect of social cognition. It is widely accepted that patients with schizophrenia and bipolar disorders have deficits in social cognition; however, previous studies on these disorders did not use tasks that replicate everyday situations.

Methodology/Principal Findings

This study evaluates the performance of patients with schizophrenia and bipolar disorders on social cognition tasks (emotional processing, empathy, and social norms knowledge) that incorporate different levels of contextual dependence and involvement of real-life scenarios. Furthermore, we explored the association between social cognition measures, clinical symptoms and executive functions. Using a logistic regression analysis, we explored whether the involvement of more basic skills in emotional processing predicted performance on empathy tasks. The results showed that both patient groups exhibited deficits in social cognition tasks with greater context sensitivity and involvement of real-life scenarios. These deficits were more severe in schizophrenic than in bipolar patients. Patients did not differ from controls in tasks involving explicit knowledge. Moreover, schizophrenic patients’ depression levels were negatively correlated with performance on empathy tasks.

Conclusions/Significance

Overall performance on emotion recognition predicted performance on intentionality attribution during the more ambiguous situations of the empathy task. These results suggest that social cognition deficits could be related to a general impairment in the capacity to implicitly integrate contextual cues. Important implications for the assessment and treatment of individuals with schizophrenia and bipolar disorders, as well as for neurocognitive models of these pathologies are discussed.

SCN1A affects brain structure and the neural activity of the aging brain

BACKGROUND

The aging of the human brain is accompanied by changes in cortical structure as well as functional activity and variable degrees of cognitive decline. One-third of the observable inter-individual differences in cognitive decline are thought to be heritable. SCN1A encodes the sodium channel α subunit and is considered to be a susceptibility gene for several neurological disorders with prominent cognitive deficits. In a recent genome-wide association study the C allele of the SCN1A variant rs10930201 was observed to be significantly associated with poor short-term memory performance. rs10930201 was further observed to be related to differences in neural activity during a working memory task.

METHODS

The aim of the present study was to explore whether SCN1A modifies the vulnerability to aging processes of the human brain. Therefore we assessed the interacting effects of the SCN1A vulnerability allele rs10930201 and age in terms of brain activity and brain morphology in 62 healthy volunteers between 21 and 82 years of age.

RESULTS

In C allele carriers, activity in the right inferior frontal cortex and the posterior cingulate cortex increased with age. Moreover, exploratory analysis revealed regional effects of rs10930201 on brain structure, indicating reduced gray matter densities in the frontal and insular regions in the C allele carriers.

CONCLUSIONS

Collectively, the present results suggest that the SCN1A polymorphism has modulatory effects on brain morphology and vulnerability to age-related alterations in brain activity of cortical regions that subserve working memory.

Effects of age on the structure of functional connectivity networks during episodic and working memory demand

The aim of the study was to investigate age-related differences in large-scale functional connectivity networks during episodic and working memory challenge. A graph theoretical approach was used providing an exhaustive set of topological measures to quantify age-related differences in the network structure on various scales. In a single session, 10 young (22-30 years) and 10 senior (62-77 years) subjects performed an episodic and a working memory task during functional magnetic resonance imaging. Networks of functional connectivity were constructed by correlating the blood oxygenation level-dependent (BOLD) signal for every pair of voxels. Statistical network parameters yield a global characterization of the network topology, the quantification of the importance of specific regions, and shifts in local connectivity. An age-related increase in the density and size of the networks and loss of small-worldness was observed, related to an expanded distribution of brain activity during both memory demands in seniors, and a more specific and localized activity in young subjects. In addition, we found highly symmetrical neural networks in young subjects accompanied by a strong coupling between parietal and occipital regions. In contrast, seniors showed pronounced left-hemispheric asymmetry with decreased connectivity within occipital areas, but increased connectivity within parietal areas. Moreover, seniors engaged an additional frontal network strongly connected to parietal areas. In contrast to young subjects, seniors showed an almost identical structure of network connectivity during both memory tasks. The chosen network approach is explorative and hypothesis-free. Our results extend seed-based and BOLD-signal intensity focused studies, and support present hypotheses like compensation and dedifferentiation.

On the origin and characteristics of noise-induced Lévy walks of E. coli

Lévy walks as a random search strategy have recently attracted a lot of attention, and have been described in many animal species. However, very little is known about one of the most important issues, namely how Lévy walks are generated by biological organisms. We study a model of the chemotaxis signaling pathway of E. coli, and demonstrate that stochastic fluctuations and the specific design of the signaling pathway in concert enable the generation of Lévy walks. We show that Lévy walks result from the superposition of an ensemble of exponential distributions, which occurs due to the shifts in the internal enzyme concentrations following the stochastic fluctuations. With our approach we derive the power-law analytically from a model of the chemotaxis signaling pathway, and obtain a power-law exponent μ ≈ 2.2, which coincides with experimental results. This work provides a means to confirm Lévy walks as natural phenomenon by providing understanding on the process through which they emerge. Furthermore, our results give novel insights into the design aspects of biological systems that are capable of translating additive noise on the microscopic scale into beneficial macroscopic behavior.

An integrated genome research network for studying the genetics of alcohol addiction

Alcohol drinking is highly prevalent in many cultures and contributes to the global burden of disease. In fact, it was shown that alcohol constitutes 3.2% of all worldwide deaths in the year 2006 and is linked to more than 60 diseases, including cancers, cardiovascular diseases, liver cirrhosis, neuropsychiatric disorders, injuries and foetal alcohol syndrome. Alcoholism, which has been proven to have a high genetic load, is one potentially fatal consequence of chronic heavy alcohol consumption, and may be regarded as one of the most prevalent neuropsychiatric diseases afflicting our society today. The aim of the integrated genome research network 'Genetics of Alcohol Addiction'--which is a German inter-/trans-disciplinary life science consortium consisting of molecular biologists, behavioural pharmacologists, system biologists with mathematicians, human geneticists and clinicians--is to better understand the genetics of alcohol addiction by identifying and validating candidate genes and molecular networks involved in the aetiology of this pathology. For comparison, addictive behaviour to other drugs of abuse (e.g. cocaine) is studied as well. Here, we present an overview of our research consortium, the current state of the art on genetic research in the alcohol field, and list finally several of our recently published research highlights. As a result of our scientific efforts, better insights into the molecular and physiological processes underlying addictive behaviour will be obtained, new targets and target networks in the addicted brain will be defined, and subsequently, novel and individualized treatment strategies for our patients will be delivered.

Interactive molecular networks obtained by computer-aided conversion of microarray data from brains of alcohol-drinking rats

Lists of differentially expressed genes in a disease have become increasingly more comprehensive with improvements on all technical levels. Despite statistical cutoffs of 99% or 95% confidence intervals, the number of genes can rise to several hundreds or even thousands, which is barely amenable to a researcher's understanding. This report describes some ways of processing those data by mathematical algorithms. Gene lists obtained from 53 microarrays (two brain regions (amygdala and caudate putamen), three rat strains drinking alcohol or being abstinent) have been used. They resulted from analyses on Affymetrix chips and encompassed approximately 6 000 genes that passed our quality filters. They have been subjected to four mathematical ways of processing: (a) basic statistics, (b) principal component analysis, (c) hierarchical clustering, and (d) introduction into Bayesian networks. It turns out, by using the p-values or the log-ratios, that they best subdivide into brain areas, followed by a fairly good discrimination into the rat strains and the least good discrimination into alcohol-drinking vs. abstinent. Nevertheless, despite the fact that the relation to alcohol-drinking was the weakest signal, attempts have been made to integrate the genes related to alcohol-drinking into Bayesian networks to learn more about their inter-relationships. The study shows, that the tools employed here are extremely useful for (a) quality control of datasets, (b) for constructing interactive (molecular) networks, but (c) have limitations in integration of larger numbers into the networks. The study also shows that it is often pivotal to balance out the number of experimental conditions with the number of animals.

Biosynthetic potentials of metabolites and their hierarchical organization

A major challenge in systems biology is to understand how complex and highly connected metabolic networks are organized. The structure of these networks is investigated here by identifying sets of metabolites that have a similar biosynthetic potential. We measure the biosynthetic potential of a particular compound by determining all metabolites than can be produced from it and, following a terminology introduced previously, call this set the scope of the compound. To identify groups of compounds with similar scopes, we apply a hierarchical clustering method. We find that compounds within the same cluster often display similar chemical structures and appear in the same metabolic pathway. For each cluster we define a consensus scope by determining a set of metabolites that is most similar to all scopes within the cluster. This allows for a generalization from scopes of single compounds to scopes of a chemical family. We observe that most of the resulting consensus scopes overlap or are fully contained in others, revealing a hierarchical ordering of metabolites according to their biosynthetic potential. Our investigations show that this hierarchy is not only determined by the chemical complexity of the metabolites, but also strongly by their biological function. As a general tendency, metabolites which are necessary for essential cellular processes exhibit a larger biosynthetic potential than those involved in secondary metabolism. A central result is that chemically very similar substances with different biological functions may differ significantly in their biosynthetic potentials. Our studies provide an important step towards understanding fundamental design principles of metabolic networks determined by the structural and functional complexity of metabolites.

Diffusion versus network models as descriptions for the spread of prion diseases in the brain

In this paper we will discuss different modeling approaches for the spread of prion diseases in the brain. Firstly, we will compare reaction-diffusion models with models of epidemic diseases on networks. The solutions of the resulting reaction-diffusion equations exhibit traveling wave behavior on a one-dimensional domain, and the wave speed can be estimated. The models can be tested for diffusion-driven (Turing) instability, which could present a possible mechanism for the formation of plaques. We also show that the reaction-diffusion systems are capable of reproducing experimental data on prion spread in the mouse visual system. Secondly, we study classical epidemic models on networks, and use these models to study the influence of the network topology on the disease progression.