Mathisca de Gunst

Identification of candidate transcriptional modulators involved in successful regeneration after nerve injury

Successful regeneration of injured neurons requires a complex molecular response that involves the expression, modification and transport of a large number of proteins. The identity of neuronal proteins responsible for the initiation of regenerative neurite outgrowth is largely unknown. Dorsal root ganglion (DRG) neurons display robust and successful regeneration following lesion of their peripheral neurite, whereas outgrowth of central neurites is weak and does not lead to functional recovery. We have utilized this differential response to gain insight in the early transcriptional events associated with successful regeneration. Surprisingly, our study shows that peripheral and central nerve crushes elicit very distinct transcriptional activation, revealing a large set of novel genes that are differentially regulated within the first 24 h after the lesion. Here we show that Ankrd1, a gene known to act as a transcriptional modulator, is involved in neurite outgrowth of a DRG neuron‐derived cell line as well as in cultured adult DRG neurons. This gene, and others identified in this study, may be part of the transcriptional regulatory module that orchestrates the onset of successful regeneration.

Morphine exposure and abstinence define specific stages of gene expression in the rat nucleus accumbens

Intermittent exposure to addictive drugs causes long‐lasting changes in responsiveness to these substances due to persistent molecular and cellular alterations within the meso‐corticolimbic system. In this report, we studied the expression profiles of 159 genes in the rat nucleus accumbens during morphine exposure (14 days, 10 mg/kg s.c.) and drug‐abstinence (3 weeks). We used real‐time quantitative PCR to monitor gene expression after establishing its sensitivity and resolution to resolve small changes in expression for genes in various abundance classes. Morphine‐exposure (5 time points) and subsequent abstinence (6 time points) induced phase‐specific temporal gene expression of distinct functional groups of genes, for example, short‐term homeostatic responses. Opiate withdrawal appeared to be a new stimulus in terms of gene expression and mediates a marked wave of gene repression. Prolonged abstinence resulted in persistently changed expression levels of genes involved in neuronal outgrowth and re‐wiring. Our findings substantiate the hypothesis that this new gene program, initiated upon morphine‐withdrawal, may subserve long‐term neuronal plasticity involved in the persistent behavioral consequences of repeated drug‐exposure.

Identification of context-specific gene regulatory networks with GEMULA—gene expression modeling using LAsso

MOTIVATION Gene regulatory networks, in which edges between nodes describe interactions between transcriptional regulators and their target genes, determine the coordinated spatiotemporal expression of genes. Especially in higher organisms, context-specific combinatorial regulation by transcription factors (TFs) is believed to determine cellular states and fates. TF-target gene interactions can be studied using high-throughput techniques such as ChIP-chip or ChIP-Seq. These experiments are time and cost intensive, and further limited by, for instance, availability of high affinity TF antibodies. Hence, there is a practical need for methods that can predict TF-TF and TF-target gene interactions in silico, i.e. from gene expression and DNA sequence data alone. We propose GEMULA, a novel approach based on linear models to predict TF-gene expression associations and TF-TF interactions from experimental data. GEMULA is based on linear models, fast and considers a wide range of biologically plausible models that describe gene expression data as a function of predicted TF binding to gene promoters. RESULTS We show that models inferred with GEMULA are able to explain roughly 70% of the observed variation in gene expression in the yeast heat shock response. The functional relevance of the inferred TF-TF interactions in these models are validated by different sources of independent experimental evidence. We also have applied GEMULA to an in vitro model of neuronal outgrowth. Our findings confirm existing knowledge on gene regulatory interactions underlying neuronal outgrowth, but importantly also generate new insights into the temporal dynamics of this gene regulatory network that can now be addressed experimentally. AVAILABILITY The GEMULA R-package is available from http://www.few.vu.nl/~degunst/gemula_1.0.tar.gz.

Independently outgrowing neurons and geometry-based synapse formation produce networks with realistic synaptic connectivity.

Neuronal signal integration and information processing in cortical networks critically depend on the organization of synaptic connectivity. During development, neurons can form synaptic connections when their axonal and dendritic arborizations come within close proximity of each other. Although many signaling cues are thought to be involved in guiding neuronal extensions, the extent to which accidental appositions between axons and dendrites can already account for synaptic connectivity remains unclear. To investigate this, we generated a local network of cortical L2/3 neurons that grew out independently of each other and that were not guided by any extracellular cues. Synapses were formed when axonal and dendritic branches came by chance within a threshold distance of each other. Despite the absence of guidance cues, we found that the emerging synaptic connectivity showed a good agreement with available experimental data on spatial locations of synapses on dendrites and axons, number of synapses by which neurons are connected, connection probability between neurons, distance between connected neurons, and pattern of synaptic connectivity. The connectivity pattern had a small-world topology but was not scale free. Together, our results suggest that baseline synaptic connectivity in local cortical circuits may largely result from accidentally overlapping axonal and dendritic branches of independently outgrowing neurons.

Existence and uniqueness of the maximum likelihood estimator for models with a Kronecker product covariance structure

This paper deals with multivariate Gaussian models for which the covariance matrix is a Kronecker product of two matrices. We consider maximum likelihood estimation of the model parameters, in particular of the covariance matrix. There is no explicit expression for the maximum likelihood estimator of a Kronecker product covariance matrix. We investigate whether the maximum likelihood estimator of the covariance matrix exists and whether it is unique. We consider models with general, with double diagonal, and with one diagonal Kronecker product covariance matrices, and find different results.

LLM3D: a log-linear modeling-based method to predict functional gene regulatory interactions from genome-wide expression data

All cellular processes are regulated by condition-specific and time-dependent interactions between transcription factors and their target genes. While in simple organisms, e.g. bacteria and yeast, a large amount of experimental data is available to support functional transcription regulatory interactions, in mammalian systems reconstruction of gene regulatory networks still heavily depends on the accurate prediction of transcription factor binding sites. Here, we present a new method, log-linear modeling of 3D contingency tables (LLM3D), to predict functional transcription factor binding sites. LLM3D combines gene expression data, gene ontology annotation and computationally predicted transcription factor binding sites in a single statistical analysis, and offers a methodological improvement over existing enrichment-based methods. We show that LLM3D successfully identifies novel transcriptional regulators of the yeast metabolic cycle, and correctly predicts key regulators of mouse embryonic stem cell self-renewal more accurately than existing enrichment-based methods. Moreover, in a clinically relevant in vivo injury model of mammalian neurons, LLM3D identified peroxisome proliferator-activated receptor γ (PPARγ) as a neuron-intrinsic transcriptional regulator of regenerative axon growth. In conclusion, LLM3D provides a significant improvement over existing methods in predicting functional transcription regulatory interactions in the absence of experimental transcription factor binding data.

Quantitative analysis of two-dimensional observations of premalignant clones in the presence or absence of malignant tumors

Based on a two-mutation model for carcinogenesis the mathematical theory is developed which is needed for the quantitative analysis of premalignant clones induced by specific carcinogens. In particular, the article deals with the situation where additional knowledge about the presence or absence of malignant tumors in the tissue of interest is available. The main difficulty arises from the fact that the data on premalignant clones are as a rule obtained from observation of a two-dimensional plane section of the tissue, so that the model needs to be translated from three dimensions into two before it is applicable to the data.

Novel Candidate Genes Associated with Hippocampal Oscillations

The hippocampus is critical for a wide range of emotional and cognitive behaviors. Here, we performed the first genome-wide search for genes influencing hippocampal oscillations. We measured local field potentials (LFPs) using 64-channel multi-electrode arrays in acute hippocampal slices of 29 BXD recombinant inbred mouse strains. Spontaneous activity and carbachol-induced fast network oscillations were analyzed with spectral and cross-correlation methods and the resulting traits were used for mapping quantitative trait loci (QTLs), i.e., regions on the genome that may influence hippocampal function. Using genome-wide hippocampal gene expression data, we narrowed the QTLs to eight candidate genes, including Plcb1, a phospholipase that is known to influence hippocampal oscillations. We also identified two genes coding for calcium channels, Cacna1b and Cacna1e, which mediate presynaptic transmitter release and have not been shown to regulate hippocampal network activity previously. Furthermore, we showed that the amplitude of the hippocampal oscillations is genetically correlated with hippocampal volume and several measures of novel environment exploration.

Two Mutation Model for Carcinogenesis: Relative Roles of Somatic Mutations and Cell Proliferation in Determining Risk

Two experimental data sets are analyzed within the framework of a two-event model for carcinogenesis. In the first, the number and size distribution of altered hepatic foci, which are thought to be premaligant lesions, are analyzed as functions of dose of an administered agent (N-Nitrosomorpholine, NNM). Definitions of initiation and promotion potencies are proposed. Results of the analysis indicate that NNM is a strong initiator and a weak promoter. In the second, the time to appearance and the probability of malignant lung tumors in rats exposed to radon are analyzed as functions of total exposure and rate of exposure. The results indicate that fractionation of exposure increases the lifetime probability of tumor, and that the efficiency of fractionation can be explained by the relative effects of radon daughters on the mutation rates and the kinetics of growth of initiated cells.

A Morpho-Density Approach to Estimating Neural Connectivity

Neuronal signal integration and information processing in cortical neuronal networks critically depend on the organization of synaptic connectivity. Because of the challenges involved in measuring a large number of neurons, synaptic connectivity is difficult to determine experimentally. Current computational methods for estimating connectivity typically rely on the juxtaposition of experimentally available neurons and applying mathematical techniques to compute estimates of neural connectivity. However, since the number of available neurons is very limited, these connectivity estimates may be subject to large uncertainties. We use a morpho-density field approach applied to a vast ensemble of model-generated neurons. A morpho-density field (MDF) describes the distribution of neural mass in the space around the neural soma. The estimated axonal and dendritic MDFs are derived from 100,000 model neurons that are generated by a stochastic phenomenological model of neurite outgrowth. These MDFs are then used to estimate the connectivity between pairs of neurons as a function of their inter-soma displacement. Compared with other density-field methods, our approach to estimating synaptic connectivity uses fewer restricting assumptions and produces connectivity estimates with a lower standard deviation. An important requirement is that the model-generated neurons reflect accurately the morphology and variation in morphology of the experimental neurons used for optimizing the model parameters. As such, the method remains subject to the uncertainties caused by the limited number of neurons in the experimental data set and by the quality of the model and the assumptions used in creating the MDFs and in calculating estimating connectivity. In summary, MDFs are a powerful tool for visualizing the spatial distribution of axonal and dendritic densities, for estimating the number of potential synapses between neurons with low standard deviation, and for obtaining a greater understanding of the relationship between neural morphology and network connectivity.