Integrated methodologies to study human transcriptional regulation from functional genomics data

Abstract : The human body is composed of a huge number of cells acting together in a concerted manner. The current understanding is that proteins perform most of the necessary activities in keeping a cell alive. The DNA, on the other hand, stores the information on how to produce the different proteins in the genome. Regulating gene transcription is the first important step that can thus affect the life of a cell, modify its functions and its responses to the environment. Regulation is a complex operation that involves specialized proteins, the transcription factors. Transcription factors (TFs) can bind to DNA and activate the processes leading to the expression of genes into new proteins. Errors in this process may lead to diseases. In particular, some transcription factors have been associated with a lethal pathological state, commonly known as cancer, associated with uncontrolled cellular proliferation, invasiveness of healthy tissues and abnormal responses to stimuli. Understanding cancer-related regulatory programs is a difficult task, often involving several TFs interacting together and influencing each other's activity. This Thesis presents new computational methodologies to study gene regulation. In addition we present applications of our methods to the understanding of cancer-related regulatory programs. The understanding of transcriptional regulation is a major challenge. We address this difficult question combining computational approaches with large collections of heterogeneous experimental data. In detail, we design signal processing tools to recover transcription factors binding sites on the DNA from genome-wide surveys like chromatin immunoprecipitation assays on tiling arrays (ChIP-chip). We then use the localization about the binding of TFs to explain expression levels of regulated genes. In this way we identify a regulatory synergy between two TFs, the oncogene C-MYC and SP1. C-MYC and SP1 bind preferentially at promoters and when SP1 binds next to C-NIYC on the DNA, the nearby gene is strongly expressed. The association between the two TFs at promoters is reflected by the binding sites conservation across mammals, by the permissive underlying chromatin states 'it represents an important control mechanism involved in cellular proliferation, thereby involved in cancer. Secondly, we identify the characteristics of TF estrogen receptor alpha (hERa) target genes and we study the influence of hERa in regulating transcription. hERa, upon hormone estrogen signaling, binds to DNA to regulate transcription of its targets in concert with its co-factors. To overcome the scarce experimental data about the binding sites of other TFs that may interact with hERa, we conduct in silico analysis of the sequences underlying the ChIP sites using the collection of position weight matrices (PWMs) of hERa partners, TFs FOXA1 and SP1. We combine ChIP-chip and ChIP-paired-end-diTags (ChIP-pet) data about hERa binding on DNA with the sequence information to explain gene expression levels in a large collection of cancer tissue samples and also on studies about the response of cells to estrogen. We confirm that hERa binding sites are distributed anywhere on the genome. However, we distinguish between binding sites near promoters and binding sites along the transcripts. The first group shows weak binding of hERa and high occurrence of SP1 motifs, in particular near estrogen responsive genes. The second group shows strong binding of hERa and significant correlation between the number of binding sites along a gene and the strength of gene induction in presence of estrogen. Some binding sites of the second group also show presence of FOXA1, but the role of this TF still needs to be investigated. Different mechanisms have been proposed to explain hERa-mediated induction of gene expression. Our work supports the model of hERa activating gene expression from distal binding sites by interacting with promoter bound TFs, like SP1. hERa has been associated with survival rates of breast cancer patients, though explanatory models are still incomplete: this result is important to better understand how hERa can control gene expression. Thirdly, we address the difficult question of regulatory network inference. We tackle this problem analyzing time-series of biological measurements such as quantification of mRNA levels or protein concentrations. Our approach uses the well-established penalized linear regression models where we impose sparseness on the connectivity of the regulatory network. We extend this method enforcing the coherence of the regulatory dependencies: a TF must coherently behave as an activator, or a repressor on all its targets. This requirement is implemented as constraints on the signs of the regressed coefficients in the penalized linear regression model. Our approach is better at reconstructing meaningful biological networks than previous methods based on penalized regression. The method is tested on the DREAM2 challenge of reconstructing a five-genes/TFs regulatory network obtaining the best performance in the "undirected signed excitatory" category. Thus, these bioinformatics methods, which are reliable, interpretable and fast enough to cover large biological dataset, have enabled us to better understand gene regulation in humans.

Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations.

Brain fluctuations at rest are not random but are structured in spatial patterns of correlated activity across different brain areas. The question of how resting-state functional connectivity (FC) emerges from the brain's anatomical connections has motivated several experimental and computational studies to understand structure-function relationships. However, the mechanistic origin of resting state is obscured by large-scale models' complexity, and a close structure-function relation is still an open problem. Thus, a realistic but simple enough description of relevant brain dynamics is needed. Here, we derived a dynamic mean field model that consistently summarizes the realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network, in which connectivity is constrained by diffusion imaging data from human subjects. The dynamic mean field approximates the ensemble dynamics, whose temporal evolution is dominated by the longest time scale of the system. With this reduction, we demonstrated that FC emerges as structured linear fluctuations around a stable low firing activity state close to destabilization. Moreover, the model can be further and crucially simplified into a set of motion equations for statistical moments, providing a direct analytical link between anatomical structure, neural network dynamics, and FC. Our study suggests that FC arises from noise propagation and dynamical slowing down of fluctuations in an anatomically constrained dynamical system. Altogether, the reduction from spiking models to statistical moments presented here provides a new framework to explicitly understand the building up of FC through neuronal dynamics underpinned by anatomical connections and to drive hypotheses in task-evoked studies and for clinical applications.

Improving plant functional groups for dynamic models of biodiversity: at the crossroads between functional and community ecology

The pace of on-going climate change calls for reliable plant biodiversity scenarios. Traditional dynamic vegetation models use plant functional types that are summarized to such an extent that they become meaningless for biodiversity scenarios. Hybrid dynamic vegetation models of intermediate complexity (hybrid-DVMs) have recently been developed to address this issue. These models, at the crossroads between phenomenological and process-based models, are able to involve an intermediate number of well-chosen plant functional groups (PFGs). The challenge is to build meaningful PFGs that are representative of plant biodiversity, and consistent with the parameters and processes of hybrid-DVMs. Here, we propose and test a framework based on few selected traits to define a limited number of PFGs, which are both representative of the diversity (functional and taxonomic) of the flora in the Ecrins National Park, and adapted to hybrid-DVMs. This new classification scheme, together with recent advances in vegetation modeling, constitutes a step forward for mechanistic biodiversity modeling.

A dual functional origin of transfer in the ICEclc genomic island of Pseudomonas knackmussii B13.

Genomic islands (GEIs) are large DNA segments, present in most bacterial genomes, that are most likely acquired via horizontal gene transfer. Here, we study the self-transfer system of the integrative and conjugative element ICEclc of Pseudomonas knackmussii B13, which stands model for a larger group of ICE/GEI with syntenic core gene organization. Functional screening revealed that unlike conjugative plasmids and other ICEs ICEclc carries two separate origins of transfer, with different sequence context but containing a similar repeat motif. Conjugation experiments with GFP-labelled ICEclc variants showed that both oriTs are used for transfer and with indistinguishable efficiencies, but that having two oriTs results in an estimated fourfold increase of ICEclc transfer rates in a population compared with having a single oriT. A gene for a relaxase essential for ICEclc transfer was also identified, but in vivo strand exchange assays suggested that the relaxase processes both oriTs in a different manner. This unique dual origin of transfer system might have provided an evolutionary advantage for distribution of ICE, a hypothesis that is supported by the fact that both oriT regions are conserved in several GEIs related to ICEclc.

Spinal cord T-cell infiltration in the spared nerve injury model of neuropathic pain: a time course study

Background : Numerous studies have shown that immune cells infiltrate the spinal cord after peripheral nerve injury and that they play a major contribution to sensory hypersensitivity in rodents. In particular, the role of monocyte-derived cells and T lymphocytes seems to be prominent in this process. This exciting new perspective in research on neuropathic pain opens many different areas of work, including the understanding of the function of these cells and how they impact on neural function. However, no systematic description of the time course or cell types that characterize this infiltration has been published yet, although this seems to be the rational first step of an overall understanding of the phenomenon. Objective : Describe the time course and cell characteristics of T lymphocyte infiltration in the spinal cord in the Spared Nerve Injury (SNI) model of neuropathic pain in rats. Methods : Collect of lumbar spinal cords of rats at days 2, 7, 21 and 40 after SNI or sham operation (n=4). Immunofluorescence detecting different proteins of T cell subgroups (CD2+CD4+, CD2+CD8+, Th1 markers, Th2 markers, Th17 markers). Quantification of the infiltration rate of the different subgroups. Expected results : First, we expect to see an infiltration of T cells in the spinal cord ipsilateral to nerve injury, higher in SNI rats than in sham animals. Second, we anticipate that different subtypes of T cells penetrate at different time points. Finally, the number of T lymphocytes are expected to decrease at the latest time point, showing a resolution of the process underlying their infiltrating the spinal cord in the first place. Impact : A systematic description of the infiltration of T cells in the spinal cord after peripheral nerve injury is needed to have a better understanding of the role of immune cells in neuropathic pain. The time course that we want to establish will provide the scientific community with new perspectives. First, it will confirm that T cells do indeed infiltrate the spinal cord after SNI in rats. Second, the type of T cells infiltrating at different time points will give clues about their function, in particular their inflammatory or anti-inflammatory profile. From there on, other studies could be lead, investigating the functional side of the specific subtypes put to light by us. Ultimately, this could lead to the discovery of new drugs targeting T cells or their infiltration, in the hope of improving neuropathic pain.

Why is the cladoceran Simocephalus vetulus (Müller) not a 'bang-bang strategist '? A critique of the optimal-body-size model

Lynch's (1980a) optimal-body-size model is designed to explain some major trends in cladoceran life histories; in particular the fact that large and littoral species seem to be bang-bang strategists (they grow first and the reproduce) whereas smaller planktonic species seem to be intermediate strategists (they grow and reproduce simultaneously). Predation is assumed to be an important selective pressure for these trends. Simocephalus vetulus (Müller) does not fit this pattern; being a littoral and relatively large species but an intermediate strategist. As shown by computer simulations, this species would reduce its per capita rate of increase by adopting the strategy predicted by the optimal-body-size model. Two aspects of the model are criticized: (1) the optimization criterion is shown to be incorrect and (2) the prediction of an intermediate strategy is not justified. Structural constraints are suggested to be responsible for the intermediate strategy of S.vetulus. Biotic interactions seem to have little effect on the observed life-history patterns of this species.

Functional homogenization of bumblebee communities in alpine landscapes under projected climate change

Background: Bumblebees represent an active pollinator group in mountain regions and assure the pollination of many different plant species from low to high elevations. Plant-pollinator interactions are mediated by functional traits. Shift in bumblebee functional structure under climate change may impact plant-pollinator interactions in mountains. Here, we estimated bumblebee upward shift in elevation, community turnover, and change in functional structure under climate change. Method: We sampled bumblebee species at 149 sites along the elevation gradient. We used stacked species distribution models (S-SDMs) forecasted under three climate change scenarios (A2, A1B, RCP3PD) to model the potential distribution of the Bombus species. Furthermore, we used species proboscis length measurements to assess the functional change in bumblebee assemblages along the elevation gradient. Results: We found species-specific response of bumblebee species to climate change. Species differed in their predicted rate of range contraction and expansion. Losers were mainly species currently restricted to high elevation. Under the most severe climate change scenarios (A2), we found a homogenization of proboscis length structure in bumblebee communities along the elevation gradient through the upward colonization of high elevation by species with longer proboscides. Conclusions: Here, we show that in addition to causing the shift in the distribution of bumblebee species, climate change may impact the functional structure of communities. The colonization of high elevation areas by bumblebee species with long proboscides may modify the structure of plant-pollination interaction networks by increasing the diversity of pollination services at high elevation.

Critical single proximal left arterial descending coronary artery stenosis to mimic chronic myocardial ischemia: a new model induced by minimal invasive technology.

BACKGROUND/AIMS: The present report examines a new pig model for progressive induction of high-grade stenosis, for the study of chronic myocardial ischemia and the dynamics of collateral vessel growth. METHODS: Thirty-nine Landrace pigs were instrumented with a novel experimental stent (GVD stent) in the left anterior descending coronary artery. Eight animals underwent transthoracic echocardiography at rest and under low-dose dobutamine. Seven animals were examined by nuclear PET and SPECT analysis. Epi-, mid- and endocardial fibrosis and the numbers of arterial vessels were examined by histology. RESULTS: Functional analysis showed a significant decrease in global left ventricular ejection fraction (24.5 +/- 1.6%) 3 weeks after implantation. There was a trend to increased left ventricular ejection fraction after low-dose dobutamine stress (36.0 +/- 6.6%) and a significant improvement of the impaired regional anterior wall motion. PET and SPECT imaging documented chronic hibernation. Myocardial fibrosis increased significantly in the ischemic area with a gradient from epi- to endocardial. The number of arterial vessels in the ischemic area increased and coronary angiography showed abundant collateral vessels of Rentrop class 1. CONCLUSION: The presented experimental model mimics the clinical situation of chronic myocardial ischemia secondary to 1-vessel coronary disease.

Deleterious Actions Of Vegf On The Blood Retinal Barrier And Photoreceptor Survival In The Light-damage Model

Purpose: The retinal balance between pro- and anti-angiogenic factors is critical for angiogenesis control, but is also involved in cell survival. We previously reported upregulation of VEGF and photoreceptor (PR) cell death in the Light-damage (LD) model. Preliminary results showed that anti-VEGF can rescue PR from cell death. Thus, we investigated the role of VEGF on the retina and we herein described the effect of anti-VEGF antibody delivered by lentiviral gene transfer in this model.Methods: To characterize the action of VEGF during the LD, we exposed Balb/c mice subretinally injected with LV-anti-VEGF, or not, to 5'000 lux for 1h. We next evaluated the retinal function, PR survival and protein expression (VEGF, VEGFR1/2, Src, PEDF, p38MAPK, Akt, Peripherin, SWL-opsin) after LD. We analyzed Blood retinal barrier (BRB) integrity on flat-mounted RPE and cryosections stained with β-catenin, ZO-1, N-cadherin and albumin.Results: Results indicate that the VEGF pathway is modulated after LD. LD leads to extravascular albumin leakage and BRB breakdown: β-catenin, ZO-1 and N-cadherin translocate to the cytoplasm of RPE cells showing loss of cell cohesion. This phenomenon is in adequacy with the VEGF time-course expression. Assessment of the retinal function reveals that PR rescue correlates with the level of LV-anti-VEGF expression. Rhodopsin content was higher in the LV-anti-VEGF group than in controls and measures of the ONL thickness indicate that LV-anti-VEGF preserves by 82% the outer nuclear layer from degeneration. Outer segments (OS) appeared well organized with an appropriate length in the LV-anti-VEGF group compared to controls, and the expression of SWL-opsin is maintained in the OS without being mislocalized as in the LV-GFP group. Finally, LV-anti-VEGF treatment prevents BRB breakdown and maintained RPE cell integrity.Conclusions: This study involves VEGF in LD and highlights the prime importance of the BRB integrity for PR survival. Taken together, these results show that anti-VEGF is neuroprotective in this model and maintains functional PR layer in LD-treated mice.

Evaluating fear of falling among community-dwelling older people: A qualitative pattern model based on the experience of falls

This research was conducted in the context of the project IRIS 8A Health and Society (2002-2008) and financially supported by the University of Lausanne. It was aomed at developping a model based on the elder people's experience and allowed us to develop a "Portrait evaluation" of fear of falling using their examples and words. It is a very simple evaluation, which can be used by professionals, but by the elder people themselves. The "Portrait evaluation" and the user's guide are on free access, but we would very much approciate to know whether other people or scientists have used it and collect their comments. (contact: Chantal.Piot-Ziegler@unil.ch)The purpose of this study is to create a model grounded in the elderly people's experience allowing the development of an original instrument to evaluate FOF.In a previous study, 58 semi-structured interviews were conducted with community-dwelling elderly people. The qualitative thematic analysis showed that fear of falling was defined through the functional, social and psychological long-term consequences of falls (Piot-Ziegler et al., 2007).In order to reveal patterns in the expression of fear of falling, an original qualitative thematic pattern analysis (QUAlitative Pattern Analysis - QUAPA) is developed and applied on these interviews.The results of this analysis show an internal coherence across the three dimensions (functional, social and psychological). Four different patterns are found, corresponding to four degrees of fear of falling. They are formalized in a fear of falling intensity model.This model leads to a portrait-evaluation for fallers and non-fallers. The evaluation must be confronted to large samples of elderly people, living in different environments. It presents an original alternative to the concept of self-efficacy to evaluate fear of falling in older people.The model of FOF presented in this article is grounded on elderly people's experience. It gives an experiential description of the three dimensions constitutive of FOF and of their evolution as fear increases, and defines an evaluation tool using situations and wordings based on the elderly people's discourse.

An integer-based score to predict functional outcome in acute ischemic stroke: The ASTRAL score.

OBJECTIVE: To develop and validate a simple, integer-based score to predict functional outcome in acute ischemic stroke (AIS) using variables readily available after emergency room admission. METHODS: Logistic regression was performed in the derivation cohort of previously independent patients with AIS (Acute Stroke Registry and Analysis of Lausanne [ASTRAL]) to identify predictors of unfavorable outcome (3-month modified Rankin Scale score >2). An integer-based point-scoring system for each covariate of the fitted multivariate model was generated by their β-coefficients; the overall score was calculated as the sum of the weighted scores. The model was validated internally using a 2-fold cross-validation technique and externally in 2 independent cohorts (Athens and Vienna Stroke Registries). RESULTS: Age (A), severity of stroke (S) measured by admission NIH Stroke Scale score, stroke onset to admission time (T), range of visual fields (R), acute glucose (A), and level of consciousness (L) were identified as independent predictors of unfavorable outcome in 1,645 patients in ASTRAL. Their β-coefficients were multiplied by 4 and rounded to the closest integer to generate the score. The area under the receiver operating characteristic curve (AUC) of the score in the ASTRAL cohort was 0.850. The score was well calibrated in the derivation (p = 0.43) and validation cohorts (0.22 [Athens, n = 1,659] and 0.49 [Vienna, n = 653]). AUCs were 0.937 (Athens), 0.771 (Vienna), and 0.902 (when pooled). An ASTRAL score of 31 indicates a 50% likelihood of unfavorable outcome. CONCLUSIONS: The ASTRAL score is a simple integer-based score to predict functional outcome using 6 readily available items at hospital admission. It performed well in double external validation and may be a useful tool for clinical practice and stroke research.

A dynamic model of keratinocyte stem cell renewal and differentiation: role of the p21WAF1/Cip1 and Notch1 signaling pathways.

We present here a dynamic model of functional equilibrium between keratinocyte stem cells, transit amplifying populations and cells that are reversibly versus irreversibly committed to differentiation. According to this model, the size of keratinocyte stem cell populations can be controlled at multiple levels, including relative late steps in the sequence of events leading to terminal differentiation and by the influences of a heterogeneous extra-cellular environment. We discuss how work in our laboratory, on the interconnection between the cyclin/CDK inhibitor p21WAF1/Cip1 and the Notch1 signaling pathways, provides strong support to this dynamic model of stem cell versus committed and/or differentiated keratinocyte populations.

Predicting current and future spatial community patterns of plant functional traits

Community-level patterns of functional traits relate to community assembly and ecosystem functioning. By modelling the changes of different indices describing such patterns - trait means, extremes and diversity in communities - as a function of abiotic gradients, we could understand their drivers and build projections of the impact of global change on the functional components of biodiversity. We used five plant functional traits (vegetative height, specific leaf area, leaf dry matter content, leaf nitrogen content and seed mass) and non-woody vegetation plots to model several indices depicting community-level patterns of functional traits from a set of abiotic environmental variables (topographic, climatic and edaphic) over contrasting environmental conditions in a mountainous landscape. We performed a variation partitioning analysis to assess the relative importance of these variables for predicting patterns of functional traits in communities, and projected the best models under several climate change scenarios to examine future potential changes in vegetation functional properties. Not all indices of trait patterns within communities could be modelled with the same level of accuracy: the models for mean and extreme values of functional traits provided substantially better predictive accuracy than the models calibrated for diversity indices. Topographic and climatic factors were more important predictors of functional trait patterns within communities than edaphic predictors. Overall, model projections forecast an increase in mean vegetation height and in mean specific leaf area following climate warming. This trend was important at mid elevation particularly between 1000 and 2000 m asl. With this study we showed that topographic, climatic and edaphic variables can successfully model descriptors of community-level patterns of plant functional traits such as mean and extreme trait values. However, which factors determine the diversity of functional traits in plant communities remains unclear and requires more investigations.

Functional genomics of intracellular bacteria.

During the genomic era, a large amount of whole-genome sequences accumulated, which identified many hypothetical proteins of unknown function. Rapidly, functional genomics, which is the research domain that assign a function to a given gene product, has thus been developed. Functional genomics of intracellular pathogenic bacteria exhibit specific peculiarities due to the fastidious growth of most of these intracellular micro-organisms, due to the close interaction with the host cell, due to the risk of contamination of experiments with host cell proteins and, for some strict intracellular bacteria such as Chlamydia, due to the absence of simple genetic system to manipulate the bacterial genome. To identify virulence factors of intracellular pathogenic bacteria, functional genomics often rely on bioinformatic analyses compared with model organisms such as Escherichia coli and Bacillus subtilis. The use of heterologous expression is another common approach. Given the intracellular lifestyle and the many effectors that are used by the intracellular bacteria to corrupt host cell functions, functional genomics is also often targeting the identification of new effectors such as those of the T4SS of Brucella and Legionella.

Identification of HMX1 target genes: a predictive promoter model approach.

PURPOSE: A homozygous mutation in the H6 family homeobox 1 (HMX1) gene is responsible for a new oculoauricular defect leading to eye and auricular developmental abnormalities as well as early retinal degeneration (MIM 612109). However, the HMX1 pathway remains poorly understood, and in the first approach to better understand the pathway's function, we sought to identify the target genes. METHODS: We developed a predictive promoter model (PPM) approach using a comparative transcriptomic analysis in the retina at P15 of a mouse model lacking functional Hmx1 (dmbo mouse) and its respective wild-type. This PPM was based on the hypothesis that HMX1 binding site (HMX1-BS) clusters should be more represented in promoters of HMX1 target genes. The most differentially expressed genes in the microarray experiment that contained HMX1-BS clusters were used to generate the PPM, which was then statistically validated. Finally, we developed two genome-wide target prediction methods: one that focused on conserving PPM features in human and mouse and one that was based on the co-occurrence of HMX1-BS pairs fitting the PPM, in human or in mouse, independently. RESULTS: The PPM construction revealed that sarcoglycan, gamma (35kDa dystrophin-associated glycoprotein) (Sgcg), teashirt zinc finger homeobox 2 (Tshz2), and solute carrier family 6 (neurotransmitter transporter, glycine) (Slc6a9) genes represented Hmx1 targets in the mouse retina at P15. Moreover, the genome-wide target prediction revealed that mouse genes belonging to the retinal axon guidance pathway were targeted by Hmx1. Expression of these three genes was experimentally validated using a quantitative reverse transcription PCR approach. The inhibitory activity of Hmx1 on Sgcg, as well as protein tyrosine phosphatase, receptor type, O (Ptpro) and Sema3f, two targets identified by the PPM, were validated with luciferase assay. CONCLUSIONS: Gene expression analysis between wild-type and dmbo mice allowed us to develop a PPM that identified the first target genes of Hmx1.