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.

Functional avidity: a measure to predict the efficacy of effector T cells?

The functional avidity is determined by exposing T-cell populations in vitro to different amounts of cognate antigen. T-cells with high functional avidity respond to low antigen doses. This in vitro measure is thought to correlate well with the in vivo effector capacity of T-cells. We here present the multifaceted factors determining and influencing the functional avidity of T-cells. We outline how changes in the functional avidity can occur over the course of an infection. This process, known as avidity maturation, can occur despite the fact that T-cells express a fixed TCR. Furthermore, examples are provided illustrating the importance of generating T-cell populations that exhibit a high functional avidity when responding to an infection or tumors. Furthermore, we discuss whether criteria based on which we evaluate an effective T-cell response to acute infections can also be applied to chronic infections such as HIV. Finally, we also focus on observations that high-avidity T-cells show higher signs of exhaustion and facilitate the emergence of virus escape variants. The review summarizes our current understanding of how this may occur as well as how T-cells of different functional avidity contribute to antiviral and anti-tumor immunity. Enhancing our knowledge in this field is relevant for tumor immunotherapy and vaccines design.

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.

Extent and limitations of density functional theory in describing magnetic systems

The performance of density-functional theory to solve the exact, nonrelativistic, many-electron problem for magnetic systems has been explored in a new implementation imposing space and spin symmetry constraints, as in ab initio wave function theory. Calculations on selected systems representative of organic diradicals, molecular magnets and antiferromagnetic solids carried out with and without these constraints lead to contradictory results, which provide numerical illustration on this usually obviated problem. It is concluded that the present exchange-correlation functionals provide reasonable numerical results although for the wrong physical reasons, thus evidencing the need for continued search for more accurate expressions.

Electronic and magnetic structure of LaMnO3 from hybrid periodic density-functional theory

The electronic and magnetic structures of the LaMnO3 compound have been studied by means of periodic calculations within the framework of spin polarized hybrid density-functional theory. In order to quantify the role of approximations to electronic exchange and correlation three different hybrid functionals have been used which mix nonlocal Fock and local Dirac-Slater exchange. Periodic Hartree-Fock results are also reported for comparative purposes. The A-antiferromagnetic ground state is properly predicted by all methods including Hartree-Fock exchange. In general, the different hybrid methods provide a rather accurate description of the band gap and of the two magnetic coupling constants, strongly suggesting that the corresponding description of the electronic structure is also accurate. An important conclusion emerging from this study is that the nature of the occupied states near the Fermi level is intermediate between the Hartree-Fock and local density approximation descriptions with a comparable participation of both Mn and O states.

Surface structure and stability of PdZn and PtZn alloys: Density-functional slab model studies

Geometric parameters of binary (1:1) PdZn and PtZn alloys with CuAu-L10 structure were calculated with a density functional method. Based on the total energies, the alloys are predicted to feature equal formation energies. Calculated surface energies of PdZn and PtZn alloys show that (111) and (100) surfaces exposing stoichiometric layers are more stable than (001) and (110) surfaces comprising alternating Pd (Pt) and Zn layers. The surface energy values of alloys lie between the surface energies of the individual components, but they differ from their composition weighted averages. Compared with the pure metals, the valence d-band widths and the Pd or Pt partial densities of states at the Fermi level are dramatically reduced in PdZn and PtZn alloys. The local valence d-band density of states of Pd and Pt in the alloys resemble that of metallic Cu, suggesting that a similar catalytic performance of these systems can be related to this similarity in the local electronic structures.

Antiferromagnetic exchange interactions from hybrid density functional theory

A hybrid theory which combines the full nonlocal ¿exact¿ exchange interaction with the local spin-density approximation of density-functional theory is shown to lead to marked improvement in the description of antiferromagnetically coupled systems. Semiquantitative agreement with experiment is found for the magnitude of the coupling constant in La2CuO4, KNiF3, and K2NiF4. The magnitude of the unpaired spin population on the metal site is in excellent agreement with experiment for La2CuO4.

Extent and limitations of density functional theory in describing magnetic systems

The performance of density-functional theory to solve the exact, nonrelativistic, many-electron problem for magnetic systems has been explored in a new implementation imposing space and spin symmetry constraints, as in ab initio wave function theory. Calculations on selected systems representative of organic diradicals, molecular magnets and antiferromagnetic solids carried out with and without these constraints lead to contradictory results, which provide numerical illustration on this usually obviated problem. It is concluded that the present exchange-correlation functionals provide reasonable numerical results although for the wrong physical reasons, thus evidencing the need for continued search for more accurate expressions.

Functional responses and heterogeneities: an experimental test with cladocerans

The functional response of predators is usually modelled as a function of absolute prey density. Arditi and Ginzburg have suggested that it should often depend instead on the prey available per capita of predators, i.e. on the prey/predator ratio. Theory suggests that these two forms of dependence are related to the degree of spatial and temporal heterogeneity. Experiments using four filter-feeding cladoceran species were designed to test this hypothesis and to investigate the relation between individual behaviour and population dynamics. The patterns of population abundance that the cladocerans reached at equilibrium match the expectation that species with homogeneous spatial behaviour follow prey-dependent dynamics while those with heterogeneous behaviour follow ratio-dependent dynamics.

Characterization of surface functional groups present on laboratory-generated and ambient aerosol particles by means of heterogeneous titration reactions

A Knudsen flow reactor has been used to quantify surface functional groups on aerosols collected in the field. This technique is based on a heterogeneous titration reaction between a probe gas and a specific functional group on the particle surface. In the first part of this work, the reactivity of different probe gases on laboratory-generated aerosols (limonene SOA, Pb(NO3)2, Cd(NO3)2) and diesel reference soot (SRM 2975) has been studied. Five probe gases have been selected for the quantitative determination of important functional groups: N(CH3)3 (for the titration of acidic sites), NH2OH (for carbonyl functions), CF3COOH and HCl (for basic sites of different strength), and O3 (for oxidizable groups). The second part describes a field campaign that has been undertaken in several bus depots in Switzerland, where ambient fine and ultrafine particles were collected on suitable filters and quantitatively investigated using the Knudsen flow reactor. Results point to important differences in the surface reactivity of ambient particles, depending on the sampling site and season. The particle surface appears to be multi-functional, with the simultaneous presence of antagonistic functional groups which do not undergo internal chemical reactions, such as acid-base neutralization. Results also indicate that the surface of ambient particles was characterized by a high density of carbonyl functions (reactivity towards NH2OH probe in the range 0.26-6 formal molecular monolayers) and a low density of acidic sites (reactivity towards N(CH3)3 probe in the range 0.01-0.20 formal molecular monolayer). Kinetic parameters point to fast redox reactions (uptake coefficient ?0>10-3 for O3 probe) and slow acid-base reactions (?0<10-4 for N(CH3)3 probe) on the particle surface. [Authors]

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.

SNP2GO: functional analysis of genome-wide association studies.

Genome-wide association studies (GWAS) are designed to identify the portion of single-nucleotide polymorphisms (SNPs) in genome sequences associated with a complex trait. Strategies based on the gene list enrichment concept are currently applied for the functional analysis of GWAS, according to which a significant overrepresentation of candidate genes associated with a biological pathway is used as a proxy to infer overrepresentation of candidate SNPs in the pathway. Here we show that such inference is not always valid and introduce the program SNP2GO, which implements a new method to properly test for the overrepresentation of candidate SNPs in biological pathways.

Postanoxic functional recovery of the developing heart is slightly altered by endogenous or exogenous nitric oxide.

Nitric oxide synthase (NOS) is strongly and transiently expressed in the developing heart but its function is not well documented. This work examined the role, either protective or detrimental, that endogenous and exogenous NO could play in the functioning of the embryonic heart submitted to hypoxia and reoxygenation. Spontaneously beating hearts isolated from 4-day-old chick embryos were either homogenized to determine basal inducible NOS (iNOS) expression and activity or submitted to 30 min anoxia followed by 100 min reoxygenation. The chrono-, dromo- and inotropic responses to anoxia/reoxygenation were determined in the presence of NOS substrate (L-arginine 10 mM), NOS inhibitor L-NIO (1-5 mM), or NO donor (DETA NONOate 10-100 microM). Myocardial iNOS was detectable by immunoblotting and its activity was specifically decreased by 53% in the presence of 5 mM L-NIO. L-Arginine, L-NIO and DETA NONOate at 10 microM had no significant effect on the investigated functional parameters during anoxia/reoxygenation. However, irrespective of anoxia/reoxygenation, DETA NONOate at 100 microM decreased ventricular shortening velocity by about 70%, and reduced atrio-ventricular propagation by 23%. None of the used drugs affected atrial activity and hearts of all experimental groups fully recovered at the end of reoxygenation. These findings indicate that (1) by contrast with adult heart, endogenously released NO plays a minor role in the early response of the embryonic heart to reoxygenation, (2) exogenous NO has to be provided at high concentration to delay postanoxic functional recovery, and (3) sinoatrial pacemaker cells are the less responsive to NO.

The use of heterogeneous chemistry for the characterization of functional groups at the gas/particle interface of soot and TiO(2) nanoparticles

Six gases (N((CH3)3), NH2OH, CF3COOH, HCl, NO2, O3) were selected to probe the surface of seven combustion aerosol (amorphous carbon, flame soot) and three types of TiO2 nanoparticles using heterogeneous, that is gas-surface reactions. The gas uptake to saturation of the probes was measured under molecular flow conditions in a Knudsen flow reactor and expressed as a density of surface functional groups on a particular aerosol, namely acidic (carboxylic) and basic (conjugated oxides such as pyrones, N-heterocycles) sites, carbonyl (R1-C(O)-R2) and oxidizable (olefinic, -OH) groups. The limit of detection was generally well below 1% of a formal monolayer of adsorbed probe gas. With few exceptions most investigated aerosol samples interacted with all probe gases which points to the coexistence of different functional groups on the same aerosol surface such as acidic and basic groups. Generally, the carbonaceous particles displayed significant differences in surface group density: Printex 60 amorphous carbon had the lowest density of surface functional groups throughout, whereas Diesel soot recovered from a Diesel particulate filter had the largest. The presence of basic oxides on carbonaceous aerosol particles was inferred from the ratio of uptakes of CF3COOH and HCl owing to the larger stability of the acetate compared to the chloride counterion in the resulting pyrylium salt. Both soots generated from a rich and a lean hexane diffusion flame had a large density of oxidizable groups similar to amorphous carbon FS 101. TiO2 15 had the lowest density of functional groups among the three studied TiO2 nanoparticles for all probe gases despite the smallest size of its primary particles. The used technique enabled the measurement of the uptake probability of the probe gases on the various supported aerosol samples. The initial uptake probability, g0, of the probe gas onto the supported nanoparticles differed significantly among the various investigated aerosol samples but was roughly correlated with the density of surface groups, as expected. [Authors]