Christelle Reynes

Rationalizing the chemical space of protein-protein interaction inhibitors.

Protein-protein interactions (PPIs) are one of the next major classes of therapeutic targets, although they are too intricate to tackle with standard approaches. This is due, in part, to the inadequacy of todays chemical libraries. However, the emergence of a growing number of experimentally validated inhibitors of PPIs (i-PPIs) allows drug designers to use chemoinformatics and machine learning technologies to unravel the nature of the chemical space covered by the reported compounds. Key characteristics of i-PPIs can then be revealed and highlight the importance of specific shapes and/or aromatic bonds, enabling the design of i-PPI-enriched focused libraries and, therefore, of cost-effective screening strategies.

Designing Focused Chemical Libraries Enriched in Protein-Protein Interaction Inhibitors using Machine-Learning Methods

Protein-protein interactions (PPIs) may represent one of the next major classes of therapeutic targets. So far, only a minute fraction of the estimated 650,000 PPIs that comprise the human interactome are known with a tiny number of complexes being drugged. Such intricate biological systems cannot be cost-efficiently tackled using conventional high-throughput screening methods. Rather, time has come for designing new strategies that will maximize the chance for hit identification through a rationalization of the PPI inhibitor chemical space and the design of PPI-focused compound libraries (global or target-specific). Here, we train machine-learning-based models, mainly decision trees, using a dataset of known PPI inhibitors and of regular drugs in order to determine a global physico-chemical profile for putative PPI inhibitors. This statistical analysis unravels two important molecular descriptors for PPI inhibitors characterizing specific molecular shapes and the presence of a privileged number of aromatic bonds. The best model has been transposed into a computer program, PPI-HitProfiler, that can output from any drug-like compound collection a focused chemical library enriched in putative PPI inhibitors. Our PPI inhibitor profiler is challenged on the experimental screening results of 11 different PPIs among which the p53/MDM2 interaction screened within our own CDithem platform, that in addition to the validation of our concept led to the identification of 4 novel p53/MDM2 inhibitors. Collectively, our tool shows a robust behavior on the 11 experimental datasets by correctly profiling 70% of the experimentally identified hits while removing 52% of the inactive compounds from the initial compound collections. We strongly believe that this new tool can be used as a global PPI inhibitor profiler prior to screening assays to reduce the size of the compound collections to be experimentally screened while keeping most of the true PPI inhibitors. PPI-HitProfiler is freely available on request from our CDithem platform website, www.CDithem.com.

Simultaneous Hyper‐ and Hypomethylation at Imprinted Loci in a Subset of Patients with GNAS Epimutations Underlies a Complex and Different Mechanism of Multilocus Methylation Defect in Pseudohypoparathyroidism Type 1b

Most patients with pseudohypoparathyroidism type 1b (PHP‐1b) display a loss of imprinting (LOI) encompassing the GNAS locus resulting in PTH resistance. In other imprinting disorders, such as Russell–Silver or Beckwith–Wiedemann syndrome, we and others have shown that the LOI is not restricted to one imprinted locus but may affect other imprinted loci for some patients. Therefore, we hypothesized that patients with PHP‐1b might present multilocus imprinting defects. We investigated, in 63 patients with PHP‐1b, the methylation pattern of eight imprinted loci: GNAS, ZAC1, PEG1/MEST, ICR1, and ICR2 on chromosome 11p15, SNRPN, DLK1/GTL2 IG‐DMR, and L3MBTL1. We found multilocus imprinting defects in four PHP‐1b patients carrying broad LOI at the GNAS locus (1) simultaneous hypermethylation at L3MBTL1 differentially methylated region 3 (DMR3), and hypomethylation at PEG1/MEST DMR (n = 1), (2) hypermethylation at the L3MBTL1 (DMR3) (n = 1) and at the DLK1/GTL2 IG‐DMR (n = 1), and (3) hypomethylation at the L3MBTL1 DMR3 (n = 1). We suggest that mechanisms underlying multilocus imprinting defects in PHP‐1b differ from those of other imprinting disorders having only multilocus loss of methylation. Furthermore, our results favor the hypothesis of “epidominance”, that is, the phenotype is controlled by the most severely affected imprinted locus.

Spatial Distribution of Cerebral White Matter Lesions Predicts Progression to Mild Cognitive Impairment and Dementia

Context White matter lesions (WML) increase the risk of dementia. The relevance of WML location is less clear. We sought to determine whether a particular WML profile, based on the density and location of lesions, could be associated with an increased risk of mild cognitive impairment (MCI) or dementia over the following 7 years. Methods In 426 healthy subjects from a cohort of community-dwelling people aged 65 years and over (ESPRIT Project), standardized cognitive and neurological evaluations were repeated after 2, 4 and 7 years. Patterns of WML were computed with a supervised data mining approach (decision trees) using the regional WML volumes (frontal, parietal, temporal, and occipital regions) and the total WML volume estimated at baseline. Cox proportional hazard models were then constructed to study the association between WML patterns and risk of MCI/dementia. Results Total WML volume and percentage of WML in the temporal region proved to be the best predictors of progression to MCI and dementia. Specifically, severe total WML load with a high proportion of lesions in the temporal region was significantly associated with the risk of developing MCI or dementia. Conclusions Above a certain threshold of damage, a pattern of WML clustering in the temporal region identifies individuals at increased risk of MCI or dementia. As this WML pattern is observed before the onset of clinical symptoms, it may facilitate the detection of patients at risk of MCI/dementia.

Insights into an original pocket-ligand pair classification: a promising tool for ligand profile prediction.

Pockets are today at the cornerstones of modern drug discovery projects and at the crossroad of several research fields, from structural biology to mathematical modeling. Being able to predict if a small molecule could bind to one or more protein targets or if a protein could bind to some given ligands is very useful for drug discovery endeavors, anticipation of binding to off- and anti-targets. To date, several studies explore such questions from chemogenomic approach to reverse docking methods. Most of these studies have been performed either from the viewpoint of ligands or targets. However it seems valuable to use information from both ligands and target binding pockets. Hence, we present a multivariate approach relating ligand properties with protein pocket properties from the analysis of known ligand-protein interactions. We explored and optimized the pocket-ligand pair space by combining pocket and ligand descriptors using Principal Component Analysis and developed a classification engine on this paired space, revealing five main clusters of pocket-ligand pairs sharing specific and similar structural or physico-chemical properties. These pocket-ligand pair clusters highlight correspondences between pocket and ligand topological and physico-chemical properties and capture relevant information with respect to protein-ligand interactions. Based on these pocket-ligand correspondences, a protocol of prediction of clusters sharing similarity in terms of recognition characteristics is developed for a given pocket-ligand complex and gives high performances. It is then extended to cluster prediction for a given pocket in order to acquire knowledge about its expected ligand profile or to cluster prediction for a given ligand in order to acquire knowledge about its expected pocket profile. This prediction approach shows promising results and could contribute to predict some ligand properties critical for binding to a given pocket, and conversely, some key pocket properties for ligand binding.

Genome-wide diversity and gene expression profiling of Babesia microti isolates identify polymorphic genes that mediate host-pathogen interactions

Babesia microti, a tick-transmitted, intraerythrocytic protozoan parasite circulating mainly among small mammals, is the primary cause of human babesiosis. While most cases are transmitted by Ixodes ticks, the disease may also be transmitted through blood transfusion and perinatally. A comprehensive analysis of genome composition, genetic diversity, and gene expression profiling of seven B. microti isolates revealed that genetic variation in isolates from the Northeast United States is almost exclusively associated with genes encoding the surface proteome and secretome of the parasite. Furthermore, we found that polymorphism is restricted to a small number of genes, which are highly expressed during infection. In order to identify pathogen-encoded factors involved in host-parasite interactions, we screened a proteome array comprised of 174 B. microti proteins, including several predicted members of the parasite secretome. Using this immuno-proteomic approach we identified several novel antigens that trigger strong host immune responses during the onset of infection. The genomic and immunological data presented herein provide the first insights into the determinants of B. microti interaction with its mammalian hosts and their relevance for understanding the selective pressures acting on parasite evolution.

Effect of exposure to polycyclic aromatic hydrocarbons on basal ganglia and attention-deficit hyperactivity disorder symptoms in primary school children

BACKGROUND Polycyclic aromatic hydrocarbons (PAHs) have been proposed as environmental risk factors for attention deficit hyperactivity disorder (ADHD). The effects of these pollutants on brain structures potentially involved in the pathophysiology of ADHD are unknown. OBJECTIVE The aim of this study was to investigate the effects of PAHs on basal ganglia volumes and ADHD symptoms in school children. METHODS We conducted an imaging study in 242 children aged 8-12years, recruited through a set of representative schools of the city of Barcelona, Spain. Indoor and outdoor PAHs and benzo[a]pyrene (BPA) levels were assessed in the school environment, one year before the MRI assessment. Whole-brain volumes and basal ganglia volumes (caudate nucleus, globus pallidus, putamen) were derived from structural MRI scans using automated tissue segmentation. ADHD symptoms (ADHD/DSM-IV Scales, American Psychiatric Association 2002) were reported by teachers, and inattentiveness was evaluated with standard error of hit reaction time in the attention network computer-based test. RESULTS Total PAHs and BPA were associated with caudate nucleus volume (CNV) (i.e., an interquartile range increase in BPA outdoor level (67pg/m3) and indoor level (76pg/m3) was significantly linked to a decrease in CNV (mm3) (β=-150.6, 95% CI [-259.1, -42.1], p=0.007, and β=-122.4, 95% CI [-232.9, -11.8], p=0.030 respectively) independently of intracranial volume, age, sex, maternal education and socioeconomic vulnerability index at home). ADHD symptoms and inattentiveness increased in children with higher exposure to BPA, but these associations were not statistically significant. CONCLUSIONS Exposure to PAHs, and in particular to BPA, is associated with subclinical changes on the caudate nucleus, even below the legislated annual target levels established in the European Union. The behavioral consequences of this induced brain change were not identified in this study, but given the caudate nucleus involvement in many crucial cognitive and behavior processes, this volume reduction is concerning for the childrens neurodevelopment.

Whole-genome duplication increases tumor cell sensitivity to MPS1 inhibition

Several lines of evidence indicate that whole-genome duplication resulting in tetraploidy facilitates carcinogenesis by providing an intermediate and metastable state more prone to generate oncogenic aneuploidy. Here, we report a novel strategy to preferentially kill tetraploid cells based on the abrogation of the spindle assembly checkpoint (SAC) via the targeting of TTK protein kinase (better known as monopolar spindle 1, MPS1). The pharmacological inhibition as well as the knockdown of MPS1 kills more efficiently tetraploid cells than their diploid counterparts. By using time-lapse videomicroscopy, we show that tetraploid cells do not survive the aborted mitosis due to SAC abrogation upon MPS1 depletion. On the contrary diploid cells are able to survive up to at least two more cell cycles upon the same treatment. This effect might reflect the enhanced difficulty of cells with whole-genome doubling to tolerate a further increase in ploidy and/or an elevated level of chromosome instability in the absence of SAC functions. We further show that MPS1-inhibited tetraploid cells promote mitotic catastrophe executed by the intrinsic pathway of apoptosis, as indicated by the loss of mitochondrial potential, the release of the pro-apoptotic cytochrome c from mitochondria, and the activation of caspases. Altogether, our results suggest that MPS1 inhibition could be used as a therapeutic strategy for targeting tetraploid cancer cells.

A new genetic algorithm in proteomics: Feature selection for SELDI-TOF data

Mass spectrometry from clinical specimens is used in order to identify biomarkers in a diagnosis. Thus, a reliable method for both feature selection and classification is required. A novel method is proposed to find biomarkers in SELDI-TOF in order to perform robust classification.The feature selection is based on a new genetic algorithm. Concerning the classification, a method which takes into account the great variability on intensity by using decision stumps has been developed. Moreover, as the samples are often small, it is more appropriate to use the decision stumps simultaneously than building a complete tree. The thresholds of the decision stumps are determined in the same genetic algorithm. Finally, the method was generalized to more than two groups based on pairwise coupling. The obtained algorithm was applied on two data sets: a publicly available one containing two groups allowing a comparison with other methods from the literature and a new one containing three groups.

Extensions of simple component analysis and simple linear discriminant analysis using genetic algorithms

Extensions of Simple Component Analysis are proposed. Two methods are obtained: a new Simple Component Analysis and a Simple Linear Discriminant Analysis. These two methodologies use Genetic Algorithms, optimize a criterion (derived from the usual method) and add constraints. The objective is to obtain loadings constituted of a small number of integers determining blocks of variables. The programs implementing the methods have been developed using the R^(C) language. Four applications are made and show a good robustness of the algorithms and a proximity to the optimal solution (from the usual PCA and LDA).