TARGET VALIDATION OF UK-101 AND FUNCTIONAL STUDIES OF β1i

β1i is a major catalytic subunit of the immunoproteasome, an alternative form of the constitutive proteasome, and its upregulation has been demonstrated in a variety of disease states including cancer. Our lab has developed a small molecule inhibitor of β1i, dubbed UK-101. While UK-101 causes apoptosis in cancer cell lines, it was not clear whether this apoptotic effect was directly mediated by its irreversible inhibition of β1i. Since off-target effects are major roadblocks for the development of new and effective pharmaceuticals, target validation studies in this system would assist in the further progression of β1i inhibitors towards preclinical trials. Our hypothesis was that the expression and catalytic activity of β1i is important for the growth and proliferation of the PC-3 prostate cancer cell line, therefore the apoptotic effect seen upon treatment of PC-3 cells with UK-101 was due solely to its covalent inhibition of β1i. To test this hypothesis, a number of complementary approaches were used. The expression of β1i in PC-3 cells was increased by the treatment of these cells with interferon-gamma or tumor necrosis factor-alpha, natural inducers of the immunoproteasome. The expression of β1i in PC-3 cells was decreased using small interfering RNA or short hairpin RNA, in a transient or stable manner, respectively. All of these cells were then treated with UK-101. The efficacy of UK-101 decreased in the interferon-gamma treated cells but did not change in any other the other cell lines, suggesting that UK-101 was not specific for β1i. This was confirmed using a molecular probe of the proteasome and demonstrated that UK-101 bound to other proteasome catalytic subunits. Additional experiments were performed to determine the effect of β1i on the proliferation of PC-3 cells. Simply removing the β1i using small interfering RNA reduces the viability of these cells. Other studies demonstrated that a mutation of β1i which inhibited its catalytic activity reduced the viability of cells when compared to those containing the wild type protein. Overall, our data indicate that β1i is a potential therapeutic target in prostate cancer. Further medicinal chemistry efforts will be required develop UK-101 into a truly selective proteasome inhibitor

Classifying pairs with trees for supervised biological network inference

Networks are ubiquitous in biology and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used to complete a partially known network by integrating various measurements. Two main supervised frameworks have been proposed: the local approach, which trains a separate model for each network node, and the global approach, which trains a single model over pairs of nodes. Here, we systematically investigate, theoretically and empirically, the exploitation of tree-based ensemble methods in the context of these two approaches for biological network inference. We first formalize the problem of network inference as classification of pairs, unifying in the process homogeneous and bipartite graphs and discussing two main sampling schemes. We then present the global and the local approaches, extending the later for the prediction of interactions between two unseen network nodes, and discuss their specializations to tree-based ensemble methods, highlighting their interpretability and drawing links with clustering techniques. Extensive computational experiments are carried out with these methods on various biological networks that clearly highlight that these methods are competitive with existing methods.Comment: 22 page

Computer Aided Diagnosis System Based on Random Forests for the Prognosis of Alzheimer’s Disease

peer reviewedIn this abstract, we propose an original CAD system consisting in the combination of brain parcelling, ensemble of trees methods, and selection of (groups of) features using the importance scores embedded in tree-based methods. Indeed, on top of their ease of use and accuracy without ad hoc parameter tuning, tree ensemble methods such as random forests (RF) (Breiman, 2001) or extremely randomized trees (ET) (Geurts et al., 2006) provide interpretable results in the form of feature importance scores. We also compare the performance and interpretability of our proposed method to standard RF and ET approaches, without feature selection, and to multiple kernel learning (MKL). The latter was shown to be an efficient method notably capable of dealing with anatomically defined regions of the brain by the use of multiple kernels

Random Forests Based Group Importance Scores and Their Statistical Interpretation: Application for Alzheimer's Disease

Machine learning approaches have been increasingly used in the neuroimaging field for the design of computer-aided diagnosis systems. In this paper, we focus on the ability of these methods to provide interpretable information about the brain regions that are the most informative about the disease or condition of interest. In particular, we investigate the benefit of group-based, instead of voxel-based, analyses in the context of Random Forests. Assuming a prior division of the voxels into non overlapping groups (defined by an atlas), we propose several procedures to derive group importances from individual voxel importances derived from Random Forests models. We then adapt several permutation schemes to turn group importance scores into more interpretable statistical scores that allow to determine the truly relevant groups in the importance rankings. The good behaviour of these methods is first assessed on artificial datasets. Then, they are applied on our own dataset of FDG-PET scans to identify the brain regions involved in the prognosis of Alzheimer's disease

Identificación de nuevos componentes del divisoma de Corynebacterineae mediante una estrategia proteómica de marcado por proximidad en las células vivas

La división celular bacteriana es un proceso dirigido por el divisoma, un complejo macromolecular cuyo ensamblaje comienza con la polimerización de la proteína FtsZ en el sitio de división. FtsZ participa en el posterior reclutamiento de otras proteínas del divisoma, que en el caso de Escherichia coli y Bacillus subtilis han sido en identificadas y caracterizadas. Sin embargo, el suborden Corynebacterineae (que incluye importantes patógenos humanos) carece de homólogos reconocibles para muchas de estas proteínas de división celular. Este trabajo se centra en descifrar la arquitectura molecular del divisoma en este grupo de bacterias. Para ello, desarrollamos y optimizamos una estrategia proteómica basada en la biotinilación por proximidad para estudiar el divisoma de Corynebacterium glutamicum. Generamos una cepa que expresa FtsZ fusionada a una ascorbato peroxidasa ingenierizada (APEX2). APEX2 cataliza la oxidación de fenol biotina en presencia de H2O2 dando lugar a un radical que reacciona con aminoácidos de proteínas cercanas. Esto nos permitió marcar el entorno proteómico de FtsZ en la célula viva para su purificación e identificación por Espectrometría de Masa. Obtuvimos así una lista de 159 proteínas, la cual fue filtrada utilizando criterios proteómicos y un exhaustivo análisis bibliográfico para seleccionar candidatos a validar. Se expresaron en C. glutamicum los candidatos fusionados a mNeon para evaluar su localización subcelular. Con esto pudimos identificar 6 proteínas sin función asignada previamente que localizan en el septo, como promitentes nuevos integrantes del divisoma. Futuros estudios permitirán la caracterización de estas proteínas y su rol en la división celular de Corynebacterineae.Agencia Nacinal de Investigación e Innovació

Proteomic mass spectra classification using decision tree based ensemble methods.

MOTIVATION: Modern mass spectrometry allows the determination of proteomic fingerprints of body fluids like serum, saliva or urine. These measurements can be used in many medical applications in order to diagnose the current state or predict the evolution of a disease. Recent developments in machine learning allow one to exploit such datasets, characterized by small numbers of very high-dimensional samples. RESULTS: We propose a systematic approach based on decision tree ensemble methods, which is used to automatically determine proteomic biomarkers and predictive models. The approach is validated on two datasets of surface-enhanced laser desorption/ionization time of flight measurements, for the diagnosis of rheumatoid arthritis and inflammatory bowel diseases. The results suggest that the methodology can handle a broad class of similar problems

Discovery of new rheumatoid arthritis biomarkers using the surface-enhanced laser desorption/ionization time-of-flight mass spectrometry ProteinChip approach.

peer reviewedOBJECTIVE: To identify serum protein biomarkers specific for rheumatoid arthritis (RA), using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) technology. METHODS: A total of 103 serum samples from patients and healthy controls were analyzed. Thirty-four of the patients had a diagnosis of RA, based on the American College of Rheumatology criteria. The inflammation control group comprised 20 patients with psoriatic arthritis (PsA), 9 with asthma, and 10 with Crohn's disease. The noninflammation control group comprised 14 patients with knee osteoarthritis and 16 healthy control subjects. Serum protein profiles were obtained by SELDI-TOF-MS and compared in order to identify new biomarkers specific for RA. Data were analyzed by a machine learning algorithm called decision tree boosting, according to different preprocessing steps. RESULTS: The most discriminative mass/charge (m/z) values serving as potential biomarkers for RA were identified on arrays for both patients with RA versus controls and patients with RA versus patients with PsA. From among several candidates, the following peaks were highlighted: m/z values of 2,924 (RA versus controls on H4 arrays), 10,832 and 11,632 (RA versus controls on CM10 arrays), 4,824 (RA versus PsA on H4 arrays), and 4,666 (RA versus PsA on CM10 arrays). Positive results of proteomic analysis were associated with positive results of the anti-cyclic citrullinated peptide test. Our observations suggested that the 10,832 peak could represent myeloid-related protein 8. CONCLUSION: SELDI-TOF-MS technology allows rapid analysis of many serum samples, and use of decision tree boosting analysis as the main statistical method allowed us to propose a pattern of protein peaks specific for RA

Eukaryotic-like gephyrin and cognate membrane receptor coordinate corynebacterial cell division and polar elongation

The order Corynebacteriales includes major industrial and pathogenic Actinobacteria such as Corynebacterium glutamicum or Mycobacterium tuberculosis. These bacteria have multi-layered cell walls composed of the mycolyl-arabinogalactan-peptidoglycan complex and a polar growth mode, thus requiring tight coordination between the septal divisome, organized around the tubulin-like protein FtsZ, and the polar elongasome, assembled around the coiled-coil protein Wag31. Here, using C. glutamicum, we report the discovery of two divisome members: a gephyrin-like repurposed molybdotransferase (Glp) and its membrane receptor (GlpR). Our results show how cell cycle progression requires interplay between Glp/GlpR, FtsZ and Wag31, showcasing a crucial crosstalk between the divisome and elongasome machineries that might be targeted for anti-mycobacterial drug discovery. Further, our work reveals that Corynebacteriales have evolved a protein scaffold to control cell division and morphogenesis, similar to the gephyrin/GlyR system that mediates synaptic signalling in higher eukaryotes through network organization of membrane receptors and the microtubule cytoskeleton.Agencia Nacional de Investigación e InnovaciónECOS-Sud France-Uruguay U20B02FOCEM-COF 03/11Agence Nationale de la Recherche ANR-18-CE11-0017/ANR-21-CE11-000

Coping with Environmental Constraints: Geographically Divergent Adaptive Evolution and Germination Plasticity in the Transcontinental \u3cem\u3ePopulus tremuloides\u3c/em\u3e

Societal Impact Statement Syntheses clearly show that global warming is affecting ecosystems and biodiversity around the world. New methods and measures are needed to predict the climate resilience of plant species critical to ecosystem stability, to improve ecological management and to support habitat restoration and human well-being. Widespread keystone species such as aspen are important targets in the study of resilience to future climate conditions because they play a crucial role in maintaining various ecosystem functions and may contain genetic material with untapped adaptive potential. Here, we present a new framework in support of climate-resilient revegetation based on comprehensively understood patterns of genetic variation in aspen. Summary Elucidating species\u27 genetic makeup and seed germination plasticity is essential to inform tree conservation efforts in the face of climate change. Populus tremuloides Michx. (aspen) occurs across diverse landscapes and reaches from Alaska to central Mexico, thus representing an early-successional model for ecological genomics. Within drought-affected regions, aspen shows ploidy changes and/or shifts from sexual to clonal reproduction, and reduced diversity and dieback have already been observed. We genotyped over 1000 individuals, covering aspen\u27s entire range, for approximately 44,000 single-nucleotide polymorphisms (SNPs) to assess large-scale and fine-scale genetic structure, variability in reproductive type (sexual/clonal), polyploidy and genomic regions under selection. We developed and implemented a rapid and reliable analysis pipeline (FastPloidy) to assess the presence of polyploidy. To gain insights into plastic responses, we contrasted seed germination from western US and eastern Canadian natural populations under elevated temperature and water stress. Four major genetic clusters were identified range wide; a preponderance of triploids and clonemates was found within western and southern North American regions, respectively. Genomic regions involving approximately 1000 SNPs under selection were identified with association to temperature and precipitation variation. Under drought stress, western US genotypes exhibited significantly lower germination rates compared with those from eastern North America, a finding that was unrelated to differences in mutation load (ploidy). This study provided new insights into the adaptive evolution of a key indicator tree that provisions crucial ecosystem services across North America, but whose presence is steadily declining within its western distribution. We uncovered untapped adaptive potential across the species\u27 range which can form the basis for climate-resilient revegetation

Biomarker discovery for inflammatory bowel disease, using proteomic serum profiling

peer reviewedCrohn's disease and ulcerative colitis known as inflammatory bowel diseases (IBD) are chronic immuno-inflammatory pathologies of the gastrointestinal tract. These diseases are multifactorial, polygenic and of unknown etiology. Clinical presentation is non-specific and diagnosis is based on clinical, endoscopic, radiological and histological criteria. Novel markers are needed to improve early diagnosis and classification of these pathologies. We performed a study with 120 serum samples collected from patients classified in 4 groups (30 Crohn, 30 ulcerative colitis, 30 inflammatory controls and 30 healthy controls) according to accredited criteria. We compared protein sera profiles obtained with a Surface Enhanced Laser Desorption Ionization-Time of Flight-Mass Spectrometer (SELDI-TOF-MS). Data analysis with univariate process and a multivariate statistical method based on multiple decision trees algorithms allowed us to select some potential biomarkers. Four of them were identified by mass spectrometry and antibody based methods. Multivariate analysis generated models that could classify samples with good sensitivity and specificity (minimum 80%) discriminating groups of patients. This analysis was used as a tool to classify peaks according to differences in level on spectra through the four categories of patients. Four biomarkers showing important diagnostic value were purified, identified (PF4, MRP8, FIBA and Hpalpha2) and two of these: PF4 and Hpalpha2 were detected in sera by classical methods. SELDI-TOF-MS technology and use of the multiple decision trees method led to protein biomarker patterns analysis and allowed the selection of potential individual biomarkers. Their downstream identification may reveal to be helpful for IBD classification and etiology understanding