Industry-scale application and evaluation of deep learning for drug target prediction

Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.Web of Science121art. no. 2

Retinoic acid receptor α as a novel contributor to adrenal cortex structure and function through interactions with Wnt and Vegfa signalling

International audiencePrimary aldosteronism (PA) is the most frequent form of secondary arterial hypertension. Mutations in different genes increase aldosterone production in PA, but additional mechanisms may contribute to increased cell proliferation and aldosterone producing adenoma (APA) development. We performed transcriptome analysis in APA and identified retinoic acid receptor alpha (RARα) signaling as a central molecular network involved in nodule formation. To understand how RARα modulates adrenal structure and function, we explored the adrenal phenotype of male and female Rarα knockout mice. inactivation of Rarα in mice led to significant structural disorganization of the adrenal cortex in both sexes, with increased adrenal cortex size in female mice and increased cell proliferation in males. Abnormalities of vessel architecture and extracellular matrix were due to decreased Vegfa expression and modifications in extracellular matrix components. On the molecular level, Rarα inactivation leads to inhibition of non-canonical Wnt signaling, without affecting the canonical Wnt pathway nor PKA signaling. Our study suggests that Rarα contributes to the maintenance of normal adrenal cortex structure and cell proliferation, by modulating Wnt signaling. Dysregulation of this interaction may contribute to abnormal cell proliferation, creating a propitious environment for the emergence of specific driver mutations in PA. Primary aldosteronism (PA) is the most common and curable form of secondary arterial hypertension, with prevalence estimations of up to 10% of cases in referred hypertensive patients, 4% of patients in primary care 1,2 and 20% of patients with resistant hypertension 3,4. Rapid diagnosis and treatment are important to prevent severe cardiovas-cular consequences of long term aldosterone exposure, which are independent of blood pressure levels and are du

KCNJ5 mutations in aldosterone producing adenoma and relationship with adrenal cortex remodeling

15th Conference on the Adrenal Cortex (Adrenal), League City, TX, JUN 19-22, 2012International audienceSomatic mutations of KCNJ5, coding for the potassium channel GIRK4, have recently been implicated in the formation of aldosterone producing adenoma (APA). While a causal link between KCNJ5 mutations, membrane depolarization and aldosterone production has been established, the precise mechanism by which these mutations promote cell proliferation and APA formation remains unclear. The aim of our study was to correlate KCNJ5 mutation status with morphological and functional characteristics of the adrenal cortex adjacent to APA. While GIRK4 was expressed in APA and in the zona glomerulosa of the adjacent cortex, significantly lower levels were detected in APA harboring a KCNJ5 mutation. There was no correlation between KCNJ5 mutation status and the morphological measures of adrenal cortex remodeling, including nodulation, vascularization and expression of CYP11B2. The cell composition of APA was not significantly different between groups. These results indicate that KCNJ5 mutations are not correlated with adrenal cortex remodeling in APA

Mast Cell Hyperplasia Is Associated With Aldosterone Hypersecretion in a Subset of Aldosterone-Producing Adenomas

International audienceContext: Adrenal mast cells can stimulate aldosterone secretion through the local release of serotonin (5-HT) and activation of the 5-HT4 receptor (5-HT4). In aldosterone-producing adenomas (APAs), 5-HT4 receptor is overexpressed and the administration of 5-HT4 receptor agonists to patients with APA increases plasma aldosterone levels. These data and the well-documented role of mast cells in tumorigenesis suggest that mast cells may be involved in the pathophysiology of APA. Objective: The study aimed at investigating the occurrence of mast cells in a series of APA tissues and to examine the influence of mast cells on aldosterone secretion. Design: The occurrence of mast cells in APAs was investigated by immunohistochemistry. Mast cell densities were compared with clinical data. The influence of mast cells on aldosterone production was studied by using cultures of human mast cell and adrenocortical cell lines. Results: In APA tissues, the density of mast cells was found to be increased in comparison with normal adrenals. Mast cells were primarily observed in adrenal cortex adjacent to adenomas or in the adenomas themselves, distinguishing two groups of APAs. A subset of adenomas was found to contain a high density of intratumoral mast cells, which was correlated with aldosterone synthase expression and in vivo aldosterone secretory parameters. Administration of conditioned medium from cultures of human mast cell lines to human adrenocortical cells induced a significant increase in aldosterone synthase (CYP11B2) mRNA expression and aldosterone production. Conclusion: APA tissues commonly contain numerous mast cells that may influence aldosterone secretion through the local release of regulatory factors

MOESM2 of ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics

Additional file 2: Table S1. The list of selected activity types in the PubChem

MOESM1 of ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics

Additional file 1. The protocol for structure standardisation

MOESM3 of ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics

Additional file 3: Table S2. The list of targets in the final dataset

Proteome and computational analyses reveal new insights into the mechanisms of hepatitis C virus-mediated liver disease posttransplantation

International audienceLiver transplant tissues offer the unique opportunity to model the longitudinal protein abundance changes occurring during hepatitis C virus (HCV)-associated liver disease progression in vivo. In this study, our goal was to identify molecular signatures, and potential key regulatory proteins, representative of the processes influencing early progression to fibrosis. We performed global protein profiling analyses on 24 liver biopsy specimens obtained from 15 HCV+ liver transplant recipients at 6 and/or 12 months posttransplantation. Differentially regulated proteins associated with early progression to fibrosis were identified by analysis of the area under the receiver operating characteristic curve. Analysis of serum metabolites was performed on samples obtained from an independent cohort of 60 HCV+ liver transplant patients. Computational modeling approaches were applied to identify potential key regulatory proteins of liver fibrogenesis. Among 4,324 proteins identified, 250 exhibited significant differential regulation in patients with rapidly progressive fibrosis. Patients with rapid fibrosis progression exhibited enrichment in differentially regulated proteins associated with various immune, hepatoprotective, and fibrogenic processes. The observed increase in proinflammatory activity and impairment in antioxidant defenses suggests that patients who develop significant liver injury experience elevated oxidative stresses. This was supported by an independent study demonstrating the altered abundance of oxidative stress-associated serum metabolites in patients who develop severe liver injury. Computational modeling approaches further highlight a potentially important link between HCV-associated oxidative stress and epigenetic regulatory mechanisms impacting on liver fibrogenesis. Conclusion: Our proteome and metabolome analyses provide new insights into the role for increased oxidative stress in the rapid fibrosis progression observed in HCV+ liver transplant recipients. These findings may prove useful in prognostic applications for predicting early progression to fibrosis. (HEPATOLOGY 2012;56:2838