Representation and quantification of module activity from omics data with rROMA.

The efficiency of analyzing high-throughput data in systems biology has been demonstrated in numerous studies, where molecular data, such as transcriptomics and proteomics, offers great opportunities for understanding the complexity of biological processes. One important aspect of data analysis in systems biology is the shift from a reductionist approach that focuses on individual components to a more integrative perspective that considers the system as a whole, where the emphasis shifted from differential expression of individual genes to determining the activity of gene sets. Here, we present the rROMA software package for fast and accurate computation of the activity of gene sets with coordinated expression. The rROMA package incorporates significant improvements in the calculation algorithm, along with the implementation of several functions for statistical analysis and visualizing results. These additions greatly expand the package's capabilities and offer valuable tools for data analysis and interpretation. It is an open-source package available on github at: www.github.com/sysbio-curie/rROMA . Based on publicly available transcriptomic datasets, we applied rROMA to cystic fibrosis, highlighting biological mechanisms potentially involved in the establishment and progression of the disease and the associated genes. Results indicate that rROMA can detect disease-related active signaling pathways using transcriptomic and proteomic data. The results notably identified a significant mechanism relevant to cystic fibrosis, raised awareness of a possible bias related to cell culture, and uncovered an intriguing gene that warrants further investigation.

Exploring isofunctional molecules: Design of a benchmark and evaluation of prediction performance.

Identification of novel chemotypes with biological activity similar to a known active molecule is an important challenge in drug discovery called 'scaffold hopping'. Small-, medium-, and large-step scaffold hopping efforts may lead to increasing degrees of chemical structure novelty with respect to the parent compound. In the present paper, we focus on the problem of large-step scaffold hopping. We assembled a high quality and well characterized dataset of scaffold hopping examples comprising pairs of active molecules and including a variety of protein targets. This dataset was used to build a benchmark corresponding to the setting of real-life applications: one active molecule is known, and the second active is searched among a set of decoys chosen in a way to avoid statistical bias. This allowed us to evaluate the performance of computational methods for solving large-step scaffold hopping problems. In particular, we assessed how difficult these problems are, particularly for classical 2D and 3D ligand-based methods. We also showed that a machine-learning chemogenomic algorithm outperforms classical methods and we provided some useful hints for future improvements.

Differential CFTR-Interactome Proximity Labeling Procedures Identify Enrichment in Multiple SLC Transporters.

Proteins interacting with CFTR and its mutants have been intensively studied using different experimental approaches. These studies provided information on the cellular processes leading to proper protein folding, routing to the plasma membrane, recycling, activation and degradation. Recently, new approaches have been developed based on the proximity labeling of protein partners or proteins in close vicinity and their subsequent identification by mass spectrometry. In this study, we evaluated TurboID- and APEX2-based proximity labeling of WT CFTR and compared the obtained data to those reported in databases. The CFTR-WT interactome was then compared to that of two CFTR (G551D and W1282X) mutants and the structurally unrelated potassium channel KCNK3. The two proximity labeling approaches identified both known and additional CFTR protein partners, including multiple SLC transporters. Proximity labeling approaches provided a more comprehensive picture of the CFTR interactome and improved our knowledge of the CFTR environment.

Drug Target Identification with Machine Learning: How to Choose Negative Examples.

Identification of the protein targets of hit molecules is essential in the drug discovery process. Target prediction with machine learning algorithms can help accelerate this search, limiting the number of required experiments. However, Drug-Target Interactions databases used for training present high statistical bias, leading to a high number of false positives, thus increasing time and cost of experimental validation campaigns. To minimize the number of false positives among predicted targets, we propose a new scheme for choosing negative examples, so that each protein and each drug appears an equal number of times in positive and negative examples. We artificially reproduce the process of target identification for three specific drugs, and more globally for 200 approved drugs. For the detailed three drug examples, and for the larger set of 200 drugs, training with the proposed scheme for the choice of negative examples improved target prediction results: the average number of false positives among the top ranked predicted targets decreased, and overall, the rank of the true targets was improved.Our method corrects databases' statistical bias and reduces the number of false positive predictions, and therefore the number of useless experiments potentially undertaken.

Recurrent Mutations in Cyclin D3 Confer Clinical Resistance to FLT3 Inhibitors in Acute Myeloid Leukemia.

PURPOSE: Biomarkers of response and resistance to FLT3 tyrosine kinase inhibitors (TKI) are still emerging, and optimal clinical combinations remain unclear. The purpose of this study is to identify co-occurring mutations that influence clinical response to the novel FLT3 inhibitor pexidartinib (PLX3397). EXPERIMENTAL DESIGN: We performed targeted sequencing of pretreatment blasts from 29 patients with FLT3 internal tandem duplication (ITD) mutations treated on the phase I/II trial of pexidartinib in relapsed/refractory FLT3-ITD+ acute myeloid leukemia (AML). We sequenced 37 samples from 29 patients with available material, including 8 responders and 21 non-responders treated at or above the recommended phase II dose of 3,000 mg. RESULTS: Consistent with other studies, we identified mutations in NRAS, TP53, IDH2, and a variety of epigenetic and transcriptional regulators only in non-responders. Among the most frequently mutated genes in non-responders was Cyclin D3 (CCND3). A total of 3 individual mutations in CCND3 (Q276*, S264R, and T283A) were identified in 2 of 21 non-responders (one patient had both Q276* and S264R). No CCND3 mutations were found in pexidartinib responders. Expression of the Q276* and T283A mutations in FLT3-ITD MV4;11 cells conferred resistance to apoptosis, decreased cell-cycle arrest, and increased proliferation in the presence of pexidartinib and other FLT3 inhibitors. Inhibition of CDK4/6 activity in CCND3 mutant MV4;11 cells restored pexidartinib-induced cell-cycle arrest but not apoptosis. CONCLUSIONS: Mutations in CCND3, a gene not commonly mutated in AML, are a novel cause of clinical primary resistance to FLT3 inhibitors in AML and may have sensitivity to CDK4/6 inhibition.

Mapping and Modeling of Discussions Related to Gastrointestinal Discomfort in French-Speaking Online Forums: Results of a 15-Year Retrospective Infodemiology Study.

BACKGROUND: Gastrointestinal (GI) discomfort is prevalent and known to be associated with impaired quality of life. Real-world information on factors of GI discomfort and solutions used by people is, however, limited. Social media, including online forums, have been considered a new source of information to examine the health of populations in real-life settings. OBJECTIVE: The aims of this retrospective infodemiology study are to identify discussion topics, characterize users, and identify perceived determinants of GI discomfort in web-based messages posted by users of French social media. METHODS: Messages related to GI discomfort posted between January 2003 and August 2018 were extracted from 14 French-speaking general and specialized publicly available online forums. Extracted messages were cleaned and deidentified. Relevant medical concepts were determined on the basis of the Medical Dictionary for Regulatory Activities and vernacular terms. The identification of discussion topics was carried out by using a correlated topic model on the basis of the latent Dirichlet allocation. A nonsupervised clustering algorithm was applied to cluster forum users according to the reported symptoms of GI discomfort, discussion topics, and activity on online forums. Users' age and gender were determined by linear regression and application of a support vector machine, respectively, to characterize the identified clusters according to demographic parameters. Perceived factors of GI discomfort were classified by a combined method on the basis of syntactic analysis to identify messages with causality terms and a second topic modeling in a relevant segment of phrases. RESULTS: A total of 198,866 messages associated with GI discomfort were included in the analysis corpus after extraction and cleaning. These messages were posted by 36,989 separate web users, most of them being women younger than 40 years. Everyday life, diet, digestion, abdominal pain, impact on the quality of life, and tips to manage stress were among the most discussed topics. Segmentation of users identified 5 clusters corresponding to chronic and acute GI concerns. Diet topic was associated with each cluster, and stress was strongly associated with abdominal pain. Psychological factors, food, and allergens were perceived as the main causes of GI discomfort by web users. CONCLUSIONS: GI discomfort is actively discussed by web users. This study reveals a complex relationship between food, stress, and GI discomfort. Our approach has shown that identifying web-based discussion topics associated with GI discomfort and its perceived factors is feasible and can serve as a complementary source of real-world evidence for caregivers.

Reconstructing Mayaro virus circulation in French Guiana shows frequent spillovers.

Characterizing the circulation of Mayaro virus (MAYV), an emerging arbovirus threat, is essential for risk assessment but challenging due to cross-reactivity with other alphaviruses such as chikungunya virus (CHIKV). Here, we develop an analytical framework to jointly assess MAYV epidemiology and the extent of cross-reactivity with CHIKV from serological data collected throughout French Guiana (N = 2697). We find strong evidence of an important sylvatic cycle for MAYV with most infections occurring near the natural reservoir in rural areas and in individuals more likely to go to the forest (i.e., adult males) and with seroprevalences of up to 18% in some areas. These findings highlight the need to strengthen MAYV surveillance in the region and showcase how modeling can improve interpretation of cross-reacting assays.

Wire-Active Microrheology to Differentiate Viscoelastic Liquids from Soft Solids.

Viscoelastic liquids are characterized by a finite static viscosity and a yield stress of zero, whereas soft solids have an infinite viscosity and a non-zero yield stress. The rheological nature of viscoelastic materials has long been a challenge and is still a matter of debate. Here, we provide for the first time the constitutive equations of linear viscoelasticity for magnetic wires in yield-stress materials, together with experimental measurements by using magnetic rotational spectroscopy (MRS). In MRS, the wires were subjected to a rotational magnetic field as a function of frequency and the motion of the wire was monitored by using time-lapse microscopy. The studied soft solids were aqueous dispersions of gel-forming polysaccharide (gellan gum) at concentrations above the gelification point. It was found that soft solids exhibited a clear and distinctive signature compared with viscous and viscoelastic liquids. In particular, the average wire rotation velocity equaled zero over a broad frequency range. We also showed that the MRS technique is quantitative. The equilibrium elastic modulus was retrieved from the wire oscillation amplitudes, and agrees with polymer-dynamics theory.