Novel pharmacological maps of protein lysine methyltransferases: key for target deorphanization

Epigenetic therapies are being investigated for the treatment of cancer, cognitive disorders, metabolic alterations and autoinmune diseases. Among the diferent epigenetic target families, protein lysine methyltransferases (PKMTs), are especially interesting because it is believed that their inhibition may be highly specifc at the functional level. Despite its relevance, there are currently known inhibitors against only 10 out of the 50 SET-domain containing members of the PKMT family. Accordingly, the identifcation of chemical probes for the validation of the therapeutic impact of epigenetic modulation is key. Moreover, little is known about the mechanisms that dictate their substrate specifcity and ligand selectivity. Consequently, it is desirable to explore novel methods to characterize the pharmacological similarity of PKMTs, going beyond classical phylogenetic relationships. Such characterization would enable the prediction of ligand of-target efects caused by lack of ligand selectivity and the repurposing of known compounds against alternative targets. This is particularly relevant in the case of orphan targets with unreported inhibitors. Here, we frst perform a systematic study of binding modes of cofactor and substrate bound ligands with all available SET domain-containing PKMTs. Protein ligand interaction fngerprints were applied to identify conserved hot spots and contact-specifc residues across subfamilies at each binding site; a relevant analysis for guiding the design of novel, selective compounds. Then, a recently described methodology (GPCR-CoINPocket) that incorporates ligand contact information into classical alignment-based comparisons was applied to the entire family of 50 SET-containing proteins to devise pharmacological similarities between them. The main advantage of this approach is that it is not restricted to proteins for which crystallographic data with bound ligands is available. The resulting family organization from the separate analysis of both sites (cofactor and substrate) was retrospectively and prospectively validated. Of note, three hits (inhibition>50% at 10 µM) were identifed for the orphan NSD1

Towards the understanding of the activity of G9a inhibitors: an activity landscape and molecular modeling approach

In this work, we analyze the structure–activity relationships (SAR) of epigenetic inhibitors (lysine mimetics) against lysine methyltransferase (G9a or EHMT2) using a combined activity landscape, molecular docking and molecular dynamics approach. The study was based on a set of 251 G9a inhibitors with reported experimental activity. The activity landscape analysis rapidly led to the identifcation of activity clifs, scafolds hops and other active an inactive molecules with distinct SAR. Structure-based analysis of activity clifs, scafold hops and other selected active and inactive G9a inhibitors by means of docking followed by molecular dynamics simulations led to the identifcation of interactions with key residues involved in activity against G9a, for instance with ASP 1083, LEU 1086, ASP 1088, TYR 1154 and PHE 1158. The outcome of this work is expected to further advance the development of G9a inhibitors

OpenFog-Compliant Application-Aware Platform: A Kubernetes Extension

Distributed computing paradigms have evolved towards low latency and highly virtualized environments. Fog Computing, as its latest iteration, enables the usage of Cloud-like services closer to the generators and consumers of data. The processing in this layer is performed by Fog Applications, which are decomposed into smaller components following the microservice paradigm and encapsulated into containers. Current state-of-the-art container orchestrators can manage hundreds of simultaneous containers. However, Kubernetes, being the de facto standard, does not consider the application itself as a top-level entity, which limits its orchestration capabilities. This raises the need to rearchitect Kubernetes to benefit from application-awareness, which refers to an orchestration method optimized for managing the applications and the set of components that comprise them. Thus, this paper proposes an application-aware and OpenFog-compliant architecture that manages applications as first-level entities during their lifecycle. Furthermore, the proposed architecture allows the definition of organizational structures to group subordinated applications based on user-defined hierarchies. This logical structuring makes it possible to outline how orchestration should be shaped to reflect the operating model of a system or an organization. The proposed architecture is implemented as a Kubernetes extension and provided as an operator.This research was funded by the project PES18/48 funded by the University of the Basque Country (UPV/EHU) and by the PhD fellowship granted under the frame of the PIF 2022 call funded by the University of the Basque Country (UPV/EHU), grant number PIF22/188

Evaluation of chemical and gene/protein entity recognition systems at BioCreative V.5: the CEMP and GPRO patents tracks

This paper presents the results of the BioCreative V.5 offline tasks related to the evaluation of the performance as well as assess progress made by strategies used for the automatic recognition of mentions of chemical names and gene in running text of medicinal chemistry patent abstracts. A total of 21 teams submitted results for at least one of these tasks. The CEMP (chemical entity mention in patents) task entailed the detection of chemical named entity mentions. A total of 14 teams submitted 56 runs. The top performing team reached an F-score of 0.90 with a precision of 0.88 and a recall of 0.93. The GPRO (gene and protein related object) task focused on the detection of mentions of gene and protein related objects. The 7 participating teams (30 runs) had to detect gene/protein mentions that could be linked to at least one biological database, such as SwissProt or EntrezGene. The best F-score, recall and precision in this task were of 0.79, 0.83 and 0.77, respectively. The CEMP and GPRO gold standard corpora included training sets of 21,000 records and test sets of 9,000 records. Similar to the previous BioCreative CHEMDNER tasks, evaluation was based on micro-averaged F-score. The BeCalm platform supported prediction submission and evaluation (http://www.becalm.eu).We acknowledge the OpenMinted (654021) and the ELIXIREXCELERATE (676559) H2020 projects, and the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology for funding. The Spanish National Bioinformatics Institute (INB) unit at the Spanish National Cancer Research Centre (CNIO) is a member of the INB, PRB2-ISCIII and is supported by grant PT13/0001/0030, of the PE I+D+i 2013-2016, funded by ISCIII and ERDF.info:eu-repo/semantics/publishedVersio

Systematic Roadmap for Cancer Drug Screening Using Zebrafish Embryo Xenograft Cancer Models: Melanoma Cell Line as a Case Study

From MDPI via Jisc Publications RouterHistory: accepted 2021-07-19, pub-electronic 2021-07-23Publication status: PublishedFunder: Gobierno de Navarra; Grant(s): 0011-1408-2016-000004, 0011-1365-2020-000292Zebrafish embryo tumor transplant models are widely utilized in cancer research. Compared with traditional murine models, the small size and transparency of zebrafish embryos combined with large clutch sizes that increase statistical power and cheap husbandry make them a cost-effective and versatile tool for in vivo drug discovery. However, the lack of a comprehensive analysis of key factors impacting the successful use of these models impedes the establishment of basic guidelines for systematic screening campaigns. Thus, we explored the following crucial factors: (i) user-independent inclusion criteria, focusing on sample homogeneity; (ii) metric definition for data analysis; (iii) tumor engraftment criteria; (iv) image analysis versus quantification of human cancer cells using qPCR (RNA and gDNA); (v) tumor implantation sites; (vi) compound distribution (intratumoral administration versus alternative inoculation sites); and (vii) efficacy (intratumoral microinjection versus compound solution in media). Based on these analyses and corresponding assessments, we propose the first roadmap for systematic drug discovery screening in zebrafish xenograft cancer models using a melanoma cell line as a case study. This study aims to help the wider cancer research community to consider the adoption of this versatile model for cancer drug screening projects

The Markyt visualisation, prediction and benchmark platform for chemical and gene entity recognition at BioCreative/CHEMDNER challenge

Biomedical text mining methods and technologies have improved significantly in the last decade. Considerable efforts have been invested in understanding the main challenges of biomedical literature retrieval and extraction and proposing solutions to problems of practical interest. Most notably, community-oriented initiatives such as the BioCreative challenge have enabled controlled environments for the comparison of automatic systems while pursuing practical biomedical tasks. Under this scenario, the present work describes the Markyt Web-based document curation platform, which has been implemented to support the visualisation, prediction and benchmark of chemical and gene mention annotations at BioCreative/CHEMDNER challenge. Creating this platform is an important step for the systematic and public evaluation of automatic prediction systems and the reusability of the knowledge compiled for the challenge. Markyt was not only critical to support the manual annotation and annotation revision process but also facilitated the comparative visualisation of automated results against the manually generated Gold Standard annotations and comparative assessment of generated results. We expect that future biomedical text mining challenges and the text mining community may benefit from the Markyt platform to better explore and interpret annotations and improve automatic system predictions. Database URL: http://www.markyt.org, https://github.com/sing-group/MarkytThis work was partially funded by the [14VI05] Contract-Programme from the University of Vigo and the Agrupamento INBIOMED from DXPCTSUG-FEDER unha maneira de facer Europa (2012/273) as well as by the Foundation for Applied Medical Research, University of Navarra (Pamplona, Spain). The research leading to these results has received funding from the European Union's Seventh Framework Programme FP7/REGPOT-2012-2013.1 under grant agreement no 316265, BIOCAPS

CHEMDNER: The drugs and chemical names extraction challenge

Natural language processing (NLP) and text mining technologies for the chemical domain (ChemNLP or chemical text mining) are key to improve the access and integration of information from unstructured data such as patents or the scientific literature. Therefore, the BioCreative organizers posed the CHEMDNER (chemical compound and drug name recognition) community challenge, which promoted the development of novel, competitive and accessible chemical text mining systems. This task allowed a comparative assessment of the performance of various methodologies using a carefully prepared collection of manually labeled text prepared by specially trained chemists as Gold Standard data. We evaluated two important aspects: one covered the indexing of documents with chemicals (chemical document indexing - CDI task), and the other was concerned with finding the exact mentions of chemicals in text (chemical entity mention recognition - CEM task). 27 teams (23 academic and 4 commercial, a total of 87 researchers) returned results for the CHEMDNER tasks: 26 teams for CEM and 23 for the CDI task. Top scoring teams obtained an F-score of 87.39% for the CEM task and 88.20% for the CDI task, a very promising result when compared to the agreement between human annotators (91%). The strategies used to detect chemicals included machine learning methods (e.g. conditional random fields) using a variety of features, chemistry and drug lexica, and domain-specific rules. We expect that the tools and resources resulting from this effort will have an impact in future developments of chemical text mining applications and will form the basis to find related chemical information for the detected entities, such as toxicological or pharmacogenomic properties

Targeting aberrant DNA methylation in mesenchymal stromal cells as a treatment for myeloma bone disease

© The Author(s) 2021.Multiple myeloma (MM) progression and myeloma-associated bone disease (MBD) are highly dependent on bone marrow mesenchymal stromal cells (MSCs). MM-MSCs exhibit abnormal transcriptomes, suggesting the involvement of epigenetic mechanisms governing their tumor-promoting functions and prolonged osteoblast suppression. Here, we identify widespread DNA methylation alterations of bone marrow-isolated MSCs from distinct MM stages, particularly in Homeobox genes involved in osteogenic differentiation that associate with their aberrant expression. Moreover, these DNA methylation changes are recapitulated in vitro by exposing MSCs from healthy individuals to MM cells. Pharmacological targeting of DNMTs and G9a with dual inhibitor CM-272 reverts the expression of hypermethylated osteogenic regulators and promotes osteoblast differentiation of myeloma MSCs. Most importantly, CM-272 treatment prevents tumor-associated bone loss and reduces tumor burden in a murine myeloma model. Our results demonstrate that epigenetic aberrancies mediate the impairment of bone formation in MM, and its targeting by CM-272 is able to reverse MBD.We thank CERCA Program/Generalitat de Catalunya and the Josep Carreras Foundation for institutional support. E.B. was funded by the Spanish Ministry of Science and Innovation (grant numbers SAF2014-55942-R and SAF2017-88086-R), co-funded by FEDER funds/European Regional Development Fund (ERDF)—a way to build Europe, and a Senior Research Award from the Multiple Myeloma Research Foundation (MMRF). C.O.-d.-S. was funded by the Spanish Ministry of Science, Innovation and Universities, under grant RTI2018-094494-B-C22 (MCIU/AEI/FEDER, UE). M.G. received financial support from the Spanish FIS-ISCIII (PI15/02156 and PI19/01384) and FEDER. A.G.G is funded by a postdoctoral contract of the Asociación Española Contra el Cáncer (AECC). F.P. was funded by grants from Instituto de Salud Carlos III (ISCIII), PI17/00701 and PI19/01352, TRASCAN (EPICA and Immunocell), Fundació La Marató de TV3, the Accelerator award CRUK/AIRC/AECC joint funder-partnership, CIBERONC (CB16/12/00489) and co-financed with FEDER funds and Fundación Ramón Areces (PREMAMM)

Trimethylamine-N-Oxide (TMAO) Predicts Cardiovascular Mortality in Peripheral Artery Disease

Peripheral artery disease (PAD) is a major cause of acute and chronic illness, with extremely poor prognosis that remains underdiagnosed and undertreated. Trimethylamine-N-Oxide (TMAO), a gut derived metabolite, has been associated with atherosclerotic burden. We determined plasma levels of TMAO by mass spectrometry and evaluated their association with PAD severity and prognosis. 262 symptomatic PAD patients (mean age 70 years, 87% men) categorized in intermittent claudication (IC, n = 147) and critical limb ischemia (CLI, n = 115) were followed-up for a mean average of 4 years (min 1-max 102 months). TMAO levels were increased in CLI compared to IC (P 2.26 µmol/L exhibited higher risk of cardiovascular death (sub-hazard ratios ≥2, P < 0.05) that remained significant after adjustment for confounding factors. TMAO levels were associated to disease severity and CV-mortality in our cohort, suggesting an improvement of PAD prognosis with the measurement of TMAO. Overall, our results indicate that the intestinal bacterial function, together with the activity of key hepatic enzymes for TMA oxidation (FMO3) and renal function, should be considered when designing therapeutic strategies to control gut-derived metabolites in vascular patients

Targeting aberrant DNA methylation in mesenchymal stromal cells as a treatment for myeloma bone disease

Multiple myeloma (MM) progression and myeloma-associated bone disease (MBD) are highly dependent on bone marrow mesenchymal stromal cells (MSCs). MM-MSCs exhibit abnormal transcriptomes, suggesting the involvement of epigenetic mechanisms governing their tumor-promoting functions and prolonged osteoblast suppression. Here, we identify widespread DNA methylation alterations of bone marrow-isolated MSCs from distinct MM stages, particularly in Homeobox genes involved in osteogenic differentiation that associate with their aberrant expression. Moreover, these DNA methylation changes are recapitulated in vitro by exposing MSCs from healthy individuals to MM cells. Pharmacological targeting of DNMTs and G9a with dual inhibitor CM-272 reverts the expression of hypermethylated osteogenic regulators and promotes osteoblast differentiation of myeloma MSCs. Most importantly, CM-272 treatment prevents tumor-associated bone loss and reduces tumor burden in a murine myeloma model. Our results demonstrate that epigenetic aberrancies mediate the impairment of bone formation in MM, and its targeting by CM-272 is able to reverse MBD. Mesenchymal stromal cells (MSCs) have been shown to support multiple myeloma (MM) development. Here, MSCs isolated from the bone marrow of MM patients are shown to have altered DNA methylation patterns and a methyltransferase inhibitor reverts MM-associated bone loss and reduces tumour burden in MM murine models