A genome-wide computational approach to define microRNA-Polycomb/ trithorax gene regulatory circuits in drosophila

Characterization of gene regulatory networks is fundamental to understanding homeostatic development. This process can be simplified by analyzing relatively simple genomes such as the genome of Drosophila melanogaster. In this work we have developed a computational framework in Drosophila to explore for the presence of gene regulatory circuits between two large groups of transcriptional regulators: the epigenetic group of the Polycomb/ trithorax (PcG/trxG) proteins and the microRNAs (miRNAs). We have searched genome-wide for miRNA targets in PcG/trxG transcripts as well as for Polycomb Response Elements (PREs) in miRNA genes. Our results show that 10% of the analyzed miRNAs could be controlling PcG/trxG gene expression, while 40% of those miRNAs are putatively controlled by the selected set of PcG/trxG proteins. The integration of these analyses has resulted in the predicted existence of 3 classes of miRNA-PcG/trxG crosstalk interactions that define potential regulatory circuits. In the first class, miRNA-PcG circuits are defined by miRNAs that reciprocally crosstalk with PcG. In the second, miRNA-trxG circuits are defined by miRNAs that reciprocally crosstalk with trxG. In the third class, miRNA-PcG/ trxG shared circuits are defined by miRNAs that crosstalk with both PcG and trxG regulators. These putative regulatory circuits may uncover a novel mechanism in Drosophila for the control of PcG/trxG and miRNAs levels of expression. The computational framework developed here for Drosophila melanogaster can serve as a model case for similar analyses in other species. Moreover, our work provides, for the first time, a new and useful resource for the Drosophila community to consult prior to experimental studies investigating the epigenetic regulatory networks of miRNA-PcG/trxG mediated gene expressionWe thank Dr. Peter Freddolino (University of Michigan Medical School, USA) for kindly providing us with the Polycomb Response Element genome-wide predictor (Khabiri and Freddolino, 2019) and Keith Harshman for carefully reading the manuscript. This work was supported by PID2020-114533 GB-C21 grant from Spanish Agencia Estatal de Investigacion/Ministerio de Ciencia e Innovaci on and by institutional grants from Fundacion Areces and Banco Santande

Automatic identification of informative regions with epigenomic changes associated to hematopoiesis

Hematopoiesis is one of the best characterized biological systems but the connection between chromatin changes and lineage differentiation is not yet well understood. We have developed a bioinformatic workflow to generate a chromatin space that allows to classify 42 human healthy blood epigenomes from the BLUEPRINT, NIH ROADMAP and ENCODE consortia by their cell type. This approach let us to distinguish different cells types based on their epigenomic profiles, thus recapitulating important aspects of human hematopoiesis. The analysis of the orthogonal dimension of the chromatin space identify 32,662 chromatin determinant regions (CDRs), genomic regions with different epigenetic characteristics between the cell types. Functional analysis revealed that these regions are linked with cell identities. The inclusion of leukemia epigenomes in the healthy hematological chromatin sample space gives us insights on the healthy cell types that are more epigenetically similar to the disease samples. Further analysis of tumoral epigenetic alterations in hematopoietic CDRs points to sets of genes that are tightly regulated in leukemic transformations and commonly mutated in other tumors. Our method provides an analytical approach to study the relationship between epigenomic changes and cell lineage differentiation. Method availability: https://github.com/david-juan/ChromDet.European Union’s Seventh Framework Programme [FP7/2007–2013, 282510 (BLUEPRINT)]; Spanish Ministry of Economy, Industry and Competitiveness and European Regional Development Fund [Project Retos BFU2015–71241-R]. Funding for open access charge: Project Retos BFU2015–71241-R (to A.V.).Peer ReviewedPostprint (published version

Gut Microbiota Induced by Pterostilbene and Resveratrol in High-Fat-High-Fructose Fed Rats: Putative Role in Steatohepatitis Onset

Resveratrol and its 2-methoxy derivative pterostilbene are two phenolic compounds that occur in foodstuffs and feature hepato-protective effects. This study is devoted to analysing and comparing the metabolic effects of pterostilbene and resveratrol on gut microbiota composition in rats displaying NAFLD induced by a diet rich in saturated fat and fructose. The associations among changes induced by both phenolic compounds in liver status and those induced in gut microbiota composition were also analysed. For this purpose, fifty Wistar rats were distributed in five experimental groups: a group of animals fed a standard diet (CC group) and four additional groups fed a high-fat high-fructose diet alone (HFHF group) or supplemented with 15 or 30 mg/kg bw/d of pterostilbene (PT15 and PT30 groups, respectively) or 30 mg/kg bw/d of resveratrol (RSV30 group). The dramatic changes induced by high-fat high-fructose feeding in the gut microbiota were poorly ameliorated by pterostilbene or resveratrol. These results suggest that the specific changes in microbiota composition induced by pterostilbene (increased abundances of Akkermansia and Erysipelatoclostridium, and lowered abundance of Clostridum sensu stricto 1) may not entirely explain the putative preventive effects on steatohepatitis.This research was funded by Ministerio de Economía y Competitividad-Fondo Europeo de Desarrollo Regional (grant number AGL-2015-65719-R MINECO/FEDER, UE), Instituto de Salud Carlos III CIBERobn (grant number CB12/03/30007); University of the Basque Country (grant number GIU 18/173)

AI4Food-NutritionFW: A Novel Framework for the Automatic Synthesis and Analysis of Eating Behaviours

Nowadays millions of images are shared on social media and web platforms. In particular, many of them are food images taken from a smartphone over time, providing information related to the individual's diet. On the other hand, eating behaviours are directly related to some of the most prevalent diseases in the world. Exploiting recent advances in image processing and Artificial Intelligence (AI), this scenario represents an excellent opportunity to: i) create new methods that analyse the individuals' health from what they eat, and ii) develop personalised recommendations to improve nutrition and diet under specific circumstances (e.g., obesity or COVID). Having tunable tools for creating food image datasets that facilitate research in both lines is very much needed. This paper proposes AI4Food-NutritionFW, a framework for the creation of food image datasets according to configurable eating behaviours. AI4Food-NutritionFW simulates a user-friendly and widespread scenario where images are taken using a smartphone. In addition to the framework, we also provide and describe a unique food image dataset that includes 4,800 different weekly eating behaviours from 15 different profiles and 1,200 subjects. Specifically, we consider profiles that comply with actual lifestyles from healthy eating behaviours (according to established knowledge), variable profiles (e.g., eating out, holidays), to unhealthy ones (e.g., excess of fast food or sweets). Finally, we automatically evaluate a healthy index of the subject's eating behaviours using multidimensional metrics based on guidelines for healthy diets proposed by international organisations, achieving promising results (99.53% and 99.60% accuracy and sensitivity, respectively). We also release to the research community a software implementation of our proposed AI4Food-NutritionFW and the mentioned food image dataset created with it.Comment: 10 pages, 5 figures, 4 table

Leveraging Automatic Personalised Nutrition: Food Image Recognition Benchmark and Dataset based on Nutrition Taxonomy

Maintaining a healthy lifestyle has become increasingly challenging in today's sedentary society marked by poor eating habits. To address this issue, both national and international organisations have made numerous efforts to promote healthier diets and increased physical activity. However, implementing these recommendations in daily life can be difficult, as they are often generic and not tailored to individuals. This study presents the AI4Food-NutritionDB database, the first nutrition database that incorporates food images and a nutrition taxonomy based on recommendations by national and international health authorities. The database offers a multi-level categorisation, comprising 6 nutritional levels, 19 main categories (e.g., "Meat"), 73 subcategories (e.g., "White Meat"), and 893 specific food products (e.g., "Chicken"). The AI4Food-NutritionDB opens the doors to new food computing approaches in terms of food intake frequency, quality, and categorisation. Also, we present a standardised experimental protocol and benchmark including three tasks based on the nutrition taxonomy (i.e., category, subcategory, and final product recognition). These resources are available to the research community, including our deep learning models trained on AI4Food-NutritionDB, which can serve as pre-trained models, achieving accurate recognition results for challenging food image databases.Comment: 12 pages, 4 figures, 4 table

Effectiveness of a training programme to improve hand hygiene compliance in primary healthcare

<p>Abstract</p> <p>Background</p> <p>Hand hygiene is the most effective measure for preventing infections related to healthcare, and its impact on the reduction of these infections is estimated at 50%. Non-compliance has been highlighted in several studies in hospitals, although none have been carried out in primary healthcare.</p> <p>Main objective</p> <p>To evaluated the effect of a "Hand Hygiene for the reduction of healthcare-associated infections" training program for primary healthcare workers, measured by variation from correct hand hygiene compliance, according to regulatory and specific criteria, 6 months after the baseline, in the intervention group (group receiving a training program) and in the control group (a usual clinical practice).</p> <p>Secondary objectives</p> <p>-To describe knowledges, attitudes and behaviors as regards hand hygiene among the professionals, and their possible association with "professional burnout", stratifying the results by type of group (intervention and usual clinical practice).</p> <p>-To estimate the logistic regression model that best explains hand hygiene compliance.</p> <p>Methods/Design</p> <p>Experimental study of parallel groups, with a control group, and random assignment by Health Center.</p> <p>Area of study.- Health centers in north-eastern Madrid (Spain).</p> <p>Sample studied.- Healthcare workers (physicians, odontostomatologists, pediatricians, nurses, dental hygienists, midwife and nursing auxiliaries).</p> <p>Intervention.- A hand hygiene training program, including a theoretical-practical workshop, provision of alcohol-based solutions and a reminder strategy in the workplace.</p> <p>Other variables: sociodemographic and professional knowledges, attitudes, and behaviors with regard to hand hygiene.</p> <p>Statistical Analysis: descriptive and inferential, using multivariate methods (covariance analysis and logistic regression).</p> <p>Discussion</p> <p>This study will provide valuable information on the prevalence of hand hygiene non-compliance, and improve healthcare.</p

Combined MEK and PI3K/p110β Inhibition as a Novel Targeted Therapy for Malignant Mesothelioma Displaying Sarcomatoid Features

Among malignant mesotheliomas (MM), the sarcomatoid subtype is associated with higher chemoresistance and worst survival. Due to its low incidence, there has been little progress in the knowledge of the molecular mechanisms associated with sarcomatoid MM, which might help to define novel therapeutic targets. In this work, we show that loss of PTEN expression is frequent in human sarcomatoid MM and PTEN expression levels are lower in sarcomatoid MM than in the biphasic and epithelioid subtypes. Combined Pten and Trp53 deletion in mouse mesothelium led to nonepithelioid MM development. In Pten;Trp53-null mice developing MM, the Gαi2-coupled receptor subunit activated MEK/ERK and PI3K, resulting in aggressive, immune-suppressed tumors. Combined inhibition of MEK and p110β/PI3K reduced mouse tumor cell growth in vitro. Therapeutic inhibition of MEK and p110β/PI3K using selumetinib (AZD6244, ARRY-142886) and AZD8186, two drugs that are currently in clinical trials, increased the survival of Pten;Trp53-null mice without major toxicity. This drug combination effectively reduced the proliferation of primary cultures of human pleural (Pl) MM, implicating nonepithelioid histology and high vimentin, AKT1/2, and Gαi2 expression levels as predictive markers of response to combined MEK and p110β/PI3K inhibition. Our findings provide a rationale for the use of selumetinib and AZD8186 in patients with MM with sarcomatoid features. This constitutes a novel targeted therapy for a poor prognosis and frequently chemoresistant group of patients with MM, for whom therapeutic options are currently lacking.[Significance] Mesothelioma is highly aggressive; its sarcomatoid variants have worse prognosis. Building on a genetic mouse model, a novel combination therapy is uncovered that is relevant to human tumors.This work was supported, in part, by grants from Asociación Española Contra el Cáncer (F.X. Real), Spanish Ministry of Economy and Competitivity, Plan Estatal de I+D+I 2013-2016, ISCIII (FIS PI15/00045 to A. Carnero), RTICC (Instituto de Salud Carlos III, grants RD12/0036/0034 to F.X. Real and A. Carnero, respectively), and CIBERONC (CB16/12/00453 and CD16/12/00275 to F.X. Real and A. Carnero, respectively), cofunded by FEDER from Regional Development European Funds (European Union) and Inserm (Institut national de la santé et de la recherche médicale). M. Marqués was supported by a Sara Borrell Fellowship from Instituto de Salud Carlos III. CNIO is supported by Ministerio de Ciencia, Innovación y Universidades as a Centro de Excelencia Severo Ochoa SEV-2015-0510

CD8+ T Cells from Human Neonates Are Biased toward an Innate Immune Response

To better understand why human neonates show a poor response to intracellular pathogens, we compared gene expression and histone modification profiles of neonatal naive CD8+ T cells with that of their adult counterparts. We found that neonatal lymphocytes have a distinct epigenomic landscape associated with a lower expression of genes involved in T cell receptor (TCR) signaling and cytotoxicity and a higher expression of genes involved in the cell cycle and innate immunity. Functional studies corroborated that neonatal CD8+ T cells are less cytotoxic, transcribe antimicrobial peptides, and produce reactive oxygen species. Altogether, our results show that neonatal CD8+ T cells have a specific genetic program biased toward the innate immune response. These findings will contribute to better diagnosis and management of the neonatal immune response.This project was specifically supported by a joint EcosNord-Anuies-SEP-Con-acyt project (M11S01). Work in the M.A.S. laboratory is supported by grantsfrom Consejo Nacional de Ciencia y Tecnologı ́a(CONACYT; CB-2011-01168182) and Programa de Mejoramiento del Profesorado (PROMEPSI-UAEM/13/342). Work in the S.S. laboratory is supported by recurrent fundingfrom the Inserm and Aix-Marseille University and by specific grants from theEuropean Union’s FP7 Program (agreement 282510-BLUEPRINT), the Associ-ation pour la Recherche contre le Cancer (ARC) (project SFI20111203756), andthe Aix-Marseille initiative d’excelence (A*MIDEX) project ANR-11-IDEX-0001-02. We thank Centro Estatal de la Transfusio ́n Sanguı ́nea in Cuernavaca for thedonation of leukocyte concentrates and the mothers and babies of HospitalGeneral Parres in Cuernavaca for the donation of cord blood. This study makesuse of data generated by the Blueprint and Roadmap consortia. A full list of theinvestigators who contributed to the generation of the data is availablefromwww.blueprint-epigenome.euandhttp://www.roadmapepigenomics.org/. Funding for the Blueprint project was provided by the European Union’sSeventh Framework Program (FP7/2007-2013) under grant agreement282510 – BLUEPRINT. The Roadmap consortium is financed by the NIH. Weare grateful to Professor C.I. Pogson for critical reading of the manuscript.S

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach