GeNNius: an ultrafast drug-target interaction inference method based on graph neural networks.

MOTIVATION: Drug-target interaction (DTI) prediction is a relevant but challenging task in the drug repurposing field. In-silico approaches have drawn particular attention as they can reduce associated costs and time commitment of traditional methodologies. Yet, current state-of-the-art methods present several limitations: existing DTI prediction approaches are computationally expensive, thereby hindering the ability to use large networks and exploit available datasets and, the generalization to unseen datasets of DTI prediction methods remains unexplored, which could potentially improve the development processes of DTI inferring approaches in terms of accuracy and robustness. RESULTS: In this work, we introduce GeNNius (Graph Embedding Neural Network Interaction Uncovering System), a Graph Neural Network (GNN)-based method that outperforms state-of-the-art models in terms of both accuracy and time efficiency across a variety of datasets. We also demonstrated its prediction power to uncover new interactions by evaluating not previously known DTIs for each dataset. We further assessed the generalization capability of GeNNius by training and testing it on different datasets, showing that this framework can potentially improve the DTI prediction task by training on large datasets and testing on smaller ones. Finally, we investigated qualitatively the embeddings generated by GeNNius, revealing that the GNN encoder maintains biological information after the graph convolutions while diffusing this information through nodes, eventually distinguishing protein families in the node embedding space. AVAILABILITY AND IMPLEMENTATION: GeNNius code is available at https://github.com/ubioinformat/GeNNius.

Single-cell dissection of aggression in honeybee colonies.

Understanding how genotypic variation results in phenotypic variation is especially difficult for collective behaviour because group phenotypes arise from complex interactions among group members. A genome-wide association study identified hundreds of genes associated with colony-level variation in honeybee aggression, many of which also showed strong signals of positive selection, but the influence of these 'colony aggression genes' on brain function was unknown. Here we use single-cell (sc) transcriptomics and gene regulatory network (GRN) analyses to test the hypothesis that genetic variation for colony aggression influences individual differences in brain gene expression and/or gene regulation. We compared soldiers, which respond to territorial intrusion with stinging attacks, and foragers, which do not. Colony environment showed stronger influences on soldier-forager differences in brain gene regulation compared with brain gene expression. GRN plasticity was strongly associated with colony aggression, with larger differences in GRN dynamics detected between soldiers and foragers from more aggressive relative to less aggressive colonies. The regulatory dynamics of subnetworks composed of genes associated with colony aggression genes were more strongly correlated with each other across different cell types and brain regions relative to other genes, especially in brain regions involved with olfaction and vision and multimodal sensory integration, which are known to mediate bee aggression. These results show how group genetics can shape a collective phenotype by modulating individual brain gene regulatory network architecture.

Uncovering perturbations in human hematopoiesis associated with healthy aging and myeloid malignancies at single-cell resolution.

Early hematopoiesis is a continuous process in which hematopoietic stem and progenitor cells (HSPCs) gradually differentiate toward specific lineages. Aging and myeloid malignant transformation are characterized by changes in the composition and regulation of HSPCs. In this study, we used single-cell RNA sequencing (scRNA-seq) to characterize an enriched population of human HSPCs obtained from young and elderly healthy individuals.Based on their transcriptional profile, we identified changes in the proportions of progenitor compartments during aging, and differences in their functionality, as evidenced by gene set enrichment analysis. Trajectory inference revealed that altered gene expression dynamics accompanied cell differentiation, which could explain aging-associated changes in hematopoiesis. Next, we focused on key regulators of transcription by constructing gene regulatory networks (GRNs) and detected regulons that were specifically active in elderly individuals. Using previous findings in healthy cells as a reference, we analyzed scRNA-seq data obtained from patients with myelodysplastic syndrome (MDS) and detected specific alterations of the expression dynamics of genes involved in erythroid differentiation in all patients with MDS such as TRIB2. In addition, the comparison between transcriptional programs and GRNs regulating normal HSPCs and MDS HSPCs allowed identification of regulons that were specifically active in MDS cases such as SMAD1, HOXA6, POU2F2, and RUNX1 suggesting a role of these transcription factors (TFs) in the pathogenesis of the disease.In summary, we demonstrate that the combination of single-cell technologies with computational analysis tools enable the study of a variety of cellular mechanisms involved in complex biological systems such as early hematopoiesis and can be used to dissect perturbed differentiation trajectories associated with perturbations such as aging and malignant transformation. Furthermore, the identification of abnormal regulatory mechanisms associated with myeloid malignancies could be exploited for personalized therapeutic approaches in individual patients.

Our blood contains many different types of cells; red blood cells carry oxygen through the body, platelets help to stop bleeding and a variety of white blood cells fight infections. All of these critical components come from a pool of immature cells in bone marrow, which can develop and specialise into any of these. However, as we get older, these immature cells can accumulate damage, including mutations in specific genes. This increases the risk of diseases such as myelodysplastic syndromes (MDS), a type of cancer in which the cells cannot develop and the patient does not have enough healthy mature blood cells. The changes in gene activity in the immature cells have previously been studied using samples from young and elderly people, as well as individuals with MDS. These studies examined large numbers of cells together, revealing differences between young and elderly people, and individuals with MDS. However, this does not describe how the different types alter their behaviour. To address this, Ainciburu, Ezponda et al. used a technique called single-cell RNA sequencing to study the gene activity in individual immature blood cells. This revealed changes associated with maturation that may account for the different combinations of cell populations in younger and older people. The results confirmed findings from previous studies and suggested new genes involved in ageing or MDS. Ainciburu, Ezponda et al. used these results to create an analytical system that highlights gene activity differences in individual MDS patients that are independent of age-related changes. These results provide new insights that could help further research into the development of MDS and the ageing process. In addition, scientists could study other diseases using this approach of analysing individual patients' gene activity. In future, this could help to personalise clinical decisions on diagnosis and treatment.

The transcription factor DDIT3 is a potential driver of dyserythropoiesis in myelodysplastic syndromes.

Myelodysplastic syndromes (MDS) are hematopoietic stem cell (HSC) malignancies characterized by ineffective hematopoiesis, with increased incidence in older individuals. Here we analyze the transcriptome of human HSCs purified from young and older healthy adults, as well as MDS patients, identifying transcriptional alterations following different patterns of expression. While aging-associated lesions seem to predispose HSCs to myeloid transformation, disease-specific alterations may trigger MDS development. Among MDS-specific lesions, we detect the upregulation of the transcription factor DNA Damage Inducible Transcript 3 (DDIT3). Overexpression of DDIT3 in human healthy HSCs induces an MDS-like transcriptional state, and dyserythropoiesis, an effect associated with a failure in the activation of transcriptional programs required for normal erythroid differentiation. Moreover, DDIT3 knockdown in CD34+ cells from MDS patients with anemia is able to restore erythropoiesis. These results identify DDIT3 as a driver of dyserythropoiesis, and a potential therapeutic target to restore the inefficient erythroid differentiation characterizing MDS patients.

CAR density influences antitumoral efficacy of BCMA CAR T cells and correlates with clinical outcome.

Identification of new markers associated with long-term efficacy in patients treated with CAR T cells is a current medical need, particularly in diseases such as multiple myeloma. In this study, we address the impact of CAR density on the functionality of BCMA CAR T cells. Functional and transcriptional studies demonstrate that CAR T cells with high expression of the CAR construct show an increased tonic signaling with up-regulation of exhaustion markers and increased in vitro cytotoxicity but a decrease in in vivo BM infiltration. Characterization of gene regulatory networks using scRNA-seq identified regulons associated to activation and exhaustion up-regulated in CARHigh T cells, providing mechanistic insights behind differential functionality of these cells. Last, we demonstrate that patients treated with CAR T cell products enriched in CARHigh T cells show a significantly worse clinical response in several hematological malignancies. In summary, our work demonstrates that CAR density plays an important role in CAR T activity with notable impact on clinical response.

SimiC enables the inference of complex gene regulatory dynamics across cell phenotypes.

Single-cell RNA-Sequencing has the potential to provide deep biological insights by revealing complex regulatory interactions across diverse cell phenotypes at single-cell resolution. However, current single-cell gene regulatory network inference methods produce a single regulatory network per input dataset, limiting their capability to uncover complex regulatory relationships across related cell phenotypes. We present SimiC, a single-cell gene regulatory inference framework that overcomes this limitation by jointly inferring distinct, but related, gene regulatory dynamics per phenotype. We show that SimiC uncovers key regulatory dynamics missed by previously proposed methods across a range of systems, both model and non-model alike. In particular, SimiC was able to uncover CAR T cell dynamics after tumor recognition and key regulatory patterns on a regenerating liver, and was able to implicate glial cells in the generation of distinct behavioral states in honeybees. SimiC hence establishes a new approach to quantitating regulatory architectures between distinct cellular phenotypes, with far-reaching implications for systems biology.

JIND: joint integration and discrimination for automated single-cell annotation.

MOTIVATION: An important step in the transcriptomic analysis of individual cells involves manually determining the cellular identities. To ease this labor-intensive annotation of cell-types, there has been a growing interest in automated cell annotation, which can be achieved by training classification algorithms on previously annotated datasets. Existing pipelines employ dataset integration methods to remove potential batch effects between source (annotated) and target (unannotated) datasets. However, the integration and classification steps are usually independent of each other and performed by different tools. We propose JIND (joint integration and discrimination for automated single-cell annotation), a neural-network-based framework for automated cell-type identification that performs integration in a space suitably chosen to facilitate cell classification. To account for batch effects, JIND performs a novel asymmetric alignment in which unseen cells are mapped onto the previously learned latent space, avoiding the need of retraining the classification model for new datasets. JIND also learns cell-type-specific confidence thresholds to identify cells that cannot be reliably classified. RESULTS: We show on several batched datasets that the joint approach to integration and classification of JIND outperforms in accuracy existing pipelines, and a smaller fraction of cells is rejected as unlabeled as a result of the cell-specific confidence thresholds. Moreover, we investigate cells misclassified by JIND and provide evidence suggesting that they could be due to outliers in the annotated datasets or errors in the original approach used for annotation of the target batch. AVAILABILITY AND IMPLEMENTATION: Implementation for JIND is available at https://github.com/mohit1997/JIND and the data underlying this article can be accessed at https://doi.org/10.5281/zenodo.6246322. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Whole exome sequencing characterization of individuals presenting extreme phenotypes of high and low risk of developing tobacco-induced lung adenocarcinoma.

BACKGROUND: Tobacco is the main risk factor for developing lung cancer. Yet, some heavy smokers do not develop lung cancer at advanced ages while others develop it at young ages. Here, we assess for the first time the genetic background of these clinically relevant extreme phenotypes using whole exome sequencing (WES). METHODS: We performed WES of germline DNA from heavy smokers who either developed lung adenocarcinoma at an early age (extreme cases, n=50) or did not present lung adenocarcinoma or other tumors at an advanced age (extreme controls, n=50). We selected non-synonymous variants located in exonic regions and consensus splice sites of the genes that showed significantly different allelic frequencies between both cohorts. We validated our results in all the additional extreme cases (i.e., heavy smokers who developed lung adenocarcinoma at an early age) available from The Cancer Genome Atlas (TCGA). RESULTS: The mean age for the extreme cases and controls was respectively 49.7 and 77.5 years. Mean tobacco consumption was 43.6 and 56.8 pack-years. We identified 619 significantly different variants between both cohorts, and we validated 108 of these in extreme cases selected from TCGA. Nine validated variants, located in relevant cancer related genes, such as PARP4, HLA-A or NQO1, among others, achieved statistical significance in the False Discovery Rate test. The most significant validated variant (P=4.48×10-5) was located in the tumor-suppressor gene ALPK2. CONCLUSIONS: We describe genetic variants associated with extreme phenotypes of high and low risk for the development of tobacco-induced lung adenocarcinoma. Our results and our strategy may help to identify high-risk subjects and to develop new therapeutic approaches.

Girasol, a sky imaging and global solar irradiance dataset.

The energy available in a microgrid that is powered by solar energy is tightly related to the weather conditions at the moment of generation. A very short-term forecast of solar irradiance provides the microgrid with the capability of automatically controlling the dispatch of energy. We propose a dataset to forecast Global Solar Irradiance (GSI) using a data acquisition system (DAQ) that simultaneously records sky imaging and GSI measurements, with the objective of extracting features from clouds and use them to forecast the power produced by a Photovoltaic (PV) system. The DAQ system is nicknamed the Girasol Machine (Girasol means Sunflower in Spanish). The sky imaging system consists of a longwave infrared (IR) camera and a visible (VI) light camera with a fisheye lens attached to it. The cameras are installed inside a weatherproof enclosure that it is mounted on a solar tracker. The tracker updates its pan and tilt every second using a solar position algorithm to maintain the Sun in the center of the IR and VI images. A pyranometer is situated on a horizontal mount next to the DAQ system to measure GSI. The dataset, composed of IR images, VI images, GSI measurements, and the Sun's positions, has been tagged with timestamps.

DREIMT: a drug repositioning database and prioritization tool for immunomodulation.

MOTIVATION: Drug immunomodulation modifies the response of the immune system and can be therapeutically exploited in pathologies such as cancer and autoimmune diseases. RESULTS: DREIMT is a new hypothesis-generation web tool, which performs drug prioritization analysis for immunomodulation. DREIMT provides significant immunomodulatory drugs targeting up to 70 immune cells subtypes through a curated database that integrates 4960 drug profiles and ∼2600 immune gene expression signatures. The tool also suggests potential immunomodulatory drugs targeting user-supplied gene expression signatures. Final output includes drug-signature association scores, FDRs and downloadable plots and results tables. AVAILABILITYAND IMPLEMENTATION: http://www.dreimt.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fractura de pelvis inestable. Estudio de cohorte longitudinal / Unstable pelvic Fractures. A Cohort Study

Introducción No hay consenso sobre el paradigma de tratamiento óptimo para pacientes que se presentan con fractura de pelvis inestable y más si se asocia a inestabilidad hemodinámica. El estudio se realizó para determinar la experiencia en el manejo de pacientes con una fractura inestable de la pelvis, con o sin inestabilidad hemodinámica, en un centro de referencia de trauma de Medellín, Colombia, como un paso inicial para avanzar protocolos adaptados a las necesidades particulares de nuestra población. Materiales & Métodos Se realizó un estudio retrospectivo, observacional que incluyó pacientes con fractura pélvica por traumatismo. Se recogieron datos demográficos, signos vitales de admisión, presencia de shock al ingreso (según escala ATLS), método de control de hemorragias, requerimiento de transfusión y mortalidad global. Se realizó análisis de un subgrupo de pacientes que se presentaron con signos de Shock hemodinámica grave asociado, definido como ATLS III- IV al ingreso y requerimiento de hemoderivados de más de 8UI en las primeras 48 horas. Resultados Un total de 567 pacientes con fractura pélvica de cualquier tipo, entre enero de 2011 y diciembre de 2018, identificados en las bases de datos de la institución, de los cuales 149 pacientes presentaron un patrón de fractura inestable de la pelvis (FIP). El 68.1% de los pacientes eran varones, con una edad media de 34 RIQ; 30 años y una puntuación de gravedad de la lesión (ISS) de 34 RIQ: 20. La mortalidad hospitalaria fue del 13.3%. La angioembolización y la colocación de fijadores externos fueron el método más común de control de hemorragias utilizado. Un total de 37 pacientes (27%) se sometieron intervención para el control de la hemorragia en las primeras 48 horas. Hubo 37 pacientes con fractura pélvica admitidos en estado de shock grave o requerimiento de más de 8U hemoderivados, 17 pacientes requirieron intervención en la pelvis, únicas o combinadas. La mortalidad calculada para estos pacientes fué de 32%. Discusión Los pacientes con FPI admitidos en nuestra institución tienen una alta mortalidad y es aún mayor en los pacientes quienes se presentan con Shock grave. Se utilizaron varios métodos para el control de la hemorragia de forma semejante a las indicadas en la literatura actual. Requerimos un esfuerzo institucional sostenido para tratar las fracturas pélvicas y disminuir la mortalidad de nuestros pacientes y conocer nuestra población nos permite orientar las estrategias de manejo.

Background There is no consensus about treatment for patients with an unstable pelvic fracture and even less when hemodynamic instability is associated with. Study was conducted to determine the outcome in the management of patients with an unstable fracture of the pelvis, with or without hemodynamic instability, in a trauma referral center in Medellín, Colombia. Methods A retrospective, longitudinal cohort study was conducted including patients with pelvic fracture due to trauma. Demographic data, vital signs on admission, presence of hemodynamic shock on admission (according to the ATLS scale), method of bleeding control, transfusion requirement and overall mortality rate were collected. An analysis was performed on a subgroup of patients who presented with signs of associated severe hemodynamic shock, defined as ATLS III-IV upon admission and a requirement for blood products of more than 8IU in the first 48hours. Results A total of 567 patients with any type of pelvic fracture, between January 2011 and December 2018, identified in the institution's databases, of which 149 patients presented an unstable pelvic fracture pattern (FIP). 68.1% of the patients were male, with a mean age of 34 IQR; 30 years and an Injury Severity Score (ISS) of 34 IQR: 20. Hospital mortality was 13.3%. Angioembolization and external fixator placement were the most used method of bleeding control. A total of 37 patients (27%) underwent intervention to control bleeding in the first 48hours. There were 37 patients with pelvic fracture admitted in a state of severe shock or requiring more than 8U of blood products, 17 patients required intervention in the pelvis, using single or combined ways. The mortality calculated for these patients was 32%. Discussion Patients with unstable pelvis fractures admitted to our institution have a high mortality rate and it is even higher in patients who present with severe shock. Various methods were used to control bleeding similar to those indicated in the current literature. We require a sustained institutional effort to treat pelvic fractures and reduce the mortality of our patients, and knowing our population characteristcs allows us to guide management strategies.

UPEFinder: A Bioinformatic Tool for the Study of Uncharacterized Proteins Based on Gene Expression Correlation and the PageRank Algorithm.

The Human Proteome Project (HPP) is leading the international effort to characterize the human proteome. Although the main goal of this project was first focused on the detection of missing proteins, a new challenge arose from the need to assign biological functions to the uncharacterized human proteins and describe their implications in human diseases. Not only the proteins with experimental evidence (uPE1 proteins) but also the uncharacterized missing proteins (uMPs) were the objects of study in this challenge, neXt-CP50. In this work, we developed a new bioinformatic approach to infer biological annotations for the uPE1 proteins and uMPs based on a "guilt-by-association" analysis using public RNA-Seq data sets. We used the correlation of these proteins with the well-characterized PE1 proteins to construct a network. In this way, we applied the PageRank algorithm to this network to identify the most relevant nodes, which were the biological annotations of the uncharacterized proteins. All of the generated information was stored in a database. In addition, we implemented the web application UPEFinder (https://upefinder.proteored.org) to facilitate the access to this new resource. This information is especially relevant for the researchers of the HPP who are interested in the generation and validation of new hypotheses about the functions of these proteins. Both the database and the web application are publicly available (https://github.com/ubioinformat/UPEfinder).

Vascular clinical anatomy of the submandibular gland.

OBJECTIVE: The aim of this study is to describe in depth the precise anatomy of the vascular supply of the submandibular gland, trying to determine the existence of patterns of glandular vascularization. Knowledge of these patterns could facilitate surgical management of the gland and the submandibular gland flap. MATERIAL AND METHODS: Neck dissections of formaldehyde preserved human cadavers were performed. Submandibular and transmandibular approaches were used during the dissections. All the vascular branches found were registered and classified into 2groups: main or accessory branches. The anatomical data analyzed was: The diameter and length of the main and accessory branches, as well as the most important measurements of the submandibular gland flap pedicle. RESULTS: 33 glands were dissected to study the arterial supply of the submandibular gland (17 right, 16 left; 17 males, 16 females) and 29 were dissected to study the venous supply (15 left, 14 right; 15 males,14 females). A total of 123 arterial branches were found reaching the 33 submandibular glands (47 main and 76 accessories) and 116 venous branches were found draining the 29 submandibular glands (47 main branches and 69 accessory branches). A constant main venous branch that ran parallel to the Wharton duct and drained in the sublingual vein was found in all of cases (Concomitant Wharton Duct Vein or CWDV). CONCLUSION: The CWDV is a constant venous branch for the drainage of the gland and should be considered as venous pedicle during the dissection of submandibular gland flaps.

DeepMSPeptide: peptide detectability prediction using deep learning.

SUMMARY: The protein detection and quantification using high-throughput proteomic technologies is still challenging due to the stochastic nature of the peptide selection in the mass spectrometer, the difficulties in the statistical analysis of the results and the presence of degenerated peptides. However, considering in the analysis only those peptides that could be detected by mass spectrometry, also called proteotypic peptides, increases the accuracy of the results. Several approaches have been applied to predict peptide detectability based on the physicochemical properties of the peptides. In this manuscript, we present DeepMSPeptide, a bioinformatic tool that uses a deep learning method to predict proteotypic peptides exclusively based on the peptide amino acid sequences. AVAILABILITY AND IMPLEMENTATION: DeepMSPeptide is available at https://github.com/vsegurar/DeepMSPeptide. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Hsa-miR-139-5p is a prognostic thyroid cancer marker involved in HNRNPF-mediated alternative splicing.

It is critical to identify biomarkers and functional networks associated with aggressive thyroid cancer to anticipate disease progression and facilitate personalized patient management. We performed miRNome sequencing of 46 thyroid tumors enriched with advanced disease patients with a median follow-up of 96 months. MiRNome profiles correlated with tumor-specific histopathological and molecular features, such as stromal cell infiltration and tumor driver mutation. Differential expression analysis revealed a consistent hsa-miR-139-5p downexpression in primary carcinomas from patients with recurrent/metastatic disease compared to disease-free patients, sustained in paired local metastases and validated in publicly available thyroid cancer series. Exogenous expression of hsa-miR-139-5p significantly reduced migration and proliferation of anaplastic thyroid cancer cells. Proteomic analysis indicated RICTOR, SMAD2/3 and HNRNPF as putative hsa-miR-139-5p targets in our cell system. Abundance of HNRNPF mRNA, encoding an alternative splicing factor involved in cryptic exon inclusion/exclusion, inversely correlated with hsa-miR-139-5p expression in human tumors. RNA sequencing analysis revealed 174 splicing events differentially regulated upon HNRNPF repression in our cell system, affecting genes involved in RTK/RAS/MAPK and PI3K/AKT/MTOR signaling cascades among others. These results point at the hsa-miR-139-5p/HNRNPF axis as a novel regulatory mechanism associated with the modulation of major thyroid cancer signaling pathways and tumor virulence.

Role of bulge epidermal stem cells and TSLP signaling in psoriasis.

Psoriasis is a common inflammatory skin disease involving a cross-talk between epidermal and immune cells. The role of specific epidermal stem cell populations, including hair follicle stem cells (HF-SCs) in psoriasis is not well defined. Here, we show reduced expression of c-JUN and JUNB in bulge HF-SCs in patients with scalp psoriasis. Using lineage tracing in mouse models of skin inflammation with inducible deletion of c-Jun and JunB, we found that mutant bulge HF-SCs initiate epidermal hyperplasia and skin inflammation. Mechanistically, thymic stromal lymphopoietin (TSLP) was identified in mutant cells as a paracrine factor stimulating proliferation of neighboring non-mutant epidermal cells, while mutant inter-follicular epidermal (IFE) cells are lost over time. Blocking TSLP in psoriasis-like mice reduced skin inflammation and decreased epidermal proliferation, VEGFα expression, and STAT5 activation. These findings unravel distinct roles of HF-SCs and IFE cells in inflammatory skin disease and provide novel mechanistic insights into epidermal cell interactions in inflammation.

Identification of Coding and Long Noncoding RNAs Differentially Expressed in Tumors and Preferentially Expressed in Healthy Tissues.

The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets allow unprecedented gene expression analyses. Here, using these datasets, we performed pan-cancer and pan-tissue identification of coding and long noncoding RNA (lncRNA) transcripts differentially expressed in tumors and preferentially expressed in healthy tissues and/or tumors. Pan-cancer comparison of mRNAs and lncRNAs showed that lncRNAs were deregulated in a more tumor-specific manner. Given that lncRNAs are more tissue-specific than mRNAs, we identified healthy tissues that preferentially express lncRNAs upregulated in tumors and found that testis, brain, the digestive tract, and blood/spleen were the most prevalent. In addition, specific tumors also upregulate lncRNAs preferentially expressed in other tissues, generating a unique signature for each tumor type. Most tumors studied downregulated lncRNAs preferentially expressed in their tissue of origin, probably as a result of dedifferentiation. However, the same lncRNAs could be upregulated in other tumors, resulting in "bimorphic" transcripts. In hepatocellular carcinoma (HCC), the upregulated genes identified were expressed at higher levels in patients with worse prognosis. Some lncRNAs upregulated in HCC and preferentially expressed in healthy testis or brain were predicted to function as oncogenes and were significantly associated with higher tumor burden, and poor prognosis, suggesting their relevance in hepatocarcinogenesis and/or tumor evolution. Taken together, therapies targeting oncogenic lncRNAs should take into consideration the healthy tissue, where the lncRNAs are preferentially expressed, to predict and decrease unwanted secondary effects and increase potency. SIGNIFICANCE: Comprehensive analysis of coding and noncoding genes expressed in different tumors and normal tissues, which should be taken into account to predict side effects from potential coding and noncoding gene-targeting therapies.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/79/20/5167/F1.large.jpg.

Specific Contributions of Cohesin-SA1 and Cohesin-SA2 to TADs and Polycomb Domains in Embryonic Stem Cells.

Cohesin exists in two variants carrying either STAG/SA1 or SA2. Here we have addressed their specific contributions to the unique spatial organization of the mouse embryonic stem cell genome, which ensures super-enhancer-dependent transcription of pluripotency factors and repression of lineage-specification genes within Polycomb domains. We find that cohesin-SA2 facilitates Polycomb domain compaction through Polycomb repressing complex 1 (PRC1) recruitment and promotes the establishment of long-range interaction networks between distant Polycomb-bound promoters that are important for gene repression. Cohesin-SA1, in contrast, disrupts these networks, while preserving topologically associating domain (TAD) borders. The diverse effects of both complexes on genome topology may reflect two modes of action of cohesin. One, likely involving loop extrusion, establishes overall genome arrangement in TADs together with CTCF and prevents excessive segregation of same-class compartment regions. The other is required for organization of local transcriptional hubs such as Polycomb domains and super-enhancers, which define cell identity.

Complete Regression of Advanced Pancreatic Ductal Adenocarcinomas upon Combined Inhibition of EGFR and C-RAF.

Five-year survival for pancreatic ductal adenocarcinoma (PDAC) patients remains below 7% due to the lack of effective treatments. Here, we report that combined ablation of EGFR and c-RAF expression results in complete regression of a significant percentage of PDAC tumors driven by Kras/Trp53 mutations in genetically engineered mice. Moreover, systemic elimination of these targets induces toxicities that are well tolerated. Response to this targeted therapy correlates with transcriptional profiles that resemble those observed in human PDACs. Finally, inhibition of EGFR and c-RAF expression effectively blocked tumor progression in nine independent patient-derived xenografts carrying KRAS and TP53 mutations. These results open the door to the development of targeted therapies for PDAC patients.

Rinosinusitis fúngica no invasiva producida por micetomas. Presentación de 2 casos y revisión de la literatura / Non-invasive fungal rhinosinusitis produced by mycetomas. Presentation of 2 cases and a review of the literature

La invasión de los senos paranasales por hongos o rinosinusitis fúngica es una enfermedad relativamente infrecuente que puede manifestarse como distintos cuadros clínicos, en función de la agresividad del microorganismo causante, la edad y las enfermedades asociadas del paciente. Presentamos 2 casos de rinosinusitis fúngica no invasiva en pacientes inmunocompetentes, donde se observan imágenes radiológicas de bolas fúngicas intrasinusales de una inusual radioopacidad metálica. Realizamos una revisión de la literatura de las rinosinusitis fúngicas, con el objetivo de actualizar la terminología y clasificación de estos cuadros y revisamos la descripción de las bolas fúngicas intrasinusales (AU)

The invasion of the paranasal sinuses by fungi, or fungal rhinosinusitis, is a relatively uncommon disorder that may present as different clinical pictures depending on the aggressiveness of the microorganism responsible, age and other illnesses associated with the patient. Two cases are presented on two immunocompetent patients with non-invasive fungal rhinosinusitis, in whom sinus fungal balls of unusual metallic radio-opacity were observed in the X-ray images. A literature review was performed on fungal rhinosinusitis, with the aim of updating the terminology and classification of these clinical pictures, as well as a review on the description of sinus fungal balls (AU)