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To date, no immunotherapy approaches have managed to fully overcome T-cell exhaustion, which remains a mandatory fate for chronically activated effector cells and a major therapeutic challenge. Understanding how to reprogram CD8+ tumor-infiltrating lymphocytes away from exhausted effector states remains an elusive goal. Our work provides evidence that orthogonal gene engineering of T cells to secrete an interleukin (IL)-2 variant binding the IL-2Rβγ receptor and the alarmin IL-33 reprogrammed adoptively transferred T cells to acquire a novel, synthetic effector state, which deviated from canonical exhaustion and displayed superior effector functions. These cells successfully overcame homeostatic barriers in the host and led—in the absence of lymphodepletion or exogenous cytokine support—to high levels of engraftment and tumor regression. Our work unlocks a new opportunity of rationally engineering synthetic CD8+ T-cell states endowed with the ability to avoid exhaustion and control advanced solid tumors.

SummaryInterleukin-2 receptor (IL-2R)-mediated intracellular signaling is a key regulator of T-cell fate decisions. While the potent signals generated by IL-2 engagement execute effector differentiation, elevated metabolic activities and rapid cellular expansion, IL-15 binding induces a stemness/memory phenotype and a quiescent metabolic state. Here, we demonstrate that weak but sustained signaling generated by a non-IL-2Rα-binding variant of IL-2 (IL-2v) drive proliferation/metabolic and stemness transcriptional programs, thereby reprogramming CD8+T cells into a hybrid ‘metabolically active stem-like state’. We further show that IL-2v-induced T cells are capable of superior engraftment, persistence, and tumor control when utilized in adoptive cell therapy. Taken together, our study highlights the ability to fine-tune cytokine engagement of cognate receptors in order to generate therapeutically relevant T-cell states and further reveals the metabolic plasticity of the T-cell memory program.

Developing strategies to inflame tumors is critical for increasing response to immunotherapy. Here, we report that low-dose radiotherapy (LDRT) of murine tumors promotes T-cell infiltration and enables responsiveness to combinatorial immunotherapy in an IFN-dependent manner. Treatment efficacy relied upon mobilizing both adaptive and innate immunity and depended on both cytotoxic CD4+ and CD8+ T cells. LDRT elicited predominantly CD4+ cells with features of exhausted effector cytotoxic cells, with a subset expressing NKG2D and exhibiting proliferative capacity, as well as a unique subset of activated dendritic cells expressing the NKG2D ligand RAE1. We translated these findings to a phase I clinical trial administering LDRT, low-dose cyclophosphamide, and immune checkpoint blockade to patients with immune-desert tumors. In responsive patients, the combinatorial treatment triggered T-cell infiltration, predominantly of CD4+ cells with Th1 signatures. Our data support the rational combination of LDRT with immunotherapy for effectively treating low T cell–infiltrated tumors.Significance:Low-dose radiation reprogrammed the tumor microenvironment of tumors with scarce immune infiltration and together with immunotherapy induced simultaneous mobilization of innate and adaptive immunity, predominantly CD4+ effector T cells, to achieve tumor control dependent on NKG2D. The combination induced important responses in patients with metastatic immune-cold tumors.This article is highlighted in the In This Issue feature, p. 1

Supplementary Figure from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy

Supplementary Data from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy

Supplementary Table from Low-Dose Radiotherapy Reverses Tumor Immune Desertification and Resistance to Immunotherapy

Adoptive cell therapy (ACT) usingex vivoexpanded tumor-infiltrating T lymphocytes (TILs) can mediate responses in metastatic melanoma, but long-term efficacy remains limited to a fraction of patients. Here we interrogated tumor-microenvironment (TME) cellular states and interactions of longitudinal samples from 13 metastatic melanoma patients treated with TIL-ACT in our clinical study (NCT03475134). We performed single-cell RNA-seq and spatial proteomic analyses in pre- and post-ACT tumor tissues and showed that responders exhibited higher tumor cell-intrinsic immunogenicity. Also, endogenous CD8+TILs and myeloid cells of responders were characterized by increased cytotoxicity, exhaustion and costimulation and type-I IFN signaling, respectively. Cell-cell interaction prediction analyses corroborated by spatial neighborhood analyses revealed that responders have rich baseline intratumoral and stromal tumor-reactive T-cell networks with activated myeloid populations. Successful TIL-ACT therapy further reprogrammed the myeloid compartment and increased TIL-myeloid networks. Our systematic target discovery study reveals CD8+T-cell network-based biomarkers that could improve patient selection and guide the design of ACT clinical trials.One-Sentence SummaryResponse to adoptive TIL therapy in melanoma is determined by CD8+TIL-myeloid cell networks

Persistent exposure to antigen during chronic infection or cancer renders T cells dysfunctional. The molecular mechanisms regulating this state of exhaustion are thought to be common in infection and cancer, despite obvious differences in their microenvironments. We discovered that NFAT5, an NFAT family member lacking an AP-1 docking site, is highly expressed in exhausted T cells from murine and human tumors and is a central player in tumor-induced exhaustion. While NFAT5 overexpression reduced tumor control, NFAT5 deletion improved tumor control by promoting the accumulation of tumor-specific CD8+ T cells that expressed less TOX and PD-1 and produced more cytokines particularly among precursor exhausted cells. Conversely, NFAT5 had no effect on chronic infection-induced T cell exhaustion. Mechanistically we found that TCR triggering induced NFAT5 expression and that hyperosmolarity stimulated transcriptional activity of NFAT5. We propose that NFAT5 takes over NFAT1/2 to promote exhaustion specifically in tumor-infiltrating CD8+ T cells.

Graphical abstract, Highlights • Dendritic cell-based vaccines are a promising tool to treat cancer patients. • Clinical outcomes have been so far modest, calling for an urgent need to enhance vaccine immunogenicity. • Integration of immunomodulatory agents constitutes a great opportunity to further improve vaccine efficacy. • A pipeline combining an in vitro screening of single agents with a bioinformatic algorithm predicting the effects of the possible dual combinations will guide the selection of the most promising combinations to integrate in future vaccine formulations.., Dendritic cell (DC)-based vaccines have been largely used in the adjuvant setting for the treatment of cancer, however, despite their proven safety, clinical outcomes still remain modest. In order to improve their efficacy, DC-based vaccines are often combined with one or multiple immunomodulatory agents. However, the selection of the most promising combinations is hampered by the plethora of agents available and the unknown interplay between these different agents. To address this point, we developed a hybrid experimental and computational platform to predict the effects and immunogenicity of dual combinations of stimuli once combined with DC vaccination, based on the experimental data of a variety of assays to monitor different aspects of the immune response after a single stimulus. To assess the stimuli behavior when used as single agents, we first developed an in vitro co-culture system of T cell priming using monocyte-derived DCs loaded with whole tumor lysate to prime autologous peripheral blood mononuclear cells in the presence of the chosen stimuli, as single adjuvants, and characterized the elicited response assessing 18 different phenotypic and functional traits important for an efficient anti-cancer response. We then developed and applied a prediction algorithm, generating a ranking for all possible dual combinations of the different single stimuli considered here. The ranking generated by the prediction tool was then validated with experimental data showing a strong correlation with the predicted scores, confirming that the top ranked conditions globally significantly outperformed the worst conditions. Thus, the method developed here constitutes an innovative tool for the selection of the best immunomodulatory agents to implement in future DC-based vaccines.

Developing strategies to inflame tumors is critical for increasing response to immunotherapy. Here, we report that low-dose radiotherapy (LDRT) of murine tumors promotes T-cell infiltration and enables responsiveness to combinatorial immunotherapy in an IFN-dependent manner. Treatment efficacy relied upon mobilizing both adaptive and innate immunity and depended on both cytotoxic CD4+ and CD8+ T cells. LDRT elicited predominantly CD4+ cells with features of exhausted effector cytotoxic cells, with a subset expressing NKG2D and exhibiting proliferative capacity, as well as a unique subset of activated dendritic cells expressing the NKG2D ligand RAE1. We translated these findings to a phase I clinical trial administering LDRT, low-dose cyclophosphamide, and immune checkpoint blockade to patients with immune-desert tumors. In responsive patients, the combinatorial treatment triggered T-cell infiltration, predominantly of CD4+ cells with Th1 signatures. Our data support the rational combination of LDRT with immunotherapy for effectively treating low T cell–infiltrated tumors.Significance:Low-dose radiation reprogrammed the tumor microenvironment of tumors with scarce immune infiltration and together with immunotherapy induced simultaneous mobilization of innate and adaptive immunity, predominantly CD4+ effector T cells, to achieve tumor control dependent on NKG2D. The combination induced important responses in patients with metastatic immune-cold tumors.This article is highlighted in the In This Issue feature, p. 1

Macrophage plasticity has been studied in vitro, but transcriptional regulation upon injury is poorly understood. We generated a valuable dataset that captures transcriptional changes in the healthy heart and after myocardial injury, revealing a dynamic transcriptional landscape of macrophage activation. Partial deconvolution suggested that post-injury macrophages exhibit overlapping activation of pro-inflammatory and anti-inflammatory programs rather than aligning to canonical M1/M2 programs. Furthermore, simulated dynamics and experimental validation of a regulatory core of the underlying gene-regulatory network revealed a negative-feedback loop that limits initial inflammation via hypoxia-mediated upregulation of ll10. Our results also highlight the prominence of post-transcriptional regulation (miRNAs, mRNA decay, and lincRNAs) in attenuating the myocardial injury-induced inflammatory response. We also identified a cardiac-macrophage-specific gene signature (e.g., Egfr and Lifr) and time-specific markers for macrophage populations (e.g., Lyve1, Cd40, and Mrc1). Altogether, these data provide a core resource for deciphering the transcriptional network in cardiac macrophages in vivo. We thank S. Bartlett (CNIC) for editorial assistance and the CNIC Cellomics and Genomics Units for technical help. This work was supported by grants from the Spanish Ministry of Economy and Competitiveness (SAF2015-64287R and SAF2015-71878-REDT) to M.R., and Fundacion Marato TV3 (121931) and the European Commission FP7 (CardioNext-ITN-608027) to M.R. and A.G.A. L.A.-H. is funded by a fellowship from La Caixa-CNIC. The CNIC is supported by MEIC and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (MEIC award SEV-2015-0505). Sí

The mechanisms regulating exhaustion of tumor-infiltrating lymphocytes (TIL) and responsiveness to PD-1 blockade remain partly unknown. In human ovarian cancer we show that tumor-specific CD8(+) TIL accumulate in tumor islets, where they engage antigen and upregulate PD-1, which restrains their functions. Intraepithelial PD-1(+)CD8(+) TIL can be however polyfunctional. PD-1(+) TIL indeed exhibit a continuum of exhaustion states, with variable levels of CD28 costimulation, which is provided by antigen-presenting cells (APC) in intraepithelial tumor myeloid niches. CD28 costimulation is associated with improved effector fitness of exhausted CD8(+) TIL and is required for their activation upon PD-1 blockade, which also requires tumor myeloid APCs. Exhausted TIL lacking proper CD28 costimulation in situ fail to respond to PD-1 blockade, and their response may be rescued by local CTLA-4 blockade and tumor APC stimulation via CD40L.

2533 Background: Adoptive cell therapy (ACT) using tumor-infiltrating lymphocytes (TIL) has demonstrated a curative potential for patients with metastatic melanoma (MM). Nevertheless, activity remains unsatisfactory in many patients, requiring development of biomarkers that predict therapeutic efficacy. We report results of a single-center phase I study to assess feasibility, safety and efficacy of TIL-ACT in MM patients (NCT03475134). Methods: Patients with MM refractory to at least one prior line of therapy received TIL therapy with lymphodepleting chemotherapy before T-cell infusion, followed by high-dose interleukin-2. RDG- and FDG-PET imaging was performed before and after TIL infusion. Multispectral immuno-fluorescence (mIF) imaging and bulk-RNA sequencing (Seq) were performed on tumor samples pre-ACT and post-ACT (day+30 and upon progression). Single-cell RNA-Seq and TCR-Seq were performed on pre-ACT tumor and ACT product, as well as on tumor-reactive and neoantigen-specific TILs and on longitudinal blood samples. Results: As of 02/02/2021, thirteen patients (enrolled between March 2018 and December 2020) have successfully completed TIL-ACT therapy, with a median follow-up of 9.5 months (IQR 3.0 -24.6). Median age was 53 years (range 20-69) and all were previously treated with PD-1 based blockade. Median number of TILs infused was 55.0 x109 cells (range 12.8-84.7). The best overall response rate by RECIST 1.1 and disease control rate in evaluable patients was 41.7% (5/12) and 50% (6/12) respectively at 3 months. Two patients have an ongoing near-complete response at 3 years. Up to data cut-off, 10 patients have progressed by RECIST v1.1, with median PFS of 4.8 months (95% CI 1.5 - 9.6), while median OS is not reached. mIF revealed biomarkers of response, which may allow proper identification of patients in subsequent studies. In addition, deep sequencing of bulk and neoepitope-specific TIL clonotypes highlighted transcriptomic signatures revealing cell programs regulating in vitro expansion, in vivo blood persistence as well as tumor infiltration post-ACT. RGD-PET data will also be presented. Conclusions: We demonstrate reproducibility of TIL-ACT in our center, consistently with previous reports. Comprehensive translational studies reveal immune correlates of clinical responses that contribute to the understanding of mechanisms of TIL potency and will guide the development of next-generation cell products. Clinical trial information: NCT03475134.

Knowledgebases play an increasingly important role in scientific research, where the expert curation of biological knowledge in forms that are amenable to computational analysis (using ontologies for example)–provides a significant added value and enables new types of computational analyses for high throughput datasets. In this work, we demonstrate how expert curation can also play a more direct role in research, by supporting the use of network-based dynamical models to study a specific biological process. This curation effort is focused on the regulatory interactions between biological entities, such as genes or proteins and compounds, which may interact with each other in a complex manner, including regulatory complexes and conditional dependencies between co-regulators. This critical information has to be captured and encoded in a computable manner, which is currently far beyond the current capabilities of automatically constructed network. As a case study, we report here the prior knowledge network constructed by the sysVASC consortium to model the biological events leading to the formation of atherosclerotic plaques, during the onset of cardiovascular disease and discuss some specific examples to illustrate the main pitfalls and added value provided by the expert curation during this endeavor. Database URL: http://biomodels.caltech.edu

For decades, cancer research has been focused on understanding the neoplastic transformation of normal cells into cancerous ones from a cell-centric perspective. However, it is increasingly evident that the surrounding tumor microenvironment (TME) is equally important for tumor growth, progression and dissemination. The TME is a complex and heterogeneous system of interplaying elements strongly intertwined with normal processes of the surrounding hosting tissue. Cancerous cells and stromal cells, including different types of infiltrating immune cells and resident tissue cells, interact with each other and with extracellular matrix components in a very convoluted way. In addition, all of these cells may have phenotypically distinct variants exhibiting variability in cell traits, such as cell-cell adhesion, migration capability, proliferation rate and responsiveness to specific treatments; the composition of the cell population can differ between different regions of the tumor and between different tumors of the same or different patients, which result in both intratumor and across tumor heterogeneity. Altogether, the complexity and heterogeneity of the TME hinder the elucidation of cancer driving mechanisms and biomarkers and render the tumor behavior difficult to anticipate. Ultimately, that slows down the development of novel cancer therapies and makes difficult the choice of suitable treatments for specific patients. Mathematical and computational models may help on describing, explaining and predicting cancer in a new generation of experimental design assisted by computer simulations. These novel experimental and computational approaches face new challenges in the era of precision medicine and personalized cancer therapies, such as capturing the spatiotemporal structure of the TME, vertical and horizontal integration of multiple-omics data and dealing with heterogeneity at both intratumor and patient population level.

Las complicaciones cardiovasculares, que se caracterizan por la disfunción endotelial y aterosclerosis acelerada, son la principal causa de morbilidad y mortalidad asociadas con la diabetes, enfermedades cardiovasculares e inflamatorias. Cada vez hay más evidencia de que la generación de exceso de radicales libres altamente reactivos -debido en gran parte a la hiperglucemia-, conduce al estrés oxidativo, que exacerba aún más el desarrollo y progresión de la diabetes y sus complicaciones. La sobreproducción y/o eliminación insuficiente de estos radicales libres da como resultado la disfunción vascular, el daño a las proteínas celulares, los lípidos de membrana y los ácidos nucleicos. A pesar de la abrumadora evidencia sobre las consecuencias perjudiciales del estrés oxidativo y su papel en la diabetes experimental, los grandes ensayos clínicos con los antioxidantes clásicos no han demostrado beneficio alguno para los pacientes diabéticos. A medida que nuestra comprensión de los mecanismos de generación de radicales libres se desarrolla, va quedando claro que, en lugar de limitarse a eliminar los radicales reactivos de oxígeno, un enfoque más amplio se encamina a prevenir la generación de estas especies reactivas. Por lo tanto, se debe aplicar nuevas estrategias con antioxidantes, tanto clásicos como nuevos, en el tratamiento de la diabetes.

Es importante reconocer el síndrome de resistencia a la insulina, lo que a menudo se está denominando enfermedad de la civilización occidental. El predominio de estas enfermedades se encuentra incrementado con la asociación de la epidemia de obesidad en países desarrollados. Se hace una revisión sobre obesidad, resistencia a la insulina, hipertensión arterial y síndrome de ovario poliquístico. It is important to recognize insulin resistance syndrome, frequently called the occidental civilization disease. Predominance of these diseases is increasing in association with epidemics of obesity in developed countries. A review on obesity, insulin resistance, arterial hypertension and polycystic ovary syndrome is done.

Parkinson's disease (PD) is a major neurodegenerative chronic disease, most likely caused by a complex interplay of genetic and environmental factors. Information on various aspects of PD pathogenesis is rapidly increasing and needs to be efficiently organized, so that the resulting data is available for exploration and analysis. Here we introduce a computationally tractable, comprehensive molecular interaction map of PD. This map integrates pathways implicated in PD pathogenesis such as synaptic and mitochondrial dysfunction, impaired protein degradation, alpha-synuclein pathobiology and neuroinflammation. We also present bioinformatics tools for the analysis, enrichment and annotation of the map, allowing the research community to open new avenues in PD research. The PD map is accessible at http://minerva.uni.lu/pd_map. Electronic supplementary material The online version of this article (doi:10.1007/s12035-013-8489-4) contains supplementary material, which is available to authorized users.

In molecular and cell biology, most of the data presented in published papers are not available in accessible formats that would allow for analysis and systematic mining. Here we present SourceData (http://sourcedata.embo.org), a platform that allows researchers and publishers to share scientific figures and, when available, the underlying source data in a way that is machine-readable and findable. SourceData has therefore developed tools to generate machine-readable descriptive metadata from figures in published manuscripts. Experimentally tested hypotheses are represented as directed relationships between standardized biological entities, which can be connected into a searchable data-oriented ′knowledge graph′. SourceData focuses on the core of scientific evidence - data presented in figures - and makes papers searchable based on their data content. By coupling data availability to improved discoverability, SourceData aims at establishing a self-reinforcing data ′ecosystem′ that bridges the conventional visual and narrative description of research findings with a machine-readable representation of data and hypotheses.

Background Prior knowledge networks (PKNs) provide a framework for the development of computational biological models, including Boolean models of regulatory networks which are the focus of this work. PKNs are created by a painstaking process of literature curation, and generally describe all relevant regulatory interactions identified using a variety of experimental conditions and systems, such as specific cell types or tissues. Certain of these regulatory interactions may not occur in all biological contexts of interest, and their presence may dramatically change the dynamical behaviour of the resulting computational model, hindering the elucidation of the underlying mechanisms and reducing the usefulness of model predictions. Methods are therefore required to generate optimized contextual network models from generic PKNs. Results We developed a new approach to generate and optimize Boolean networks, based on a given PKN. Using a genetic algorithm, a model network is built as a sub-network of the PKN and trained against experimental data to reproduce the experimentally observed behaviour in terms of attractors and the transitions that occur between them under specific perturbations. The resulting model network is therefore contextualized to the experimental conditions and constitutes a dynamical Boolean model closer to the observed biological process used to train the model than the original PKN. Such a model can then be interrogated to simulate response under perturbation, to detect stable states and their properties, to get insights into the underlying mechanisms and to generate new testable hypotheses. Conclusions Generic PKNs attempt to synthesize knowledge of all interactions occurring in a biological process of interest, irrespective of the specific biological context. This limits their usefulness as a basis for the development of context-specific, predictive dynamical Boolean models. The optimization method presented in this article produces specific, contextualized models from generic PKNs. These contextualized models have improved utility for hypothesis generation and experimental design. The general applicability of this methodological approach makes it suitable for a variety of biological systems and of general interest for biological and medical research. Our method was implemented in the software optimusqual, available online at http://www.vital-it.ch/software/optimusqual/. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1287-z) contains supplementary material, which is available to authorized users.

Survival analyses based on the Kaplan-Meier estimate have been pervasively used to support or validate the relevance of biological mechanisms in cancer research. Recently, with the appearance of gene expression high-throughput technologies, this kind of analysis has been applied to tumor transcriptomics data. In a 'bottom-up' approach, gene-expression profiles that are associated with a deregulated pathway hypothetically involved in cancer progression are first identified and then subsequently correlated with a survival effect, which statistically supports or requires the rejection of such a hypothesis. In this work, we propose a 'top-down' approach, in which the clinical outcome (survival) is the starting point that guides the identification of deregulated biological mechanisms in cancer by a non-hypothesis-driven iterative survival analysis. We show that the application of our novel method to a population of ~2,000 breast cancer patients of the METABRIC consortium allows the identification of several well-known cancer mechanisms, such as ERBB4, HNF3A and TGFB pathways, and the investigation of their paradoxical dual effect. In addition, several novel biological mechanisms are proposed as potentially involved in cancer progression. The proposed exploratory methodology can be considered both alternative and complementary to classical 'bottom-up' approaches for validation of biological hypotheses. We propose that our method may be used to better characterize cancer, and may therefore impact the future design of therapies that are truly molecularly tailored to individual patients. The method, named SURCOMED, was implemented as a web-based tool, which is publicly available at http://surcomed.vital-it.ch. R scripts are also available at http://surcomed.sourceforge.net).

Background Networks have become a popular way to conceptualize a system of interacting elements, such as electronic circuits, social communication, metabolism or gene regulation. Network inference, analysis, and modeling techniques have been developed in different areas of science and technology, such as computer science, mathematics, physics, and biology, with an active interdisciplinary exchange of concepts and approaches. However, some concepts seem to belong to a specific field without a clear transferability to other domains. At the same time, it is increasingly recognized that within some biological systems—such as the tumor microenvironment—where different types of resident and infiltrating cells interact to carry out their functions, the complexity of the system demands a theoretical framework, such as statistical inference, graph analysis and dynamical models, in order to asses and study the information derived from high-throughput experimental technologies. Results In this article we propose to adopt and adapt the concepts of influence and investment from the world of social network analysis to biological problems, and in particular to apply this approach to infer causality in the tumor microenvironment. We showed that constructing a bidirectional network of influence between cell and cell communication molecules allowed us to determine the direction of inferred regulations at the expression level and correctly recapitulate cause-effect relationships described in literature. Conclusions This work constitutes an example of a transfer of knowledge and concepts from the world of social network analysis to biomedical research, in particular to infer network causality in biological networks. This causality elucidation is essential to model the homeostatic response of biological systems to internal and external factors, such as environmental conditions, pathogens or treatments. Electronic supplementary material The online version of this article (doi:10.1186/s13104-016-1976-8) contains supplementary material, which is available to authorized users.

The zebrafish has the capacity to regenerate its heart after severe injury. While the function of a few genes during this process has been studied, we are far from fully understanding how genes interact to coordinate heart regeneration. To enable systematic insights into this phenomenon, we generated and integrated a dynamic co-expression network of heart regeneration in the zebrafish and linked systems-level properties to the underlying molecular events. Across multiple post-injury time points, the network displays topological attributes of biological relevance. We show that regeneration steps are mediated by modules of transcriptionally coordinated genes, and by genes acting as network hubs. We also established direct associations between hubs and validated drivers of heart regeneration with murine and human orthologs. The resulting models and interactive analysis tools are available at http://infused.vital-it.ch. Using a worked example, we demonstrate the usefulness of this unique open resource for hypothesis generation and in silico screening for genes involved in heart regeneration. We thank C. Hoffmann and J. Esposito for technical support, A. Feenstra (VUA) for co-supervision of N.K., and R. Schneider (LCSB) for co-supervision of G.A. during her work at LIH and current support at LCSB. This research was funded by Luxembourg's National Research Fund (FNR) and the Swiss National Research Foundation (SNF), INFUSED project (www.infused-project.eu). N.M. was supported by Comunidad de Madrid (Fibroteam P2010/BMD-2321) and the ERC (Starting Grant 2013 337703 zebraHeart). Zebrafish drawing in Figure 1 was adapted from Wikimedia Commons (http://bit.ly/1PL92vj). Sí

In molecular and cell biology, most of the data presented in published papers are not available in accessible formats that would allow for analysis and systematic mining. Here we present SourceData (http://sourcedata.embo.org), a platform that allows researchers and publishers to share scientific figures and, when available, the underlying source data in a way that is machine-readable and findable. SourceData has therefore developed tools to generate machine-readable descriptive metadata from figures in published manuscripts. Experimentally tested hypotheses are represented as directed relationships between standardized biological entities, which can be connected into a searchable data-oriented ′knowledge graph′. SourceData focuses on the core of scientific evidence - data presented in figures - and makes papers searchable based on their data content. By coupling data availability to improved discoverability, SourceData aims at establishing a self-reinforcing data ′ecosystem′ that bridges the conventional visual and narrative description of research findings with a machine-readable representation of data and hypotheses.

The use of genotype information as an aid to selection can be a cheap and effective way to improve the genetic progress in beef cattle breeds, specially in the case of high cost phenotypic recording which is true for many economic traits in beef cattle. SNPs located at candidate genes underlying economic traits allow prediction of the genetic merit of individuals and, combined with parentage and traceability analysis, guarantee consumer protection. Here we present a cost-effective technology, the Capillary Primer-Extension Assay, to genotype validated mutations which identify differences between individuals in candidate genes associated directly or potentially with meat tenderness, marbling and muscle growth, milk yield, protein and fat content, sex or coat colour. We genotyped 70 SNPs in 8 beef, 3 dairy and one semi-feral (never selected for any production trait) breeds and present a panel of 53 SNPs with the aim of enabling a reasonable tool for parentage analysis, animal identification and production of markers usable in GAS in small local breeds for which other tools are unaffordable.

Con el objetivo, de proponer un grupo de recomendaciones educativas y acciones basadas en actividades físicas encaminadas a que las personas adultas obesas en edades comprendidas entre 50 y 55 años, del consultorio # 10 del municipio Guane, tengan un control sobre su peso corporal, las que como elementos fundamentales después de un diagnostico poseen un índice de masa corporal (peso esperado para talla) por encima de 30kg/m², y el conocimiento sobre la practica de ejercicios físicos era casi nulo, así como también sobre la noción en lo relativo en normas y patrones de conducta alimentarias son incorrectos. Se tomo como unidad de análisis 36 personas obesas de ambos sexos y como muestra de estas 15 .Utilizando métodos teóricos y métodos empíricos, al comparar la fase inicial y la evolutiva respecto a las variaciones de su IMC estudiados se han encontraron cambios significativos en los mismos, así se espera concluir que la combinación de tratamientos no farmacológico basados en el ejercicio en combinación conjunta en la educación de sus patrones alimentarios produce cambios significativos en el peso corporal, además de crear las condiciones necesarias para influir en la formación de estilos de vida sanos, y lograr que la población se incorpore a las transformaciones del medio social que los rodea y, de esta forma, se modifican ellos mismos, e incorporan valores y conductas de vida mucho más sanas que harán posible los cambios futuros que se esperan en el país dentro del campo de la salud . Teniendo en cuenta que el abordaje terapéutico de la obesidad, por su naturaleza multifactorial, debe ser objeto de múltiples sectores o miembros de la sociedad y la comunidad. Y en correspondencia con la realidad toda la comunidad una sus esfuerzos con vistas evitar el incremento progresivo de esta enfermedad.

Dysfunctional uterine bleeding - Bark hypothalamus pituitary, ovary and uterus. Hemorragia Uterina Disfuncional - Corteza Hipotálamo Hipófisis, Ovario y útero.

Angiogenesis plays a key role in tumor growth and cancer progression. TIE-2-expressing monocytes (TEM) have been reported to critically account for tumor vascularization and growth in mouse tumor experimental models, but the molecular basis of their pro-angiogenic activity are largely unknown. Moreover, differences in the pro-angiogenic activity between blood circulating and tumor infiltrated TEM in human patients has not been established to date, hindering the identification of specific targets for therapeutic intervention. In this work, we investigated these differences and the phenotypic reversal of breast tumor pro-angiogenic TEM to a weak pro-angiogenic phenotype by combining Boolean modelling and experimental approaches. Firstly, we show that in breast cancer patients the pro-angiogenic activity of TEM increased drastically from blood to tumor, suggesting that the tumor microenvironment shapes the highly pro-angiogenic phenotype of TEM. Secondly, we predicted in silico all minimal perturbations transitioning the highly pro-angiogenic phenotype of tumor TEM to the weak pro-angiogenic phenotype of blood TEM and vice versa. In silico predicted perturbations were validated experimentally using patient TEM. In addition, gene expression profiling of TEM transitioned to a weak pro-angiogenic phenotype confirmed that TEM are plastic cells and can be reverted to immunological potent monocytes. Finally, the relapse-free survival analysis showed a statistically significant difference between patients with tumors with high and low expression values for genes encoding transitioning proteins detected in silico and validated on patient TEM. In conclusion, the inferred TEM regulatory network accurately captured experimental TEM behavior and highlighted crosstalk between specific angiogenic and inflammatory signaling pathways of outstanding importance to control their pro-angiogenic activity. Results showed the successful in vitro reversion of such an activity by perturbation of in silico predicted target genes in tumor derived TEM, and indicated that targeting tumor TEM plasticity may constitute a novel valid therapeutic strategy in breast cancer., Author Summary Tumor vascularization is essential for tumor growth and cancer progression. In breast cancer, monocytes are angiogenic, i.e. able to induce tumor vascularization. In patients, blood circulating monocytes drastically increase their angiogenic activity when reaching the tumor, suggesting that the tumor microenvironment shapes their angiogenic activity. The identification of the tumor signals inducing the angiogenic activity of monocyte is of paramount significance because it represents the rationale for anti-angiogenic therapies in breast cancer. This goal was achieved by constructing an integrative model of monocyte behavior based on experimental data. The model predicted treatments abrogating the angiogenic activity of monocytes, which were experimentally validated in monocytes isolated from patient breast carcinoma. Importantly, these treatments reverted angiogenic monocytes into immunological potent cells. The main outcome of this modeling strategy for experimental and clinical oncology is the identification of effective treatments abrogating the angiogenic activity of monocytes and thus simultaneously revealing their functional plasticity.

High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell’s response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs) represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using context specific datasets (PKN contextualization), are necessary conditions to construct reliable, predictive models, which are current challenges in the systems biology of cell signalling. Here we present PRUNET, a user-friendly software tool designed to address the contextualization of a PKNs to specific experimental conditions. As the input, the algorithm takes a PKN and the expression profile of two given stable steady states or cellular phenotypes. The PKN is iteratively pruned using an evolutionary algorithm to perform an optimization process. This optimization rests in a match between predicted attractors in a discrete logic model (Boolean) and a Booleanized representation of the phenotypes, within a population of alternative subnetworks that evolves iteratively. We validated the algorithm applying PRUNET to four biological examples and using the resulting contextualized networks to predict missing expression values and to simulate well-characterized perturbations. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the implemented algorithm makes PRUNET suitable for a variety of biological processes, for instance cellular reprogramming or transitions between healthy and disease states.

With defined culture protocol, human embryonic stem cells (hESCs) are able to generate cardiomyocytes in vitro, therefore providing a great model for human heart development, and holding great potential for cardiac disease therapies. In this study, we successfully generated a highly pure population of human cardiomyocytes (hCMs) (>95% cTnT+) from hESC line, which enabled us to identify and characterize an hCM-specific signature, at both the gene expression and DNA methylation levels. Gene functional association network and gene-disease network analyses of these hCM-enriched genes provide new insights into the mechanisms of hCM transcriptional regulation, and stand as an informative and rich resource for investigating cardiac gene functions and disease mechanisms. Moreover, we show that cardiac-structural genes and cardiac-transcription factors have distinct epigenetic mechanisms to regulate their gene expression, providing a better understanding of how the epigenetic machinery coordinates to regulate gene expression in different cell types. Electronic supplementary material The online version of this article (doi:10.1007/s13238-013-0016-x) contains supplementary material, which is available to authorized users.

Background Cellular differentiation and reprogramming are processes that are carefully orchestrated by the activation and repression of specific sets of genes. An increasing amount of experimental results show that despite the large number of genes participating in transcriptional programs of cellular phenotypes, only few key genes, which are coined here as reprogramming determinants, are required to be directly perturbed in order to induce cellular reprogramming. However, identification of reprogramming determinants still remains a combinatorial problem, and the state-of-art methods addressing this issue rests on exhaustive experimentation or prior knowledge to narrow down the list of candidates. Results Here we present a computational method, without any preliminary selection of candidate genes, to identify reduced subsets of genes, which when perturbed can induce transitions between cellular phenotypes. The method relies on the expression profiles of two stable cellular phenotypes along with a topological analysis stability elements in the gene regulatory network that are necessary to cause this multi-stability. Since stable cellular phenotypes can be considered as attractors of gene regulatory networks, cell fate and cellular reprogramming involves transition between these attractors, and therefore current method searches for combinations of genes that are able to destabilize a specific initial attractor and stabilize the final one in response to the appropriate perturbations. Conclusions The method presented here represents a useful framework to assist researchers in the field of cellular reprogramming to design experimental strategies with potential applications in the regenerative medicine and disease modelling.

Transcription factor cross-repression is an important concept in cellular differentiation. A bistable toggle switch constitutes a molecular mechanism that determines cellular commitment and provides stability to transcriptional programs of binary cell fate choices. Experiments support that perturbations of these toggle switches can interconvert these binary cell fate choices, suggesting potential reprogramming strategies. However, more complex types of cellular transitions could involve perturbations of combinations of different types of multistable motifs. Here, we introduce a method that generalizes the concept of transcription factor cross-repression to systematically predict sets of genes, whose perturbations induce cellular transitions between any given pair of cell types. Furthermore, to our knowledge, this is the first method that systematically makes these predictions without prior knowledge of potential candidate genes and pathways involved, providing guidance on systems where little is known. Given the increasing interest of cellular reprogramming in medicine and basic research, our method represents a useful computational methodology to assist researchers in the field in designing experimental strategies.

Network analysis is an essential component of systems biology approaches toward understanding the molecular and cellular interactions underlying biological systems functionalities and their perturbations in disease. Regulatory and signalling pathways involve DNA, RNA, proteins and metabolites as key elements to coordinate most aspects of cellular functioning. Cellular processes depend on the structure and dynamics of gene regulatory networks and can be studied by employing a network representation of molecular interactions. This chapter describes several types of biological networks, how combination of different analytic approaches can be used to study diseases, and provides a list of selected tools for network visualization and analysis. It also introduces protein–protein interaction networks, gene regulatory networks, signalling networks and metabolic networks to illustrate concepts underlying network representation of cellular processes and molecular interactions. It finally discusses how the level of accuracy in inferring functional relationships influences the choice of methods applied for the analysis of a particular biological network type.

Background The activation of immune cells in the brain is believed to be one of the earliest events in prion disease development, where misfolded PrionSc protein deposits are thought to act as irritants leading to a series of events that culminate in neuronal cell dysfunction and death. The role of these events in prion disease though is still a matter of debate. To elucidate the mechanisms leading from abnormal protein deposition to neuronal injury, we have performed a detailed network analysis of genes differentially expressed in several mouse prion models. Results We found a master regulatory core of genes related to immune response controlling other genes involved in prion protein replication and accumulation, and neuronal cell death. This regulatory core determines the existence of two stable states that are consistent with the transcriptome analysis comparing prion infected versus uninfected mouse brain. An in silico perturbation analysis demonstrates that core genes are individually capable of triggering the transition and that the network remains locked once the diseased state is reached. Conclusions We hypothesize that this locking may be the cause of the sustained immune response observed in prion disease. Our analysis supports the hypothesis that sustained brain inflammation is the main pathogenic process leading to neuronal dysfunction and loss, which, in turn, leads to clinical symptoms in prion disease.

The development of new high-throughput technologies enables us to measure genome-wide transcription levels, protein abundance, metabolite concentration, etc. Nevertheless, these experimental data are often noisy and incomplete, which hinders data analysis, modeling and prediction. Here, we propose a method to predict expression values of genes involved in stable cellular phenotypes from the expression values of the remaining genes in a literature-based gene regulatory network. The consistency between predicted and known stable states from experimental data is used to guide an iterative network pruning that contextualizes the network to the biological conditions under which the expression data were obtained. Using the contextualized network and the property of network stability we predict gene expression values missing from experimental data. The prediction method assumes a Boolean model to compute steady states of networks and an evolutionary algorithm to iteratively prune the networks. The evolutionary algorithm samples the probability distribution of positive feedback loops or positive circuits and individual interactions within the subpopulation of the best-pruned networks at each iteration. The resulting expression inference is based not only on previous knowledge about local connectivity but also on a global network property (stability), providing robustness in the predictions.

Peroxisome proliferator-activated receptor gamma (PPARγ) is a key regulator of adipocyte differentiation and has an important role in metabolic syndrome. Phosphorylation of the receptor's ligand-binding domain at serine 273 has been shown to change the expression of a large number of genes implicated in obesity. The difference in gene expression seen when comparing wild-type phosphorylated with mutant non-phosphorylated PPARγ may have important consequences for the cellular molecular network, the state of which can be shifted from the healthy to a stable diseased state. We found that a group of differentially expressed genes are involved in bi-stable switches and form a core network, the state of which changes with disease progression. These findings support the idea that bi-stable switches may be a mechanism for locking the core gene network into a diseased state and for efficiently propagating perturbations to more distant regions of the network. A structural analysis of the PPARγ-RXRα dimer complex supports the hypothesis of a major structural change between the two states, and this may represent an important mechanism leading to the differential expression observed in the core network.