PD-1-induced T cell exhaustion is controlled by a Drp1-dependent mechanism

Programmed cell death-1 (PD-1) signaling downregulates the T-cell response, promoting an exhausted state in tumor-infiltrating T cells, through mostly unveiled molecular mechanisms. Dynamin-related protein-1 (Drp1)-dependent mitochondrial fission plays a crucial role in sustaining T-cell motility, proliferation, survival, and glycolytic engagement. Interestingly, such processes are exactly those inhibited by PD-1 in tumor-infiltrating T cells. Here, we show that PD-1pos CD8+ T cells infiltrating an MC38 (murine adenocarcinoma)-derived murine tumor mass have a downregulated Drp1 activity and more elongated mitochondria compared with PD-1neg counterparts. Also, PD-1pos lymphocytic elements infiltrating a human colon cancer rarely express active Drp1. Mechanistically, PD-1 signaling directly prevents mitochondrial fragmentation following T-cell stimulation by downregulating Drp1 phosphorylation on Ser616, via regulation of the ERK1/2 and mTOR pathways. In addition, downregulation of Drp1 activity in tumor-infiltrating PD-1pos CD8+ T cells seems to be a mechanism exploited by PD-1 signaling to reduce motility and proliferation of these cells. Overall, our data indicate that the modulation of Drp1 activity in tumor-infiltrating T cells may become a valuable target to ameliorate the anticancer immune response in future immunotherapy approaches

Spatial correlations in attribute communities

Community detection is an important tool for exploring and classifying the properties of large complex networks and should be of great help for spatial networks. Indeed, in addition to their location, nodes in spatial networks can have attributes such as the language for individuals, or any other socio-economical feature that we would like to identify in communities. We discuss in this paper a crucial aspect which was not considered in previous studies which is the possible existence of correlations between space and attributes. Introducing a simple toy model in which both space and node attributes are considered, we discuss the effect of space-attribute correlations on the results of various community detection methods proposed for spatial networks in this paper and in previous studies. When space is irrelevant, our model is equivalent to the stochastic block model which has been shown to display a detectability-non detectability transition. In the regime where space dominates the link formation process, most methods can fail to recover the communities, an effect which is particularly marked when space-attributes correlations are strong. In this latter case, community detection methods which remove the spatial component of the network can miss a large part of the community structure and can lead to incorrect results.Comment: 10 pages and 7 figure

Inibição dos lentivírus de pequenos ruminantes por drogas antivirais.

Inibidores da enzima transcriptase reversa e da protease foram avaliados quanto ao seu efeito inibitório na replicação do Vírus da Artrite Encefalite Caprina (CAEV) cepa CAEV Cork e do vírus Maedi-Visna (MVV) cepa K1514 cultivados em células fibroblásticas de caprinos. Os fármacos utilizados foram: lamivudina, didanosina, estavudina, zidovudina, efavirenz, atazanavir e lopinavir/ritonavir. A maior concentração utilizada para lamivudina, estavudina, zidovudina e efavirenz foi 500 μM, para atazanavir foi 50 μM e 5,0 μM para lopinavir/r e didanosina. A atividade antiviral in vitro foi pesquisada por meio da avaliação da viabilidade celular através da redução do MTT e pela pesquisa de inibição dos efeitos citopáticos (CPE) dos vírus. A replicação dos vírus só não foi completamente bloqueada pelos inibidores de protease (IP) atazanavir e lopinavir/r enquanto os demais apresentaram eficácia antiviral significativa em diferentes concentrações. Os IP juntamente com o efavirenz, não mostraram atividade antiviral quando foram avaliados pela técnica de redução do MTT. Esses dados indicam que os fármacos inibidores da transcriptase reversa lamivudina, didanosina, estavudina e zidovudina são eficazes na inibição in vitro dos lentivírus de pequenos ruminantes

JNK1 and ERK1/2 modulate lymphocyte homeostasis via BIM and DRP1 upon AICD induction

The Activation-Induced Cell Death (AICD) is a stimulation-dependent form of apoptosis used by the organism to shutdown T-cell response once the source of inflammation has been eliminated, while allowing the generation of immune memory. AICD is thought to progress through the activation of the extrinsic Fas/FasL pathway of cell death, leading to cytochrome-C release through caspase-8 and Bid activation. We recently described that, early upon AICD induction, mitochondria undergo structural alterations, which are required to promote cytochrome-C release and execute cell death. Here, we found that such alterations do not depend on the Fas/FasL pathway, which is instead only lately activated to amplify the cell death cascade. Instead, such alterations are primarily dependent on the MAPK proteins JNK1 and ERK1/2, which, in turn, regulate the activity of the pro-fission protein Drp1 and the pro-apoptotic factor Bim. The latter regulates cristae disassembly and cooperate with Drp1 to mediate the Mitochondrial Outer Membrane Permeabilization (MOMP), leading to cytochrome-C release. Interestingly, we found that Bim is also downregulated in T-cell Acute Lymphoblastic Leukemia (T-ALL) cells, this alteration favouring their escape from AICD-mediated control

Correction: JNK1 and ERK1/2 modulate lymphocyte homeostasis via BIM and DRP1 upon AICD induction

This Article was originally published under Nature Researchʼs License to Publish, but has now been made available under a [CC BY 4.0] license. The PDF and HTML versions of the Article have been modified accordingly

Prediction of incident cardiovascular events using machine learning and CMR radiomics.

OBJECTIVES: Evaluation of the feasibility of using cardiovascular magnetic resonance (CMR) radiomics in the prediction of incident atrial fibrillation (AF), heart failure (HF), myocardial infarction (MI), and stroke using machine learning techniques. METHODS: We identified participants from the UK Biobank who experienced incident AF, HF, MI, or stroke during the continuous longitudinal follow-up. The CMR indices and the vascular risk factors (VRFs) as well as the CMR images were obtained for each participant. Three-segmented regions of interest (ROIs) were computed: right ventricle cavity, left ventricle (LV) cavity, and LV myocardium in end-systole and end-diastole phases. Radiomics features were extracted from the 3D volumes of the ROIs. Seven integrative models were built for each incident cardiovascular disease (CVD) as an outcome. Each model was built with VRF, CMR indices, and radiomics features and a combination of them. Support vector machine was used for classification. To assess the model performance, the accuracy, sensitivity, specificity, and AUC were reported. RESULTS: AF prediction model using the VRF+CMR+Rad model (accuracy: 0.71, AUC 0.76) obtained the best result. However, the AUC was similar to the VRF+Rad model. HF showed the most significant improvement with the inclusion of CMR metrics (VRF+CMR+Rad: 0.79, AUC 0.84). Moreover, adding only the radiomics features to the VRF reached an almost similarly good performance (VRF+Rad: accuracy 0.77, AUC 0.83). Prediction models looking into incident MI and stroke reached slightly smaller improvement. CONCLUSIONS: Radiomics features may provide incremental predictive value over VRF and CMR indices in the prediction of incident CVDs. KEY POINTS: • Prediction of incident atrial fibrillation, heart failure, stroke, and myocardial infarction using machine learning techniques. • CMR radiomics, vascular risk factors, and standard CMR indices will be considered in the machine learning models. • The experiments show that radiomics features can provide incremental predictive value over VRF and CMR indices in the prediction of incident cardiovascular diseases

Desenvolvimento de dendezeiro em área reabilitada com leguminosas arbóreas.

O objetivo desse trabalho foi avaliar o desenvolvimento de 8 cultivares de dendê, plantadas em tanques de deposição de rejeito de bauxita, anteriormente com um substrato de cerca de 50 cm de cinza vegetal e revegetado com leguminosas arbóreas fixadoras de nitrogênio

Generalisability of deep learning models in low-resource imaging settings: A fetal ultrasound study in 5 African countries

Most artificial intelligence (AI) research have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower progress has been made in limited-resource environments where medical imaging is needed. For example, in Sub-Saharan Africa the rate of perinatal mortality is very high due to limited access to antenatal screening. In these countries, AI models could be implemented to help clinicians acquire fetal ultrasound planes for diagnosis of fetal abnormalities. So far, deep learning models have been proposed to identify standard fetal planes, but there is no evidence of their ability to generalise in centres with limited access to high-end ultrasound equipment and data. This work investigates different strategies to reduce the domain-shift effect for a fetal plane classification model trained on a high-resource clinical centre and transferred to a new low-resource centre. To that end, a classifier trained with 1,792 patients from Spain is first evaluated on a new centre in Denmark in optimal conditions with 1,008 patients and is later optimised to reach the same performance in five African centres (Egypt, Algeria, Uganda, Ghana and Malawi) with 25 patients each. The results show that a transfer learning approach can be a solution to integrate small-size African samples with existing large-scale databases in developed countries. In particular, the model can be re-aligned and optimised to boost the performance on African populations by increasing the recall to $0.92 \pm 0.04$ and at the same time maintaining a high precision across centres. This framework shows promise for building new AI models generalisable across clinical centres with limited data acquired in challenging and heterogeneous conditions and calls for further research to develop new solutions for usability of AI in countries with less resources