Neutrophil extracellular trap formation requires OPA1-dependent glycolytic ATP production

Optic atrophy 1 (OPA1) is a mitochondrial inner membrane protein that has an important role in mitochondrial fusion and structural integrity. Dysfunctional OPA1 mutations cause atrophy of the optic nerve leading to blindness. Here, we show that OPA1 has an important role in the innate immune system. Using conditional knockout mice lacking Opa1 in neutrophils (Opa1(N Delta)), we report that lack of OPA1 reduces the activity of mitochondrial electron transport complex I in neutrophils. This then causes a decline in adenosine-triphosphate (ATP) production through glycolysis due to lowered NAD(+) availability. Additionally, we show that OPA1-dependent ATP production in these cells is required for microtubule network assembly and for the formation of neutrophil extracellular traps. Finally, we show that Opa1(N Delta) mice exhibit a reduced antibacterial defense capability against Pseudomonas aeruginosa.Peer reviewe

Optic atrophy 1 (OPA1) is essential for NET formation and antibacterial functions in neutrophils

The precise nature of anti-bacterial and anti-fungal inflammatory responses has not yet been completely defined. Besides intracellular killing, neutrophils are able to exert an antibacterial effect in the extracellular space by forming so-called neutrophil extracellular traps (NETs) containing released DNA and granule proteins. We have data indicating that mitochondrial dynamics, namely mitochondrial fusion, is essential for mtDNA release, underlining the active role of mitochondrial dynamics during NET formation. Mitochondrial functions, including energy production, are linked to mitochondrial morphology and dynamics; a process so-called mitochondrial fusion and fission, controlled by a family of GTP-dependent dynamin related proteins. Optic Atrophy 1 (OPA1) is one of the three fusion proteins that mediates fusion of inner membrane of mitochondria and keeps mitochondria cristae junction tight. In this thesis, we present evidence showing that lack of OPA1 reduces mitochondrial electron transport complex I activity, causes reduction of glycolysis metabolism in neutrophils, which then results in lower adenosine-triphosphate (ATP) production, leading to tubulin network disruption. In absence of proper tubulin network formation, activated neutrophils exhibit disorientation of mitochondria localization and defect in mitochondrial DNA (mtDNA) release and neutrophil extracellular trap (NET) formation. NETs are formed by neutrophils as a part of innate immune response against microorganisms. Neutrophils isolated from the autosomal dominant optic atrophy (ADOA) patient genetically deficient in functional Opa1 protein exhibited disrupted microtubule (MT) network formation, and had defect in NET formation upon activation. Conditional knockout mouse lacking Opa1 in myelocyte population (Opa1N∆), exhibited also defect in mitochondrial complex I activity, glycolysis metabolism and lower adenosine-triphosphate (ATP) production by neutrophils. Similar to ADOA patient’ neutrophils, Opa1-deficient mouse neutrophils displayed disruption of tubulin network formation and lacked ability to form NETs. Moreover, less bacterial clearance was detected in lung of Opa1N∆ mice, despite higher neutrophil infiltration upon Pseudomonas aeruginosa intranasal infection compared to control mice. Lack of bacterial clearance was mainly due to deficient NET formation. In addition, using genetically deficient mice and pharmacological approaches, we could clearly show that NET formation by mouse and human neutrophils occurs independently of both RIPK3 and MLKL signalling, and hence, is independent of necroptosis. These findings 7 extend the importance of mitochondria to a function in innate immunity beyond their role in energy production

Vaccine Development in the Time of COVID-19: The Relevance of the Risklick AI to Assist in Risk Assessment and Optimize Performance [perspective].

The 2019 coronavirus (COVID-19) pandemic revealed the urgent need for the acceleration of vaccine development worldwide. Rapid vaccine development poses numerous risks for each category of vaccine technology. By using the Risklick artificial intelligence (AI), we estimated the risks associated with all types of COVID-19 vaccine during the early phase of vaccine development. We then performed a postmortem analysis of the probability and the impact matrix calculations by comparing the 2020 prognosis to the contemporary situation. We used the Risklick AI to evaluate the risks and their incidence associated with vaccine development in the early stage of the COVID-19 pandemic. Our analysis revealed the diversity of risks among vaccine technologies currently used by pharmaceutical companies providing vaccines. This analysis highlighted the current and future potential pitfalls connected to vaccine production during the COVID-19 pandemic. Hence, the Risklick AI appears as an essential tool in vaccine development for the treatment of COVID-19 in order to formally anticipate the risks, and increases the overall performance from the production to the distribution of the vaccines. The Risklick AI could, therefore, be extended to other fields of research and development and represent a novel opportunity in the calculation of production-associated risks

Classification of hierarchical text using geometric deep learning:

We consider the hierarchical representation of documents as graphs and use geometric deep learning to classify them into different categories. While graph neural networks can efficiently handle the variable structure of hierarchical documents using the permutation invariant message passing operations, we show that we can gain extra performance improvements using our proposed selective graph pooling operation that arises from the fact that some parts of the hierarchy are invariable across different documents. We applied our model to classify clinical trial (CT) protocols into completed and terminated categories. We use bag-of-words based, as well as pre-trained transformer-based embeddings to featurize the graph nodes, achieving f1-scores ' 0:85 on a publicly available large scale CT registry of around 360K protocols. We further demonstrate how the selective pooling can add insights into the CT termination status prediction. We make the source code and dataset splits accessible

Utilizing Artificial Intelligence to Manage COVID-19 Scientific Evidence Torrent with Risklick AI: A Critical Tool for Pharmacology and Therapy Development.

INTRODUCTION The SARS-CoV-2 pandemic has led to one of the most critical and boundless waves of publications in the history of modern science. The necessity to find and pursue relevant information and quantify its quality is broadly acknowledged. Modern information retrieval techniques combined with artificial intelligence (AI) appear as one of the key strategies for COVID-19 living evidence management. Nevertheless, most AI projects that retrieve COVID-19 literature still require manual tasks. METHODS In this context, we pre-sent a novel, automated search platform, called Risklick AI, which aims to automatically gather COVID-19 scientific evidence and enables scientists, policy makers, and healthcare professionals to find the most relevant information tailored to their question of interest in real time. RESULTS Here, we compare the capacity of Risklick AI to find COVID-19-related clinical trials and scientific publications in comparison with clinicaltrials.gov and PubMed in the field of pharmacology and clinical intervention. DISCUSSION The results demonstrate that Risklick AI is able to find COVID-19 references more effectively, both in terms of precision and recall, compared to the baseline platforms. Hence, Risklick AI could become a useful alternative assistant to scientists fighting the COVID-19 pandemic

Classification of hierarchical text using geometric deep learning: the case of clinical trials corpus

We consider the hierarchical representation of documents as graphs and use geometric deep learning to classify them into different categories. While graph neural networks can efficiently handle the variable structure of hierarchical documents using the permutation invariant message passing operations, we show that we can gain extra performance improvements using our proposed selective graph pooling operation that arises from the fact that some parts of the hierarchy are invariable across different documents. We applied our model to classify clinical trial (CT) protocols into completed and terminated categories. We use bag-of-words based, as well as pre-trained transformer-based embeddings to featurize the graph nodes, achieving f1-scores around 0.85 on a publicly available large scale CT registry of around 360K protocols. We further demonstrate how the selective pooling can add insights into the CT termination status prediction. We make the source code and dataset splits accessible

Deep learning-based risk prediction for interventional clinical trials based on protocol design ::a retrospective study

Success rate of clinical trials (CTs) is low, with the protocol design itself being considered a major risk factor. We aimed to investigate the use of deep learning methods to predict the risk of CTs based on their protocols. Considering protocol changes and their final status, a retrospective risk assignment method was proposed to label CTs according to low, medium, and high risk levels. Then, transformer and graph neural networks were designed and combined in an ensemble model to learn to infer the ternary risk categories. The ensemble model achieved robust performance (area under the receiving operator characteristic curve [AUROC] of 0.8453 [95% confidence interval: 0.8409–0.8495]), similar to the individual architectures but significantly outperforming a baseline based on bag-of-words features (0.7548 [0.7493–0.7603] AUROC). We demonstrate the potential of deep learning in predicting the risk of CTs from their protocols, paving the way for customized risk mitigation strategies during protocol design

On Graph Construction for Classification of Clinical Trials Protocols Using Graph Neural Networks

A recent trend in health-related machine learning proposes the use of Graph Neural Networks (GNN’s) to model biomedical data. This is justified due to the complexity of healthcare data and the modelling power of graph abstractions. Thus, GNN’s emerge as the natural choice to learn from increasing amounts of healthcare data. While formulating the problem, however, there are usually multiple design choices and decisions that can affect the final performance. In this work, we focus on Clinical Trial (CT) protocols consisting of hierarchical documents, containing free text as well as medical codes and terms, and design a classifier to predict each CT protocol termination risk as “low” or “high”. We show that while using GNN’s to solve this classification task is very successful, the way the graph is constructed is also of importance and one can benefit from making a priori useful information more explicit. While a natural choice is to consider each CT protocol as an independent graph and pose the problem as a graph classification, consistent performance improvements can be achieved by considering them as super-nodes in one unified graph and connecting them according to some metadata, like similar medical condition or intervention, and finally approaching the problem as a node classification task rather than graph classification. We validate this hypothesis experimentally on a large-scale manually labeled CT database. This provides useful insights on the flexibility of graph-based modeling for machine learning in the healthcare domain

NET formation can occur independently of RIPK3 and MLKL signaling

The importance of neutrophil extracellular traps (NETs) in innate immunity is well established but the molecular mechanisms responsible for their formation are still a matter of scientific dispute. Here, we aim to characterize a possible role of the receptor-interacting protein kinase 3 (RIPK3) and the mixed lineage kinase domain-like (MLKL) signaling pathway, which are known to cause necroptosis, in NET formation. Using genetic and pharmacological approaches, we investigated whether this programmed form of necrosis is a prerequisite for NET formation. NETs have been defined as extracellular DNA scaffolds associated with the neutrophil granule protein elastase that are capable of killing bacteria. Neither Ripk3-deficient mouse neutrophils nor human neutrophils in which MLKL had been pharmacologically inactivated, exhibited abnormalities in NET formation upon physiological activation or exposure to low concentrations of PMA. These data indicate that NET formation occurs independently of both RIPK3 and MLKL signaling