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- Medina-Dols, Aina, et al.
(författare)
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Role of PATJ in stroke prognosis by modulating endothelial to mesenchymal transition through the Hippo/Notch/PI3K axis
- 2024
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Ingår i: Cell Death Discovery. - : Springer Nature. - 2058-7716. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Through GWAS studies we identified PATJ associated with functional outcome after ischemic stroke (IS). The aim of this study was to determine PATJ role in brain endothelial cells (ECs) in the context of stroke outcome. PATJ expression analyses in patient's blood revealed that: (i) the risk allele of rs76221407 induces higher expression of PATJ, (ii) PATJ is downregulated 24 h after IS, and (iii) its expression is significantly lower in those patients with functional independence, measured at 3 months with the modified Rankin scale ((mRS) <= 2), compared to those patients with marked disability (mRS = 4-5). In mice brains, PATJ was also downregulated in the injured hemisphere at 48 h after ischemia. Oxygen-glucose deprivation and hypoxia-dependent of Hypoxia Inducible Factor-1 alpha also caused PATJ depletion in ECs. To study the effects of PATJ downregulation, we generated PATJ-knockdown human microvascular ECs. Their transcriptomic profile evidenced a complex cell reprogramming involving Notch, TGF-ss, PI3K/Akt, and Hippo signaling that translates in morphological and functional changes compatible with endothelial to mesenchymal transition (EndMT). PATJ depletion caused loss of cell-cell adhesion, upregulation of metalloproteases, actin cytoskeleton remodeling, cytoplasmic accumulation of the signal transducer C-terminal transmembrane Mucin 1 (MUC1-C) and downregulation of Notch and Hippo signaling. The EndMT phenotype of PATJ-depleted cells was associated with the nuclear recruitment of MUC1-C, YAP/TAZ, beta-catenin, and ZEB1. Our results suggest that PATJ downregulation 24 h after IS promotes EndMT, an initial step prior to secondary activation of a pro-angiogenic program. This effect is associated with functional independence suggesting that activation of EndMT shortly after stroke onset is beneficial for stroke recovery.
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- Akkaya, Munir, et al.
(författare)
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A single-nucleotide polymorphism in a Plasmodium berghei ApiAP2 transcription factor alters the development of host immunity
- 2020
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Ingår i: Science Advances. - : American Association for the Advancement of Science. - 2375-2548. ; 6:6
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Tidskriftsartikel (refereegranskat)abstract
- The acquisition of malaria immunity is both remarkably slow and unpredictable. At present, we know little about the malaria parasite genes that influence the host's ability to mount a protective immune response. Here, we show that a single-nucleotide polymorphism (SNP) resulting in a single amino acid change (S to F) in an ApiAP2 transcription factor in the rodent malaria parasite Plasmodium berghei (Pb) NK65 allowed infected mice to mount a T helper cell 1 (T(H)1)-type immune response that controlled subsequent infections. As compared to PbNK65(S), PbNK65(F) parasites differentially expressed 46 genes, most of which are predicted to play roles in immune evasion. PbNK65(F) infections resulted in an early interferon-gamma response and a later expansion of germinal centers, resulting in high levels of infected red blood cell-specific T(H)1-type immunoglobulin G2b (IgG2b) and IgG2c antibodies. Thus, the Pb ApiAP2 transcription factor functions as a critical parasite virulence factor in malaria infections.
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- Amorim, Joni A., et al.
(författare)
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Cyber Security Training Perspectives
- 2013
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Konferensbidrag (refereegranskat)abstract
- Building comprehensive cyber security strategies to protect people, infrastructure and assets demands research on methods and practices to reduce risks. Once the methods and practices are identified, there is a need to develop training for the manystakeholders involved, from security experts to the end user. In thispaper, we discuss new approaches for training, which includes the development of serious games for training on cyber security. The identification of the theoretical framework to be used for situation and threat assessment receives special consideration.
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- Conn, Jonathan G.M., et al.
(författare)
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Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models
- 2023
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Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-960X .- 1549-9596. ; 63:4, s. 1099-1113
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Tidskriftsartikel (refereegranskat)abstract
- Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state of the art, the American Chemical Society organized a "Second Solubility Challenge"in 2019, in which competitors were invited to submit blinded predictions of the solubilities of 132 drug-like molecules. In the first part of this article, we describe the development of two models that were submitted to the Blind Challenge in 2019 but which have not previously been reported. These models were based on computationally inexpensive molecular descriptors and traditional machine learning algorithms and were trained on a relatively small data set of 300 molecules. In the second part of the article, to test the hypothesis that predictions would improve with more advanced algorithms and higher volumes of training data, we compare these original predictions with those made after the deadline using deep learning models trained on larger solubility data sets consisting of 2999 and 5697 molecules. The results show that there are several algorithms that are able to obtain near state-of-the-art performance on the solubility challenge data sets, with the best model, a graph convolutional neural network, resulting in an RMSE of 0.86 log units. Critical analysis of the models reveals systematic differences between the performance of models using certain feature sets and training data sets. The results suggest that careful selection of high quality training data from relevant regions of chemical space is critical for prediction accuracy but that other methodological issues remain problematic for machine learning solubility models, such as the difficulty in modeling complex chemical spaces from sparse training data sets.
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