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Plasmodium parasites cause malaria, a global health disease that is responsible for more than 200 million clinical cases and 600 000 deaths each year. Most deaths are caused by various complications, including malaria-associated acute respiratory distress syndrome (MA-ARDS). Despite the very rapid and efficient killing of parasites with antimalarial drugs, 15% of patients with complicated malaria succumb. This stresses the importance of investigating resolution mechanisms that are involved in the recovery from these complications once the parasite is killed. To study the resolution of MA-ARDS, P. berghei NK65-infected C57BL/6 mice were treated with antimalarial drugs after onset of symptoms, resulting in 80% survival. Micro-computed tomography revealed alterations of the lungs upon infection, with an increase in total and non-aerated lung volume due to edema. Whole body plethysmography confirmed a drastically altered lung ventilation, which was restored during resolution. Single-cell RNA sequencing indicated an increased inflammatory state in the lungs upon infection, which was accompanied by a drastic decrease in endothelial cells, consistent with CD8+ T cell-mediated killing. During resolution, anti-inflammatory pathways were upregulated and proliferation of endothelial cells was observed. MultiNicheNet interactome analysis identified important changes in the ligand-receptor interactions during disease resolution that warrant further exploration in order to develop new therapeutic strategies. In conclusion, our study provides insights in pro-resolving pathways that limit inflammation and promote endothelial cell proliferation in experimental MA-ARDS. This information may be useful for the design of adjunctive treatments to enhance resolution after Plasmodium parasite killing by antimalarial drugs.

Subgraph matching algorithms are used to find and enumerate specific interconnection structures in networks. By enumerating these specific structures/subgraphs, the fundamental properties of the network can be derived. More specifically in biological networks, subgraph matching algorithms are used to discover network motifs, specific patterns occurring more often than expected by chance. Finding these network motifs yields information on the underlying biological relations modelled by the network. In this work, we present the Index-based Subgraph Matching Algorithm with General Symmetries (ISMAGS), an improved version of the Index-based Subgraph Matching Algorithm (ISMA). ISMA quickly finds all instances of a predefined motif in a network by intelligently exploring the search space and taking into account easily identifiable symmetric structures. However, more complex symmetries (possibly involving switching multiple nodes) are not taken into account, resulting in superfluous output. ISMAGS overcomes this problem by using a customised symmetry analysis phase to detect all symmetric structures in the network motif subgraphs. These structures are then converted to symmetry-breaking constraints used to prune the search space and speed up calculations. The performance of the algorithm was tested on several types of networks (biological, social and computer networks) for various subgraphs with a varying degree of symmetry. For subgraphs with complex (multi-node) symmetric structures, high speed-up factors are obtained as the search space is pruned by the symmetry-breaking constraints. For subgraphs with no or simple symmetric structures, ISMAGS still reduces computation times by optimising set operations. Moreover, the calculated list of subgraph instances is minimal as it contains no instances that differ by only a subgraph symmetry. An implementation of the algorithm is freely available at https://github.com/mhoubraken/ISMAGS.

Subgraph matching algorithms are designed to find all instances of predefined subgraphs in a large graph or network and play an important role in the discovery and analysis of so-called network motifs, subgraph patterns which occur more often than expected by chance. We present the index-based subgraph matching algorithm (ISMA), a novel tree-based algorithm. ISMA realizes a speedup compared to existing algorithms by carefully selecting the order in which the nodes of a query subgraph are investigated. In order to achieve this, we developed a number of data structures and maximally exploited symmetry characteristics of the subgraph. We compared ISMA to a naive recursive tree-based algorithm and to a number of well-known subgraph matching algorithms. Our algorithm outperforms the other algorithms, especially on large networks and with large query subgraphs. An implementation of ISMA in Java is freely available at http://sourceforge.net/projects/isma/.