Computational and Experimental Approaches to Predict Host-Parasite Protein-Protein Interactions

Trichinella spiralis cathepsin B bound and degraded host's intestinal type I collagen

Trichinellosis is a zoonotic parasitic disease that poses threats to human health, the meat industry, food safety, and huge financial losses. The critical stage of Trichinella spiralis (T. spiralis) infection is the invasion of intestinal larvae into the host's intestinal epithelial cells (IECs). T. spiralis Cathepsin B (TsCB) specifically interacts with IECs to facilitate the invasion of larvae. This study aims to look at how TsCB affects mouse IECs. TsCB was successfully cloned, expressed, and characterized, demonstrating its natural cysteine protease hydrolysis activity. A total of 140 proteins that interact with rTsCB were identified by GST pull-down combined with LC-MS/MS, including type I collagen, an essential component of the host's intestinal epithelial barrier system and intimately related to intestinal epithelial damage. TsCB transcription and expression levels rise, whereas type I collagen in the host's intestinal mucosa declines when the T. spiralis larvae invaded. Besides, it was discovered that TsCB bound to and degraded type I collagen of the host's intestine. This research can serve as a foundation for clarifying how T. spiralis invades the host's intestinal barrier and might provide information on potential targets for the creation of novel treatments to treat parasite illnesses.

Plasmodium manipulates the expression of host long non-coding RNA during red blood cell intracellular infection

BACKGROUND

Parasites interact with their host through "direct" and/or "indirect" mechanisms. Plasmodium, for example, either mediates direct physical interactions with host factors or triggers the immune system of the host indirectly, leading to changes in infectious outcomes. Long non-coding RNAs (lncRNAs) participate in regulating biological processes, especially host-pathogen interactions. However, research on the role of host lncRNAs during Plasmodium infection is limited.

METHODS

A RNA sequencing method (RNA-seq) was used to confirm the differential expression profiles of lncRNAs in Plasmodium yeolii 17XL (P.y17XL)-infected BALB/c mice. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to elucidate the potential functions of Plasmodium-induced genes. Subsequently, the effect of specific lncRNAs on the modulation of immune-related signaling pathways in malaria was determined by fluorescence-activated cell sorting, western blot and enzyme-linked immunosorbent assay.

RESULTS

The data showed that in P.y17XL-infected BALB/c mice, Plasmodium upregulated the expression of 132 lncRNAs and downregulated the expression of 159 lncRNAs. Differentially expressed lncRNAs clearly associated with malaria infection were annotated, including four novel dominant lncRNAs: ENMSUSG00000111521.1, XLOC_038009, XLOC_058629 and XLOC_065676. GO and KEGG pathway analyses demonstrated that these four differentially expressed lncRNAs were associated with co-localized/co-expressed protein-coding genes that were totally enriched in malaria and with the transforming growth factor beta (TGF-β) signaling pathway. Using the models of P.y17XL-infected BALB/c mice, data certified that the level of TGF-β production and activation of TGF-β/Smad_2/3 signaling pathway were obviously changed in malaria infection.

CONCLUSIONS

These differentially expressed immune-related genes were deemed to have a role in the process of Plasmodium infection in the host via dendritic/T regulatory cells and the TGF-β/Smad_2/3 signaling pathway. The results of the present study confirmed that Plasmodium infection-induced lncRNA expression is a novel mechanism used by Plasmodium parasites to modify host immune signaling. These results further enhance current understanding of the interaction between Plasmodium and host cells.

Machine Learning and Artificial Intelligence for the Prediction of Host-Pathogen Interactions: A Viral Case

The research of interactions between the pathogens and their hosts is key for understanding the biology of infection. Commencing on the level of individual molecules, these interactions define the behavior of infectious agents and the outcomes they elicit. Discovery of host-pathogen interactions (HPIs) conventionally involves a stepwise laborious research process. Yet, amid the global pandemic the urge for rapid discovery acceleration through the novel computational methodologies has become ever so poignant. This review explores the challenges of HPI discovery and investigates the efforts currently undertaken to apply the latest machine learning (ML) and artificial intelligence (AI) methodologies to this field. This includes applications to molecular and genetic data, as well as image and language data. Furthermore, a number of breakthroughs, obstacles, along with prospects of AI for host-pathogen interactions (HPI), are discussed.

Analysis of Predicted Host-Parasite Interactomes Reveals Commonalities and Specificities Related to Parasitic Lifestyle and Tissues Tropism

The study of molecular host–parasite interactions is essential to understand parasitic infection and adaptation within the host system. As well, prevention and treatment of infectious diseases require a clear understanding of the molecular crosstalk between parasites and their hosts. Yet, large-scale experimental identification of host–parasite molecular interactions remains challenging, and the use of computational predictions becomes then necessary. Here, we propose a computational integrative approach to predict host—parasite protein—protein interaction (PPI) networks resulting from the human infection by 15 different eukaryotic parasites. We used an orthology-based approach to transfer high-confidence intraspecies interactions obtained from the STRING database to the corresponding interspecies homolog protein pairs in the host–parasite system. Our approach uses either the parasites predicted secretome and membrane proteins, or only the secretome, depending on whether they are uni- or multi-cellular, respectively, to reduce the number of false predictions. Moreover, the host proteome is filtered for proteins expressed in selected cellular localizations and tissues supporting the parasite growth. We evaluated the inferred interactions by analyzing the enriched biological processes and pathways in the predicted networks and their association with known parasitic invasion and evasion mechanisms. The resulting PPI networks were compared across parasites to identify common mechanisms that may define a global pathogenic hallmark. We also provided a study case focusing on a closer examination of the human–S. mansoni predicted interactome, detecting central proteins that have relevant roles in the human–S. mansoni network, and identifying tissue-specific interactions with key roles in the life cycle of the parasite. The predicted PPI networks can be visualized and downloaded at http://orthohpi.jensenlab.org.