Hepatic macrophages play critical roles in the establishment and growth of hydatid cysts in the liver during Echinococcus granulosus sensu stricto infection.

Cystic echinococcosis (CE) is a worldwide neglected zoonotic disease caused by infection with the larval stage of the tapeworm Echinococcus granulosus sensu lato (E. granulosus s.l.), which predominantly resides in the liver accompanied by mild inflammation. Macrophages constitute the main cellular component of the liver and play a central role in controlling the progression of inflammation and liver fibrosis. However, the role of hepatic macrophages in the establishment and growth of hydatid cysts in the liver during E. granulosus sensu stricto (E. granulosus s.s.) infection has not been fully elucidated. Here, we showed that CD68+ macrophages accumulated in pericystic areas of the liver and that the expression of CD163, a marker of anti-inflammatory macrophages, was more evident in active CE patients than in inactive CE patients. Moreover, in a mouse model of E. granulosus s.s. infection, the pool of hepatic macrophages expanded dramatically through the attraction of massive amounts of monocyte-derived macrophages (MoMFs) to the infection site. These infiltrating macrophages preferentially polarized toward an iNOS+ proinflammatory phenotype at the early stage and then toward a CD206+ anti-inflammatory phenotype at the late stage. Notably, the resident Kupffer cells (KCs) predominantly maintained an anti-inflammatory phenotype to favor persistent E. granulosus s.s. infection. In addition, depletion of hepatic macrophages promoted E. granulosus s.s. larval establishment and growth partially by inhibiting CD4+ T-cell recruitment and liver fibrosis. The above findings demonstrated that hepatic macrophages play a vital role in the progression of CE, contributing to a better understanding of the local inflammatory responses surrounding hydatid cysts and possibly facilitating the design of novel therapeutic approaches for CE.

Upregulation of LAG3 modulates the immune imbalance of CD4+ T-cell subsets and exacerbates disease progression in patients with alveolar echinococcosis and a mouse model.

Infection with the cestode Echinococcus multilocularis (E. multilocularis) causes alveolar echinococcosis (AE), a tumor-like disease predominantly affecting the liver but able to spread to any organ. T cells develop functional defects during chronic E. multilocularis infection, mostly due to upregulation of inhibitory receptors such as T-cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domains (TIGIT) and programmed death-1 (PD-1). However, the role of lymphocyte activation gene-3 (LAG3), an inhibitory receptor, in AE infection remains to be determined. Here, we discovered that high expression of LAG3 was mainly found in CD4+ T cells and induced regulatory T cells (iTregs) in close liver tissue (CLT) from AE patients. In a mouse model of E. multilocularis infection, LAG3 expression was predominantly found in T helper 2 (Th2) and Treg subsets, which secreted significantly more IL-4 and IL-10, resulting in host immune tolerance and disease progression at a late stage. Furthermore, LAG3 deficiency was found to drive the development of effector memory CD4+ T cells and enhance the type 1 CD4+ T-cell immune response, thus inhibiting metacestode growth in vivo. In addition, CD4+ T cells from LAG3-deficient mice produced more IFN-γ and less IL-4 when stimulated by E. multilocularis protoscoleces (EmP) antigen in vitro. Finally, adoptive transfer experiments showed that LAG3-knockout (KO) CD4+ T cells were more likely to develop into Th1 cells and less likely to develop into Tregs in recipient mice. Our work reveals that high expression of LAG3 accelerates AE disease progression by modulating the immune imbalance of CD4+ T-cell subsets. These findings may provide a novel immunotherapeutic strategy against E. multilocularis infection.

Echinococcus granulosus: The establishment of the metacestode in the liver is associated with control of the CD4+ T-cell-mediated immune response in patients with cystic echinococcosis and a mouse model.

The larval stage of the tapeworm Echinococcus granulosus sensu lato (E. granulosus s.l.) caused a chronic infection, known as cystic echinococcosis (CE), which is a worldwide public health problem. The human secondary CE is caused by the dissemination of protoscoleces (PSCs) when fertile cysts are accidentally ruptured, followed by development of PSCs into new metacestodes. The local immune mechanisms responsible for the establishment and established phases after infection with E. granulosus s.l. are not clear. Here, we showed that T cells were involved in the formation of the immune environment in the liver in CE patients and Echinococcus granulosus sensu strict (E. granulosus s.s.)-infected mice, with CD4+ T cells being the dominant immune cells; this process was closely associated with cyst viability and establishment. Local T2-type responses in the liver were permissive for early infection establishment by E. granulosus s.s. between 4 and 6 weeks in the experimental model. CD4+ T-cell deficiency promoted PSC development into cysts in the liver in E. granulosus s.s.-infected mice. In addition, CD4+ T-cell-mediated cellular immune responses and IL-10-producing CD8+ T cells play a critical role in the establishment phase of secondary E. granulosus s.s. PSC infection. These data contribute to the understanding of local immune responses to CE and the design of new therapies by restoring effective immune responses and blocking evasion mechanisms during the establishment phase of infection.

Prediction of Local Quality of Protein Structure Models Considering Spatial Neighbors in Graphical Models.

Protein tertiary structure prediction methods have matured in recent years. However, some proteins defy accurate prediction due to factors such as inadequate template structures. While existing model quality assessment methods predict global model quality relatively well, there is substantial room for improvement in local quality assessment, i.e. assessment of the error at each residue position in a model. Local quality is a very important information for practical applications of structure models such as interpreting/designing site-directed mutagenesis of proteins. We have developed a novel local quality assessment method for protein tertiary structure models. The method, named Graph-based Model Quality assessment method (GMQ), explicitly considers the predicted quality of spatially neighboring residues using a graph representation of a query protein structure model. GMQ uses conditional random field as its core of the algorithm, and performs a binary prediction of the quality of each residue in a model, indicating if a residue position is likely to be within an error cutoff or not. The accuracy of GMQ was improved by considering larger graphs to include quality information of more surrounding residues. Moreover, we found that using different edge weights in graphs reflecting different secondary structures further improves the accuracy. GMQ showed competitive performance on a benchmark for quality assessment of structure models from the Critical Assessment of Techniques for Protein Structure Prediction (CASP).

Assessment of protein side-chain conformation prediction methods in different residue environments.

Computational prediction of side-chain conformation is an important component of protein structure prediction. Accurate side-chain prediction is crucial for practical applications of protein structure models that need atomic-detailed resolution such as protein and ligand design. We evaluated the accuracy of eight side-chain prediction methods in reproducing the side-chain conformations of experimentally solved structures deposited to the Protein Data Bank. Prediction accuracy was evaluated for a total of four different structural environments (buried, surface, interface, and membrane-spanning) in three different protein types (monomeric, multimeric, and membrane). Overall, the highest accuracy was observed for buried residues in monomeric and multimeric proteins. Notably, side-chains at protein interfaces and membrane-spanning regions were better predicted than surface residues even though the methods did not all use multimeric and membrane proteins for training. Thus, we conclude that the current methods are as practically useful for modeling protein docking interfaces and membrane-spanning regions as for modeling monomers.