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Human guanylate binding proteins (GBPs) are key players of interferon–gamma (IFNγ)-induced cell intrinsic defense mechanisms targeting intracellular pathogens. In this study, we combine the well-established Toxoplasma gondii infection model with three in vitro macrophage culture systems to delineate the contribution of individual GBP family members to control this apicomplexan parasite. Use of high-throughput imaging assays and genome engineering allowed us to define a role for GBP1, 2 and 5 in parasite infection control. While GBP1 performs a pathogen-proximal, parasiticidal and growth-restricting function through accumulation at the parasitophorous vacuole of intracellular Toxoplasma , GBP2 and GBP5 perform a pathogen-distal, growth-restricting role. We further find that mutants of the GTPase or isoprenylation site of GBP1/2/5 affect their normal function in Toxoplasma control by leading to mis-localization of the proteins. [ABSTRACT FROM AUTHOR]

To study the dynamics of infection processes, it is common to manually enumerate imaging‐based infection assays. However, manual counting of events from imaging data is biased, error‐prone and a laborious task. We recently presented HRMAn (Host Response to Microbe Analysis), an automated image analysis program using state‐of‐the‐art machine learning and artificial intelligence algorithms to analyse pathogen growth and host defence behaviour. With HRMAn, we can quantify intracellular infection by pathogens such as Toxoplasma gondii and Salmonella in a variety of cell types in an unbiased and highly reproducible manner, measuring multiple parameters including pathogen growth, pathogen killing and activation of host cell defences. Since HRMAn is based on the KNIME Analytics platform, it can easily be adapted to work with other pathogens and produce more readouts from quantitative imaging data. Here we showcase improvements to HRMAn resulting in the release of HRMAn 2.0 and new applications of HRMAn 2.0 for the analysis of host–pathogen interactions using the established pathogen T. gondii and further extend it for use with the bacterial pathogen Chlamydia trachomatis and the fungal pathogen Cryptococcus neoformans. [ABSTRACT FROM AUTHOR]

I Toxoplasma gondii i ( I Tg i ) is an apicomplexan parasite able to invade any nucleated cell in warm-blooded animals. Right: Cellular response to Tg-infection: IFN and potentially other cytokines trigger infected cells to mount a cell-intrinsic defence against the PV. We recently showed that the IFN dose-dependent restriction of I Tg i -infection depends on cell type, with epithelial cells displaying a sharp parasiticidal effect, in contrast to macrophages, fibroblasts, and endothelial cells demonstrating a dose-dependent response [[24]]. This cell death phenotype is dependent on IFN -induced guanylate binding proteins (GBPs), of which GBP1 translocates to I Tg i vacuoles and releases I Tg i -derived molecular ligands of cellular receptors [[27]]. [Extracted from the article]

The article focuses on how cells protect themselves from self-damage caused by interferon-stimulated genes (ISGs), specifically guanylate-binding proteins (GBPs), in response to interferon-g (IFN-g) stimulation, and how the kinase PIM1 controls GBP1 activity to prevent cellular damage while maintaining its antimicrobial function in human macrophages.

Research on Toxoplasma gondii and its interplay with the host is often performed using fluorescence microscopy-based imaging experiments combined with manual quantification of acquired images. We present here an accurate and unbiased quantification method for host-pathogen interactions. We describe how to plan experiments and prepare, stain and image infected specimens and analyze them with the program HRMAn (Host Response to Microbe Analysis). HRMAn is a high-content image analysis method based on KNIME Analytics Platform. Users of this guide will be able to perform infection studies in high-throughput volume and to a greater level of detail. Relying on cutting edge machine learning algorithms, HRMAn can be trained and tailored to many experimental settings and questions.

Background : Infections cause the production of inflammatory cytokines such as Interferon gamma (IFNγ). IFNγ in turn prompts the upregulation of a range of host defence proteins including members of the family of guanylate binding proteins (Gbps). In humans and mice alike, GBPs restrict the intracellular replication of invasive microbes and promote inflammation. To study the physiological functions of Gbp family members, the most commonly chosen in vivo models are mice harbouring loss-of-function mutations in either individual Gbp genes or the entire Gbp gene cluster on mouse chromosome 3. Individual Gbp deletion strains differ in their design, as some strains exist on a pure C57BL/6 genetic background, while other strains contain a 129-derived genetic interval encompassing the Gbp gene cluster on an otherwise C57BL/6 genetic background. Methods : To determine whether the presence of 129 alleles of paralogous Gbps could influence the phenotypes of 129-congenic Gbp -deficient strains, we studied the expression of Gbps in both C57BL/6J and 129/Sv mice following in vivo stimulation with adjuvants and after infection with either Toxoplasma gondii or Shigella flexneri . Results : We show that C57BL/6J relative to 129/Sv mice display moderately elevated expression of Gbp2, but more prominently, are also defective for Gbp2b (formerly Gbp1) mRNA induction upon immune priming. Notably, Toxoplasma infections induce robust Gbp2b protein expression in both strains of mice, suggestive of a Toxoplasma -activated mechanism driving Gbp2b protein translation. We further find that the higher expression of Gbp2b mRNA in 129/Sv mice correlates with a gene duplication event at the Gbp2b locus resulting in two copies of the Gbp2b gene on the haploid genome of the 129/Sv strain. Conclusions : Our findings demonstrate functional differences between 129 and C57BL/6 Gbp alleles which need to be considered in the design and interpretation of studies utilizing mouse models, particularly for phenotypes influenced by Gbp2 or Gbp2b expression., Competing Interests: No competing interests were disclosed.

For image-based infection biology, accurate unbiased quantification of host-pathogen interactions is essential, yet often performed manually or using limited enumeration employing simple image analysis algorithms based on image segmentation. Host protein recruitment to pathogens is often refractory to accurate automated assessment due to its heterogeneous nature. An intuitive intelligent image analysis program to assess host protein recruitment within general cellular pathogen defense is lacking. We present HRMAn (Host Response to Microbe Analysis), an open-source image analysis platform based on machine learning algorithms and deep learning. We show that HRMAn has the capacity to learn phenotypes from the data, without relying on researcher-based assumptions. Using Toxoplasma gondii and Salmonella enterica Typhimurium we demonstrate HRMAn's capacity to recognize, classify and quantify pathogen killing, replication and cellular defense responses. HRMAn thus presents the only intelligent solution operating at human capacity suitable for both single image and high content image analysis., Editorial Note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter)., Competing Interests: DF, AY, BC, JW, MB, MH, JM, EF No competing interests declared, (© 2019, Fisch et al.)