On the road to systems biology of host-pathogen interactions

Important challenges to finding new leads for new antibiotics

Identification of new antibiotics remains a huge challenge. The last antibiotic of new chemical class and mechanism was discovered more than 30 years ago. Advances since have been largely incremental modifications to a limited number of chemical scaffolds. Discovering and developing truly new antibiotics is challenging: the science is complex, and the development process is time consuming and expensive. Herein, we focus on the discovery phase of modern antibacterial research and development. We argue that antibacterial discovery has been challenged by a poor understanding of bacterial permeability, by generic in vitro conventions that ignore the host, and by the inherent complexity of bacterial systems. Together, these factors have colluded to challenge modern, industrial, and reductionist approaches to antibiotic discovery. Nevertheless, advances in our understanding of many of these obstacles, including a new appreciation for the complexity of both host and pathogen biology, bode well for future efforts.

Artificial Intelligence Methods in Infection Biology Research

Despite unprecedented achievements, the domain-specific application of artificial intelligence (AI) in the realm of infection biology was still in its infancy just a couple of years ago. This is largely attributable to the proneness of the infection biology community to shirk quantitative techniques. The so-called "sorting machine" paradigm was prevailing at that time, meaning that AI applications were primarily confined to the automation of tedious laboratory tasks. However, fueled by the severe acute respiratory syndrome coronavirus 2 pandemic, AI-driven applications in infection biology made giant leaps beyond mere automation. Instead, increasingly sophisticated tasks were successfully tackled, thereby ushering in the transition to the "Swiss army knife" paradigm. Incentivized by the urgent need to subdue a raging pandemic, AI achieved maturity in infection biology and became a versatile tool. In this chapter, the maturation of AI in the field of infection biology from the "sorting machine" paradigm to the "Swiss army knife" paradigm is outlined. Successful applications are illustrated for the three data modalities in the domain, that is, images, molecular data, and language data, with a particular emphasis on disentangling host-pathogen interactions. Along the way, fundamental terminology mentioned in the same breath as AI is elaborated on, and relationships between the subfields these terms represent are established. Notably, in order to dispel the fears of infection biologists toward quantitative methodologies and lower the initial hurdle, this chapter features a hands-on guide on software installation, virtual environment setup, data preparation, and utilization of pretrained models at its very end.

Prediction of GCRV virus-host protein interactome based on structural motif-domain interactions

BACKGROUND

Grass carp hemorrhagic disease, caused by grass carp reovirus (GCRV), is the most fatal causative agent in grass carp aquaculture. Protein-protein interactions between virus and host are one avenue through which GCRV can trigger infection and induce disease. Experimental approaches for the detection of host-virus interactome have many inherent limitations, and studies on protein-protein interactions between GCRV and its host remain rare.

RESULTS

In this study, based on known motif-domain interaction information, we systematically predicted the GCRV virus-host protein interactome by using motif-domain interaction pair searching strategy. These proteins derived from different domain families and were predicted to interact with different motif patterns in GCRV. JAM-A protein was successfully predicted to interact with motifs of GCRV Sigma1-like protein, and shared the similar binding mode compared with orthoreovirus. Differentially expressed genes during GCRV infection process were extracted and mapped to our predicted interactome, the overlapped genes displayed different tissue expression distributions on the whole, the overall expression level in intestinal is higher than that of other three tissues, which may suggest that the functions of these genes are more active in intestinal. Function annotation and pathway enrichment analysis revealed that the host targets were largely involved in signaling pathway and immune pathway, such as interferon-gamma signaling pathway, VEGF signaling pathway, EGF receptor signaling pathway, B cell activation, and T cell activation.

CONCLUSIONS

Although the predicted PPIs may contain some false positives due to limited data resource and poor research background in non-model species, the computational method still provide reasonable amount of interactions, which can be further validated by high throughput experiments. The findings of this work will contribute to the development of system biology for GCRV infectious diseases, and help guide the identification of novel receptors of GCRV in its host.

Correlative Förster Resonance Electron Transfer-Proximity Ligation Assay (FRET-PLA) Technique for Studying Interactions Involving Membrane Proteins

This unit provides a guide and detailed protocol for studying membrane protein-protein interactions (PPI) using the acceptor-sensitized Förster resonance electron transfer (FRET) method in combination with the proximity ligation assay (PLA). The protocol in this unit is focused on the preparation of FRET-PLA samples and the detection of correlative FRET/PLA signals as well as on the analysis of FRET-PLA data and interpretation of correlative results when using cyan fluorescent protein (CFP) as a FRET donor and yellow fluorescent protein (YFP) as a FRET acceptor. The correlative application of FRET and PLA combines two powerful tools for monitoring PPI, yielding results that are more reliable than with either technique alone. © 2016 by John Wiley & Sons, Inc.

Comparative proteomics analysis of host cells infected with Brucella abortus A19

We carried out a proteomic analysis of THP-1-derived macrophages with and without Brucella abortus A19 (B. abortus A19) infection in order to study the cellular responses to B. abortus A19. The proteins were analyzed at different time points after infection with 2DE followed by MALDI-TOF/TOF identification. Comparative analysis of multiple 2DE gels revealed that the majority of changes in protein abundance appeared between 48 and 96 h after infection. MS identified 44 altered proteins, including 20 proteins increased in abundance and 24 proteins decreased in abundance, which were found to be involved in cytoskeleton, signal transduction, energy metabolism, host macromolecular biosynthesis, and stress response. Moreover, 22 genes corresponding to the altered proteins were quantified by real-time RT-PCR to examine the transcriptional profiles between infected and uninfected THP-1-derived macrophages. Finally, we mapped the altered pathways and networks using ingenuity pathway analysis, which suggested that the altered protein species were heavily favored germ cell-Sertoli cell junction signaling as the primary pathway. Furthermore, mechanisms of viral exit from host cell and macrophage stimulating protein-recepteur d'origine nantais signaling appeared to be major pathways modulated in infected cells. This study effectively provides useful dynamic protein-related information concerning B. abortus infection in macrophages.

The effects of ingested mammalian blood factors on vector arthropod immunity and physiology

The blood feeding behavior of disease-transmitting arthropods creates a unique intersection between vertebrate and invertebrate physiology. Here, we review host blood-derived factors that persist through blood digestion to affect the lifespan, reproduction, and immune responses of some of the most common arthropod vectors of human disease.

Global identification of prokaryotic glycoproteins based on an Escherichia coli proteome microarray

Glycosylation is one of the most abundant protein posttranslational modifications. Protein glycosylation plays important roles not only in eukaryotes but also in prokaryotes. To further understand the roles of protein glycosylation in prokaryotes, we developed a lectin binding assay to screen glycoproteins on an Escherichia coli proteome microarray containing 4,256 affinity-purified E.coli proteins. Twenty-three E.coli proteins that bound Wheat-Germ Agglutinin (WGA) were identified. PANTHER protein classification analysis showed that these glycoprotein candidates were highly enriched in metabolic process and catalytic activity classes. One sub-network centered on deoxyribonuclease I (sbcB) was identified. Bioinformatics analysis suggests that prokaryotic protein glycosylation may play roles in nucleotide and nucleic acid metabolism. Fifteen of the 23 glycoprotein candidates were validated by lectin (WGA) staining, thereby increasing the number of validated E. coli glycoproteins from 3 to 18. By cataloguing glycoproteins in E.coli, our study greatly extends our understanding of protein glycosylation in prokaryotes.

A comparative lipidomics platform for chemotaxonomic analysis of Mycobacterium tuberculosis

The lipidic envelope of Mycobacterium tuberculosis promotes virulence in many ways, so we developed a lipidomics platform for a broad survey of cell walls. Here we report two new databases (MycoMass, MycoMap), 30 lipid fine maps, and mass spectrometry datasets that comprise a static lipidome. Further, by rapidly regenerating lipidomic datasets during biological processes, comparative lipidomics provides statistically valid, organism-wide comparisons that broadly assess lipid changes during infection or among clinical strains of mycobacteria. Using stringent data filters, we tracked more than 5,000 molecular features in parallel with few or no false-positive molecular discoveries. The low error rates allowed chemotaxonomic analyses of mycobacteria, which describe the extent of chemical change in each strain and identified particular strain-specific molecules for use as biomarkers.

The challenge of relating gene expression to the virulence of Salmonella enterica serovar Typhimurium

The first decade of transcriptomic studies of Salmonella enterica serovar Typhimurium focused upon gene expression in vitro, and during the infection of mammalian cells. The published regulons and stimulons show that the three Type Three Secretion Systems of S. Typhimurium respond to a diverse range of environmental conditions, and are controlled by a hierarchy of regulatory proteins. The integration of in vitro generated transcriptomic data with global gene expression of S. Typhimurium during infection is beginning to yield valuable information. The coordinated regulation of Salmonella gene expression is a key process for survival, adaptation and virulence capacities of the pathogen.

Analysis of phagosomal proteomes: from latex-bead to bacterial phagosomes

Phagosomal proteome characterization has contributed significantly to the understanding of host-pathogen interaction and the mechanism of infectious diseases caused by intracellular bacteria. The latex bead-containing phagosome has been widely used as a model system to study phagosomal proteomes at a global level. In contrast, the study of bacteria-containing phagosomes at a similar level has just begun. A number of intracellular microbial species are studied for their proteomes during the invasion of a host, providing insight into their metabolic adaptation in host cells and interaction with host-cell antimicrobial environments. In this review, we attempt to summarize the most recent advancements in the proteomic study of microbial phagosomes, especially those originating from mouse or human cells. We also briefly describe the proteomics of latex bead-containing phagosomes because they are often used as model phagosomes for study. We provide descriptions on major biological and technological components in phagosomal proteome studies. We also discuss the role of phagosomal proteome study in the broader horizon of systems biology and the technological challenges in phagosomal proteome characterization.

Reductionistic and holistic science

A reductionistic approach to science, epitomized by molecular biology, is often contrasted with the holistic approach of systems biology. However, molecular biology and systems biology are actually interdependent and complementary ways in which to study and make sense of complex phenomena.