scholarly journals Technologies for Proteome-Wide Discovery of Extracellular Host-Pathogen Interactions

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Nadia Martinez-Martin
Keyword(s):  
Cell Surface ◽  
Protein Interactions ◽  
Extracellular Protein ◽  
Interaction Networks ◽  
Productive Infection ◽  
Surface Barrier ◽  
Host Pathogen Interaction ◽  
Genome Wide ◽  
Host Pathogen ◽  
Extracellular Molecules

Pathogens have evolved unique mechanisms to breach the cell surface barrier and manipulate the host immune response to establish a productive infection. Proteins exposed to the extracellular environment, both cell surface-expressed receptors and secreted proteins, are essential targets for initial invasion and play key roles in pathogen recognition and subsequent immunoregulatory processes. The identification of the host and pathogen extracellular molecules and their interaction networks is fundamental to understanding tissue tropism and pathogenesis and to inform the development of therapeutic strategies. Nevertheless, the characterization of the proteins that function in the host-pathogen interface has been challenging, largely due to the technical challenges associated with detection of extracellular protein interactions. This review discusses available technologies for the high throughput study of extracellular protein interactions between pathogens and their hosts, with a focus on mammalian viruses and bacteria. Emerging work illustrates a rich landscape for extracellular host-pathogen interaction and points towards the evolution of multifunctional pathogen-encoded proteins. Further development and application of technologies for genome-wide identification of extracellular protein interactions will be important in deciphering functional host-pathogen interaction networks, laying the foundation for development of novel therapeutics.

PROGRESS IN COMPUTATIONAL STUDIES OF HOST–PATHOGEN INTERACTIONS

2013 ◽  
Vol 11 (02) ◽  
pp. 1230001 ◽  
Author(s):  
HUFENG ZHOU ◽  
JINGJING JIN ◽  
LIMSOON WONG
Keyword(s):  
Protein Interactions ◽  
Computational Studies ◽  
Interaction Data ◽  
Protein Protein Interactions ◽  
Host Pathogen Interactions ◽  
Basic Principles ◽  
Host Pathogen Interaction ◽  
Treatment And Prevention ◽  
Host Pathogen ◽  
Pathogen Protein

Host–pathogen interactions are important for understanding infection mechanism and developing better treatment and prevention of infectious diseases. Many computational studies on host–pathogen interactions have been published. Here, we review recent progress and results in this field and provide a systematic summary, comparison and discussion of computational studies on host–pathogen interactions, including prediction and analysis of host–pathogen protein–protein interactions; basic principles revealed from host–pathogen interactions; and database and software tools for host–pathogen interaction data collection, integration and analysis.

scholarly journals Genome-Wide Host-Pathogen Interaction Unveiled by Transcriptomic Response of Diamondback Moth to Fungal Infection

PLoS ONE ◽  
2016 ◽  
Vol 11 (4) ◽  
pp. e0152908 ◽  
Author(s):  
Zhen-Jian Chu ◽  
Yu-Jun Wang ◽  
Sheng-Hua Ying ◽  
Xiao-Wei Wang ◽  
Ming-Guang Feng
Keyword(s):  
Fungal Infection ◽  
Diamondback Moth ◽  
Transcriptomic Response ◽  
Host Pathogen Interaction ◽  
Genome Wide ◽  
Host Pathogen

scholarly journals High-Content Imaging for Large-Scale Detection of Low-Affinity Extracellular Protein Interactions

2019 ◽  
Vol 24 (10) ◽  
pp. 987-999
Author(s):  
Laura Wood ◽  
Gavin J. Wright
Keyword(s):  
Cell Surface ◽  
Protein Interactions ◽  
Large Scale ◽  
Local Environment ◽  
Surface Expression ◽  
Extracellular Protein ◽  
Automated Image Analysis ◽  
Cell Surface Expression ◽  
High Content Imaging ◽  
Pathogen Invasion

Extracellular protein interactions coordinate cellular responses with their local environment and have important roles in pathogen invasion and disease. Due to technical challenges associated with studying binding events at the cell surface, the systematic and reliable identification of novel ligand–receptor pairs remains difficult. Here, we describe the development of a cell-based assay using large-scale transient transfections and high-content imaging (HCI) to detect extracellular binding events. We optimized the parameters for efficient transfection of human cells with cDNA plasmids encoding full-length cell surface receptors in 384-well plates. Using a range of well-characterized structurally diverse low-affinity cell surface interactions, we show that transfected cells probed with highly avid ligands can be used to successfully identify ligand–receptor pairs using an HCI platform and automated image analysis software. To establish the high-throughput potential of this approach, we also screened a pool of ligands against a collection of 2455 cell surface expression clones and found that known ligand–receptor interactions could be robustly and consistently detected across the library using this technology.

High-throughput identification of transient extracellular protein interactions

2010 ◽  
Vol 38 (4) ◽  
pp. 919-922 ◽  
Author(s):  
Gavin J. Wright ◽  
Stephen Martin ◽  
K. Mark Bushell ◽  
Christian Söllner
Keyword(s):  
High Throughput ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Living Cell ◽  
Protein Interaction Networks ◽  
Extracellular Protein ◽  
Interaction Networks ◽  
Receptor Proteins ◽  
Major Class ◽  
Extracellular Environment

Protein interactions are highly diverse in their biochemical nature, varying in affinity and are often dependent on the surrounding biochemical environment. Given this heterogeneity, it seems unlikely that any one method, and particularly those capable of screening for many protein interactions in parallel, will be able to detect all functionally relevant interactions that occur within a living cell. One major class of interactions that are not detected by current popular high-throughput methods are those that occur in the extracellular environment, especially those made by membrane-embedded receptor proteins. In the present article, we discuss some of our recent research in the development of a scalable assay to identify this class of protein interaction and some of the findings from its application in the construction of extracellular protein interaction networks.

scholarly journals Specialization for resistance in wild host-pathogen interaction networks

2015 ◽  
Vol 6 ◽  
Author(s):  
Luke G. Barrett ◽  
Francisco Encinas-Viso ◽  
Jeremy J. Burdon ◽  
Peter H. Thrall
Keyword(s):  
Interaction Networks ◽  
Host Pathogen Interaction ◽  
Wild Host ◽  
Host Pathogen

Global translation variations in host cells upon attack of lytic and sublytic Staphylococcus aureus α-haemolysin1

2015 ◽  
Vol 472 (1) ◽  
pp. 83-95 ◽  
Author(s):  
Massimiliano Clamer ◽  
Toma Tebaldi ◽  
Marta Marchioretto ◽  
Paola Bernabò ◽  
Efrem Bertini ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Translational Control ◽  
Host Cells ◽  
Alpha Hemolysin ◽  
Host Pathogen Interaction ◽  
Genome Wide ◽  
Host Pathogen ◽  
Global Translation

Staphylococcal alpha-hemolysin (AHL) is a clinically relevant toxin, whose effects on host translation are poorly understood. We characterized genome-wide alterations induced at transcriptional and transational levels by lytic and sublytic AHL, pinpointing the importance of translational control during host-pathogen interaction.

scholarly journals Genome-Wide Association Mapping of bc-1 and bc-u Reveals Candidate Genes and New Adjustments to the Host-Pathogen Interaction for Resistance to Bean Common Mosaic Necrosis Virus in Common Bean

2021 ◽  
Vol 12 ◽  
Author(s):  
Alvaro Soler-Garzón ◽  
Phillip E. McClean ◽  
Phillip N. Miklas
Keyword(s):  
Common Bean ◽  
Candidate Genes ◽  
Genome Wide Association ◽  
Bzip Transcription Factor ◽  
Necrosis Virus ◽  
Host Group ◽  
Host Pathogen Interaction ◽  
Genome Wide ◽  
A Genome ◽  
Host Pathogen

Bean common mosaic necrosis virus (BCMNV) is a major disease in common bean (Phaseolus vulgaris L.). Host plant resistance is the primary disease control. We sought to identify candidate genes to better understand the host-pathogen interaction and develop tools for marker-assisted selection (MAS). A genome-wide association study (GWAS) approach using 182 lines from a race Durango Diversity Panel (DDP) challenged by BCMNV isolates NL-8 [Pathogroup (PG)-III] and NL-3 (PG-VI), and genotyped with 1.26 million single-nucleotide polymorphisms (SNPs), revealed significant peak regions on chromosomes Pv03 and Pv05, which correspond to bc-1 and bc-u resistance gene loci, respectively. Three candidate genes were identified for NL-3 and NL-8 resistance. Side-by-side receptor-like protein kinases (RLKs), Phvul.003G038700 and Phvul.003G038800 were candidate genes for bc-1. These RLKs were orthologous to linked RLKs associated with virus resistance in soybean (Glycine max). A basic Leucine Zipper (bZIP) transcription factor protein is the candidate gene for bc-u. bZIP protein gene Phvul.005G124100 carries a unique non-synonymous mutation at codon 14 in the first exon (Pv05: 36,114,516 bases), resulting in a premature termination codon that causes a nonfunctional protein. SNP markers for bc-1 and bc-u and new markers for I and bc-3 genes were used to genotype the resistance genes underpinning BCMNV phenotypes in the DDP, host group (HG) differentials, and segregating F3 families. Results revealed major adjustments to the current host-pathogen interaction model: (i) there is only one resistance allele bc-1 for the Bc-1 locus, and differential expression of the allele is based on presence vs. absence of bc-u; (ii) bc-1 exhibits dominance and incomplete dominance; (iii) bc-1 alone confers resistance to NL-8; (iv) bc-u was absent from HGs 2, 4, 5, and 7 necessitating a new gene symbol bc-ud to reflect this change; (v) bc-ud alone delays susceptible symptoms, and when combined with bc-1 enhanced resistance to NL-3; and (vi) bc-ud is on Pv05, not Pv03 as previously thought. These candidate genes, markers, and adjustments to the host-pathogen interaction will facilitate breeding for resistance to BCMNV and related Bean common mosaic virus (BCMV) in common bean.

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