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Host-Pathogen Interactions

Overview of attention for book
Cover of 'Host-Pathogen Interactions'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Genetic Association Studies in Host–Pathogen Interaction Analysis
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    Chapter 2 Bacterial Genotyping Methods: From the Basics to Modern
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    Chapter 3 Real-Time Reverse Transcription PCR as a Tool to Study Virulence Gene Regulation in Bacterial Pathogens
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    Chapter 4 Usage of a Bioluminescence Reporter System to Image Promoter Activity During Host Infection
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    Chapter 5 lacZ Reporter System as a Tool to Study Virulence Gene Regulation in Bacterial Pathogens
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    Chapter 6 Western Blotting Against Tagged Virulence Determinants to Study Bacterial Pathogenicity
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    Chapter 7 Molecular Methods to Analyze the Effect of Proteins Expressed by Salmonella During Its Intracellular Stage
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    Chapter 8 Organoids as a Model to Study Infectious Disease
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    Chapter 9 Surface Proteome Biotinylation Combined with Bioinformatic Tools as a Strategy for Predicting Pathogen Interacting Proteins
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    Chapter 10 Systems Biology Modeling to Study Pathogen–Host Interactions
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    Chapter 11 Phage Therapy: Various Perspectives on How to Improve the Art
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    Chapter 12 Application of RNA-seq and Bioimaging Methods to Study Microbe–Microbe Interactions and Their Effects on Biofilm Formation and Gene Expression
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    Chapter 13 Serial Dilution-Based Growth Curves and Growth Curve Synchronization for High-Resolution Time Series of Bacterial Biofilm Growth
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    Chapter 14 Detection of Bacterial Quorum Sensing Molecules
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    Chapter 15 Generating Chromosome-Located Transcriptional Fusions to Fluorescent Proteins for Single-Cell Gene Expression Analysis in Pseudomonas syringae
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    Chapter 16 Introduction of Genetic Material in Ralstonia solanacearum Through Natural Transformation and Conjugation
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    Chapter 17 In Vitro and In Vivo Secretion/Translocation Assays to Identify Novel Ralstonia solanacearum Type 3 Effectors
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    Chapter 18 Plant Pathogenicity Phenotyping of Ralstonia solanacearum Strains
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    Chapter 19 Methods to Quantify Biotic-Induced Stress in Plants
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    Chapter 20 From Sample to Data: Preparing, Obtaining, and Analyzing Images of Plant-Pathogen Interactions Using Confocal Microscopy
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    Chapter 21 Screening of c-di-GMP-Regulated Exopolysaccharides in Host Interacting Bacteria
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    Chapter 22 Primary Characterization of Small RNAs in Symbiotic Nitrogen-Fixing Bacteria
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    Chapter 23 A New, Nondestructive, Split-Root System for Local and Systemic Plant Responses Studies with Soybean
  25. Altmetric Badge
    Chapter 24 Methods for the Characterization of Plant-Growth Promoting Rhizobacteria
Attention for Chapter 9: Surface Proteome Biotinylation Combined with Bioinformatic Tools as a Strategy for Predicting Pathogen Interacting Proteins
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  • Good Attention Score compared to outputs of the same age and source (68th percentile)

Mentioned by

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2 tweeters

Citations

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2 Dimensions

Readers on

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16 Mendeley
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Chapter title
Surface Proteome Biotinylation Combined with Bioinformatic Tools as a Strategy for Predicting Pathogen Interacting Proteins
Chapter number 9
Book title
Host-Pathogen Interactions
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7604-1_9
Pubmed ID
Book ISBNs
978-1-4939-7603-4, 978-1-4939-7604-1
Authors

Anita Horvatić, Josipa Kuleš, Nicolas Guillemin, Franjo Martinković, Iva Štimac, Vladimir Mrljak, Mangesh Bhide

Abstract

Constant advancements in methodology and mass spectrometry instrumentation, genome sequencing and bioinformatic tools have enabled the identification of numerous pathogen proteomes. Identifying the pathogen interacting proteins by means of high-throughput techniques is key for understanding pathogen invasion and survival mechanisms and in such a way proposing specific proteins as pharmaceutical targets. Herein we describe the methodology for the enrichment and identification of pathogen surface proteome using cell surface protein biotinylation followed by LC-MS/MS and bioinformatic analyses of such data. This strategy is to be employed for the determination of protein subcellular localization and prediction of potential pathogen interacting proteins.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 13%
Student > Master 1 6%
Student > Doctoral Student 1 6%
Other 1 6%
Professor 1 6%
Other 2 13%
Unknown 8 50%
Readers by discipline Count As %
Medicine and Dentistry 3 19%
Veterinary Science and Veterinary Medicine 2 13%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Social Sciences 1 6%
Nursing and Health Professions 1 6%
Other 0 0%
Unknown 8 50%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 January 2018.
All research outputs
#9,327,444
of 15,227,010 outputs
Outputs from Methods in molecular biology
#2,762
of 8,961 outputs
Outputs of similar age
#214,701
of 403,568 outputs
Outputs of similar age from Methods in molecular biology
#492
of 1,814 outputs
Altmetric has tracked 15,227,010 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,961 research outputs from this source. They receive a mean Attention Score of 2.6. This one has gotten more attention than average, scoring higher than 65% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 403,568 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,814 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.