scholarly journals LectinOracle – A Generalizable Deep Learning Model for Lectin-Glycan Binding Prediction

2021 ◽  
Author(s):  
Jon Lundstrøm ◽  
Emma Korhonen ◽  
Frédérique Lisacek ◽  
Daniel Bojar
Keyword(s):  
Cell Biology ◽  
Focal Point ◽  
Sequence Similarity ◽  
Quantitative Agreement ◽  
Binding Prediction ◽  
Model Combining ◽  
Wide Range ◽  
Microbe Interactions ◽  
Host Microbe Interactions ◽  
Viral Cell Entry

AbstractRanging from bacterial cell adhesion over viral cell entry to human innate immunity, glycan-binding proteins or lectins abound in nature. Widely used as staining and characterization reagents in cell biology, and crucial for understanding the interactions in biological systems, lectins are a focal point of study in glycobiology. Yet the sheer breadth and depth of specificity for diverse oligosaccharide motifs has made studying lectins a largely piecemeal approach, with few options to generalize. Here, we present LectinOracle, a model combining transformer-based representations for proteins and graph convolutional neural networks for glycans to predict their interaction. Using a curated dataset of 564,647 unique protein-glycan interactions, we show that LectinOracle predictions agree with literature-annotated specificities for a wide range of lectins. We further identify clusters of lectins with related binding specificity that are not clustered based on sequence similarity. Using a range of specialized glycan arrays, we show that LectinOracle predictions generalize to new glycans and lectins, with qualitative and quantitative agreement with experimental data. We further demonstrate that LectinOracle can analyze whole lectomes and their role in host-microbe interactions. We envision that the herein presented platform will advance both the study of lectins and their role in (glyco)biology.

scholarly journals Identification of Erwinia amylovora Genes Induced during Infection of Immature Pear Tissue

2005 ◽  
Vol 187 (23) ◽  
pp. 8088-8103 ◽  
Author(s):  
Youfu Zhao ◽  
Sara E. Blumer ◽  
George W. Sundin
Keyword(s):  
Pseudomonas Syringae ◽  
Erwinia Amylovora ◽  
Sequence Similarity ◽  
Type Ii Secretion ◽  
Knockout Mutant ◽  
Technology System ◽  
Microbe Interactions ◽  
Host Microbe Interactions ◽  
Blight Disease

ABSTRACT The enterobacterium Erwinia amylovora is a devastating plant pathogen causing necrotrophic fire blight disease of apple, pear, and other rosaceous plants. In this study, we used a modified in vivo expression technology system to identify E. amylovora genes that are activated during infection of immature pear tissue, a process that requires the major pathogenicity factors of this organism. We identified 394 unique pear fruit-induced (pfi) genes on the basis of sequence similarity to known genes and separated them into nine putative function groups including host-microbe interactions (3.8%), stress response (5.3%), regulation (11.9%), cell surface (8.9%), transport (13.5%), mobile elements (1.0%), metabolism (20.3%), nutrient acquisition and synthesis (15.5%), and unknown or hypothetical proteins (19.8%). Known virulence genes, including hrp/hrc components of the type III secretion system, the major effector gene dspE, type II secretion, levansucrase (lsc), and regulators of levansucrase and amylovoran biosynthesis, were upregulated during pear tissue infection. Known virulence factors previously identified in E. (Pectobacterium) carotovora and Pseudomonas syringae were identified for the first time in E. amylovora and included HecA hemagglutinin family adhesion, Peh polygalacturonase, new effector HopPtoCEA, and membrane-bound lytic murein transglycosylase MltEEA. An insertional mutation within hopPtoC EA did not result in reduced virulence; however, an mltE EA knockout mutant was reduced in virulence and growth in immature pears. This study suggests that E. amylovora utilizes a variety of strategies during plant infection and to overcome the stressful and poor nutritional environment of its plant hosts.

scholarly journals Integrative developmental ecology: a review of density-dependent effects on life-history traits and host-microbe interactions in non-social holometabolous insects

2020 ◽  
Vol 34 (5) ◽  
pp. 659-680 ◽  
Author(s):  
Anh The Than ◽  
Fleur Ponton ◽  
Juliano Morimoto
Keyword(s):  
Population Dynamics ◽  
Life History ◽  
Life History Traits ◽  
Larval Density ◽  
Future Research ◽  
Density Dependent ◽  
Integrative Framework ◽  
Wide Range ◽  
Microbe Interactions ◽  
Host Microbe Interactions

Abstract Population density modulates a wide range of eco-evolutionary processes including inter- and intra-specific competition, fitness and population dynamics. In holometabolous insects, the larval stage is particularly susceptible to density-dependent effects because the larva is the resource-acquiring stage. Larval density-dependent effects can modulate the expression of life-history traits not only in the larval and adult stages but also downstream for population dynamics and evolution. Better understanding the scope and generality of density-dependent effects on life-history traits of current and future generations can provide useful knowledge for both theory and experiments in developmental ecology. Here, we review the literature on larval density-dependent effects on fitness of non-social holometabolous insects. First, we provide a functional definition of density to navigate the terminology in the literature. We then classify the biological levels upon which larval density-dependent effects can be observed followed by a review of the literature produced over the past decades across major non-social holometabolous groups. Next, we argue that host-microbe interactions are yet an overlooked biological level susceptible to density-dependent effects and propose a conceptual model to explain how density-dependent effects on host-microbe interactions can modulate density-dependent fitness curves. In summary, this review provides an integrative framework of density-dependent effects across biological levels which can be used to guide future research in the field of ecology and evolution.

scholarly journals Meta-analysis of Diets Used in Drosophila Microbiome Research and Introduction of the Drosophila Dietary Composition Calculator (DDCC)

2020 ◽  
Vol 10 (7) ◽  
pp. 2207-2211 ◽  
Author(s):  
Danielle N. A. Lesperance ◽  
Nichole A. Broderick
Keyword(s):  
Meta Analysis ◽  
Dietary Composition ◽  
Nutritional Content ◽  
Web Based ◽  
Future Studies ◽  
Microbiome Composition ◽  
Wide Range ◽  
Microbiome Research ◽  
Microbe Interactions ◽  
Host Microbe Interactions

Nutrition is a major factor influencing many aspects of Drosophila melanogaster physiology. However, a wide range of diets, many of which are termed “standard” in the literature, are utilized for D. melanogaster research, leading to inconsistencies in reporting of nutrition-dependent phenotypes across the field. This is especially evident in microbiome studies, as diet has a pivotal role in microbiome composition and resulting host-microbe interactions. Here, we performed a meta-analysis of diets used in fly microbiome research and provide a web-based tool for researchers to determine the nutritional content of diets of interest. While our meta-analysis primarily focuses on microbiome studies, our goal in developing these resources is to aid the broader community in contextualizing past and future studies across the scope of D. melanogaster research to better understand how individual lab diets can contribute to observed phenotypes.

Exploring Host-Microbe Interactions in Hydra

2009 ◽  
Vol 4 (10) ◽  
pp. 457-462 ◽  
Author(s):  
Sebastian Fraune ◽  
Thomas C. G. Bosch ◽  
René Augustin
Keyword(s):  
Microbe Interactions ◽  
Host Microbe Interactions

Pharmacology of T2R Mediated Host–Microbe Interactions

2021 ◽  
Author(s):  
Manoj Reddy Medapati ◽  
Anjali Y. Bhagirath ◽  
Nisha Singh ◽  
Prashen Chelikani
Keyword(s):  
Microbe Interactions ◽  
Host Microbe Interactions

scholarly journals Organoids to Dissect Gastrointestinal Virus–Host Interactions: What Have We Learned?

Viruses ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 999
Author(s):  
Sue E. Crawford ◽  
Sasirekha Ramani ◽  
Sarah E. Blutt ◽  
Mary K. Estes
Keyword(s):  
Cell Types ◽  
Human Infection ◽  
Viral Pathogens ◽  
Host Interactions ◽  
Complex Interactions ◽  
Microbe Interactions ◽  
Host Microbe Interactions ◽  
Human Intestinal Epithelium ◽  
Human Viruses ◽  
Human Infections

Historically, knowledge of human host–enteric pathogen interactions has been elucidated from studies using cancer cells, animal models, clinical data, and occasionally, controlled human infection models. Although much has been learned from these studies, an understanding of the complex interactions between human viruses and the human intestinal epithelium was initially limited by the lack of nontransformed culture systems, which recapitulate the relevant heterogenous cell types that comprise the intestinal villus epithelium. New investigations using multicellular, physiologically active, organotypic cultures produced from intestinal stem cells isolated from biopsies or surgical specimens provide an exciting new avenue for understanding human specific pathogens and revealing previously unknown host–microbe interactions that affect replication and outcomes of human infections. Here, we summarize recent biologic discoveries using human intestinal organoids and human enteric viral pathogens.

scholarly journals Understanding the host-microbe interactions using metabolic modeling

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Jack Jansma ◽  
Sahar El Aidy
Keyword(s):  
Human Health ◽  
Flux Balance Analysis ◽  
Bacterial Species ◽  
Flux Balance ◽  
Interaction Field ◽  
In Silico Simulation ◽  
Balance Analysis ◽  
Microbe Interactions ◽  
Host Microbe Interactions ◽  
Genome Scale

AbstractThe human gut harbors an enormous number of symbiotic microbes, which is vital for human health. However, interactions within the complex microbiota community and between the microbiota and its host are challenging to elucidate, limiting development in the treatment for a variety of diseases associated with microbiota dysbiosis. Using in silico simulation methods based on flux balance analysis, those interactions can be better investigated. Flux balance analysis uses an annotated genome-scale reconstruction of a metabolic network to determine the distribution of metabolic fluxes that represent the complete metabolism of a bacterium in a certain metabolic environment such as the gut. Simulation of a set of bacterial species in a shared metabolic environment can enable the study of the effect of numerous perturbations, such as dietary changes or addition of a probiotic species in a personalized manner. This review aims to introduce to experimental biologists the possible applications of flux balance analysis in the host-microbiota interaction field and discusses its potential use to improve human health.

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