scholarly journals Taxonomy-aware, sequence similarity ranking reliably predicts phage-host relationships

2021 ◽  
Author(s):  
Andrzej Zielezinski ◽  
Jakub Barylski ◽  
Wojciech M. Karlowski
Keyword(s):  
Prediction Accuracy ◽  
Phage Genome ◽  
Sequence Similarity ◽  
Area Under The Curve ◽  
Genome Sequences ◽  
Host Interactions ◽  
Host Interaction ◽  
Percentage Points ◽  
Host Relationships ◽  
Blast Alignment

ABSTRACTMotivationSimilar regions in virus and host genomes provide strong evidence for phage-host interaction, and BLAST is one of the leading tools to predict hosts from phage sequences. However, BLAST-based host prediction has three limitations: (i) top-scoring prokaryotic sequences do not always point to the actual host, (ii) mosaic phage genomes may produce matches to many, typically related, bacteria, and (iii) phage and host sequences may diverge beyond the point where their relationship can be detected by a BLAST alignment.ResultsWe created an extension to BLAST, named Phirbo, that improves host prediction quality beyond what is obtainable from standard BLAST searches. The tool harnesses information concerning sequence similarity and bacteria relatedness to predict phage-host interactions. Phirbo was evaluated on two benchmark sets of known phage-host pairs, and it improved precision and recall by 25 percentage points, as well as the discriminatory power for the recognition of phage-host relationships by 10 percentage points (Area Under the Curve = 0.95). Phirbo also yielded a mean host prediction accuracy of 60% and 70% at the genus and family levels, respectively, representing a 5% improvement over BLAST. When using only a fraction of phage genome sequences (3 kb), the prediction accuracy of Phirbo was 5-11% higher than BLAST at all taxonomic levels.ConclusionOur results suggest that Phirbo is an effective, unsupervised tool for predicting phage-host relationships.AvailabilityPhirbo is available at https://github.com/aziele/phirbo.

scholarly journals Taxonomy-aware, sequence similarity ranking reliably predicts phage–host relationships

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Andrzej Zielezinski ◽  
Jakub Barylski ◽  
Wojciech M. Karlowski
Keyword(s):  
Sequence Similarity ◽  
Area Under The Curve ◽  
Prediction Method ◽  
Host Interactions ◽  
Percentage Points ◽  
Host Relationships ◽  
Blast Alignment ◽  
Virus Genomes ◽  
Viral Sequences

Abstract Background Characterizing phage–host interactions is critical to understanding the ecological role of both partners and effective isolation of phage therapeuticals. Unfortunately, experimental methods for studying these interactions are markedly slow, low-throughput, and unsuitable for phages or hosts difficult to maintain in laboratory conditions. Therefore, a number of in silico methods emerged to predict prokaryotic hosts based on viral sequences. One of the leading approaches is the application of the BLAST tool that searches for local similarities between viral and microbial genomes. However, this prediction method has three major limitations: (i) top-scoring sequences do not always point to the actual host; (ii) mosaic virus genomes may match to many, typically related, bacteria; and (iii) viral and host sequences may diverge beyond the point where their relationship can be detected by a BLAST alignment. Results We created an extension to BLAST, named Phirbo, that improves host prediction quality beyond what is obtainable from standard BLAST searches. The tool harnesses information concerning sequence similarity and bacteria relatedness to predict phage–host interactions. Phirbo was evaluated on three benchmark sets of known virus–host pairs, and it improved precision and recall by 11–40 percentage points over currently available, state-of-the-art, alignment-based, alignment-free, and machine-learning host prediction tools. Moreover, the discriminatory power of Phirbo for the recognition of virus–host relationships surpassed the results of other tools by at least 10 percentage points (area under the curve = 0.95), yielding a mean host prediction accuracy of 57% and 68% at the genus and family levels, respectively, and drops by 12 percentage points when using only a fraction of viral genome sequences (3 kb). Finally, we provide insights into a repertoire of protein and ncRNA genes that are shared between phages and hosts and may be prone to horizontal transfer during infection. Conclusions Our results suggest that Phirbo is a simple and effective tool for predicting phage–host relationships.

scholarly journals PHIST: fast and accurate prediction of prokaryotic hosts from metagenomic viral sequences

2021 ◽  
Author(s):  
Andrzej Zielezinski ◽  
Sebastian Deorowicz ◽  
Adam Gudyś
Keyword(s):  
Prediction Accuracy ◽  
Species Level ◽  
Supplementary Information ◽  
Genomic Sequences ◽  
Supplementary Data ◽  
Host Interaction ◽  
Alignment Free ◽  
Percentage Points ◽  
Search Tool ◽  
Viral Sequences

AbstractSummaryPHIST (Phage-Host Interaction Search Tool) predicts prokaryotic hosts of viruses from their genomic sequences. It improves host prediction accuracy at species level over current alignment-based tools (on average by 3 percentage points) as well as alignment-free and CRISPR-based tools (by 14–20 percentage points). PHIST is also two orders of magnitude faster than alignment-based tools making it suitable for metagenomics studies.Availability and implementationGNU-licensed C++ code wrapped in Python API available at: https://github.com/refresh-bio/[email protected], [email protected] informationSupplementary data are available at publisher Web site.

scholarly journals A Novel Approach to Predicting Interactions Between Microbes and Hosts

2021 ◽  
Author(s):  
Jie Li ◽  
Zhuo Chen ◽  
Dawei Ma ◽  
Li Zhou ◽  
Yadong Wang
Keyword(s):  
Prediction Accuracy ◽  
State Of The Art ◽  
Accurate Method ◽  
Host Interactions ◽  
Host Interaction ◽  
Novel Approach ◽  
Novel Method ◽  
Living Organisms ◽  
Host Infection ◽  
The Cost

Abstract Increasing evidence shows that microbes are important for the protection of human health and the health of other living organisms. At the same time, microbes can cause other organisms to become sick or even die. Through microbe–host interaction, we can understand intuitively the process and mechanism of host infection by microbes. Several methods are developed to predict microbe–host interactions. However, current methods are limited by the cost of interaction verification experiments and accuracy. Therefore, there is still a need for a rapid and accurate method to predict microbe–host interaction. Here, we proposed a novel method based on Integrated Similarity, KATZ measure, and Within and Between Scores (ISKATZWBS) to predict microbe–host interactions. Experimental results show that the proposed method performs well and the AUCs are 0.946, 0.981, 0.954 on the PHI-base, HPIDB, and HMDAD datasets repectively. Compared with other four state-of the-art methods: KATZHMDA, WBSMDA , NGRHMDA and NCPHMDA, the proposed method has higher prediction accuracy.

scholarly journals Identification of Epidemiological Traits by Analysis of SARS−CoV−2 Sequences

Viruses ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 764
Author(s):  
Bohu Pan ◽  
Zuowei Ji ◽  
Sugunadevi Sakkiah ◽  
Wenjing Guo ◽  
Jie Liu ◽  
...  
Keyword(s):  
Clustering Analysis ◽  
Phylogenetic Trees ◽  
Sequence Similarity ◽  
Host Age ◽  
Hierarchical Clustering Analysis ◽  
Genome Sequences ◽  
Geographic Patterns ◽  
Rapid Spread ◽  
Pairwise Sequence Similarity ◽  
Sequence Similarity Analysis

Severe acute respiratory syndrome coronavirus 2 (SARS−CoV−2) has caused the ongoing global COVID-19 pandemic that began in late December 2019. The rapid spread of SARS−CoV−2 is primarily due to person-to-person transmission. To understand the epidemiological traits of SARS−CoV−2 transmission, we conducted phylogenetic analysis on genome sequences from >54K SARS−CoV−2 cases obtained from two public databases. Hierarchical clustering analysis on geographic patterns in the resulting phylogenetic trees revealed a co-expansion tendency of the virus among neighboring countries with diverse sources and transmission routes for SARS−CoV−2. Pairwise sequence similarity analysis demonstrated that SARS−CoV−2 is transmitted locally and evolves during transmission. However, no significant differences were seen among SARS−CoV−2 genomes grouped by host age or sex. Here, our identified epidemiological traits provide information to better prevent transmission of SARS−CoV−2 and to facilitate the development of effective vaccines and therapeutics against the virus.

scholarly journals Predicting bacteriophage hosts based on sequences of annotated receptor-binding proteins

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dimitri Boeckaerts ◽  
Michiel Stock ◽  
Bjorn Criel ◽  
Hans Gerstmans ◽  
Bernard De Baets ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Receptor Binding ◽  
Bacterial Infections ◽  
Sequence Data ◽  
Sequence Similarity ◽  
Area Under The Curve ◽  
Local Alignment ◽  
Search Tool ◽  
Different Levels

AbstractNowadays, bacteriophages are increasingly considered as an alternative treatment for a variety of bacterial infections in cases where classical antibiotics have become ineffective. However, characterizing the host specificity of phages remains a labor- and time-intensive process. In order to alleviate this burden, we have developed a new machine-learning-based pipeline to predict bacteriophage hosts based on annotated receptor-binding protein (RBP) sequence data. We focus on predicting bacterial hosts from the ESKAPE group, Escherichia coli, Salmonella enterica and Clostridium difficile. We compare the performance of our predictive model with that of the widely used Basic Local Alignment Search Tool (BLAST). Our best-performing predictive model reaches Precision-Recall Area Under the Curve (PR-AUC) scores between 73.6 and 93.8% for different levels of sequence similarity in the collected data. Our model reaches a performance comparable to that of BLASTp when sequence similarity in the data is high and starts outperforming BLASTp when sequence similarity drops below 75%. Therefore, our machine learning methods can be especially useful in settings in which sequence similarity to other known sequences is low. Predicting the hosts of novel metagenomic RBP sequences could extend our toolbox to tune the host spectrum of phages or phage tail-like bacteriocins by swapping RBPs.

scholarly journals Apicortin, a Constituent of Apicomplexan Conoid/Apical Complex and Its Tentative Role in Pathogen—Host Interaction

2021 ◽  
Vol 6 (3) ◽  
pp. 118
Author(s):  
Ferenc Orosz
Keyword(s):  
Plasmodium Falciparum ◽  
Toxoplasma Gondii ◽  
Therapeutic Target ◽  
In Silico ◽  
Free Living ◽  
Binding Properties ◽  
Host Interactions ◽  
Trichoplax Adhaerens ◽  
Host Interaction ◽  
Apical Complex

In 2009, apicortin was identified in silico as a characteristic protein of apicomplexans that also occurs in the placozoa, Trichoplax adhaerens. Since then, it has been found that apicortin also occurs in free-living cousins of apicomplexans (chromerids) and in flagellated fungi. It contains a partial p25-α domain and a doublecortin (DCX) domain, both of which have tubulin/microtubule binding properties. Apicortin has been studied experimentally in two very important apicomplexan pathogens, Toxoplasma gondii and Plasmodium falciparum. It is localized in the apical complex in both parasites. In T. gondii, apicortin plays a key role in shaping the structure of a special tubulin polymer, conoid. In both parasites, its absence or downregulation has been shown to impair pathogen–host interactions. Based on these facts, it has been suggested as a therapeutic target for treatment of malaria and toxoplasmosis.

scholarly journals Quantifying relative virulence: when μ max fails and AUC alone just is not enough

2020 ◽  
Author(s):  
Ruben Michael Ceballos ◽  
Carson Len Stacy
Keyword(s):  
Specific Growth ◽  
Area Under The Curve ◽  
Maximum Specific Growth Rate ◽  
Curve Analysis ◽  
Growth Curve Analysis ◽  
Virus Infections ◽  
Susceptible Host ◽  
Host Interactions ◽  
Biological Phenomena ◽  
Single Host

A challenge in virology is quantifying relative virulence (V R) between two (or more) viruses that exhibit different replication dynamics in a given susceptible host. Host growth curve analysis is often used to mathematically characterize virus–host interactions and to quantify the magnitude of detriment to host due to viral infection. Quantifying V R using canonical parameters, like maximum specific growth rate (μ max), can fail to provide reliable information regarding virulence. Although area-under-the-curve (AUC) calculations are more robust, they are sensitive to limit selection. Using empirical data from Sulfolobus Spindle-shaped Virus (SSV) infections, we introduce a novel, simple metric that has proven to be more robust than existing methods for assessing V R. This metric (I SC) accurately aligns biological phenomena with quantified metrics to determine V R. It also addresses a gap in virology by permitting comparisons between different non-lytic virus infections or non-lytic versus lytic virus infections on a given host in single-virus/single-host infections.

scholarly journals Protein Tyrosine and Serine/Threonine Phosphorylation in Oral Bacterial Dysbiosis and Bacteria-Host Interaction

2022 ◽  
Vol 11 ◽  
Author(s):  
Liang Ren ◽  
Daonan Shen ◽  
Chengcheng Liu ◽  
Yi Ding
Keyword(s):  
Community Development ◽  
Oral Bacteria ◽  
Bacterial Metabolism ◽  
Oral Microbiota ◽  
Bacterial Protein ◽  
Oral Diseases ◽  
Host Interactions ◽  
Host Interaction ◽  
Systemic Health

The human oral cavity harbors approximately 1,000 microbial species, and dysbiosis of the microflora and imbalanced microbiota-host interactions drive many oral diseases, such as dental caries and periodontal disease. Oral microbiota homeostasis is critical for systemic health. Over the last two decades, bacterial protein phosphorylation systems have been extensively studied, providing mounting evidence of the pivotal role of tyrosine and serine/threonine phosphorylation in oral bacterial dysbiosis and bacteria-host interactions. Ongoing investigations aim to discover novel kinases and phosphatases and to understand the mechanism by which these phosphorylation events regulate the pathogenicity of oral bacteria. Here, we summarize the structures of bacterial tyrosine and serine/threonine kinases and phosphatases and discuss the roles of tyrosine and serine/threonine phosphorylation systems in Porphyromonas gingivalis and Streptococcus mutans, emphasizing their involvement in bacterial metabolism and virulence, community development, and bacteria-host interactions.

scholarly journals Sixth European Seminar in Virology on Virus–Host Interaction at Single Cell and Organism Level

Viruses ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 400
Author(s):  
Elisa Saccon ◽  
Adriana Vitiello ◽  
Marta Trevisan ◽  
Cristiano Salata ◽  
Giorgio Palù
Keyword(s):  
Immune Response ◽  
Single Cell ◽  
Virus Evolution ◽  
Cell Level ◽  
Host Interactions ◽  
Host Interaction ◽  
Young Researchers

The 6th European Seminar in Virology (EuSeV) was held in Bertinoro, Italy, 22–24 June 2018, and brought together international scientists and young researchers working in the field of Virology. Sessions of the meeting included: virus–host-interactions at organism and cell level; virus evolution and dynamics; regulation; immunity/immune response; and disease and therapy. This report summarizes lectures by the invited speakers and highlights advances in the field.

scholarly journals Acquisition of intact polar lipids from the prymnesiophyte <i>Phaeocystis globosa</i> by its lytic virus PgV-07T

2014 ◽  
Vol 11 (1) ◽  
pp. 185-194 ◽  
Author(s):  
D. S. Maat ◽  
N. J. Bale ◽  
E. C. Hopmans ◽  
A.-C. Baudoux ◽  
J. S. Sinninghe Damsté ◽  
...  
Keyword(s):  
Viral Infection ◽  
Lower Proportion ◽  
Specific Lipids ◽  
Phaeocystis Globosa ◽  
Host Interactions ◽  
Algal Virus ◽  
Host Interaction ◽  
Betaine Lipids ◽  
Algal Host ◽  
Intact Polar Lipids

Abstract. Recent studies showed changes in phytoplankton lipid composition during viral infection and have indicated roles for specific lipids in the mechanisms of algal virus-host interaction. To investigate the generality of these findings and obtain a better understanding of the allocation of specific lipids to viruses, we studied the intact polar lipid (IPL) composition of virally infected and non-infected cultures of the prymnesiophyte Phaeocystis globosa G(A) and its lytic virus PgV-07T. The P. globosa IPL composition was relatively stable over a diel cycle and not strongly affected by viral infection. Glycolipids, phospholipids and betaine lipids were present in both the host and virus, although specific groups such as the diacylglyceryl-hydroxymethyltrimethyl-β-alanines and the sulfoquinovosyldiacylglycerols, were present in a lower proportion or were not detected in the virus. Viral glycosphingolipids (vGSLs), which have been shown to play a role in the infection strategy of the virus EhV-86, infecting the prymnesiophyte Emiliania huxleyi CCMP374, were not encountered. Our results show that the involvement of lipids in virus–algal host interactions can be very different amongst virus–algal host systems.

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