A network-based integrated framework for predicting virus–prokaryote interactions

Wang, Weili; Ren, Jie; Tang, Kujin; Dart, Emily; Ignacio‐Espinoza, J. Cesar; Fuhrman, Jed A.; Braun, Jonathan; Sun, Fengzhu; Ahlgren, Nathan A.

doi:10.1093/nargab/lqaa044

Cited by 89 publications

(135 citation statements)

References 79 publications

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“…In extreme environments such as the Atacama Desert, where only slow microbial replication is supported (25,26), it is plausible that CRISPR-Cas may not yield fast enough immune responses (36) for it to be prevalent. To predict possible host-virus interactions for hosts that lack CRISPR systems, VirHostMatcher (37) identified 3897 putative interactions between 73 MAGs and 132 viral scaffolds using d2* threshold of 0.3 (Figure 3b shows highest confidence interactions with d2* threshold of 0.25 for visualisation, Table S3 contains sample specific overview). Most putative interactions were established with actinobacterial MAGs, which were infected by a largely shared set of multiple viruses, while Thaumarchaeota and Firmicutes only interacted with three and four viruses specific to the hosts' taxonomic group respectively.…”

Section: Oligonucleotide Frequency-based Host-virus Matches Suggest Amentioning

confidence: 99%

Diverse viruses carrying genes for microbial extremotolerance in the Atacama Desert hyperarid soil

Hwang

Rahlff

Schulze‐Makuch

et al. 2020

Preprint

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Viruses play an essential role in shaping microbial community structures and serve as reservoirs for genetic diversity in many ecosystems. In hyperarid desert environments, where life itself becomes scarce and loses diversity, the interactions between viruses and host populations have remained elusive. Here, we resolved host-virus interactions in the soil metagenomes of the Atacama Desert hyperarid core, one of the harshest terrestrial environments on Earth. We show dispersal of diverse and abundant viruses that infect a wide range of hosts over 205 km across the desert. Host genomes encoded both adaptive and innate immune systems, providing evidence of viral predation being a key selective pressure along with abiotic stresses. Viral genomes carried extremotolerance features (i.e. DNA repair proteins, enzymes against oxidative damage) and other auxiliary metabolic genes, indicating that viruses could mediate the spread of microbial resilience against environmental stress across the desert. Our results suggest that the host-virus interactions in the Atacama Desert soils are dynamic and complex, shaping uniquely adapted microbiomes in this highly selective and hostile environment.ImportanceDeserts are one of the largest and rapidly expanding terrestrial ecosystems characterized by low biodiversity and biomass. The hyperarid core of the Atacama Desert, previously thought to be devoid of life, is one of the harshest environments supporting only scant biomass of highly adapted microbes. While there is growing evidence that viruses play essential roles in shaping the diversity and structure of nearly every ecosystem, very little is known about the role of viruses in desert soils, especially where viral contact with viable hosts is significantly reduced. Our results indicate that viruses are abundant, diverse and widely dispersed across the desert, potentially spreading key stress resilience and metabolic genes to ensure host survival. The desertification accelerated by climate change expands both the ecosystem cover and the ecological significance of the desert virome. This study sheds light on the complex evolutionary dynamics that shape the unique and poorly understood host-virus relationships in desert soils.

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Section: Oligonucleotide Frequency-based Host-virus Matches Suggest Amentioning

confidence: 99%

Diverse viruses carrying genes for microbial extremotolerance in the Atacama Desert hyperarid soil

Hwang

Rahlff

Schulze‐Makuch

et al. 2020

Preprint

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“…The noted difference in vHULK's performance (primarily at the species level) between testing and validation could be explained in part by the presence of phages in the NDG dataset whose hosts are not present in vHULK's training datasets. It is known that many machine learning prediction models may struggle when facing instances for which there was no target in training (24,25,40). We have tried to mitigate this problem by providing users with entropy values associated with scores; we have shown that these entropy values can be used as proxies for prediction confidence.…”

Section: Discussionmentioning

confidence: 99%

“…According to its authors, WiSH presents 63% of mean accuracy when predicting host genus among 20 possible genera. A more recent work proposed the use of neural networks based on an ensemble of distance measurements and other features, concepts that were implemented in the tool VirHostMatchernet (VHM-net) (25). VHM-net achieved accuracies that varied from 43% to 59% at the host genus level only.…”

Section: Introductionmentioning

confidence: 99%

vHULK, a new tool for bacteriophage host prediction based on annotated genomic features and deep neural networks

Amgarten

Iha

Piroupo

et al. 2020

Preprint

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The experimental determination of a bacteriophage host is a laborious procedure. For this reason, there is a pressing need for reliable computational predictions of bacteriophage hosts in phage research in general and in phage therapy in particular. Here, we present a new program called vHULK for phage host prediction based on 9,504 phage genome features. These features take into account alignment significance scores between predicted-protein sequences in the phage genomes and a curated database of viral protein families. The features were fed to a deep neural network, and four distinct models were trained to predict 61 different host genera and 52 host species. In random controlled test sets, the program obtained 99% and 98% accuracy values at the genus and species levels, respectively. On a validation dataset with 2,178 phage genomes, mean accuracies were 82% and 52% at the genus and species levels, respectively. When compared against other phage host prediction programs on the same validation dataset, vHULK achieved substantially better performance, therefore demonstrating that the program is an advance on the state-of-art in phage host prediction. vHULK is freely available at https://github.com/LaboratorioBioinformatica/vHULK.

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“…We further evaluated the host prediction accuracy of Phirbo by selecting a top-scored prokaryotic sequence for each phage [14][15][16]18]. Briefly, host prediction accuracy is calculated as the percentage of phages whose predicted hosts have the same taxonomic affiliation as their respective known hosts (if multiple top-scoring hosts are present, the prediction is scored as correct if the true host is among the predicted hosts).…”

Section: Phirbo Preserves Blast Top-ranked Host Predictionsmentioning

confidence: 99%

“…Methods for studying phage-host interactions primarily rely on cultured virus-host systems; however, recent in silico approaches suggest a much broader range of hosts may be susceptible to viral infections [14]. These methods predict prokaryotic hosts based on sequence composition [15,16], direct sequence similarity between phages and hosts [14], analysis of CRISPR spacers or tRNAs [13,17], as well as supervised approaches that integrate several sequence-based methods [18,19].…”

Section: Introductionmentioning

confidence: 99%

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

Zieleziński

Barylski

Karłowski

2021

Preprint

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MotivationSimilar 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.

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A network-based integrated framework for predicting virus–prokaryote interactions

Cited by 89 publications

References 79 publications

Diverse viruses carrying genes for microbial extremotolerance in the Atacama Desert hyperarid soil

Diverse viruses carrying genes for microbial extremotolerance in the Atacama Desert hyperarid soil

vHULK, a new tool for bacteriophage host prediction based on annotated genomic features and deep neural networks

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

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