CHERRY: a Computational metHod for accuratE pRediction of virus-pRokarYotic interactions using a graph encoder-decoder model

Unveiling the overlooked small-sized microbiome in river ecosystems

Enriching microorganisms using a 0.22-μm pore size is a general pretreatment procedure in river microbiome research. However, it remains unclear the extent to which this method loses microbiome information. Here, we conducted a comparative metagenomics-based study on microbiomes with sizes over 0.22 μm (large-sized) and between 0.22 μm and 0.1 μm (small-sized) in a subtropical river. Although the absolute concentration of small-sized microbiome was about two orders of magnitude lower than that of large-sized microbiome, sequencing only large-sized microbiome resulted in a significant loss of microbiome diversity. Specifically, the microbial community was different between two sizes, and 347 genera were only detected in small-sized microbiome. Small-sized microbiome had much more diverse viral community than large-sized fraction. The viruses had abundant ecological functions and were hosted by 825 species of 169 families, including pathogen-related families. Small-sized microbiome had distinct antimicrobial resistance risks from large-sized microbiome, showing an enrichment of eight antibiotic resistance gene (ARG) types as well as the detection of 140 unique ARG subtypes and five enriched risk rank I ARGs. Draft genomes of five major resistant pathogens having diverse ecological and pollutant-degrading functions were only assembled in small-sized microbiome. These findings provide novel insights into river ecosystems, and highlight the overlooked small-sized microbiome in the environment.

Investigating potential auxiliary anaerobic digestion activity of phage under polyvinyl chloride microplastic stress

Polyvinyl chloride (PVC) microplastics present in sewage were trapped in sludge, thereby hindering anaerobic digestion performance of waste active sludge (WAS). Phages regulate virocell metabolism by encoding auxiliary metabolic genes (AMGs) related to energy acquisition and material degradation, supporting hosts survive in harsh environments and play a crucial role in biogeochemical cycles. This study investigated the potential effects of phages on the recovery of WAS anaerobic digestion under PVC stress. We observed a significant alteration in the phage community induced by PVC microplastics. Phages encoded AMGs related to anaerobic digestion and cell growth probably alleviate PVC microplastics inhibition on WAS anaerobic digestion, and 54.2 % of hydrolysis-related GHs and 40.8 % of acidification-related AMGs were actively transcribed in the PVC-exposed group. Additionally, the degradation of chitin and peptidoglycan during hydrolysis and the conversion of glucose to pyruvate during acidification were more susceptible to phages. Prediction of phage-host relationship indicated that the phyla Pseudomonadota were predominantly targeted hosts by hydrolysis-related and acidification-related phages, and PVC toxicity had minimal impact on phage-host interaction. Our findings highlight the importance of phages in anaerobic digestion and provide a novel strategy for using phages in the functional recovery of microplastic-exposed sludge.

Protein Set Transformer: A protein-based genome language model to power high diversity viromics

Exponential increases in microbial and viral genomic data demand transformational advances in scalable, generalizable frameworks for their interpretation. Standard homology-based functional analyses are hindered by the rapid divergence of microbial and especially viral genomes and proteins that significantly decreases the volume of usable data. Here, we present Protein Set Transformer (PST), a protein-based genome language model that models genomes as sets of proteins without considering sparsely available functional labels. Trained on >100k viruses, PST outperformed other homology- and language model-based approaches for relating viral genomes based on shared protein content. Further, PST demonstrated protein structural and functional awareness by clustering capsid-fold-containing proteins with known capsid proteins and uniquely clustering late gene proteins within related viruses. Our data establish PST as a valuable method for diverse viral genomics, ecology, and evolutionary applications. We posit that the PST framework can be a foundation model for microbial genomics when trained on suitable data.

Viromic and Metagenomic Analyses of Commercial Spirulina Fermentations Reveal Remarkable Microbial Diversity

Commercially produced cyanobacteria preparations sold under the name spirulina are widely consumed, due to their traditional use as a nutrient-rich foodstuff and subsequent marketing as a superfood. Despite their popularity, the microbial composition of ponds used to cultivate these bacteria is understudied. A total of 19 pond samples were obtained from small-scale spirulina farms and subjected to metagenome and/or virome sequencing, and the results were analysed. A remarkable level of prokaryotic and viral diversity was found to be present in the ponds, with Limnospira sp. and Arthrospira sp. sometimes being notably scarce. A detailed breakdown of prokaryotic and viral components of 15 samples is presented. Twenty putative Limnospira sp.-infecting bacteriophage contigs were identified, though no correlation between the performance of these cultures and the presence of phages was found. The high diversity of these samples prevented the identification of clear trends in sample performance over time, between ponds or when comparing successful and failed fermentations.

Isolation and identification of specific Enterococcus faecalis phage C-3 and G21-7 against Avian pathogenic Escherichia coli and its application to one-day-old geese

Colibacillosis caused by Avian pathogenic Escherichia coli (APEC), including peritonitis, respiratory tract inflammation and ovaritis, is recognized as one of the most common and economically destructive bacterial diseases in poultry worldwide. In this study, the characteristics and inhibitory potential of phages were investigated by double-layer plate method, transmission electron microscopy, whole genome sequencing, bioinformatics analysis and animal experiments. The results showed that phages C-3 and G21-7 isolated from sewage around goose farms infected multiple O serogroups (O1, O2, O18, O78, O157, O26, O145, O178, O103 and O104) Escherichia coli (E.coli) with a multiplicity of infection (MOI) of 10 and 1, respectively. According to the one-step growth curve, the incubation time of both bacteriophage C-3 and G21-7 was 10 min. Sensitivity tests confirmed that C-3 and G21-6 are stable at 4 to 50 °C and pH in the range of 4 to 11. Based on morphological and phylogenetic analysis, phages C-3 and G21-7 belong to Enterococcus faecalis (E. faecalis) phage species of the genus Saphexavirus of Herelleviridae family. According to genomic analysis, phage C-3 and G21-7 were 58,097 bp and 57,339 bp in size, respectively, with G+C content of 39.91% and 39.99%, encoding proteins of 97 CDS (105 to 3,993 bp) and 96 CDS (105 to 3,993 bp), and both contained 2 tRNAs. Both phages contained two tail proteins and holin-endolysin system coding genes, and neither carried resistance genes nor virulence factors. Phage mixture has a good safety profile and has shown good survival probability and feed efficiency in both treatment and prophylaxis experiments with one-day-old goslings. These results suggest that phage C-3 and G21-7 can be used as potential antimicrobials for the prevention and treatment of APEC.

Characterizing the gut phageome and phage-borne antimicrobial resistance genes in pigs

BACKGROUND

Mammalian intestine harbors a mass of phages that play important roles in maintaining gut microbial ecosystem and host health. Pig has become a common model for biomedical research and provides a large amount of meat for human consumption. However, the knowledge of gut phages in pigs is still limited.

RESULTS

Here, we investigated the gut phageome in 112 pigs from seven pig breeds using PhaBOX strategy based on the metagenomic data. A total of 174,897 non-redundant gut phage genomes were assembled from 112 metagenomes. A total of 33,487 gut phage genomes were classified and these phages mainly belonged to phage families such as Ackermannviridae, Straboviridae, Peduoviridae, Zierdtviridae, Drexlerviridae, and Herelleviridae. The gut phages in seven pig breeds exhibited distinct communities and the gut phage communities changed with the age of pig. These gut phages were predicted to infect a broad range of 212 genera of prokaryotes, such as Candidatus Hamiltonella, Mycoplasma, Colwellia, and Lactobacillus. The data indicated that broad KEGG and CAZy functions were also enriched in gut phages of pigs. The gut phages also carried the antimicrobial resistance genes (ARGs) and the most abundant antimicrobial resistance genotype was diaminopyrimidine resistance.

CONCLUSIONS

Our research delineates a landscape for gut phages in seven pig breeds and reveals that gut phages serve as a key reservoir of ARGs in pigs. Video Abstract.

Prediction of Klebsiella phage-host specificity at the strain level

Phages are increasingly considered promising alternatives to target drug-resistant bacterial pathogens. However, their often-narrow host range can make it challenging to find matching phages against bacteria of interest. Current computational tools do not accurately predict interactions at the strain level in a way that is relevant and properly evaluated for practical use. We present PhageHostLearn, a machine learning system that predicts strain-level interactions between receptor-binding proteins and bacterial receptors for Klebsiella phage-bacteria pairs. We evaluate this system both in silico and in the laboratory, in the clinically relevant setting of finding matching phages against bacterial strains. PhageHostLearn reaches a cross-validated ROC AUC of up to 81.8% in silico and maintains this performance in laboratory validation. Our approach provides a framework for developing and evaluating phage-host prediction methods that are useful in practice, which we believe to be a meaningful contribution to the machine-learning-guided development of phage therapeutics and diagnostics.

Insights into phage-bacteria interaction in cold seep Gigantidas platifrons through metagenomics and transcriptome analyses

Viruses are crucial for regulating deep-sea microbial communities and biogeochemical cycles. However, their roles are still less characterized in deep-sea holobionts. Bathymodioline mussels are endemic species inhabiting cold seeps and harboring endosymbionts in gill epithelial cells for nutrition. This study unveiled a diverse array of viruses in the gill tissues of Gigantidas platifrons mussels and analyzed the viral metagenome and transcriptome from the gill tissues of Gigantidas platifrons mussels collected from a cold seep in the South Sea. The mussel gills contained various viruses including Baculoviridae, Rountreeviridae, Myoviridae and Siphovirdae, but the active viromes were Myoviridae, Siphoviridae, and Podoviridae belonging to the order Caudovirales. The overall viral community structure showed significant variation among environments with different methane concentrations. Transcriptome analysis indicated high expression of viral structural genes, integrase, and restriction endonuclease genes in a high methane concentration environment, suggesting frequent virus infection and replication. Furthermore, two viruses (GP-phage-contig14 and GP-phage-contig72) interacted with Gigantidas platifrons methanotrophic gill symbionts (bathymodiolin mussels host intracellular methanotrophic Gammaproteobacteria in their gills), showing high expression levels, and have huge different expression in different methane concentrations. Additionally, single-stranded DNA viruses may play a potential auxiliary role in the virus-host interaction using indirect bioinformatics methods. Moreover, the Cro and DNA methylase genes had phylogenetic similarity between the virus and Gigantidas platifrons methanotrophic gill symbionts. This study also explored a variety of viruses in the gill tissues of Gigantidas platifrons and revealed that bacteria interacted with the viruses during the symbiosis with Gigantidas platifrons. This study provides fundamental insights into the interplay of microorganisms within Gigantidas platifrons mussels in deep sea.

Identification and classification of the genomes of novel microviruses in poultry slaughterhouse

Microviridae is a family of phages with circular ssDNA genomes and they are widely found in various environments and organisms. In this study, virome techniques were employed to explore potential members of Microviridae in a poultry slaughterhouse, leading to the identification of 98 novel and complete microvirus genomes. Using a similarity clustering network classification approach, these viruses were found to belong to at least 6 new subfamilies within Microviridae and 3 higher-level taxonomic units. Genome size, GC content and genome structure of these new taxa showed evident regularities, validating the rationality of our classification method. Our method can divide microviruses into about 45 additional detailed clusters, which may serve as a new standard for classifying Microviridae members. Furthermore, by addressing the scarcity of host information for microviruses, the current study significantly broadened their host range and discovered over 20 possible new hosts, including important pathogenic bacteria such as Helicobacter pylori and Vibrio cholerae, as well as different taxa demonstrated different host specificities. The findings of this study effectively expand the diversity of the Microviridae family, providing new insights for their classification and identification. Additionally, it offers a novel perspective for monitoring and controlling pathogenic microorganisms in poultry slaughterhouse environments.

Correlation between the gut microbiome and neurodegenerative diseases: a review of metagenomics evidence

A growing body of evidence suggests that the gut microbiota contributes to the development of neurodegenerative diseases via the microbiota-gut-brain axis. As a contributing factor, microbiota dysbiosis always occurs in pathological changes of neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. High-throughput sequencing technology has helped to reveal that the bidirectional communication between the central nervous system and the enteric nervous system is facilitated by the microbiota's diverse microorganisms, and for both neuroimmune and neuroendocrine systems. Here, we summarize the bioinformatics analysis and wet-biology validation for the gut metagenomics in neurodegenerative diseases, with an emphasis on multi-omics studies and the gut virome. The pathogen-associated signaling biomarkers for identifying brain disorders and potential therapeutic targets are also elucidated. Finally, we discuss the role of diet, prebiotics, probiotics, postbiotics and exercise interventions in remodeling the microbiome and reducing the symptoms of neurodegenerative diseases.

A Metagenomic Investigation of Potential Health Risks and Element Cycling Functions of Bacteria and Viruses in Wastewater Treatment Plants

The concentration of viruses in sewage sludge is significantly higher (10-1000-fold) than that found in natural environments, posing a potential risk for human and animal health. However, the composition of these viruses and their role in the transfer of pathogenic factors, as well as their role in the carbon, nitrogen, and phosphorus cycles remain poorly understood. In this study, we employed a shotgun metagenomic approach to investigate the pathogenic bacteria and viral composition and function in two wastewater treatment plants located on a campus. Our analysis revealed the presence of 1334 amplicon sequence variants (ASVs) across six sludge samples, with 242 ASVs (41.22% of total reads) identified as pathogenic bacteria. Arcobacter was found to be the most dominant pathogen accounting for 6.79% of total reads. The virome analysis identified 613 viral genera with Aorunvirus being the most abundant genus at 41.85%. Approximately 0.66% of these viruses were associated with human and animal diseases. More than 60% of the virome consisted of lytic phages. Host prediction analysis revealed that the phages primarily infected Lactobacillus (37.11%), Streptococcus (21.11%), and Staphylococcus (7.11%). Furthermore, our investigation revealed an abundance of auxiliary metabolic genes (AMGs) involved in carbon, nitrogen, and phosphorus cycling within the virome. We also detected a total of 113 antibiotic resistance genes (ARGs), covering major classes of antibiotics across all samples analyzed. Additionally, our findings indicated the presence of virulence factors including the clpP gene accounting for approximately 4.78%, along with toxin genes such as the RecT gene representing approximately 73.48% of all detected virulence factors and toxin genes among all samples analyzed. This study expands our understanding regarding both pathogenic bacteria and viruses present within sewage sludge while providing valuable insights into their ecological functions.

The gut ileal mucosal virome is disturbed in patients with Crohn's disease and exacerbates intestinal inflammation in mice

Gut bacteriome dysbiosis is known to be implicated in the pathogenesis of inflammatory bowel disease (IBD). Crohn's disease (CD) is an IBD subtype with extensive mucosal inflammation, yet the mucosal virome, an empirical modulator of the bacteriome and mucosal immunity, remains largely unclear regarding its composition and role. Here, we exploited trans-cohort CD patients and healthy individuals to compositionally and functionally investigate the small bowel (terminal ileum) virome and bacteriome. The CD ileal virome was characterised by an under-representation of both lytic and temperate bacteriophages (especially those targeting bacterial pathogens), particularly in patients with flare-up. Meanwhile, the virome-bacteriome ecology in CD ileal mucosa was featured by a lack of Bifidobacterium- and Lachnospiraceae-led mutualistic interactions between bacteria and bacteriophages; surprisingly it was more pronounced in CD remission than flare-up, underlining the refractory and recurrent nature of mucosal inflammation in CD. Lastly, we substantiated that ileal virions from CD patients causally exacerbated intestinal inflammation in IBD mouse models, by reshaping a gut virome-bacteriome ecology preceding intestinal inflammation (microbial trigger) and augmenting microbial sensing/defence pathways in the intestine cells (host response). Altogether, our results highlight the significance of mucosal virome in CD pathogenesis and importance of mucosal virome restoration in CD therapeutics.

Biological and bioinformatic tools for the discovery of unknown phage-host combinations

The field of microbial ecology has been transformed by metagenomics in recent decades and has culminated in vast datasets that facilitate the bioinformatic dissection of complex microbial communities. Recently, attention has turned from defining the microbiota composition to the interactions and relationships that occur between members of the microbiota. Within complex microbiota, the identification of bacteriophage-host combinations has been a major challenge. Recent developments in artificial intelligence tools to predict protein structure and function as well as the relationships between bacteria and their infecting bacteriophages allow a strategic approach to identifying and validating phage-host relationships. However, biological validation of these predictions remains essential and will serve to improve the existing predictive tools. In this review, I provide an overview of the most recent developments in both bioinformatic and experimental approaches to predicting and experimentally validating unknown phage-host combinations.

RNAVirHost: a machine learning-based method for predicting hosts of RNA viruses through viral genomes

BACKGROUND

The high-throughput sequencing technologies have revolutionized the identification of novel RNA viruses. Given that viruses are infectious agents, identifying hosts of these new viruses carries significant implications for public health and provides valuable insights into the dynamics of the microbiome. However, determining the hosts of these newly discovered viruses is not always straightforward, especially in the case of viruses detected in environmental samples. Even for host-associated samples, it is not always correct to assign the sample origin as the host of the identified viruses. The process of assigning hosts to RNA viruses remains challenging due to their high mutation rates and vast diversity.

RESULTS

In this study, we introduce RNAVirHost, a machine learning-based tool that predicts the hosts of RNA viruses solely based on viral genomes. RNAVirHost is a hierarchical classification framework that predicts hosts at different taxonomic levels. We demonstrate the superior accuracy of RNAVirHost in predicting hosts of RNA viruses through comprehensive comparisons with various state-of-the-art techniques. When applying to viruses from novel genera, RNAVirHost achieved the highest accuracy of 84.3%, outperforming the alignment-based strategy by 12.1%.

CONCLUSIONS

The application of machine learning models has proven beneficial in predicting hosts of RNA viruses. By integrating genomic traits and sequence homologies, RNAVirHost provides a cost-effective and efficient strategy for host prediction. We believe that RNAVirHost can greatly assist in RNA virus analyses and contribute to pandemic surveillance.

IPEV: identification of prokaryotic and eukaryotic virus-derived sequences in virome using deep learning

BACKGROUND

The virome obtained through virus-like particle enrichment contains a mixture of prokaryotic and eukaryotic virus-derived fragments. Accurate identification and classification of these elements are crucial to understanding their roles and functions in microbial communities. However, the rapid mutation rates of viral genomes pose challenges in developing high-performance tools for classification, potentially limiting downstream analyses.

FINDINGS

We present IPEV, a novel method to distinguish prokaryotic and eukaryotic viruses in viromes, with a 2-dimensional convolutional neural network combining trinucleotide pair relative distance and frequency. Cross-validation assessments of IPEV demonstrate its state-of-the-art precision, significantly improving the F1-score by approximately 22% on an independent test set compared to existing methods when query viruses share less than 30% sequence similarity with known viruses. Furthermore, IPEV outperforms other methods in accuracy on marine and gut virome samples based on annotations by sequence alignments. IPEV reduces runtime by at most 1,225 times compared to existing methods under the same computing configuration. We also utilized IPEV to analyze longitudinal samples and found that the gut virome exhibits a higher degree of temporal stability than previously observed in persistent personal viromes, providing novel insights into the resilience of the gut virome in individuals.

CONCLUSIONS

IPEV is a high-performance, user-friendly tool that assists biologists in identifying and classifying prokaryotic and eukaryotic viruses within viromes. The tool is available at https://github.com/basehc/IPEV.

Identifying ARG-carrying bacteriophages in a lake replenished by reclaimed water using deep learning techniques

As important mobile genetic elements, phages support the spread of antibiotic resistance genes (ARGs). Previous analyses of metaviromes or metagenome-assembled genomes (MAGs) failed to assess the extent of ARGs transferred by phages, particularly in the generation of antibiotic pathogens. Therefore, we have developed a bioinformatic pipeline that utilizes deep learning techniques to identify ARG-carrying phages and predict their hosts, with a special focus on pathogens. Using this method, we discovered that the predominant types of ARGs carried by temperate phages in a typical landscape lake, which is fully replenished by reclaimed water, were related to multidrug resistance and β-lactam antibiotics. MAGs containing virulent factors (VFs) were predicted to serve as hosts for these ARG-carrying phages, which suggests that the phages may have the potential to transfer ARGs. In silico analysis showed a significant positive correlation between temperate phages and host pathogens (R = 0.503, p < 0.001), which was later confirmed by qPCR. Interestingly, these MAGs were found to be more abundant than those containing both ARGs and VFs, especially in December and March. Seasonal variations were observed in the abundance of phages harboring ARGs (from 5.62 % to 21.02 %) and chromosomes harboring ARGs (from 18.01 % to 30.94 %). In contrast, the abundance of plasmids harboring ARGs remained unchanged. In summary, this study leverages deep learning to analyze phage-transferred ARGs and demonstrates an alternative method to track the production of potential antibiotic-resistant pathogens by metagenomics that can be extended to microbiological risk assessment.

Bacteriophages: Vectors of or weapons against the transmission of antibiotic resistance genes in hospital wastewater systems?

Antimicrobial resistance poses a serious threat to human health and is responsible for the death of millions of people annually. Hospital wastewater is an important hotspot for antibiotic-resistance genes (ARGs) and antibiotic-resistant bacteria (ARB). However, little is known about the relationship between phages and ARGs in hospital wastewater systems (HWS). In the present study, the viral diversity of 12 HWSs using data from public metagenomic databases was investigated. Viruses were widely found in both the influent and effluent of each HWS. A total of 45 unique ARGs were carried by 85 viral contigs, which accounted for only 0.14% of the total viral populations, implying that ARGs were not commonly present in phages. Three efflux pump genes were identified as shared between phages and bacterial genomes. However, the predominant types of ARGs in HWS such as aminoglycoside- and beta-lactam-resistance genes were rarely found in phages. Based on CRISPR spacer and tRNA matches, interactions between 171 viral contigs and 60 antibiotic-resistant genomes were predicted, including interactions involving phages and vancomycin-resistant Enterococcus_B faecium or beta-lactam-resistant Klebsiella pneumoniae. More than half (56.1%) of these viral contigs indicated lytic and none of them carried ARGs. As the vOTUs in this study had few ARGs and were primarily lytic, HWS may be a valuable source for phage discovery. Future studies will be able to experimentally validate these sequence-based results to confirm the suitability of HWS phages for pathogen control measures in wastewater.

Prokaryotic virus host prediction with graph contrastive augmentaion

Prokaryotic viruses, also known as bacteriophages, play crucial roles in regulating microbial communities and have the potential for phage therapy applications. Accurate prediction of phage-host interactions is essential for understanding the dynamics of these viruses and their impacts on bacterial populations. Numerous computational methods have been developed to tackle this challenging task. However, most existing prediction models can be constrained due to the substantial number of unknown interactions in comparison to the constrained diversity of available training data. To solve the problem, we introduce a model for prokaryotic virus host prediction with graph contrastive augmentation (PHPGCA). Specifically, we construct a comprehensive heterogeneous graph by integrating virus-virus protein similarity and virus-host DNA sequence similarity information. As the backbone encoder for learning node representations in the virus-prokaryote graph, we employ LGCN, a state-of-the-art graph embedding technique. Additionally, we apply graph contrastive learning to augment the node representations without the need for additional labels. We further conducted two case studies aimed at predicting the host range of multi-species phages, helping to understand the phage ecology and evolution.

Enhanced Bacterium-Phage Symbiosis in Attached Microbial Aggregates on a Membrane Surface Facing Elevated Hydraulic Stress

Phages are increasingly recognized for their importance in microbial aggregates, including their influence on microbial ecosystem services and biotechnology applications. However, the adaptive strategies and ecological functions of phages in different aggregates remain largely unexplored. Herein, we used membrane bioreactors to investigate bacterium-phage interactions and related microbial functions within suspended and attached microbial aggregates (SMA vs AMA). SMA and AMA represent distinct microbial habitats where bacterial communities display distinct patterns in terms of dominant species, keystone species, and bacterial networks. However, bacteria and phages in both aggregates exhibited high lysogenicity, with 60% lysogenic phages in the virome and 70% lysogenic metagenome-assembled genomes of bacteria. Moreover, substantial phages exhibited broad host ranges (34% in SMA and 42% in AMA) and closely interacted with habitat generalist species (43% in SMA and 49% in AMA) as adaptive strategies in stressful operation environments. Following a mutualistic pattern, phage-carried auxiliary metabolic genes (pAMGs; 238 types in total) presumably contributed to the bacterial survival and aggregate stability. The SMA-pAMGs were mainly associated with energy metabolism, while the AMA-pAMGs were mainly associated with antioxidant biosynthesis and the synthesis of extracellular polymeric substances, representing habitat-dependent patterns. Overall, this study advanced our understanding of phage adaptive strategies in microbial aggregate habitats and emphasized the importance of bacterium-phage symbiosis in the stability of microbial aggregates.

Enhancing plant growth in biofertilizer-amended soil through nitrogen-transforming microbial communities

Biofertilizers have immense potential for enhancing agricultural productivity. However, there is still a need for clarification regarding the specific mechanisms through which these biofertilizers improve soil properties and stimulate plant growth. In this research, a bacterial agent was utilized to enhance plant growth and investigate the microbial modulation mechanism of soil nutrient turnover using metagenomic technology. The results demonstrated a significant increase in soil fast-acting nitrogen (by 46.7%) and fast-acting phosphorus (by 88.6%) upon application of the bacterial agent. This finding suggests that stimulated soil microbes contribute to enhanced nutrient transformation, ultimately leading to improved plant growth. Furthermore, the application of the bacterial agent had a notable impact on the accumulation of key genes involved in nitrogen cycling. Notably, it enhanced nitrification genes (amo, hao, and nar), while denitrification genes (nir and nor) showed a slight decrease. This indicates that ammonium oxidation may be the primary pathway for increasing fast-acting nitrogen in soils. Additionally, the bacterial agent influenced the composition and functional structure of the soil microbial community. Moreover, the metagenome-assembled genomes (MAGs) obtained from the soil microbial communities exhibited complementary metabolic processes, suggesting mutual nutrient exchange. These MAGs contained widely distributed and highly abundant genes encoding plant growth promotion (PGP) traits. These findings emphasize how soil microbial communities can enhance vegetation growth by increasing nutrient availability and regulating plant hormone production. This effect can be further enhanced by introducing inoculated microbial agents. In conclusion, this study provides novel insights into the mechanisms underlying the beneficial effects of biofertilizers on soil properties and plant growth. The significant increase in nutrient availability, modulation of key genes involved in nitrogen cycling, and the presence of MAGs encoding PGP traits highlight the potential of biofertilizers to improve agricultural practices. These findings have important implications for enhancing agricultural sustainability and productivity, with positive societal and environmental impacts.

Zero-shot-capable identification of phage-host relationships with whole-genome sequence representation by contrastive learning

Accurately identifying phage-host relationships from their genome sequences is still challenging, especially for those phages and hosts with less homologous sequences. In this work, focusing on identifying the phage-host relationships at the species and genus level, we propose a contrastive learning based approach to learn whole-genome sequence embeddings that can take account of phage-host interactions (PHIs). Contrastive learning is used to make phages infecting the same hosts close to each other in the new representation space. Specifically, we rephrase whole-genome sequences with frequency chaos game representation (FCGR) and learn latent embeddings that 'encapsulate' phages and host relationships through contrastive learning. The contrastive learning method works well on the imbalanced dataset. Based on the learned embeddings, a proposed pipeline named CL4PHI can predict known hosts and unseen hosts in training. We compare our method with two recently proposed state-of-the-art learning-based methods on their benchmark datasets. The experiment results demonstrate that the proposed method using contrastive learning improves the prediction accuracy on known hosts and demonstrates a zero-shot prediction capability on unseen hosts. In terms of potential applications, the rapid pace of genome sequencing across different species has resulted in a vast amount of whole-genome sequencing data that require efficient computational methods for identifying phage-host interactions. The proposed approach is expected to address this need by efficiently processing whole-genome sequences of phages and prokaryotic hosts and capturing features related to phage-host relationships for genome sequence representation. This approach can be used to accelerate the discovery of phage-host interactions and aid in the development of phage-based therapies for infectious diseases.

PhaBOX: a web server for identifying and characterizing phage contigs in metagenomic data

Motivation

There is accumulating evidence showing the important roles of bacteriophages (phages) in regulating the structure and functions of the microbiome. However, lacking an easy-to-use and integrated phage analysis software hampers microbiome-related research from incorporating phages in the analysis.

Results

In this work, we developed a web server, PhaBOX, which can comprehensively identify and analyze phage contigs in metagenomic data. It supports integrated phage analysis, including phage contig identification from the metagenomic assembly, lifestyle prediction, taxonomic classification, and host prediction. Instead of treating the algorithms as a black box, PhaBOX also supports visualization of the essential features for making predictions. The web server is designed with a user-friendly graphical interface that enables both informatics-trained and nonspecialist users to analyze phages in microbiome data with ease.

Availability and implementation

The web server of PhaBOX is available via: https://phage.ee.cityu.edu.hk. The source code of PhaBOX is available at: https://github.com/KennthShang/PhaBOX.

The gut virome and the relevance of temperate phages in human health

Alterations in the gut virome impact human health. Bacteriophages, viruses that infect bacteria, dominate the gut virome and are mainly composed by virulent and temperate phages. While virulent phages exclusively replicate within and lyse their bacterial host's cell, temperate phages switch from an integrated state residing within their bacterial host's chromosome to an induced free virion state via an induction event. How often do these induction events occur and what are their implications on gut homeostasis? Here, we summarize the current knowledge of the gut virome based on metagenomics and present how the proportion of induced temperate phages varies amongst individuals, age, and disease states. Finally, we highlight the importance of building upon classical culture-dependent techniques and sequencing approaches to improve our understanding of temperate phages to enable their potential therapeutic use.

PhaVIP: Phage VIrion Protein classification based on chaos game representation and Vision Transformer

MOTIVATION

As viruses that mainly infect bacteria, phages are key players across a wide range of ecosystems. Analyzing phage proteins is indispensable for understanding phages' functions and roles in microbiomes. High-throughput sequencing enables us to obtain phages in different microbiomes with low cost. However, compared to the fast accumulation of newly identified phages, phage protein classification remains difficult. In particular, a fundamental need is to annotate virion proteins, the structural proteins, such as major tail, baseplate, etc. Although there are experimental methods for virion protein identification, they are too expensive or time-consuming, leaving a large number of proteins unclassified. Thus, there is a great demand to develop a computational method for fast and accurate phage virion protein (PVP) classification.

RESULTS

In this work, we adapted the state-of-the-art image classification model, Vision Transformer, to conduct virion protein classification. By encoding protein sequences into unique images using chaos game representation, we can leverage Vision Transformer to learn both local and global features from sequence "images". Our method, PhaVIP, has two main functions: classifying PVP and non-PVP sequences and annotating the types of PVP, such as capsid and tail. We tested PhaVIP on several datasets with increasing difficulty and benchmarked it against alternative tools. The experimental results show that PhaVIP has superior performance. After validating the performance of PhaVIP, we investigated two applications that can use the output of PhaVIP: phage taxonomy classification and phage host prediction. The results showed the benefit of using classified proteins over all proteins.

AVAILABILITY AND IMPLEMENTATION

The web server of PhaVIP is available via: https://phage.ee.cityu.edu.hk/phavip. The source code of PhaVIP is available via: https://github.com/KennthShang/PhaVIP.

Advances in the field of phage-based therapy with special emphasis on computational resources

In the current era, one of the major challenges is to manage the treatment of drug/antibiotic-resistant strains of bacteria. Phage therapy, a century-old technique, may serve as an alternative to antibiotics in treating bacterial infections caused by drug-resistant strains of bacteria. In this review, a systematic attempt has been made to summarize phage-based therapy in depth. This review has been divided into the following two sections: general information and computer-aided phage therapy (CAPT). In the case of general information, we cover the history of phage therapy, the mechanism of action, the status of phage-based products (approved and clinical trials) and the challenges. This review emphasizes CAPT, where we have covered primary phage-associated resources, phage prediction methods and pipelines. This review covers a wide range of databases and resources, including viral genomes and proteins, phage receptors, host genomes of phages, phage-host interactions and lytic proteins. In the post-genomic era, identifying the most suitable phage for lysing a drug-resistant strain of bacterium is crucial for developing alternate treatments for drug-resistant bacteria and this remains a challenging problem. Thus, we compile all phage-associated prediction methods that include the prediction of phages for a bacterial strain, the host for a phage and the identification of interacting phage-host pairs. Most of these methods have been developed using machine learning and deep learning techniques. This review also discussed recent advances in the field of CAPT, where we briefly describe computational tools available for predicting phage virions, the life cycle of phages and prophage identification. Finally, we describe phage-based therapy's advantages, challenges and opportunities.

PhaTYP: predicting the lifestyle for bacteriophages using BERT

Bacteriophages (or phages), which infect bacteria, have two distinct lifestyles: virulent and temperate. Predicting the lifestyle of phages helps decipher their interactions with their bacterial hosts, aiding phages' applications in fields such as phage therapy. Because experimental methods for annotating the lifestyle of phages cannot keep pace with the fast accumulation of sequenced phages, computational method for predicting phages' lifestyles has become an attractive alternative. Despite some promising results, computational lifestyle prediction remains difficult because of the limited known annotations and the sheer amount of sequenced phage contigs assembled from metagenomic data. In particular, most of the existing tools cannot precisely predict phages' lifestyles for short contigs. In this work, we develop PhaTYP (Phage TYPe prediction tool) to improve the accuracy of lifestyle prediction on short contigs. We design two different training tasks, self-supervised and fine-tuning tasks, to overcome lifestyle prediction difficulties. We rigorously tested and compared PhaTYP with four state-of-the-art methods: DeePhage, PHACTS, PhagePred and BACPHLIP. The experimental results show that PhaTYP outperforms all these methods and achieves more stable performance on short contigs. In addition, we demonstrated the utility of PhaTYP for analyzing the phage lifestyle on human neonates' gut data. This application shows that PhaTYP is a useful means for studying phages in metagenomic data and helps extend our understanding of microbial communities.

Expansion of Colorectal Cancer Biomarkers Based on Gut Bacteria and Viruses

Simple Summary

The current study identified microbial (including bacterial and viral) diagnostic models that could discriminate colorectal tumor patients from healthy controls, expanding the potential biomarkers for colorectal tumors. A combination of five colorectal cancer-associated gut bacteria was identified in this study for the discrimination of colorectal cancer patients from healthy controls, with verifiable performance in multiple cohorts. The gene pathways regulated by aberrant gut bacteria were also identified, providing possible directions for studying bacterial carcinogenesis mechanisms. Furthermore, this study revealed the potential interactions of gut bacteria with viruses and within bacteria in adenoma-carcinoma sequences, which may extend our understanding of dysbiosis in colorectal carcinogenesis.

Abstract

The alterations in gut bacteria are closely related to colorectal cancer. However, studies on adenoma are still scarce. Besides, the associations of gut viruses with colorectal tumor, and the interactions of bacteria with viruses in colorectal tumors are still under exploration. Therefore, a metagenomic sequencing of stool samples from patients with colorectal adenoma (CRA), colorectal cancer (CRC), and healthy controls was performed to identify changes in gut microbiome in patients with colorectal tumors. Five CRC-enriched bacteria (Peptostreptococcus stomatis, Clostridium symbiosum, Hungatella hathewayi, Parvimonas micra, and Gemella morbillorum) were identified as a diagnostic model to identify CRC patients, and the efficacy of the diagnostic model was verifiable in 1523 metagenomic samples from ten cohorts of eight different countries. We identified the positive association of Bacteroides fragilis with PD-L1 expression and PD-1 checkpoint pathway, providing a possible direction for studying bacterial carcinogenesis mechanisms. Furthermore, the increased interactions within the microbiome in patients may play roles in the development of CRC. In conclusion, this study identified novel microbiota combinations with discrimination for colorectal tumor, and revealed the potential interactions of gut bacteria with viruses in the adenoma-carcinoma sequence, which implies that the microbiome, but not only bacteria, should be paid more attention in further studies.

Comparative Genomics of Closely-Related Gordonia Cluster DR Bacteriophages

Bacteriophages infecting bacteria of the genus Gordonia have increasingly gained interest in the scientific community for their diverse applications in agriculture, biotechnology, and medicine, ranging from biocontrol agents in wastewater management to the treatment of opportunistic pathogens in pulmonary disease patients. However, due to the time and costs associated with experimental isolation and cultivation, host ranges for many bacteriophages remain poorly characterized, hindering a more efficient usage of bacteriophages in these areas. Here, we perform a series of computational genomic inferences to predict the putative host ranges of all Gordonia cluster DR bacteriophages known to date. Our analyses suggest that BiggityBass (as well as several of its close relatives) is likely able to infect host bacteria from a wide range of genera-from Gordonia to Nocardia to Rhodococcus, making it a suitable candidate for future phage therapy and wastewater treatment strategies.