Substantial viral diversity in bats and rodents from East Africa: insights into evolution, recombination, and cocirculation.

BACKGROUND: Zoonotic viruses cause substantial public health and socioeconomic problems worldwide. Understanding how viruses evolve and spread within and among wildlife species is a critical step when aiming for proactive identification of viral threats to prevent future pandemics. Despite the many proposed factors influencing viral diversity, the genomic diversity and structure of viral communities in East Africa are largely unknown. RESULTS: Using 38.3 Tb of metatranscriptomic data obtained via ultradeep sequencing, we screened vertebrate-associated viromes from 844 bats and 250 rodents from Kenya and Uganda collected from the wild. The 251 vertebrate-associated viral genomes of bats (212) and rodents (39) revealed the vast diversity, host-related variability, and high geographic specificity of viruses in East Africa. Among the surveyed viral families, Coronaviridae and Circoviridae showed low host specificity, high conservation of replication-associated proteins, high divergence among viral entry proteins, and frequent recombination. Despite major dispersal limitations, recurrent mutations, cocirculation, and occasional gene flow contribute to the high local diversity of viral genomes. CONCLUSIONS: The present study not only shows the landscape of bat and rodent viromes in this zoonotic hotspot but also reveals genomic signatures driven by the evolution and dispersal of the viral community, laying solid groundwork for future proactive surveillance of emerging zoonotic pathogens in wildlife. Video Abstract.

Identifying Phage Sequences From Metagenomic Data Using Deep Neural Network With Word Embedding and Attention Mechanism.

Phages are the functional viruses that infect bacteria and they play important roles in microbial communities and ecosystems. Phage research has attracted great attention due to the wide applications of phage therapy in treating bacterial infection in recent years. Metagenomics sequencing technique can sequence microbial communities directly from an environmental sample. Identifying phage sequences from metagenomic data is a vital step in the downstream of phage analysis. However, the existing methods for phage identification suffer from some limitations in the utilization of the phage feature for prediction, and therefore their prediction performance still need to be improved further. In this article, we propose a novel deep neural network (called MetaPhaPred) for identifying phages from metagenomic data. In MetaPhaPred, we first use a word embedding technique to encode the metagenomic sequences into word vectors, extracting the latent feature vectors of DNA words. Then, we design a deep neural network with a convolutional neural network (CNN) to capture the feature maps in sequences, and with a bi-directional long short-term memory network (Bi-LSTM) to capture the long-term dependencies between features from both forward and backward directions. The feature map consists of a set of feature patterns, each of which is the weighted feature extracted by a convolution filter with convolution kernels in the CNN slide along the input feature vectors. Next, an attention mechanism is used to enhance contributions of important features. Experimental results on both simulated and real metagenomic data with different lengths demonstrate the superiority of the proposed MetaPhaPred over the state-of-the-art methods in identifying phage sequences.

Characterization of a novel genus of jumbo phages and their application in wastewater treatment.

Phages widely exist in numerous environments from wastewater to deep ocean, representing a huge virus diversity, yet remain poorly characterized. Among them, jumbo phages are of particular interests due to their large genome (>200 kb) and unusual biology. To date, only six strains of jumbo phages infecting Klebsiella pneumoniae have been described. Here, we report the isolation and characterization of two jumbo phages from hospital wastewater representing the sixth genus: φKp5130 and φKp9438. Both phages showed lytic activity against broad range of clinical antibiotic-resistant K. pneumoniae strains and distinct physiology including long latent period, small burst size, and high resistance to thermal and pH stress. The treatment of sewage water with the phages cocktail resulted in dramatic decline in K. pneumoniae population. Overall, this study provides detailed molecular and genomics characterization of two novel jumbo phages, expands viral diversity, and provides novel candidate phages to facilitate environmental wastewater treatment.

PhaGAA: an integrated web server platform for phage genome annotation and analysis.

MOTIVATION: Phage genome annotation plays a key role in the design of phage therapy. To date, there have been various genome annotation tools for phages, but most of these tools focus on mono-functional annotation and have complex operational processes. Accordingly, comprehensive and user-friendly platforms for phage genome annotation are needed. RESULTS: Here, we propose PhaGAA, an online integrated platform for phage genome annotation and analysis. By incorporating several annotation tools, PhaGAA is constructed to annotate the prophage genome at DNA and protein levels and provide the analytical results. Furthermore, PhaGAA could mine and annotate phage genomes from bacterial genome or metagenome. In summary, PhaGAA will be a useful resource for experimental biologists and help advance the phage synthetic biology in basic and application research. AVAILABILITY AND IMPLEMENTATION: PhaGAA is freely available at http://phage.xialab.info/.

Vertical and horizontal gene transfer tradeoffs direct plasmid fitness.

Plasmid fitness is directed by two orthogonal processes-vertical transfer through cell division and horizontal transfer through conjugation. When considered individually, improvements in either mode of transfer can promote how well a plasmid spreads and persists. Together, however, the metabolic cost of conjugation could create a tradeoff that constrains plasmid evolution. Here, we present evidence for the presence, consequences, and molecular basis of a conjugation-growth tradeoff across 40 plasmids derived from clinical Escherichia coli pathogens. We discover that most plasmids operate below a conjugation efficiency threshold for major growth effects, indicating strong natural selection for vertical transfer. Below this threshold, E. coli demonstrates a remarkable growth tolerance to over four orders of magnitude change in conjugation efficiency. This tolerance fades as nutrients become scarce and horizontal transfer attracts a greater share of host resources. Our results provide insight into evolutionary constraints directing plasmid fitness and strategies to combat the spread of antibiotic resistance.

DPProm: A Two-Layer Predictor for Identifying Promoters and Their Types on Phage Genome Using Deep Learning.

With the number of phage genomes increasing, it is urgent to develop new bioinformatics methods for phage genome annotation. Promoter, a DNA region, is important for gene transcriptional regulation. In the era of post-genomics, the availability of data makes it possible to establish computational models for promoter identification with robustness. In this work, we introduce DPProm, a two-layer model composed of DPProm-1L and DPProm-2L, to predict promoters and their types for phages. On the first layer, as a dual-channel deep neural network ensemble method fusing multi-view features (sequence feature and handcrafted feature), the model DPProm-1L is proposed to identify whether a DNA sequence is a promoter or non-promoter. The sequence feature is extracted with convolutional neural network (CNN). And the handcrafted feature is the combination of free energy, GC content, cumulative skew, and Z curve features. On the second layer, DPProm-2L based on CNN is trained to predict the promoters' types (host or phage). For the realization of prediction on the whole genomes, the model DPProm, combines with a novel sequence data processing workflow, which contains sliding window and merging sequences modules. Experimental results show that DPProm outperforms the state-of-the-art methods, and decreases the false positive rate effectively on whole genome prediction. Furthermore, we provide a user-friendly web at http://bioinfo.ahu.edu.cn/DPProm. We expect that DPProm can serve as a useful tool for identification of promoters and their types.

Advances and challenges in cataloging the human gut virome.

The human gut virome, which is often referred to as the "dark matter" of the gut microbiome, remains understudied. A better understanding of the composition and variations of the gut virome across populations is critical for exploring its impact on diseases and health. A series of advances in the characterization of human gut virome have unveiled high genetic diversity and various functional potentials of gut viruses. Here, we summarize the recently available human gut virome databases and discuss their features, procedures, and challenges with the intention to provide a reference to researchers to use while choosing a profiling database. We also propose a "best practice" for cataloging the viral population.

Harnessing stepping-stone hosts to engineer, select, and reboot synthetic bacteriophages in one pot.

Advances in synthetic genomics have led to a great demand for genetic manipulation. Trimming any process to simplify and accelerate streamlining of genetic code into life holds great promise for synthesizing and studying organisms. Here, we develop a simple but powerful stepping-stone strategy to promote genome refactoring of viruses in one pot, validated by successful cross-genus and cross-order rebooting of 90 phages infecting 4 orders of popular pathogens. Genomic sequencing suggests that rebooting outcome is associated with gene number and DNA polymerase availability within phage genomes. We integrate recombineering, screening, and rebooting processes in one pot and demonstrate genome assembly and genome editing of phages by stepping-stone hosts in an efficient and economic manner. Under this framework, in vitro assembly, yeast-based assembly, or genetic manipulation of native hosts are not required. As additional stepping-stone hosts are being developed, this framework will open doors for synthetic phages targeting more pathogens and commensals.

Genomic characterization of four novel bacteriophages infecting the clinical pathogen Klebsiella pneumoniae.

Bacteriophages are an invaluable source of novel genetic diversity. Sequencing of phage genomes can reveal new proteins with potential uses as biotechnological and medical tools, and help unravel the diversity of biological mechanisms employed by phages to take over the host during viral infection. Aiming to expand the available collection of phage genomes, we have isolated, sequenced, and assembled the genome sequences of four phages that infect the clinical pathogen Klebsiella pneumoniae: vB_KpnP_FBKp16, vB_KpnP_FBKp27, vB_KpnM_FBKp34, and Jumbo phage vB_KpnM_FBKp24. The four phages show very low (0-13%) identity to genomic phage sequences deposited in the GenBank database. Three of the four phages encode tRNAs and have a GC content very dissimilar to that of the host. Importantly, the genome sequences of the phages reveal potentially novel DNA packaging mechanisms as well as distinct clades of tubulin spindle and nucleus shell proteins that some phages use to compartmentalize viral replication. Overall, this study contributes to uncovering previously unknown virus diversity, and provides novel candidates for phage therapy applications against antibiotic-resistant K. pneumoniae infections.

Population Bottlenecks and Intra-host Evolution During Human-to-Human Transmission of SARS-CoV-2.

The emergence of the novel human coronavirus, SARS-CoV-2, causes a global COVID-19 (coronavirus disease 2019) pandemic. Here, we have characterized and compared viral populations of SARS-CoV-2 among COVID-19 patients within and across households. Our work showed an active viral replication activity in the human respiratory tract and the co-existence of genetically distinct viruses within the same host. The inter-host comparison among viral populations further revealed a narrow transmission bottleneck between patients from the same households, suggesting a dominated role of stochastic dynamics in both inter-host and intra-host evolutions.

Intra-host variation and evolutionary dynamics of SARS-CoV-2 populations in COVID-19 patients.

BACKGROUND: Since early February 2021, the causative agent of COVID-19, SARS-CoV-2, has infected over 104 million people with more than 2 million deaths according to official reports. The key to understanding the biology and virus-host interactions of SARS-CoV-2 requires the knowledge of mutation and evolution of this virus at both inter- and intra-host levels. However, despite quite a few polymorphic sites identified among SARS-CoV-2 populations, intra-host variant spectra and their evolutionary dynamics remain mostly unknown. METHODS: Using high-throughput sequencing of metatranscriptomic and hybrid captured libraries, we characterized consensus genomes and intra-host single nucleotide variations (iSNVs) of serial samples collected from eight patients with COVID-19. The distribution of iSNVs along the SARS-CoV-2 genome was analyzed and co-occurring iSNVs among COVID-19 patients were identified. We also compared the evolutionary dynamics of SARS-CoV-2 population in the respiratory tract (RT) and gastrointestinal tract (GIT). RESULTS: The 32 consensus genomes revealed the co-existence of different genotypes within the same patient. We further identified 40 intra-host single nucleotide variants (iSNVs). Most (30/40) iSNVs presented in a single patient, while ten iSNVs were found in at least two patients or identical to consensus variants. Comparing allele frequencies of the iSNVs revealed a clear genetic differentiation between intra-host populations from the respiratory tract (RT) and gastrointestinal tract (GIT), mostly driven by bottleneck events during intra-host migrations. Compared to RT populations, the GIT populations showed a better maintenance and rapid development of viral genetic diversity following the suspected intra-host bottlenecks. CONCLUSIONS: Our findings here illustrate the intra-host bottlenecks and evolutionary dynamics of SARS-CoV-2 in different anatomic sites and may provide new insights to understand the virus-host interactions of coronaviruses and other RNA viruses.

Genetic signatures for lineage/sublineage classification of HPV16, 18, 52 and 58 variants.

Increasing evidences indicate that high-risk HPV variants are heterogeneous in carcinogenicity and ethnic dispersion. In this work, we identified genetic signatures for convenient determination of lineage/sublineage of HPV16, 18, 52 and 58 variants. Using publicly available genomes, we found that E2 of HPV16, L2 of HPV18, L1 and LCR of HPV52, and L2, LCR and E1 of HPV58 contain the proper genetic signature for lineage/sublineage classification. Sets of hierarchical signature nucleotide positions were further confirmed for high accuracy (>95%) by classifying HPV genomes obtained from Chinese females, which included 117 HPV16 variants, 48 HPV18 variants, 117 HPV52 variants and 89 HPV58 variants. The circulation of HPV variants posing higher cancer risk in Eastern China, such as HPV16 A4 and HPV58 A3, calls for continuous surveillance in this region. The marker genes and signature nucleotide positions may facilitate cost-effective diagnostic detections of HPV variants in clinical settings.

Temporal encoding of bacterial identity and traits in growth dynamics.

In biology, it is often critical to determine the identity of an organism and phenotypic traits of interest. Whole-genome sequencing can be useful for this but has limited power for trait prediction. However, we can take advantage of the inherent information content of phenotypes to bypass these limitations. We demonstrate, in clinical and environmental bacterial isolates, that growth dynamics in standardized conditions can differentiate between genotypes, even among strains from the same species. We find that for pairs of isolates, there is little correlation between genetic distance, according to phylogenetic analysis, and phenotypic distance, as determined by growth dynamics. This absence of correlation underscores the challenge in using genomics to infer phenotypes and vice versa. Bypassing this complexity, we show that growth dynamics alone can robustly predict antibiotic responses. These findings are a foundation for a method to identify traits not easily traced to a genetic mechanism.

Multiple approaches for massively parallel sequencing of SARS-CoV-2 genomes directly from clinical samples.

BACKGROUND: COVID-19 (coronavirus disease 2019) has caused a major epidemic worldwide; however, much is yet to be known about the epidemiology and evolution of the virus partly due to the scarcity of full-length SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) genomes reported. One reason is that the challenges underneath sequencing SARS-CoV-2 directly from clinical samples have not been completely tackled, i.e., sequencing samples with low viral load often results in insufficient viral reads for analyses. METHODS: We applied a novel multiplex PCR amplicon (amplicon)-based and hybrid capture (capture)-based sequencing, as well as ultra-high-throughput metatranscriptomic (meta) sequencing in retrieving complete genomes, inter-individual and intra-individual variations of SARS-CoV-2 from serials dilutions of a cultured isolate, and eight clinical samples covering a range of sample types and viral loads. We also examined and compared the sensitivity, accuracy, and other characteristics of these approaches in a comprehensive manner. RESULTS: We demonstrated that both amplicon and capture methods efficiently enriched SARS-CoV-2 content from clinical samples, while the enrichment efficiency of amplicon outran that of capture in more challenging samples. We found that capture was not as accurate as meta and amplicon in identifying between-sample variations, whereas amplicon method was not as accurate as the other two in investigating within-sample variations, suggesting amplicon sequencing was not suitable for studying virus-host interactions and viral transmission that heavily rely on intra-host dynamics. We illustrated that meta uncovered rich genetic information in the clinical samples besides SARS-CoV-2, providing references for clinical diagnostics and therapeutics. Taken all factors above and cost-effectiveness into consideration, we proposed guidance for how to choose sequencing strategy for SARS-CoV-2 under different situations. CONCLUSIONS: This is, to the best of our knowledge, the first work systematically investigating inter- and intra-individual variations of SARS-CoV-2 using amplicon- and capture-based whole-genome sequencing, as well as the first comparative study among multiple approaches. Our work offers practical solutions for genome sequencing and analyses of SARS-CoV-2 and other emerging viruses.

Environmental and genetic determinants of plasmid mobility in pathogenic Escherichia coli.

Plasmids are key vehicles of horizontal gene transfer (HGT), mobilizing antibiotic resistance, virulence, and other traits among bacterial populations. The environmental and genetic forces that drive plasmid transfer are poorly understood, however, due to the lack of definitive quantification coupled with genomic analysis. Here, we integrate conjugative phenotype with plasmid genotype to provide quantitative analysis of HGT in clinical Escherichia coli pathogens. We find a substantial proportion of these pathogens (>25%) able to readily spread resistance to the most common classes of antibiotics. Antibiotics of varied modes of action had less than a 5-fold effect on conjugation efficiency in general, with one exception displaying 31-fold promotion upon exposure to macrolides and chloramphenicol. In contrast, genome sequencing reveals plasmid incompatibility group strongly correlates with transfer efficiency. Our findings offer new insights into the determinants of plasmid mobility and have implications for the development of treatments that target HGT.

Prophage Hunter: an integrative hunting tool for active prophages.

Identifying active prophages is critical for studying coevolution of phage and bacteria, investigating phage physiology and biochemistry, and engineering designer phages for diverse applications. We present Prophage Hunter, a tool aimed at hunting for active prophages from whole genome assembly of bacteria. Combining sequence similarity-based matching and genetic features-based machine learning classification, we developed a novel scoring system that exhibits higher accuracy than current tools in predicting active prophages on the validation datasets. The option of skipping similarity matching is also available so that there's higher chance for novel phages to be discovered. Prophage Hunter provides a one-stop web service to extract prophage genomes from bacterial genomes, evaluate the activity of the prophages, identify phylogenetically related phages, and annotate the function of phage proteins. Prophage Hunter is freely available at https://pro-hunter.bgi.com/.

Data-driven quantitative modeling of bacterial active nematics.

Active matter comprises individual units that convert energy into mechanical motion. In many examples, such as bacterial systems and biofilament assays, constituent units are elongated and can give rise to local nematic orientational order. Such "active nematics" systems have attracted much attention from both theorists and experimentalists. However, despite intense research efforts, data-driven quantitative modeling has not been achieved, a situation mainly due to the lack of systematic experimental data and to the large number of parameters of current models. Here, we introduce an active nematics system made of swarming filamentous bacteria. We simultaneously measure orientation and velocity fields and show that the complex spatiotemporal dynamics of our system can be quantitatively reproduced by a type of microscopic model for active suspensions whose important parameters are all estimated from comprehensive experimental data. This provides unprecedented access to key effective parameters and mechanisms governing active nematics. Our approach is applicable to different types of dense suspensions and shows a path toward more quantitative active matter research.

MetaPGN: a pipeline for construction and graphical visualization of annotated pangenome networks.

Pangenome analyses facilitate the interpretation of genetic diversity and evolutionary history of a taxon. However, there is an urgent and unmet need to develop new tools for advanced pangenome construction and visualization, especially for metagenomic data. Here, we present an integrated pipeline, named MetaPGN, for construction and graphical visualization of pangenome networks from either microbial genomes or metagenomes. Given either isolated genomes or metagenomic assemblies coupled with a reference genome of the targeted taxon, MetaPGN generates a pangenome in a topological network, consisting of genes (nodes) and gene-gene genomic adjacencies (edges) of which biological information can be easily updated and retrieved. MetaPGN also includes a self-developed Cytoscape plugin for layout of and interaction with the resulting pangenome network, providing an intuitive and interactive interface for full exploration of genetic diversity. We demonstrate the utility of MetaPGN by constructing Escherichia coli pangenome networks from five E. coli pathogenic strains and 760 human gut microbiomes,revealing extensive genetic diversity of E. coli within both isolates and gut microbial populations. With the ability to extract and visualize gene contents and gene-gene physical adjacencies of a specific taxon from large-scale metagenomic data, MetaPGN provides advantages in expanding pangenome analysis to uncultured microbial taxa.

Revised inverse problem algorithm-based prediction of coronary artery stenosis readings from the clinical data of patients with coronary heart diseases.

AIM: Coronary artery stenosis readings were predicted in this study on the basis of clinical data for patients with coronary heart diseases using the inverse problem algorithm. METHOD: Five factors, including age, BSA (body surface area), MAP (mean artery pressure), sugar AC (ante cibum), and LDL-C (low-density Lipoprotein-Cholesterol) were incorporated into a nonlinear first-order regression fit analysis to develop a prediction equation with sixteen terms derived via a revised inverse problem algorithm implemented through the STATISTICA default regression fit. The clinical data acquired from ninety-three coronary heart disease patients were first normalized to the same domain range of [-1 to +1], and then processed by the above algorithm to find the compromised solution of predicted coronary artery stenosis reading. The actual reading was obtained by weighting the stenosis of three major cardiac artery branches, namely, the left anterior descending artery (LAD) (wi 0.3), left circumflex artery (LCA) (wi 0.3), and right coronary artery (RCA) (wi 0.4). RESULT: The derived regression fit possessed the final loss function value Φ = 3.589 and correlation coefficient r2 = 0.892 with variance of 79.55%. Accordingly, forty-five patients with similar syndromes were analyzed to verify the prediction, which exhibited a high coincidence. The LDL-C factor was dominant for the prediction of the largest coefficient in the derived equation, whereas the age factor exhibited a minor contribution to the regression fit. The attempts to reduce the number of influence factors to 4, 3 or 2 for the model simplification yielded the results, whose low linearity and high loss function values reflected their inappropriate setting. CONCLUSION: The algorithm proved to be an effective technique for prediction of the potential diagnosis in the medical field.

Transcriptional Regulation of the Outer Membrane Porin Gene ompW Reveals its Physiological Role during the Transition from the Aerobic to the Anaerobic Lifestyle of Escherichia coli.

Understanding bacterial physiology relies on elucidating the regulatory mechanisms and cellular functions of those differentially expressed genes in response to environmental changes. A widespread Gram-negative bacterial outer membrane protein OmpW has been implicated in the adaptation to stresses in various species. It is recently found to be present in the regulon of the global anaerobic transcription factor FNR and ArcA in Escherichia coli. However, little is known about the physiological implications of this regulatory disposition. In this study, we demonstrate that transcription of ompW is indeed mediated by a series of global regulators involved in the anaerobiosis of E. coli. We show that FNR can both activate and repress the expression of ompW through its direct binding to two distinctive sites, -81.5 and -126.5 bp respectively, on ompW promoter. ArcA also participates in repression of ompW under anaerobic condition, but in an FNR dependent manner. Additionally, ompW is also subject to the regulation by CRP and NarL which senses the availability and types of carbon sources and respiration electron acceptors in the environment respectively, implying a role of OmpW in the carbon and energy metabolism of E. coli during its anaerobic adaptation. Molecular docking reveals that OmpW can bind fumarate, an alternative electron acceptor in anaerobic respiration, with sufficient affinity. Moreover, supplement of fumarate or succinate which belongs to the C4-dicarboxylates family of metabolite, to E. coli culture rescues OmpW-mediated colicin S4 killing. Taken together, we propose that OmpW is involved in anaerobic carbon and energy metabolism to mediate the transition from aerobic to anaerobic lifestyle in E. coli.