Biology subjects

Qin, F.

Publications and source records attributed to Qin, F..

At least 19 recordsLinked to original sources

bioRxiv2026

MintCNA: A Unified Framework for Integrative Copy Number Profiling with Single-Cell Multi-Omics Data

Chromosomal copy number alterations (CNAs) are key drivers of tumor evolution, disease progression and therapeutic resistance, and the identification of them is an important step to delineate tumor clonal structure. However, accurately resolving CNA landscapes from single-cell data remains challenging. Most existing tools analyze one omics layer at a time and are susceptible to assay-specific noises, limiting their ability to recover shared or modality-specific CNAs. Recent single-cell multi-omics techniques enable joint sequencing of multiple molecular layers in the same cells, yet in silico methods that fully exploit such complementary multi-modal data for CNA analysis are still missing. Here we present a single-cell multi-omics integration framework, MintCNA, a unified framework for CNA detection from paired multi-omics data. MintCNA integrates traditional statistical modeling with embedded deep learning structure to enhance CNA profiling across multi-omics. We use an attention-guided convolutional autoencoder for data denoising and perform multivariate change-point detection utilizing a sliding-window screening and ranking procedure. Missingness-adjusted CUSUM statistics are constructed which jointly aggregate omics features by a data-adaptive projection to detect genome-wide chromosomal breakpoints. Across various simulations and applications to a colorectal cancer multi-omics dataset, MintCNA consistently outperforms existing single-omics CNA callers in detection accuracy. MintCNA provides a single-cell CNA tool that integrates paired scDNA-seq and scRNA-seq, supporting the study of intra-tumor heterogeneity and tumor evolution.

bioinformatics↗

bioRxiv2026

CN-RNN: a Deep Learning Framework for Copy Number Variation Detection with Exome Sequencing Data

Copy number variations (CNVs) are major structural genomic variants that contribute to a wide range of human diseases. Accurate detection of CNVs from whole-exome sequencing (WES) data has been a long-sought goal for clinical and population genetic studies. Despite recent progress, existing WES-based CNV callers still suffer from high false-positive rates and reduced recall for short-length variants, and current deep learning methods have not fully used complementary information in region-level genomic features. Here we present CN-RNN, a deep learning-based CNV caller for WES data. The model combines a bidirectional long short-term memory (BiLSTM) branch that captures local depth changes and contextual dependencies across neighboring exons with a parallel multi-layer perceptron (MLP) branch that encodes region-level metadata such as GC content, mappability, and exon length. CN-RNN was trained on the Autism Sequencing Consortium (ASC) parent-child trio cohort using the Mendelian rule of inheritance to ensure high-quality training sets. It was evaluated across three independent datasets, in which we showed that CN-RNN outperformed existing WES-based CNV callers and deep learning methods. CN-RNN offers a scalable, accurate tool for CNV profiling in WES-based studies and supports broader application of CNV analysis in population and clinical research. CN-RNN is available at https://github.com/FeifeiXiao-lab/CN-RNN.

genetics↗

bioRxiv2026

Single-cell lung eQTL dataset of Asian never-smokers highlights the roles of alveolar cells in lung cancer etiology

Single-cell expression quantitative trait loci (sc-eQTL) analyses are powerful in identifying context-specific susceptibility genes from genome-wide association studies (GWAS) loci. However, few studies have comprehensively investigated cells of lung cancer origin in non-European populations. Here, we built a lung sc-eQTL dataset from 129 Korean women never-smokers with epithelial cell enrichment. eQTL mapping identified 2,229 genes with an eQTL in 33 cell types, including East Asian-specific findings when compared to predominantly European datasets. Integration with single-cell chromatin accessibility data demonstrated an enrichment of cell-type specific eQTLs in cell-type matched candidate enhancers, while shared eQTLs were more frequently found near promoters. Colocalization and transcriptome-wide association study unveiled 36 susceptibility genes from 22 cell types in 22 lung cancer loci, including 10 loci not achieving genome-wide significance in prior GWAS. Around 47% of these genes were from cells of the alveoli, underscoring their importance, especially in lung adenocarcinoma (LUAD) susceptibility. Focusing on the trajectory of alveolar epithelial cell regeneration, we detected 785 cell-state-interacting QTLs, which overlapped with 28% (10) of the identified susceptibility genes. Finally, we experimentally validated East Asian-and alveolar type 2 cell-specific eQTL of TCF7L2 underlying East Asian LUAD locus, 10q25.2. Consistent with its role as a Wnt/{beta}-catenin effector, TCF7L2 displayed significant effect on lung adenocarcinoma cell growth. Our data highlighted context-specific susceptibility genes, especially from alveolar cells of lung, contributing to lung cancer etiology.

genetics↗

bioRxiv2025

Identifying High-Dimensional Genomic Factors Modulating Biological Networks Across Multi-Omics Data

Biological traits such as genes, metabolites, and microbial taxa interact within complex networks, yet how genomic factors shape these interactions remains poorly understood. Here, we introduce GFBioNet, a computationally efficient method for identifying factors that modulate direct associations between biological traits within network models. Our two-stage strategy first estimates a baseline network using Gaussian graphical models and then tests whether genomic factors modulate specific network edges (trait-trait relationships), enabling scalable analysis of high-dimensional multi-omics data while explicitly controlling the false discovery rate (FDR). Simulations demonstrate reliable FDR control and high statistical power across a broad range of settings. Applied to multiple datasets, GFBioNet reveals host genetic variants influencing oral microbiome relationships, gut microbial taxa modulating metabolite networks in colorectal cancer, and somatic mutations and copy-number alterations reshaping gene expression networks in lung adenocarcinoma. By expanding network analysis to evaluate modifiers of trait-trait relationships, GFBioNet offers a versatile tool for uncovering the genomic architecture of biological networks across multi-omics studies.

genomics↗

bioRxiv2025

Harnessing Protein Unfolding for Thermosensing: Structural Insights from TRPV3

How proteins sense temperature with high precision is a fundamental biological challenge. Here, we structurally elucidate a dynamics-based mechanism for thermoTRPs, termed suicidal gating, in which extreme thermosensitivity arises from intrinsic instability and concerted protein dynamics, coupling channel opening to partial protein unfolding. Using cryo-EM, we directly capture heat-induced partial unfolding within the channels central pore domain, a structural core of gating. Analysis of mutant structures capturing intermediate activated states reveals no large domain shifts despite high temperature dependence, only subtle interfacial changes between domains. Both mutation and heat treatment consistently impact a critical network of thermolabile "latch" interactions along the S6-TRP helix-proximal N-terminus axis, driving receptor activation from a stable closed structure to progressively decoupled states and eventual disintegration. These findings provide direct structural evidence for a new receptor mechanism and establish a broader paradigm in which distributed structural flexibility, rather than localized dedicated sensors, drives extreme biological functions.

physiology↗

bioRxiv2024

HapCNV: A Comprehensive Framework for CNV Detection in Low-input DNA Sequencing Data

Copy number variants (CNVs) are prevalent in both diploid and haploid genomes, with the latter containing a single copy of each gene. Studying CNVs in genomes from single or few cells is significantly advancing our knowledge in human disorders and disease susceptibility. Low-input including low-cell and single-cell sequencing data for haploid and diploid organisms generally displays shallow and highly non-uniform read counts resulting from the whole genome amplification steps that introduce amplification biases. In addition, haploid organisms typically possess relatively short genomes and require a higher degree of DNA amplification compared to diploid organisms. However, most CNV detection methods are specifically developed for diploid genomes without specific consideration of effects on haploid genomes. Challenges also reside in reference samples or normal controls which are used to provide baseline signals for defining copy number losses or gains. In traditional methods, references are usually pre-specified from cells that are assumed to be normal or disease-free. However, the use of pre-defined reference cells can bias results if common CNVs are present. Here, we present the development of a comprehensive statistical framework for data normalization and CNV detection in haploid single- or low-cell DNA sequencing data called HapCNV. The prominent advancement is the construction of a novel genomic location specific pseudo-reference that selects unbiased references using a preliminary cell clustering method. This approach effectively preserves common CNVs. Using simulations, we demonstrated that HapCNV outperformed existing methods by generating more accurate CNV detection, especially for short CNVs. Superior performance of HapCNV was also validated in detecting known CNVs in a real P. falciparum parasite dataset. In conclusion, HapCNV provides a novel and useful approach for CNV detection in haploid low-input sequencing datasets, with easy applicability to diploids.

bioinformatics↗

bioRxiv2024

Protein Dynamics Underlies Strong Temperature Dependence of Heat Receptors

Ion channels are generally allosteric proteins, involving specialized stimulus sensor domains conformationally linked to the gate to drive channel opening. Temperature receptors are a group of ion channels from the transient receptor potential (TRP) family. They exhibit an unprecedentedly strong temperature dependence and are responsible for temperature sensing in mammals. Despite intensive studies, however, the nature of the temperature sensor domain in these channels remains elusive. By direct calorimetry of TRPV1 proteins, we have recently provided a proof of principle that temperature sensing by ion channels may diverge from the conventional allosterity theory; rather it is intimately linked to inherent thermal instability of channel proteins. Here we tackle the generality of the hypothesis and provide key molecular evidences on the coupling of thermal transitions in the channels. We show that while wild-type channels possess a single concerted thermal transition peak, the chimera, in which strong temperature dependence becomes disrupted, results in multi-transition peaks, and the activation enthalpies are accordingly reduced. The data show that the coupling with protein unfolding drives up the energy barrier of activation, leading to a strong temperature dependence of opening. Furthermore, we pinpoint the proximal N-terminus of the channels as a linchpin in coalescing different parts of the channels into concerted activation. Thus, we suggest that coupled interaction networks in proteins underlie the strong temperature dependence of temperature receptors. SignificanceDecoding receptor mechanisms requires understanding receptor activation at molecular levels. Whereas structural studies can unravel critical residues participating in activation, functional measurements are ultimately needed to pinpoint their mechanistic roles. Temperature receptors are gateways to thermosensation and pain. Despite intensive studies, how they detect temperature remains elusive. Here, by directly measuring heat flow in the most temperature-sensitive, high-threshold noxious heat receptor TRPV2, we show that channel activation is accompanied with a heat uptake sufficient to induce protein unfolding. We present molecular evidence that heat activation and unfolding are coupled, and propose a new mechanism based on concerted activation of different parts of channels to drive up temperature sensitivity. Our findings provide a mechanistic framework for understanding thermal biological processes.

biophysics↗

bioRxiv2024

From Sight to Insight: A Multi-task Approach with the Visual Language Decoding Model

Visual neural decoding aims to unlock the mysteries of how the human brain interprets the visual world. While early studies made some progress in decoding visual activity for singular type of information, they failed to concurrently reveal the multi-level interweaving linguistic information in the brain. Here, we developed a novel Visual Language Decoding Model (VLDM) capable of decoding categories, semantic labels, and textual descriptions from visual perceptual activities simultaneously. We selected the large-scale NSD dataset to ensure the efficiency of the decoding model in joint training and evaluation across multiple tasks. For category decoding, we achieved the effective classification of 12 categories with an accuracy of nearly 70%, significantly surpassing the chance level. For label decoding, we attained the precise prediction of 80 specific semantic labels with a 16-fold improvement over the chance level. For text decoding, the scores of the decoded text surpassed the corresponding baseline levels by remarkable margins on six evaluation metrics. This study contributes significantly to extensive applications in multi-layered brain-computer interfaces, potentially leading to more natural and efficient human-computer interaction experiences.

neuroscience↗

bioRxiv2024

UBA1-CDK16: A Sex-Specific Chimeric RNA and Its Role in Immune Sexual Dimorphism

RNA processing mechanisms, such as alternative splicing and RNA editing, have been recognized as critical means to expand the transcriptome. Chimeric RNAs formed by intergenic splicing provide another potential layer of RNA diversification. By analyzing a large set of RNA-Seq data and validating results in over 1,200 blood samples, we identified UBA1-CDK16, a female-specific chimeric transcript. Intriguingly, both parental genes, are expressed in males and females. Mechanistically, UBA1-CDK16 is produced by cis-splicing between the two adjacent X-linked genes, originating from the inactive X chromosome. A female-specific chromatin loop, formed between the junction sites, facilitates the alternative splicing of its readthrough precursor. This unique chimeric transcript exhibits evolutionary conservation, evolving to be female-specific from non-human primates to humans. Furthermore, our investigation reveals that UBA1-CDK16 is enriched in the myeloid lineage and plays a regulatory role in myeloid differentiation. Notably, female COVID-19 patients who tested negative for this chimeric transcript displayed higher counts of neutrophils, highlighting its potential role in disease pathogenesis. These findings support the notion that chimeric RNAs represent a new repertoire of transcripts that can be regulated independently from the parental genes, and a new class of RNA variance with potential implications in sexual dimorphism and immune responses.

genomics↗

bioRxiv2023

AI in Paleontology

Accumulating data have led to the emergence of data-driven paleontological studies, which reveal an unprecedented picture of evolutionary history. However, the fast-growing quantity and complication of data modalities make data processing laborious and inconsistent, while also lacking clear benchmarks to evaluate data collection and generation, and the performances of different methods on similar tasks. Recently, Artificial Intelligence (AI) is widely practiced across scientific disciplines, but has not become mainstream in paleontology where manual workflows are still typical. In this study, we review more than 70 paleontological AI studies since the 1980s, covering major tasks including micro-and macrofossil classification, image segmentation, and prediction. These studies feature a wide range of techniques such as Knowledge Based Systems (KBS), neural networks, transfer learning, and many other machine learning methods to automate a variety of paleontological research workflows. Here, we discuss their methods, datasets, and performance and compare them with more conventional AI studies. We attribute the recent increase in paleontological AI studies to the lowering bar in training and deployment of AI models rather than real progress. We also present recently developed AI implementations such as diffusion model content generation and Large Language Models (LLMs) to speculate how these approaches may interface with paleontological research. Even though AI has not yet flourished in paleontological research, successful implementation of AI is growing and show promise for transformative effect on the workflow in paleontological research in the years to come. HighlightsO_LIFirst systematic review of AI applications in paleontology. C_LIO_LIThere is a 10 to 20-year gap between AI in paleontology and mainstream studies. C_LIO_LIRecent progress in paleontological AI studies is likely a result of lowering bar in training and deployment. C_LIO_LIFuture direction discussed for interactions between paleontology and AI. C_LI

paleontology↗

bioRxiv2023

Spatial pattern and differential expression analysis with spatial transcriptomic data

The emergence of spatial transcriptomic technologies has opened new avenues to investigate gene activities while preserving the spatial context of tissues. Utilizing data generated by such technologies, the identification of spatially variable (SV) genes is an essential step in investigating tissue landscapes and biological processes. Particularly in typical experimental designs such as case-control or longitudinal studies, identifying SV genes between groups is crucial for discovering significant biomarkers or developing targeted therapies for diseases. However, current methods available for analyzing spatial transcriptomic data are still in their infancy, and none of the existing methods are capable of identifying SV genes between groups. To overcome this challenge, we developed SPADE for spatial pattern and differential expression analysis to identify SV gene in spatial transcriptomic data. SPADE is based on a machine learning model of Gaussian process regression with a gene-specific Gaussian kernel, enabling the detection of SV genes both within and between groups. Through extensive simulations and real data analyses, we have demonstrated the superior performance of SPADE compared to existing methods in detecting SV genes within and between groups. The SPADE source code and documentation are publicly available at https://github.com/thecailab/SPADE.

bioinformatics↗

bioRxiv2023

A statistical learning method for simultaneous copy number estimation and subclone clustering with single cell sequencing data

The availability of single cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in SCS data using various clustering methods, since cells comprising a subpopulation are found to share genetic profile. However, currently available methods may generate spurious results (e.g., falsely identified CNAs) in the procedure of CNA detection, hence diminishing the accuracy of subclone identification from a large complex cell population. In this study, we developed a CNA detection method based on a fused lasso model, referred to as FLCNA, which can simultaneously identify subclones in single cell DNA sequencing (scDNA-seq) data. Spike-in simulations were conducted to evaluate the clustering and CNA detection performance of FLCNA benchmarking to existing copy number estimation methods (SCOPE, HMMcopy) in combination with the existing and commonly used clustering methods. Interestingly, application of FLCNA to a real scDNA-seq dataset of breast cancer revealed remarkably different genomic variation patterns in neoadjuvant chemotherapy treated samples and pre-treated samples. We show that FLCNA is a practical and powerful method in subclone identification and CNA detection with scDNA-seq data.

genomics↗

bioRxiv2023

Morphine Re-arranges Chromatin Spatial Architecture of Primate Cortical Neurons

The expression of linear DNA sequences is precisely regulated by the three-dimensional (3D) architecture of chromatin. Morphine-induced aberrant gene networks of neurons have been extensively investigated; however, how morphine impacts the 3D genomic architecture of neuorns is still unknown. Here, we applied digestion-ligation-only high-throughput chromosome conformation capture (DLO Hi-C) technology to investigate the affection of morphine on 3D chromatin architecture of primate cortical neurons. After receiving continuous morphine administration for 90 days on rhesus monkeys, we discovered that morphine re-arranged chromosome territories, with a total of 391 segmented compartments being switched. Morphine altered over half of the detected topologically associated domains (TADs), most of which exhibited a variety of shifts, followed by separating and fusing types. Analysis of the looping events at kilobase-scale resolution revealed that morphine increased not only the number but also the length of differential loops. Moreover, all identified differentially expressed genes (DEGs) from the RNA sequencing (RNA-seq) were mapped to the specific TAD boundaries or differential loops, and were further validated to be significantly changed. Collectively, an altered 3D genomic architecture of cortical neurons may regulate the gene networks associated-morphine effects. Our finding provides critical hubs connecting chromosome spatial organization and gene networks associated with the morphine effects in humans.

bioinformatics↗

bioRxiv2021

Mechanisms Driving Genome Reduction of a Novel Roseobacter Lineage Showing Vitamin B12 Auxotrophy

Members of the marine Roseobacter group are key players in the global carbon and sulfur cycles. While over 300 species have been described, only 2% possess reduced genomes (mostly 3-3.5 Mbp) compared to an average roseobacter (>4 Mbp). These taxonomic minorities are phylogenetically diverse but form a Pelagic Roseobacter Cluster (PRC) at the genome content level. Here, we cultivated eight isolates constituting a novel Roseobacter lineage which we named CHUG. Metagenomic and metatranscriptomic read recruitment analyses showed that CHUG members were globally distributed and active in marine environments. CHUG members possess some of the smallest genomes (~2.52 Mb) among all known roseobacters, but they do not exhibit canonical features of genome streamlining like higher coding density or fewer paralogues and pseudogenes compared to their sister lineages. While CHUG members are clustered with traditional PRC members at the genome content level, they show important differences. Unlike other PRC members, neither the relative abundances of CHUG members nor their gene expression levels are correlated with chlorophyll a concentration across the global samples. Moreover, CHUG members cannot synthesize vitamin B12, a key metabolite made by most roseobacters but not by many phytoplankton species and thus thought to mediate the roseobacter-phytoplankton interactions. This combination of features is evidence for the hypothesis that CHUG members may have evolved a free-living lifestyle decoupled from phytoplankton. This ecological transition was accompanied by the loss of signature genes involved in roseobacter-phytoplankton symbiosis, suggesting that relaxation of purifying selection is likely an important driver of genome reduction in CHUG.

microbiology↗

bioRxiv2020

In-depth analysis reveals complex molecular etiology of idiopathic cerebral palsy

Cerebral palsy (CP), the most prevalent physical disability in children, has long been ignored with regard to its inherent molecular mechanism. In this work, we performed in-depth clinical and molecular analysis on 120 idiopathic CP families, and identified in half of the patients the underlying risk factors. By a compilation of 117 CP-related genes, we recognized the characteristic features in terms of inheritance and function, and proposed a dichotomous classification system according to the expression patterns. In two patients with both CP and intellectual disability, we revealed that the defective TYW1, a tRNA hypermodification enzyme, caused microcephaly and problems in motion and cognition by hindering neuronal proliferation and migration. We developed an algorithm and proved in mice brain that this malfunctioning hypermodification specifically perturbed the translation of a subset of proteins involved in cell cycling. In a CP patient with normal intelligence, we identified a mitochondrial enzyme GPAM, the hypomorphic form of which led to hypomyelination of corticospinal tract. We confirmed that the aberrant Gpam in mice perturbed the lipid metabolism in astrocyte, resulting in suppressed astrocyte proliferation and a shortage of lipid contents supplied for oligodendrocyte myelination. This work broadened the scope of understanding of CP etiology and paved a way for innovative therapeutic strategies.

genetics↗

bioRxiv2020

Autographa californica Multiple Nucleopolyhedrovirus orf13 is Required for Efficient Nuclear Egress of Nucleocapsids

Autographa californica multiple nucleopolyhedrovirus (AcMNPV) orf13 (ac13) is a conserved gene in all sequenced alphabaculoviruses. However, its function in the viral life cycle remains unknown. In this study we found that ac13 was a late gene and that the encoded protein, bearing a putative nuclear localization signal motif in the DUF3627 domain, colocalized with the nuclear membrane. Deletion of ac13 did not affect viral DNA replication, gene transcription, nucleocapsid assembly or occlusion body (OB) formation, but reduced virion budding from infected cells by approximately 400-fold compared with the wild-type virus. Deletion of ac13 substantially impaired the egress of nucleocapsids from the nucleus to the cytoplasm, while the number of occlusion-derived viruses embedded within OBs was unaffected. Taken together, our results indicated that ac13 was required for efficient nuclear egress of nucleocapsids during virion budding, but was dispensable for OB formation. IMPORTANCEEgress of baculovirus nucleocapsids from the nucleus is an essential process for morphogenesis of mature budded viruses, which is required to spread infection within susceptible cells and tissues. Although many viral and host proteins are required for nucleocapsid egress, the specific mechanisms underlying this process in baculoviruses remain somewhat enigmatic. In the present study, we found that the ac13 gene, in addition to ac11, ac51, ac66, ac75, ac78, gp41, ac93, p48, exon0 and ac142, was required for efficient nuclear egress of nucleocapsids. Our results contribute to a better understanding of nucleocapsid egress in baculoviruses.

microbiology↗

bioRxiv2020

Integrating Genomic Correlation Structure Improves Copy Number Variations Detection

Copy number variation plays important roles in human complex diseases. The detection of copy number variants (CNVs) is identifying mean shift in genetic intensities to locate chromosomal breakpoints, the step of which is referred to as chromosomal segmentation. Many segmentation algorithms have been developed with a strong assumption of independent observations in the genetic loci, and they assume each locus has an equal chance to be a breakpoint (i.e., boundary of CNVs). However, this assumption is violated in the genetics perspective due to the existence of correlation among genomic positions such as linkage disequilibrium (LD). Our study showed that the LD structure is related to the location distribution of CNVs which indeed presents a non-random pattern on the genome. To generate more accurate CNVs, we therefore proposed a novel algorithm, LDcnv, that models the CNV data with its biological characteristics relating to genetic correlation (i.e., LD). To evaluate the performance of LDcnv, we conducted extensive simulations and analyzed large-scale HapMap datasets. We showed that LDcnv presents high accuracy, stability and robustness in CNV detection and higher precision in detecting short CNVs compared to existing methods. We also theoretically demonstrated the correlation structure of CNV data, which further supports the necessity of integrating biological structure in statistical methods for CNV detection. This new segmentation algorithm has a wide scope of application with next-generation sequencing data analysis and single-cell sequencing analysis. Author SummaryCopy number variants (CNVs) refers to gains or losses of the DNA segments in comparison to a reference genome. CNVs have garnered extensive interests in recent years as they play an important role susceptibility to disorders and diseases such as autism, schizophrenia and cancer [1-7]. Although innovation in modern technology is promoting the discoveries related to CNVs, the methodology for CNV detection is still lagging, which limits the novel discoveries regarding the role of CNVs in complex diseases. In this study, we are proposing a novel segmentation algorithm, LDcnv, to accurately locate the breakpoints or boundaries of CNVs in the human genome. Instead of utilizing an independent assumption of the signal intensities as has been used in traditional segmentation algorithms, LDcnv models the correlation structure in the genome in a change-point CNV detection model, which allows for accurate and fast computation with a whole genome scan. Our study showed strong theoretical evidence of the existence of correlation structure in real CNV data, and we believe that taking this evidence into consideration will improve the power of CNV detection. Extensive simulation studies have demonstrated the advantage of the LDcnv algorithm in stability, robustness and accuracy over existing methods. We also used high-quality CNV profiles to further support the superior performance of the LDcnv algorithm over existing methods. The development of the LDcnv algorithm provides great insights for new directions in developing CNV detection tools.

genetics↗

bioRxiv2019

Novel pelagiphages prevail in the ocean

Viruses play a key role in biogeochemical cycling and host mortality, metabolism, physiology and evolution in the ocean. Viruses that infect the globally abundant marine SAR11 bacteria (pelagiphages) were reported to be an important component of the marine viral community. In this study, ten pelagiphages that infect three different Pelagibacter strains were isolated from various geographical locations and were genomically characterized. All ten pelagiphages are novel, representing four new lineages of the Podoviridae family. Although they share limited homology with cultured phage isolates, they are all closely related to some environmental viral fragments. Two HTVC023P-type pelagiphages are shown to be related to the abundant VC_6 and VC_8 viral populations of the Global Oceans Viromes (GOV) datasets. Interestingly, HTVC103P-type pelagiphages contain a structural module similar to that in SAR116 phage HMO-2011. Three HTVC111P-type pelagiphages and HTVC106P are also novel and related to GOV VC_41 and VC_67 viral populations, respectively. Remarkably, these pelagiphage represented phage groups are all globally distributed and predominant. Half of the top ten most abundant known marine phage groups are represented by pelagiphages. The HTVC023P-type group is the most abundant known viral group, exceeding the abundance of HTVC010P-type and HMO-2011-type groups. Furthermore, the HTVC023P-type group is also abundant throughout the water column. Altogether, this study has greatly broadened our understanding of pelagiphages regarding their genetic diversity, phage-host interactions and the distribution pattern. Availability of these newly isolated pelagiphages and their genome sequences will allow us to further explore their phage-host interactions and ecological strategies.

ecology↗