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Vibrio alginolyticus, the causative agent of aquatic vertebrates and invertebrates, can cause severe infections (e.g., septicaemia, gill necrosis, and surface ulcers) and high mortality in aquatic organisms, leading to serious economic losses in global aquaculture. Small non-coding RNAs (sRNAs), emerging modulators of gene expression, played vital regulatory roles in virulence, pathogenicity and physiological metabolism of bacteria. In this work, the modulation of physiological functions and metabolome of V. alginolyticus by the quorum-regulatory sRNA, Qrr1, was figured out. We found that the deletion of qrr1 induced significant cell shape elongation. Meanwhile, Qrr1 could inhibit the production of alkaline serine protease by weakening the expression of main regulator LuxR in the quorum sensing (QS) system. Moreover, the untargeted metabolomics and lipidomics approaches showed most of nucleotides, organic acids, carbohydrates, and lipidome (both lipid content and category) were significantly altered in response to the qrr1 deletion. Spearman correlation analysis demonstrated that most of the intermediates involved in glutamate metabolism, sphingolipid metabolism and glycerolipid metabolism displayed high correlations with cell virulence factors. These findings illuminate the mechanism of bacterial virulence regulation and further exploit potential therapeutic targets for virulence prevention in V. alginolyticus., (© The Author(s) 2024. Published by Oxford University Press on behalf of Applied Microbiology International.)

Motivation: Viruses, with their ubiquitous presence and high diversity, play pivotal roles in ecological systems and public health. Accurate identification of viruses in various ecosystems is essential for comprehending their variety and assessing their ecological influence. Metagenomic sequencing has become a major strategy to survey the viruses in various ecosystems. However, accurate and comprehensive virus detection in metagenomic data remains difficult. Limited reference sequences prevent alignment-based methods from identifying novel viruses. Machine learning-based tools are more promising in novel virus detection but often miss short viral contigs, which are abundant in typical metagenomic data. The inconsistency in virus search results produced by available tools further highlights the urgent need for a more robust tool for virus identification., Results: In this work, we develop ViraLM for identifying novel viral contigs in metagenomic data. By using the latest genome foundation model as the backbone and training on a rigorously constructed dataset, the model is able to distinguish viruses from other organisms based on the learned genomic characteristics. We thoroughly tested ViraLM on multiple datasets and the experimental results show that ViraLM outperforms available tools in different scenarios. In particular, ViraLM improves the F1-score on short contigs by 22%., Availability and Implementation: The source code of ViraLM is available via: https://github.com/ChengPENG-wolf/ViraLM., (© The Author(s) 2024. Published by Oxford University Press.)

RNA viruses exhibit vast phylogenetic diversity and can significantly impact public health and agriculture. However, current bioinformatics tools for viral discovery from metagenomic data frequently generate false positive virus results, overestimate viral diversity, and misclassify virus sequences. Additionally, current tools often fail to determine virus-host associations, which hampers investigation of the potential threat posed by a newly detected virus. To address these issues we developed VirID, a software tool specifically designed for the discovery and characterization of RNA viruses from metagenomic data. The basis of VirID is a comprehensive RNA-dependent RNA polymerase database to enhance a workflow that includes RNA virus discovery, phylogenetic analysis, and phylogeny-based virus characterization. Benchmark tests on a simulated data set demonstrated that VirID had high accuracy in profiling viruses and estimating viral richness. In evaluations with real-world samples, VirID was able to identify RNA viruses of all types, but also provided accurate estimations of viral genetic diversity and virus classification, as well as comprehensive insights into virus associations with humans, animals, and plants. VirID therefore offers a robust tool for virus discovery and serves as a valuable resource in basic virological studies, pathogen surveillance, and early warning systems for infectious disease outbreaks., Competing Interests: Conflict of Interest: The authors declare that this research was carried out without any commercial or financial relationships that could be interpreted as potential conflicts of interest., (© The Author(s) 2024. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)

Diabetic neuropathy is a debilitating disorder characterized by spontaneous and mechanical allodynia. The role of skin mechanoreceptors in the development of mechanical allodynia is unclear. We discovered that mice with diabetic neuropathy had decreased sirtuin 1 (SIRT1) deacetylase activity in foot skin, leading to reduced expression of brain-derived neurotrophic factor (BDNF) and subsequent loss of innervation in Meissner corpuscles, a mechanoreceptor expressing the BDNF receptor TrkB. When SIRT1 was depleted from skin, the mechanical allodynia worsened in diabetic neuropathy mice, likely due to retrograde degeneration of the Meissner-corpuscle innervating Aβ axons and aberrant formation of Meissner corpuscles which may have increased the mechanosensitivity. The same phenomenon was also noted in skin-keratinocyte specific BDNF knockout mice. Furthermore, overexpression of SIRT1 in skin induced Meissner corpuscle reinnervation and regeneration, resulting in significant improvement of diabetic mechanical allodynia. Overall, the findings suggested that skin-derived SIRT1 and BDNF function in the same pathway in skin sensory apparatus regeneration and highlighted the potential of developing topical SIRT1-activating compounds as a novel treatment for diabetic mechanical allodynia., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Summary: RNA viruses are ubiquitous across a broad spectrum of ecosystems. Therefore, beyond their significant implications for public health, RNA viruses are also key players in ecological processes. High-through sequencing has accelerated the discovery of RNA viruses. Nevertheless, many of these viruses lack taxonomic annotation, posing a challenge to functional inference and evolutionary study. In particular, virus classification at the genus level remains difficult due to the limited reference data and ambiguous boundaries between some closely related genera. We introduce VirTAXA, a robust classification tool that combines remote homology search and tree-based validation to enhance the genus-level taxonomic classification of RNA viruses. VirTAXA is able to predict the genus label of an assembled viral contig and provide evidence type for each prediction. It achieves comparable accuracy to state-of-the-art methods while assigning genus labels to a greater number of sequences. Specifically, on the Global Ocean RNA metatranscriptomic data, VirTAXA can assign genus labels for 18% more contigs than the second-best classification tool. Furthermore, we demonstrated that VirTAXA can be conveniently extended to other types of viruses., Availability and Implementation: The source code and data of VirTAXA are available via https://github.com/JudithEllyn/VirTAXA., (© The Author(s) 2024. Published by Oxford University Press.)

Plants can recruit beneficial microbes to enhance their ability to resist disease. It is well established that selenium is beneficial in plant growth, but its role in mediating microbial disease resistance remains poorly understood. Here, we investigated the correlation between selenium, oilseed rape rhizosphere microbes, and Sclerotinia sclerotiorum. Soil application of 0.5 and 1.0 mg kg-1 selenium [selenate Na2SeO4, Se(VI) or selenite Na2SeO3, Se(IV)] significantly increased the resistance of oilseed rape to Sclerotinia sclerotiorum compared with no selenium application, with a disease inhibition rate higher than 20% in Se(VI)0.5, Se(IV)0.5 and Se(IV)1.0 mg kg-1 treatments. The disease resistance of oilseed rape was related to the presence of rhizosphere microorganisms and beneficial bacteria isolated from the rhizosphere inhibited Sclerotinia stem rot. Burkholderia cepacia and the synthetic community consisting of Bacillus altitudinis, Bacillus megaterium, Bacillus cereus, Bacillus subtilis, Bacillus velezensis, Burkholderia cepacia, and Flavobacterium anhui enhanced plant disease resistance through transcriptional regulation and activation of plant-induced systemic resistance. In addition, inoculation of isolated bacteria optimized the bacterial community structure of leaves and enriched beneficial microorganisms such as Bacillus, Pseudomonas, and Sphingomonas. Bacillus isolated from the leaves were sprayed on detached leaves, and it also performed a significant inhibition effect on Sclerotinia sclerotiorum. Overall, our results indicate that selenium improves plant rhizosphere microorganisms and increase resistance to Sclerotinia sclerotiorum in oilseed rape., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Safflower, an economic crop, is renowned for its flowers, which are widely used in medicines for treating cardiovascular and cerebrovascular diseases and in dyes for food and industry. The utility of safflower depends on its flavonoid glycosides. Therefore, the biosynthesis of safflower flavonoid glycosides has been a focus of attention, but the present mechanisms remain poorly understood. This study aims to identify functional genes associated with flavonoid glycoside biosynthesis in safflower through a comprehensive approach that integrates whole-genome screen and multi-omics correlation studies. CYP and UGT are two crucial genes families involved in flavonoid glycoside biosynthesis. We have screened 264 CYP genes and 140 UGT genes in the genome of safflower and conducted analyzes including phylogenetic relationships, conserved motifs, gene structures, cis -acting elements, and chromosome mapping, which provided extensive and comprehensive data on the CYP and UGT gene families. Integration of phenotype and metabolic data from safflower different tissues helped narrow down the screening by confirming that HSYA is synthesized only in flowers. Based on the gene expression patterns and phylogenetic analysis, CtOGT1 was ultimately identified, which could catalyze the generation of glycosides using various flavonoid substrates and exhibited strong substrate affinity. Moreover, molecular docking studies elucidated CtOGT1's highly active intrinsic mechanism. In conclusion, this study effectively identified genes responsible for flavonoid glycoside biosynthesis in safflower through the integration of whole-genome screen and multi-omics analysis, established a comprehensive foundation of data, methodology, and experimental evidence for further elucidating the pathways of safflower flavonoid glycoside biosynthesis., Competing Interests: The authors declare no conflict of interest., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nanjing Agricultural University.)

The target of rapamycin (TOR) kinase is a central signaling hub that plays a crucial role in precisely orchestrating plant growth, development, and stress responses. This suggests that TOR is intricately involved in maintaining the balance between plant growth and stress responses. Nevertheless, despite the observed effects, the specific mechanisms through which TOR operates in these processes remain obscure. In this study, we investigated how the tomato ( Solanum lycopersicum ) TOR ( Sl TOR) affects plant growth and cold responses. We demonstrated that Sl TOR inhibition transcriptionally primes cold stress responses, consequently enhancing tomato cold tolerance. A widely targeted metabolomics analysis revealed the disruption of amino acid metabolism homeostasis under cold stress upon Sl TOR inhibition, which led to the accumulation of two important cryoprotective metabolites: salicylic acid (SA) and putrescine (Put). Next, we discovered Sl PGH1 (2-PHOSPHO-D-GLYCERATE HYDRO-LYASE 1) as a direct substrate of Sl TOR. Inhibiting Sl TOR led to increased SlCBF1 ( C-REPEAT-BINDING FACTOR 1 ) expression via Sl PGH1, potentially triggering the activation of cold-responsive genes and subsequent metabolic alterations. Our study provides a mechanistic framework that elucidates how Sl TOR modulates amino acid-related metabolism to enhance tomato cold tolerance, which sheds light on the complex interplay between growth and stress responses orchestrated by TOR., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nanjing Agricultural University.)

Motivation: Protein embedding, which represents proteins as numerical vectors, is a crucial step in various learning-based protein annotation/classification problems, including gene ontology prediction, protein-protein interaction prediction, and protein structure prediction. However, existing protein embedding methods are often computationally expensive due to their large number of parameters, which can reach millions or even billions. The growing availability of large-scale protein datasets and the need for efficient analysis tools have created a pressing demand for efficient protein embedding methods., Results: We propose a novel protein embedding approach based on multi-teacher distillation learning, which leverages the knowledge of multiple pre-trained protein embedding models to learn a compact and informative representation of proteins. Our method achieves comparable performance to state-of-the-art methods while significantly reducing computational costs and resource requirements. Specifically, our approach reduces computational time by ∼70% and maintains ±1.5% accuracy as the original large models. This makes our method well-suited for large-scale protein analysis and enables the bioinformatics community to perform protein embedding tasks more efficiently., Availability and Implementation: The source code of MTDP is available via https://github.com/KennthShang/MTDP., (© The Author(s) 2024. Published by Oxford University Press.)

Segmented RNA viruses are a complex group of RNA viruses with multisegment genomes. Reconstructing complete segmented viruses is crucial for advancing our understanding of viral diversity, evolution, and public health impact. Using metatranscriptomic data to identify known and novel segmented viruses has sped up the survey of segmented viruses in various ecosystems. However, the high genetic diversity and the difficulty in binning complete segmented genomes present significant challenges in segmented virus reconstruction. Current virus detection tools are primarily used to identify nonsegmented viral genomes. This study presents SegVir, a novel tool designed to identify segmented RNA viruses and reconstruct their complete genomes from complex metatranscriptomes. SegVir leverages both close and remote homology searches to accurately detect conserved and divergent viral segments. Additionally, we introduce a new method that can evaluate the genome completeness and conservation based on gene content. Our evaluations on simulated datasets demonstrate SegVir's superior sensitivity and precision compared to existing tools. Moreover, in experiments using real data, we identified some virus segments missing in the NCBI database, underscoring SegVir's potential to enhance viral metagenome analysis. The source code and supporting data of SegVir are available via https://github.com/HubertTang/SegVir., (© The Author(s) 2024. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)

Motivation: In the field of drug discovery, accurately and effectively predicting the binding affinity between proteins and ligands is crucial for drug screening and optimization. However, current research primarily utilizes representations based on sequence or structure to predict protein-ligand binding affinity, with relatively less study on protein surface information, which is crucial for protein-ligand interactions. Moreover, when dealing with multimodal information of proteins, traditional approaches typically concatenate features from different modalities in a straightforward manner without considering the heterogeneity among them, which results in an inability to effectively exploit the complementary between modalities., Results: We introduce a novel multimodal feature extraction (MFE) framework that, for the first time, incorporates information from protein surfaces, 3D structures, and sequences, and uses cross-attention mechanism for feature alignment between different modalities. Experimental results show that our method achieves state-of-the-art performance in predicting protein-ligand binding affinity. Furthermore, we conduct ablation studies that demonstrate the effectiveness and necessity of protein surface information and multimodal feature alignment within the framework., Availability and Implementation: The source code and data are available at https://github.com/Sultans0fSwing/MFE., (© The Author(s) 2024. Published by Oxford University Press.)

Motivation: The microbiome of a sampled habitat often consists of microbial communities from various sources, including potential contaminants. Microbial source tracking (MST) can be used to discern the contribution of each source to the observed microbiome data, thus enabling the identification and tracking of microbial communities within a sample. Therefore, MST has various applications, from monitoring microbial contamination in clinical labs to tracing the source of pollution in environmental samples. Despite promising results in MST development, there is still room for improvement, particularly for applications where precise quantification of each source's contribution is critical., Results: In this study, we introduce a novel tool called SourceID-NMF towards more precise microbial source tracking. SourceID-NMF utilizes a non-negative matrix factorization (NMF) algorithm to trace the microbial sources contributing to a target sample. By leveraging the taxa abundance in both available sources and the target sample, SourceID-NMF estimates the proportion of available sources present in the target sample. To evaluate the performance of SourceID-NMF, we conducted a series of benchmarking experiments using simulated and real data. The simulated experiments mimic realistic yet challenging scenarios for identifying highly similar sources, irrelevant sources, unknown sources, low abundance sources, and noise sources. The results demonstrate the superior accuracy of SourceID-NMF over existing methods. Particularly, SourceID-NMF accurately estimated the proportion of irrelevant and unknown sources while other tools either over- or under-estimated them. In addition, the noise sources experiment also demonstrated the robustness of SourceID-NMF for MST., Availability and Implementation: SourceID-NMF is available online at https://github.com/ZiyiHuang0708/SourceID-NMF., (© The Author(s) 2024. Published by Oxford University Press.)

MiR156 play important roles in regulation of plant growth and development, secondary metabolite synthesis, and other biological processes by targeting the SQUAMOSA promoter binding protein-like (SPL) family. Our previous sequencing data analysis suggested that Csn-miR156d may regulate flowering and anthocyanin accumulation by cleavage and degradation of the expression of the SPL in tea plant, but it remains to be elucidated. In this study, 5'RLM-RACE experiment, tobacco transient transformation, qRT-PCR, and antisense oligonucleotide (asODN) were used to verify that CsSPL1 is the target gene of Csn-miR156d. Stable transformation of Arabidopsis revealed that Csn-miR156d could delay flowering by negatively regulating the transcript levels of FT, AP1, FUL, and SOC1, while overexpression of CsSPL1 showed an opposite effect. Additionally, overexpression of Csn-miR156d in Arabidopsis could enhance the transcription of the anthocyanin biosynthesis-related structural genes DFR, ANS, F3H, UGT78D2, and LDOX, as well as regulatory genes PAP1, MYB113, GL3, MYB11, and MYB12, leading to anthocyanin accumulation. Moreover, asODN experiment revealed that Csn-miR156d could increase the anthocyanin content in tea plant. These results suggest that Csn-miR156d regulates flowering and anthocyanin accumulation in tea plant by suppressing the expression of CsSPL1. Our study provides new insights into the development and anthocyanin accumulation in tea plant and lays a theoretical foundation for further research on the molecular mechanism of miRNAs in regulating tea plant growth and secondary metabolism., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Profile hidden Markov models (pHMMs) are able to achieve high sensitivity in remote homology search, making them popular choices for detecting novel or highly diverged viruses in metagenomic data. However, many existing pHMM databases have different design focuses, making it difficult for users to decide the proper one to use. In this review, we provide a thorough evaluation and comparison for multiple commonly used profile HMM databases for viral sequence discovery in metagenomic data. We characterized the databases by comparing their sizes, their taxonomic coverage, and the properties of their models using quantitative metrics. Subsequently, we assessed their performance in virus identification across multiple application scenarios, utilizing both simulated and real metagenomic data. We aim to offer researchers a thorough and critical assessment of the strengths and limitations of different databases. Furthermore, based on the experimental results obtained from the simulated and real metagenomic data, we provided practical suggestions for users to optimize their use of pHMM databases, thus enhancing the quality and reliability of their findings in the field of viral metagenomics., (© The Author(s) 2024. Published by Oxford University Press.)

How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment., (© The Author(s) 2024. Published by Oxford University Press.)

Motivation: Molecular representation learning plays an indispensable role in crucial tasks such as property prediction and drug design. Despite the notable achievements of molecular pre-training models, current methods often fail to capture both the structural and feature semantics of molecular graphs. Moreover, while graph contrastive learning has unveiled new prospects, existing augmentation techniques often struggle to retain their core semantics. To overcome these limitations, we propose a gradient-compensated encoder parameter perturbation approach, ensuring efficient and stable feature augmentation. By merging enhancement strategies grounded in attribute masking and parameter perturbation, we introduce MoleMCL, a new MOLEcular pre-training model based on multi-level contrastive learning., Results: Experimental results demonstrate that MoleMCL adeptly dissects the structure and feature semantics of molecular graphs, surpassing current state-of-the-art models in molecular prediction tasks, paving a novel avenue for molecular modeling., Availability and Implementation: The code and data underlying this work are available in GitHub at https://github.com/BioSequenceAnalysis/MoleMCL., (© The Author(s) 2024. Published by Oxford University Press.)

Auxin plays important roles throughout plant growth and development. However, the mechanisms of auxin regulation of plant structure are poorly understood. In this study, we identified a transcription factor (TF) of the BARLEY B RECOMBINANT/BASIC PENTACYSTEINE (BBR/BPC) family in apple (Malus × domestica), MdBPC2. It was highly expressed in dwarfing rootstocks, and it negatively regulated auxin biosynthesis. Overexpression of MdBPC2 in apple decreased plant height, altered leaf morphology, and inhibited root system development. These phenotypes were due to reduced auxin levels and were restored reversed after exogenous indole acetic acid (IAA) treatment. Silencing of MdBPC2 alone had no obvious phenotypic effect, while silencing both Class I and Class II BPCs in apple significantly increased auxin content in plants. Biochemical analysis demonstrated that MdBPC2 directly bound to the GAGA-rich element in the promoters of the auxin synthesis genes MdYUC2a and MdYUC6b, inhibiting their transcription and reducing auxin accumulation in MdBPC2 overexpression lines. Further studies established that MdBPC2 interacted with the polycomb group (PcG) protein LIKE HETEROCHROMATIN PROTEIN 1 (LHP1) to inhibit MdYUC2a and MdYUC6b expression via methylation of histone 3 lysine 27 (H3K27me3). Silencing MdLHP1 reversed the negative effect of MdBPC2 on auxin accumulation. Our results reveal a dwarfing mechanism in perennial woody plants involving control of auxin biosynthesis by a BPC transcription factor, suggesting its use for genetic improvement of apple rootstock., Competing Interests: Conflict of interest statement. None declared., (© The Author(s) 2023. Published by Oxford University Press on behalf of American Society of Plant Biologists. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., (© The Author(s) 2024. Published by Oxford University Press GigaScience.)

Motivation: With advances in metagenomic sequencing technologies, there are accumulating studies revealing the associations between the human gut microbiome and some human diseases. These associations shed light on using gut microbiome data to distinguish case and control samples of a specific disease, which is also called host disease status classification. Importantly, using learning-based models to distinguish the disease and control samples is expected to identify important biomarkers more accurately than abundance-based statistical analysis. However, available tools have not fully addressed two challenges associated with this task: limited labeled microbiome data and decreased accuracy in cross-studies. The confounding factors, such as the diet, technical biases in sample collection/sequencing across different studies/cohorts often jeopardize the generalization of the learning model., Results: To address these challenges, we develop a new tool GDmicro, which combines semi-supervised learning and domain adaptation to achieve a more generalized model using limited labeled samples. We evaluated GDmicro on human gut microbiome data from 11 cohorts covering 5 different diseases. The results show that GDmicro has better performance and robustness than state-of-the-art tools. In particular, it improves the AUC from 0.783 to 0.949 in identifying inflammatory bowel disease. Furthermore, GDmicro can identify potential biomarkers with greater accuracy than abundance-based statistical analysis methods. It also reveals the contribution of these biomarkers to the host's disease status., Availability and Implementation: https://github.com/liaoherui/GDmicro., (© The Author(s) 2023. Published by Oxford University Press.)

Surround suppression (SS) is a phenomenon whereby a neuron's response to stimuli in its central receptive field (cRF) is suppressed by stimuli extending to its surround receptive field (sRF). Recent evidence show that top-down influence contributed to SS in the primary visual cortex (V1). However, how the top-down influence from different high-level cortical areas affects SS in V1 has not been comparatively observed. The present study applied transcranial direct current stimulation (tDCS) to modulate the neural activity in area 21a (A21a) and area 7 (A7) of cats and examined the changes in the cRF and sRF of V1 neurons. We found that anode-tDCS at A21a reduced V1 neurons' cRF size and increased their response to visual stimuli in cRF, causing an improved SS strength. By contrast, anode-tDCS at A7 increased V1 neurons' sRF size and response to stimuli in cRF, also enhancing the SS. Modeling analysis based on DoG function indicated that the increased SS of V1 neurons after anode-tDCS at A21a could be explained by a center-only mechanism, whereas the improved SS after anode-tDCS at A7 might be mediated through a combined center and surround mechanism. In conclusion, A21a and A7 may affect the SS of V1 neurons through different mechanisms., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Plant pathogens are challenged by host-derived iron starvation or excess during infection, but the mechanism through which pathogens counteract iron stress is unclear. Here, we found that Fusarium graminearum encounters iron excess during the colonization of wheat heads. Deletion of heme activator protein X (FgHapX), siderophore transcription factor A (FgSreA) or both attenuated virulence. Further, we found that FgHapX activates iron storage under iron excess by promoting histone H2B deubiquitination (H2B deub1) at the promoter of the responsible gene. Meanwhile, FgSreA is shown to inhibit genes mediating iron acquisition during iron excess by facilitating the deposition of histone variant H2A.Z and histone 3 lysine 27 trimethylation (H3K27 me3) at the first nucleosome after the transcription start site. In addition, the monothiol glutaredoxin FgGrx4 is responsible for iron sensing and control of the transcriptional activity of FgHapX and FgSreA via modulation of their enrichment at target genes and recruitment of epigenetic regulators, respectively. Taken together, our findings elucidated the molecular mechanisms for adaptation to iron excess mediated by FgHapX and FgSreA during infection in F. graminearum and provide novel insights into regulation of iron homeostasis at the chromatin level in eukaryotes., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Motivation: Bacteriophages (phages for short), which prey on and replicate within bacterial cells, have a significant role in modulating microbial communities and hold potential applications in treating antibiotic resistance. The advancement of high-throughput sequencing technology contributes to the discovery of phages tremendously. However, the taxonomic classification of assembled phage contigs still faces several challenges, including high genetic diversity, lack of a stable taxonomy system and limited knowledge of phage annotations. Despite extensive efforts, existing tools have not yet achieved an optimal balance between prediction rate and accuracy., Results: In this work, we develop a learning-based model named PhaGenus, which conducts genus-level taxonomic classification for phage contigs. PhaGenus utilizes a powerful Transformer model to learn the association between protein clusters and support the classification of up to 508 genera. We tested PhaGenus on four datasets in different scenarios. The experimental results show that PhaGenus outperforms state-of-the-art methods in predicting low-similarity datasets, achieving an improvement of at least 13.7%. Additionally, PhaGenus is highly effective at identifying previously uncharacterized genera that are not represented in reference databases, with an improvement of 8.52%. The analysis of the infants' gut and GOV2.0 dataset demonstrates that PhaGenus can be used to classify more contigs with higher accuracy., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Access to accurate viral genomes is important to downstream data analysis. Third-generation sequencing (TGS) has recently become a popular platform for virus sequencing because of its long read length. However, its per-base error rate, which is higher than next-generation sequencing, can lead to genomes with errors. Polishing tools are thus needed to correct errors either before or after sequence assembly. Despite promising results of available polishing tools, there is still room to improve the error correction performance to perform more accurate genome assembly. The errors, particularly those in coding regions, can hamper analysis such as linage identification and variant monitoring. In this work, we developed a novel pipeline, HMMPolish, for correcting (polishing) errors in protein-coding regions of known RNA viruses. This tool can be applied to either raw TGS reads or the assembled sequences of the target virus. By utilizing profile Hidden Markov Models of protein families/domains in known viruses, HMMPolish can correct errors that are ignored by available polishers. We extensively validated HMMPolish on 34 datasets that covered four clinically important viruses, including HIV-1, influenza-A, norovirus, and severe acute respiratory syndrome coronavirus 2. These datasets contain reads with different properties, such as sequencing depth and platforms (PacBio or Nanopore). The benchmark results against popular/representative polishers show that HMMPolish competes favorably on error correction in coding regions of known RNA viruses., (© The Author(s) 2023. Published by Oxford University Press.)

Plasmids are mobile genetic elements that carry important accessory genes. Cataloging plasmids is a fundamental step to elucidate their roles in promoting horizontal gene transfer between bacteria. Next generation sequencing (NGS) is the main source for discovering new plasmids today. However, NGS assembly programs tend to return contigs, making plasmid detection difficult. This problem is particularly grave for metagenomic assemblies, which contain short contigs of heterogeneous origins. Available tools for plasmid contig detection still suffer from some limitations. In particular, alignment-based tools tend to miss diverged plasmids while learning-based tools often have lower precision. In this work, we develop a plasmid detection tool PLASMe that capitalizes on the strength of alignment and learning-based methods. Closely related plasmids can be easily identified using the alignment component in PLASMe while diverged plasmids can be predicted using order-specific Transformer models. By encoding plasmid sequences as a language defined on the protein cluster-based token set, Transformer can learn the importance of proteins and their correlation through positionally token embedding and the attention mechanism. We compared PLASMe and other tools on detecting complete plasmids, plasmid contigs, and contigs assembled from CAMI2 simulated data. PLASMe achieved the highest F1-score. After validating PLASMe on data with known labels, we also tested it on real metagenomic and plasmidome data. The examination of some commonly used marker genes shows that PLASMe exhibits more reliable performance than other tools., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

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., Competing Interests: The authors declare that they have no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press.)

Primary and metastatic lung cancer is a leading cause of cancer-related death and novel therapies are urgently needed. Epidermal growth factor receptor (EGFR) and death receptor (DR) 4/5 are both highly expressed in primary and metastatic non-small cell lung cancer (NSCLC); however, targeting these receptors individually has demonstrated limited therapeutic benefit in patients. In this study, we created and characterized diagnostic and therapeutic stem cells (SC), expressing EGFR-targeted nanobody (EV) fused to the extracellular domain of death DR4/5 ligand (DRL) (EVDRL) that simultaneously targets EGFR and DR4/5, in primary and metastatic NSCLC tumor models. We show that EVDRL targets both cell surface receptors, and induces caspase-mediated apoptosis in a broad spectrum of NSCLC cell lines. Utilizing real-time dual imaging and correlative immunohistochemistry, we show that allogeneic SCs home to tumors and when engineered to express EVDRL, alleviate tumor burden and significantly increase survival in primary and brain metastatic NSCLC. This study reports mechanistic insights into simultaneous targeting of EGFR- and DR4/5 in lung tumors and presents a promising approach for translation into the clinical setting., (© The Author(s) 2023. Published by Oxford University Press.)

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., (© The Author(s) 2023. Published by Oxford University Press.)

Background: Crohn's disease (CD) is a chronic autoimmune disease in which inflammation can progress to complications of stricturing and/or penetrating disease. Real-world data on burden of complicated CD phenotypes are limited., Methods: We analyzed cross-sectional data from the SPARC IBD (Study of a Prospective Adult Research Cohort with Inflammatory Bowel Disease) registry from 2016 to 2020. Four mutually exclusive phenotype cohorts were created: inflammatory CD (CD-I), complicated CD (stricturing CD, penetrating CD, and stricturing and penetrating CD [CD-SP]). Statistical analyses were performed using CD-I as the reference., Results: A total of 1557 patients were identified: CD-I (n = 674, 43.3%), stricturing CD (n = 457, 29.4%), penetrating CD (n = 166, 10.7%), and CD-SP (n = 260, 16.7%). Patients with complicated phenotypes reported significantly greater use of tumor necrosis factor inhibitors (84.2%-86.7% vs 66.0%; P < .001) and corticosteroids (75.3%-82.7% vs 68.0%; P < .001). Patients with CD-SP reported significantly more aphthous ulcer (15.4% vs 10.5%; P < .05), erythema nodosum (6.5% vs 3.6%; P < .05), inflammatory bowel disease-related arthropathy (25.8% vs 17.2%; P < .01), liquid stools (24.2% vs 9.3%; P < .001), nocturnal fecal incontinence (10.8% vs 2.5%; P < .001), and CD-related surgery (77.7% vs 12.2%; P < .001)., Conclusions: Patients with complicated CD phenotypes reported higher rates of active CD-related luminal and extraintestinal manifestations, and underwent more surgeries, despite being more likely to have received biologics than those with CD-I. The potential for early recognition and management of CD-I to prevent progression to complicated phenotypes should be explored in longitudinal studies., (© The Author(s) 2022. Published by Oxford University Press on behalf of Crohn's & Colitis Foundation.)

Motivation: As prevalent extrachromosomal replicons in many bacteria, plasmids play an essential role in their hosts' evolution and adaptation. The host range of a plasmid refers to the taxonomic range of bacteria in which it can replicate and thrive. Understanding host ranges of plasmids sheds light on studying the roles of plasmids in bacterial evolution and adaptation. Metagenomic sequencing has become a major means to obtain new plasmids and derive their hosts. However, host prediction for assembled plasmid contigs still needs to tackle several challenges: different sequence compositions and copy numbers between plasmids and the hosts, high diversity in plasmids, and limited plasmid annotations. Existing tools have not yet achieved an ideal tradeoff between sensitivity and precision on metagenomic assembled contigs., Results: In this work, we construct a hierarchical classification tool named HOTSPOT, whose backbone is a phylogenetic tree of the bacterial hosts from phylum to species. By incorporating the state-of-the-art language model, Transformer, in each node's taxon classifier, the top-down tree search achieves an accurate host taxonomy prediction for the input plasmid contigs. We rigorously tested HOTSPOT on multiple datasets, including RefSeq complete plasmids, artificial contigs, simulated metagenomic data, mock metagenomic data, the Hi-C dataset, and the CAMI2 marine dataset. All experiments show that HOTSPOT outperforms other popular methods., Availability and Implementation: The source code of HOTSPOT is available via: https://github.com/Orin-beep/HOTSPOT., (© The Author(s) 2023. Published by Oxford University Press.)

Summary: Without relying on cultivation, metagenomic sequencing greatly accelerated the novel RNA virus detection. However, it is not trivial to accurately identify RNA viral contigs from a mixture of species. The low content of RNA viruses in metagenomic data requires a highly specific detector, while new RNA viruses can exhibit high genetic diversity, posing a challenge for alignment-based tools. In this work, we developed VirBot, a simple yet effective RNA virus identification tool based on the protein families and the corresponding adaptive score cutoffs. We benchmarked it with seven popular tools for virus identification on both simulated and real sequencing data. VirBot shows its high specificity in metagenomic datasets and superior sensitivity in detecting novel RNA viruses., Availability and Implementation: https://github.com/GreyGuoweiChen/RNA&#95;virus&#95;detector., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2023. Published by Oxford University Press.)

Despite ambiguous environmental inputs, top-down attention biases our subjective perception toward the preferred percepts, via modulating prestimulus neural activity or inducing prestimulus sensory templates that carry concrete internal sensory representations of the preferred percepts. In contrast to frequent changes of behavioral goals in the typical cue-target paradigm, human beings are often engaged in a prolonged task state with only 1 specific behavioral goal. It remains unclear how prestimulus neural signals and sensory templates are modulated in the latter case. To answer this question in the present electroencephalogram study on human subjects, we manipulated sustained task demands toward one of the 2 possible percepts in the bistable Ternus display, emphasizing either temporal integration or segregation. First, the prestimulus peak alpha frequency, which gated the temporal window of temporal integration, was effectively modulated by task demands. Furthermore, time-resolved decoding analyses showed that task demands biased neural representations toward the preferred percepts after the full presentation of bottom-up stimuli. More importantly, sensory templates resembling the preferred percepts emerged even before the bottom-up sensory evidence were sufficient enough to induce explicit percepts. Taken together, task demands modulate both prestimulus alpha frequency and sensory templates, to eventually bias subjective perception toward the preferred percepts., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Analyzing and comparing the effects of labor-saving cultivation modes on photosynthesis, as well as studying their vertical canopy architecture, can improve the tree structure of high-quality and high-yield citrus and selection of labor-saving cultivation modes. The photosynthesis of 1080 leaves of two labor-saving cultivation modes (wide-row and narrow-plant mode and fenced mode) comparing with the traditional mode were measured, and nitrogen content of all leaves and photosynthetic nitrogen use efficiency (PNUE) were determined. Unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) data were used to assess the vertical architecture of three citrus cultivation modes. Results showed that for the wide-row and narrow-plant and traditional modes leaf photosynthetic CO 2 assimilation rate, stomatal conductance, and transpiration rate of the upper layer were significantly higher than those of the middle layer, and values of the middle layer were markedly higher than those of the lower layer. In the fenced mode, a significant difference in photosynthetic factors between the upper and middle layers was not observed. A vertical canopy distribution had a more significant effect on PNUE in the traditional mode. Leaves in the fenced mode had distinct photosynthetic advantages and higher PNUE. UAV-based LiDAR data effectively revealed the differences in the vertical canopy architecture of citrus trees by enabling calculating the density and height percentile of the LiDAR point cloud. The point cloud densities of three cultivation modes were significantly different for all LiDAR density slices, especially at higher canopy heights. The labor-saving modes, particularly the fenced mode, had significantly higher height percentile data., Competing Interests: The authors declare no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nanjing Agricultural University.)

Brain injuries are common complications in patients with thermal burns and are associated with unpleasant outcomes. In clinical settings, it was once believed that brain injuries were not major pathological processes after burn, at least in part due to the unavailability of specific clinical manifestations. Burn-related brain injuries have been studied for more than a century, but the underlying pathophysiology has not been completely clarified. This article reviews the pathological changes in the brain following peripheral burns at the anatomical, histological, cytological, molecular and cognitive levels. Therapeutic indications based on brain injury as well as future directions for research have been summarized and proposed., (© The Author(s) 2023. Published by Oxford University Press.)

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., (© The Author(s) 2022. Published by Oxford University Press.)

Viruses are the most ubiquitous and diverse entities in the biome. Due to the rapid growth of newly identified viruses, there is an urgent need for accurate and comprehensive virus classification, particularly for novel viruses. Here, we present PhaGCN2, which can rapidly classify the taxonomy of viral sequences at the family level and supports the visualization of the associations of all families. We evaluate the performance of PhaGCN2 and compare it with the state-of-the-art virus classification tools, such as vConTACT2, CAT and VPF-Class, using the widely accepted metrics. The results show that PhaGCN2 largely improves the precision and recall of virus classification, increases the number of classifiable virus sequences in the Global Ocean Virome dataset (v2.0) by four times and classifies more than 90% of the Gut Phage Database. PhaGCN2 makes it possible to conduct high-throughput and automatic expansion of the database of the International Committee on Taxonomy of Viruses. The source code is freely available at https://github.com/KennthShang/PhaGCN2.0., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Motivation: RNA viruses tend to mutate constantly. While many of the variants are neutral, some can lead to higher transmissibility or virulence. Accurate assembly of complete viral genomes enables the identification of underlying variants, which are essential for studying virus evolution and elucidating the relationship between genotypes and virus properties. Recently, third-generation sequencing platforms such as Nanopore sequencers have been used for real-time virus sequencing for Ebola, Zika, coronavirus disease 2019, etc. However, their high per-base error rate prevents the accurate reconstruction of the viral genome., Results: In this work, we introduce a new tool, AccuVIR, for viral genome assembly and polishing using error-prone long reads. It can better distinguish sequencing errors from true variants based on the key observation that sequencing errors can disrupt the gene structures of viruses, which usually have a high density of coding regions. Our experimental results on both simulated and real third-generation sequencing data demonstrated its superior performance on generating more accurate viral genomes than generic assembly or polish tools., Availability and Implementation: The source code and the documentation of AccuVIR are available at https://github.com/rainyrubyzhou/AccuVIR., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)

Motivation: Lacking strict proofreading mechanisms, many RNA viruses can generate progeny with slightly changed genomes. Being able to characterize highly similar genomes (i.e. haplotypes) in one virus population helps study the viruses' evolution and their interactions with the host/other microbes. High-throughput sequencing data has become the major source for characterizing viral populations. However, the inherent limitation on read length by next-generation sequencing makes complete haplotype reconstruction difficult., Results: In this work, we present a new tool named HaploDMF that can construct complete haplotypes using third-generation sequencing (TGS) data. HaploDMF utilizes a deep matrix factorization model with an adapted loss function to learn latent features from aligned reads automatically. The latent features are then used to cluster reads of the same haplotype. Unlike existing tools whose performance can be affected by the overlap size between reads, HaploDMF is able to achieve highly robust performance on data with different coverage, haplotype number and error rates. In particular, it can generate more complete haplotypes even when the sequencing coverage drops in the middle. We benchmark HaploDMF against the state-of-the-art tools on simulated and real sequencing TGS data on different viruses. The results show that HaploDMF competes favorably against all others., Availability and Implementation: The source code and the documentation of HaploDMF are available at https://github.com/dhcai21/HaploDMF., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)

Prokaryotic viruses, which infect bacteria and archaea, are key players in microbial communities. Predicting the hosts of prokaryotic viruses helps decipher the dynamic relationship between microbes. Experimental methods for host prediction cannot keep pace with the fast accumulation of sequenced phages. Thus, there is a need for computational host prediction. Despite some promising results, computational host prediction remains a challenge because of the limited known interactions and the sheer amount of sequenced phages by high-throughput sequencing technologies. The state-of-the-art methods can only achieve 43% accuracy at the species level. In this work, we formulate host prediction as link prediction in a knowledge graph that integrates multiple protein and DNA-based sequence features. Our implementation named CHERRY can be applied to predict hosts for newly discovered viruses and to identify viruses infecting targeted bacteria. We demonstrated the utility of CHERRY for both applications and compared its performance with 11 popular host prediction methods. To our best knowledge, CHERRY has the highest accuracy in identifying virus-prokaryote interactions. It outperforms all the existing methods at the species level with an accuracy increase of 37%. In addition, CHERRY's performance on short contigs is more stable than other tools., (© The Author(s) 2022. Published by Oxford University Press.)

Motivation: Bacteriophages are viruses infecting bacteria. Being key players in microbial communities, they can regulate the composition/function of microbiome by infecting their bacterial hosts and mediating gene transfer. Recently, metagenomic sequencing, which can sequence all genetic materials from various microbiome, has become a popular means for new phage discovery. However, accurate and comprehensive detection of phages from the metagenomic data remains difficult. High diversity/abundance, and limited reference genomes pose major challenges for recruiting phage fragments from metagenomic data. Existing alignment-based or learning-based models have either low recall or precision on metagenomic data., Results: In this work, we adopt the state-of-the-art language model, Transformer, to conduct contextual embedding for phage contigs. By constructing a protein-cluster vocabulary, we can feed both the protein composition and the proteins' positions from each contig into the Transformer. The Transformer can learn the protein organization and associations using the self-attention mechanism and predicts the label for test contigs. We rigorously tested our developed tool named PhaMer on multiple datasets with increasing difficulty, including quality RefSeq genomes, short contigs, simulated metagenomic data, mock metagenomic data and the public IMG/VR dataset. All the experimental results show that PhaMer outperforms the state-of-the-art tools. In the real metagenomic data experiment, PhaMer improves the F1-score of phage detection by 27%., (© The Author(s) 2022. Published by Oxford University Press.)

Background: Evidence has suggested that cytokine storms may be associated with T cell exhaustion (TEX) in COVID-19. However, the interaction mechanism between cytokine storms and TEX remains unclear., Methods: With the aim of dissecting the molecular relationship of cytokine storms and TEX through single-cell RNA sequencing data analysis, we identified 14 cell types from bronchoalveolar lavage fluid of COVID-19 patients and healthy people. We observed a novel subset of severely exhausted CD8 T cells (Exh T&#95;CD8) that co-expressed multiple inhibitory receptors, and two macrophage subclasses that were the main source of cytokine storms in bronchoalveolar., Results: Correlation analysis between cytokine storm level and TEX level suggested that cytokine storms likely promoted TEX in severe COVID-19. Cell-cell communication analysis indicated that cytokines (e.g. CXCL10, CXCL11, CXCL2, CCL2, and CCL3) released by macrophages acted as ligands and significantly interacted with inhibitory receptors (e.g. CXCR3, DPP4, CCR1, CCR2, and CCR5) expressed by Exh T&#95;CD8. These interactions formed the cytokine-receptor axes, which were also verified to be significantly correlated with cytokine storms and TEX in lung squamous cell carcinoma., Conclusions: Cytokine storms may promote TEX through cytokine-receptor axes and be associated with poor prognosis in COVID-19. Blocking cytokine-receptor axes may reverse TEX. Our finding provides novel insights into TEX in COVID-19 and new clues for cytokine-targeted immunotherapy development., (© The Author(s) 2022. Published by Oxford University Press on behalf of the West China School of Medicine & West China Hospital of Sichuan University.)

Motivation: Most RNA viruses lack strict proofreading during replication. Coupled with a high replication rate, some RNA viruses can form a virus population containing a group of genetically related but different haplotypes. Characterizing the haplotype composition in a virus population is thus important to understand viruses' evolution. Many attempts have been made to reconstruct viral haplotypes using next-generation sequencing (NGS) reads. However, the short length of NGS reads cannot cover distant single-nucleotide variants, making it difficult to reconstruct complete or near-complete haplotypes. Given the fast developments of third-generation sequencing technologies, a new opportunity has arisen for reconstructing full-length haplotypes with long reads., Results: In this work, we developed a new tool, RVHaplo to reconstruct haplotypes for known viruses from long reads. We tested it rigorously on both simulated and real viral sequencing data and compared it against other popular haplotype reconstruction tools. The results demonstrated that RVHaplo outperforms the state-of-the-art tools for viral haplotype reconstruction from long reads. Especially, RVHaplo can reconstruct the rare (1% abundance) haplotypes that other tools usually missed., Availability and Implementation: The source code and the documentation of RVHaplo are available at https://github.com/dhcai21/RVHaplo., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background and Aims: Protein profiling in patients with inflammatory bowel diseases [IBD] for diagnostic and therapeutic purposes is underexplored. This study analysed the association between phenotype, genotype, and the plasma proteome in IBD., Methods: A total of 92 inflammation-related proteins were quantified in plasma of 1028 patients with IBD (567 Crohn's disease [CD]; 461 ulcerative colitis [UC]) and 148 healthy individuals to assess protein-phenotype associations. Corresponding whole-exome sequencing and global screening array data of 919 patients with IBD were included to analyse the effect of genetics on protein levels (protein quantitative trait loci [pQTL] analysis). Intestinal mucosal RNA sequencing and faecal metagenomic data were used for complementary analyses., Results: Thirty-two proteins were differentially abundant between IBD and healthy individuals, of which 22 proteins were independent of active inflammation; 69 proteins were associated with 15 demographic and clinical factors. Fibroblast growth factor-19 levels were decreased in CD patients with ileal disease or a history of ileocecal resection. Thirteen novel cis-pQTLs were identified and 10 replicated from previous studies. One trans-pQTL of the fucosyltransferase 2 [FUT2] gene [rs602662] and two independent cis-pQTLs of C-C motif chemokine 25 [CCL25] affected plasma CCL25 levels. Intestinal gene expression data revealed an overlapping cis-expression [e]QTL-variant [rs3745387] of the CCL25 gene. The FUT2 rs602662 trans-pQTL was associated with reduced abundances of faecal butyrate-producing bacteria., Conclusions: This study shows that genotype and multiple disease phenotypes strongly associate with the plasma inflammatory proteome in IBD, and identifies disease-associated pathways that may help to improve disease management in the future., (© The Author(s) 2021. Published by Oxford University Press on behalf of European Crohn’s and Colitis Organisation.)

With advances in library construction protocols and next-generation sequencing technologies, viral metagenomic sequencing has become the major source for novel virus discovery. Conducting taxonomic classification for metagenomic data is an important means to characterize the viral composition in the underlying samples. However, RNA viruses are abundant and highly diverse, jeopardizing the sensitivity of comparison-based classification methods. To improve the sensitivity of read-level taxonomic classification, we developed an RNA-dependent RNA polymerase (RdRp) gene-based read classification tool RdRpBin. It combines alignment-based strategy with machine learning models in order to fully exploit the sequence properties of RdRp. We tested our method and compared its performance with the state-of-the-art tools on the simulated and real sequencing data. RdRpBin competes favorably with all. In particular, when the query RNA viruses share low sequence similarity with the known viruses ($\sim 0.4$), our tool can still maintain a higher F-score than the state-of-the-art tools. The experimental results on real data also showed that RdRpBin can classify more RNA viral reads with a relatively low false-positive rate. Thus, RdRpBin can be utilized to classify novel and diverged RNA viruses., (© The Author(s) 2022. Published by Oxford University Press.)

Background: It is unclear if smoking-related DNA methylation represents a causal pathway between smoking and risk of lung cancer. We sought to identify novel smoking-related DNA methylation sites in blood, with repeated measurements, and to appraise the putative role of DNA methylation in the pathway between smoking and lung cancer development., Methods: We derived a nested case-control study from the Trøndelag Health Study (HUNT), including 140 incident patients who developed lung cancer during 2009-13 and 140 controls. We profiled 850 K DNA methylation sites (Illumina Infinium EPIC array) in DNA extracted from blood that was collected in HUNT2 (1995-97) and HUNT3 (2006-08) for the same individuals. Epigenome-wide association studies (EWAS) were performed for a detailed smoking phenotype and for lung cancer. Two-step Mendelian randomization (MR) analyses were performed to assess the potential causal effect of smoking on DNA methylation as well as of DNA methylation (13 sites as putative mediators) on risk of lung cancer., Results: The EWAS for smoking in HUNT2 identified associations at 76 DNA methylation sites (P < 5 × 10-8), including 16 novel sites. Smoking was associated with DNA hypomethylation in a dose-response relationship among 83% of the 76 sites, which was confirmed by analyses using repeated measurements from blood that was collected at 11 years apart for the same individuals. Two-step MR analyses showed evidence for a causal effect of smoking on DNA methylation but no evidence for a causal link between DNA methylation and the risk of lung cancer., Conclusions: DNA methylation modifications in blood did not seem to represent a causal pathway linking smoking and the lung cancer risk., (© The Author(s) 2021. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Dysfunction of Tumour Suppressor Genes (TSGs) is a common feature in carcinogenesis. Epigenetic abnormalities including DNA hypermethylation or aberrant histone modifications in promoter regions have been described for interpreting TSG inactivation. However, in many instances, how TSGs are silenced in tumours are largely unknown. Given that miRNA with low expression in tumours is another recognized signature, we hypothesize that low expression of miRNA may reduce the activity of TSG related enhancers and further lead to inactivation of TSG during cancer development. Here, we reported that low expression of miRNA in cancer as a recognized signature leads to loss of function of TSGs in breast cancer. In 157 paired breast cancer and adjacent normal samples, tumour suppressor gene GPER1 and miR-339 are both downregulated in Luminal A/B and Triple Negative Breast Cancer subtypes. Mechanistic investigations revealed that miR-339 upregulates GPER1 expression in breast cancer cells by switching on the GPER1 enhancer, which can be blocked by enhancer deletion through the CRISPR/Cas9 system. Collectively, our findings reveal novel mechanistic insights into TSG dysfunction in cancer development, and provide evidence that reactivation of TSG by enhancer switching may be a promising alternative strategy for clinical breast cancer treatment., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Motivation: Bacteriophages (aka phages), which mainly infect bacteria, play key roles in the biology of microbes. As the most abundant biological entities on the planet, the number of discovered phages is only the tip of the iceberg. Recently, many new phages have been revealed using high-throughput sequencing, particularly metagenomic sequencing. Compared to the fast accumulation of phage-like sequences, there is a serious lag in taxonomic classification of phages. High diversity, abundance and limited known phages pose great challenges for taxonomic analysis. In particular, alignment-based tools have difficulty in classifying fast accumulating contigs assembled from metagenomic data., Results: In this work, we present a novel semi-supervised learning model, named PhaGCN, to conduct taxonomic classification for phage contigs. In this learning model, we construct a knowledge graph by combining the DNA sequence features learned by convolutional neural network and protein sequence similarity gained from gene-sharing network. Then we apply graph convolutional network to utilize both the labeled and unlabeled samples in training to enhance the learning ability. We tested PhaGCN on both simulated and real sequencing data. The results clearly show that our method competes favorably against available phage classification tools., Availability and Implementation: The source code of PhaGCN is available via: https://github.com/KennthShang/PhaGCN., (© The Author(s) 2021. Published by Oxford University Press.)

Objectives: Clinical endometritis is a common reproductive disorder in mammals that seriously endangers animal health and causes economic losses worldwide. This study aims to use lipopolysaccharide and Trueperella pyogenes exotoxin as modelling reagents (LC) to perfuse the mouse uterus in order to establish a model of clinical endometritis and to investigate the anti-inflammatory and antioxidant effects of chlorogenic acid (CGA)., Methods: In this study, five LC uterine perfusions were selected to model clinical endometritis. The anti-inflammatory and antioxidant effects of CGA were clarified. Through HE staining, proinflammatory cytokines, blood testing, NFκB and Keap1/Nrf2 signalling pathways and other index changes to explore the protection mechanism of CGA., Key Findings: After CGA treatment, the appearance, inflammatory damage and blood indicators of the mouse uterus returned to normal. Simultaneously, CGA could inhibit the activation of NFκB and reduce the release of inflammatory cytokines; CGA could also activate Keap1/Nrf2, promote the dissociation of Keap1 and Nrf2 and significantly increase the expression of the downstream genes HO-1 and NQO1., Conclusions: The above results together explain that five LC uterine perfusions can be used to establish a mouse model of clinical endometritis. CGA can treat clinical endometritis by activating Keap1/Nrf2 and inhibiting the NFκB signalling pathway., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Royal Pharmaceutical Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Sterol-regulatory element binding proteins (SREBPs) are the key transcriptional regulators of lipid metabolism. The activation of SREBP requires translocation of the SREBP precursor from the endoplasmic reticulum to the Golgi, where it is sequentially cleaved by site-1 protease (S1P) and site-2 protease and releases a nuclear form to modulate gene expression. To search for new genes regulating cholesterol metabolism, we perform a genome-wide CRISPR/Cas9 knockout screen and find that partner of site-1 protease (POST1), encoded by C12ORF49, is critically involved in the SREBP signaling. Ablation of POST1 decreases the generation of nuclear SREBP and reduces the expression of SREBP target genes. POST1 binds S1P, which is synthesized as an inactive protease (form A) and becomes fully mature via a two-step autocatalytic process involving forms B'/B and C'/C. POST1 promotes the generation of the functional S1P-C'/C from S1P-B'/B (canonical cleavage) and, notably, from S1P-A directly (non-canonical cleavage) as well. This POST1-mediated S1P activation is also essential for the cleavages of other S1P substrates including ATF6, CREB3 family members and the α/β-subunit precursor of N-acetylglucosamine-1-phosphotransferase. Together, we demonstrate that POST1 is a cofactor controlling S1P maturation and plays important roles in lipid homeostasis, unfolded protein response, lipoprotein metabolism and lysosome biogenesis.

Regulatory protein genes and microRNAs (miRNAs) play important roles in response to abiotic and biotic stress, and the biosynthesis of secondary metabolites in plants. However, their responses to selenium (Se) stimuli have not been comprehensively studied in Pueraria lobata (Willd.) Ohwi, a selenocompound-rich medicinal and edible plant. In this study, we identified a total of 436/556/1161/624 transcription factors, 134/157/308/172 transcriptional regulators, and 341/456/250/518 protein kinases, which were co-expressed with at least one selenocompound-related structural gene/sulfate transporter or phosphate transporter/reactive oxygen species (ROS) scavenging structural gene/isoflavone-related structural gene, respectively. Then, we identified a total of 87 expressed miRNAs by Se disposure, in which 11 miRNAs, including miR171f-3p, miR390b-3P, miR-N111b, miR-N118, miR-N30, miR-N38-3P, miR-N61a, miR-N61b, miR-N80-3p, miR-N84-3P, and miR-N90.2-3P, were significantly upregulated. We also identified a total of 1172 target genes for the 87 expressed miRNAs. Gene Ontology enrichment analysis of these target genes showed that regulation of transcription, DNA-templated, integral component of membrane, nucleus, ATP binding, and plasma membrane are the top five subclassifications. Finally, we revealed that 5 miRNAs targeted 10 regulatory protein genes, which are highly correlated with at least one selenocompound-related structural gene or transporter gene; 5 miRNAs targeted 10 regulatory protein genes, which are highly correlated with at least one ROS scavenging structural gene; and 5 miRNAs targeted 9 regulatory protein genes, which are potentially involved in the isoflavone biosynthesis. Overall, the study provides us the comprehensive insight into the roles of regulatory proteins and miRNAs in response to Se stimuli in P. lobata., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)