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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 (RdRP) 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 identity RNA viruses of all type, 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., (© The Author(s) 2024. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)

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., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2024. Published by Oxford University Press.)

Background: Steroid hormones (SH) during pregnancy are associated with the development of gestational diabetes mellitus (GDM). Early and mid-Down syndrome screening is used to assess the risk of Down syndrome in the fetus. It is unclear whether changes in SH during this period can be used as an early predictor of GDM., Methods: This study was a multicenter, longitudinal cohort study. GDM is diagnosed by an oral glucose tolerance test (OGTT) between 24 and 28 weeks of gestation. We measured SH levels at early and mid-Down syndrome screening, respectively. Based on the SH changes, logistic regression analysis was used to construct a prediction model for GDM. Finally, evaluated the model's predictive performance by creating a receiver operating characteristic curve (ROC) and performing external validation., Results: This study enrolled 193 pregnant women (discovery cohort, n = 157; validation cohort, n = 36). SH changes occur dynamically after pregnancy. At early Down syndrome screening, only cortisol (F) (p < 0.05, 95 % CI 4780.95-46083.68) was elevated in GDM. At mid-Down syndrome screening, free testosterone (FT) (p < 0.01, 95 % CI 0.10-0.55) and estradiol (E2) (p < 0.05, 95 % CI 203.55-1784.78) were also significantly elevated. There were significant differences in the rates of change in E2 (Fold change (FC) = 1.3425, p = 0.0072), albumin (ALB) (FC=1.5759, p = 0.0117), and dihydrotestosterone (DHT) (FC=-2.1234, p = 0.0165) between GDM and no-GDM. Stepwise logistic regression analysis resulted in the best predictive model, including six variables (Δweight, ΔF, Δcortisone (E), ΔE2, Δprogesterone (P), ΔDHT). The area under the curve for this model was 0.791, and for the external validation cohort, it was 0.799., Conclusions: A GDM prediction model can be constructed using SH measures during early and mid-Down syndrome screening., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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.)

Polyhydroxyalkanoates (PHAs) could be used to make sustainable, biodegradable plastics. However, the precise and accurate mechanistic modeling of PHA biosynthesis, especially medium-chain-length PHA (mcl-PHA), for yield improvement remains a challenge to biology. PHA biosynthesis is typically triggered by nitrogen limitation and tends to peak at an optimal carbon-to-nitrogen (C/N) ratio. Specifically, simulation of the underlying dynamic regulation mechanisms for PHA bioprocess is a bottleneck owing to surfeit model complexity and current modeling philosophies for uncertainty. To address this issue, we proposed a quantum-like decision-making model to encode gene expression and regulation events as hidden layers by the general transformation of a density matrix, which uses the interference of probability amplitudes to provide an empirical-level description for PHA biosynthesis. We implemented our framework modeling the biosynthesis of mcl-PHA in Pseudomonas putida with respect to external C/N ratios, showing its optimization production at maximum PHA production of 13.81% cell dry mass (CDM) at the C/N ratio of 40:1. The results also suggest the degree of P. putida's preference in channeling carbon towards PHA production as part of the bacterium's adaptative behavior to nutrient stress using quantum formalism. Generic parameters (k D , k N and theta θ) obtained based on such quantum formulation, representing P. putida's PHA biosynthesis with respect to external C/N ratios, was discussed. This work offers a new perspective on the use of quantum theory for PHA production, demonstrating its application potential for other bioprocesses., (© 2024. The Author(s).)

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.)

Leukemia stem cells (LSCs) are recognized as the root cause of leukemia initiation, relapse, and drug resistance. Lipid species are highly abundant and essential component of human cells, which often changed in tumor microenvironment. LSCs remodel lipid metabolism to sustain the stemness. However, there is no useful lipid related biomarker has been approved for clinical practice in AML prediction and treatment. Here, we constructed and verified fatty acid metabolism-related risk score (LFMRS) model based on TCGA database via a series of bioinformatics analysis, univariate COX regression analysis, and multivariate COX regression analysis, and found that the LFMRS model could be an independent risk factor and predict the survival time of AML patients combined with age. Moreover, we revealed that Galectin-1 (LGALS1, the key gene of LFMRS) was highly expressed in LSCs and associated with poor prognosis of AML patients, and LGALS1 repression inhibited AML cell and LSC proliferation, enhanced cell apoptosis, and decreased lipid accumulation in vitro. LGALS1 repression curbed AML progression, lipid accumulation, and CD8 + T and NK cell counts in vivo. Our study sheds light on the roles of LFMRS (especially LGALS1) model in AML, and provides information that may help clinicians improve patient prognosis and develop personalized treatment regimens for AML., (© 2024. The Author(s).)

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.)

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.)

Objectives: The respiratory tract is the portal of entry for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although a variety of respiratory pathogens other than SARS-CoV-2 have been associated with severe cases of COVID-19 disease, the dynamics of the upper respiratory microbiota during disease the course of disease, and how they impact disease manifestation, remain uncertain., Methods: We collected 349 longitudinal upper respiratory samples from a cohort of 65 COVID-19 patients (cohort 1), 28 samples from 28 recovered COVID-19 patients (cohort 2), and 59 samples from 59 healthy controls (cohort 3). All COVID-19 patients originated from the earliest stage of the epidemic in Wuhan. Based on a modified clinical scale, the disease course was divided into five clinical disease phases (pseudotimes): "Healthy" (pseudotime 0), "Incremental" (pseudotime 1), "Critical" (pseudotime 2), "Complicated" (pseudotime 3), "Convalescent" (pseudotime 4), and "Long-term follow-up" (pseudotime 5). Using meta-transcriptomics, we investigated the features and dynamics of transcriptionally active microbes in the upper respiratory tract (URT) over the course of COVID-19 disease, as well as its association with disease progression and clinical outcomes., Results: Our results revealed that the URT microbiome exhibits substantial heterogeneity during disease course. Two clusters of microbial communities characterized by low alpha diversity and enrichment for multiple pathogens or potential pathobionts (including Acinetobacter and Candida) were associated with disease progression and a worse clinical outcome. We also identified a series of microbial indicators that classified disease progression into more severe stages. Longitudinal analysis revealed that although the microbiome exhibited complex and changing patterns during COVID-19, a restoration of URT microbiomes from early dysbiosis toward more diverse status in later disease stages was observed in most patients. In addition, a group of potential pathobionts were strongly associated with the concentration of inflammatory indicators and mortality., Conclusion: This study revealed strong links between URT microbiome dynamics and disease progression and clinical outcomes in COVID-19, implying that the treatment of severe disease should consider the full spectrum of microbial pathogens present., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

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.)

As important mobile genetic elements, phages support the spread of antibiotic resistance genes (ARGs). Previous analyses of metaviromes or metagenome-assembled genomes (MAGs) failed to assess the extent of ARGs transferred by phages, particularly in the generation of antibiotic pathogens. Therefore, we have developed a bioinformatic pipeline that utilizes deep learning techniques to identify ARG-carrying phages and predict their hosts, with a special focus on pathogens. Using this method, we discovered that the predominant types of ARGs carried by temperate phages in a typical landscape lake, which is fully replenished by reclaimed water, were related to multidrug resistance and β-lactam antibiotics. MAGs containing virulent factors (VFs) were predicted to serve as hosts for these ARG-carrying phages, which suggests that the phages may have the potential to transfer ARGs. In silico analysis showed a significant positive correlation between temperate phages and host pathogens (R = 0.503, p < 0.001), which was later confirmed by qPCR. Interestingly, these MAGs were found to be more abundant than those containing both ARGs and VFs, especially in December and March. Seasonal variations were observed in the abundance of phages harboring ARGs (from 5.62 % to 21.02 %) and chromosomes harboring ARGs (from 18.01 % to 30.94 %). In contrast, the abundance of plasmids harboring ARGs remained unchanged. In summary, this study leverages deep learning to analyze phage-transferred ARGs and demonstrates an alternative method to track the production of potential antibiotic-resistant pathogens by metagenomics that can be extended to microbiological risk assessment., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

The adjuvant and/or vector significantly affect a vaccine's efficacy. Although traditional adjuvants such as alum have contributed to vaccine development, deficiencies in the induction of cellular and mucosal immunity have limited their further promotion. Salmonella vectors have unique advantages for establishing cellular and mucosal immunity due to mucosal pathways of invasion and intracellular parasitism. In addition, Salmonella vectors can activate multiple innate immune pathways, thereby promoting adaptive immune responses. In this work, the attenuated Salmonella enterica serovar Choleraesuis (S. Choleraesuis) vector rSC0016 was used to deliver the conserved protective antigen HPS&#95;06257 of Glaesserella parasuis (G. parasuis), generating a novel recombinant strain rSC0016(pS-HPS&#95;06257). The rSC0016(pS-HPS&#95;06257) can express and deliver the HPS&#95;06257 protein to the lymphatic system of the host. In comparison to HPS&#95;06257 adjuvanted with alum, rSC0016(pS-HPS&#95;06257) significantly increased TLR4 and TLR5 activation in mice as well as the levels of proinflammatory cytokines. In addition, rSC0016 promoted a greater degree of maturation in bone marrow-derived dendritic cells (BMDCs) than alum. The specific humoral, mucosal, and cellular immune responses against HPS&#95;06257 in mice immunized with rSC0016(pS-HPS&#95;06257) were significantly higher than those of HPS&#95;06257 adjuvanted with alum. HPS&#95;06257 delivered by the S. Choleraesuis vector induces a Th1-biased Th1/Th2 mixed immune response, while HPS adjuvanted with alum can only induce a Th2-biased immune response. HPS&#95;06257 adjuvanted with alum only causes opsonophagocytic activity (OPA) responses against a homologous strain (G. parasuis serotype 5, GPS5), whereas rSC0016(pS-HPS&#95;06257) could generate cross-OPA responses against a homologous strain and a heterologous strain (G. parasuis serotype 12, GPS12). Ultimately, HPS&#95;06257 adjuvanted with alum protected mice against lethal doses of GPS5 challenge by 60 % but failed to protect mice against lethal doses of GPS12. In contrast, mice immunized with rSC0016(pS-HPS&#95;06257) had 100 % or 80 % survival when challenged with lethal doses of GPS5 or GPS12, respectively. Altogether, the S. Choleraesuis vector rSC0016 could potentially generate an improved innate immune response and an improved adaptive immunological response compared to the traditional alum adjuvant, offering a novel concept for the development of a universal G. parasuis vaccine., Competing Interests: Declaration of Competing Interest The authors declare that they have no competing interests., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Inflammation and nutrition related proteins participate in the development of acute myeloid leukemia (AML). It has been reported that the albumin-to-fibrinogen ratio (AFR) could serve as a prognostic indicator in patients with malignancy, but the precise relevance of AML is unclear. This study aimed to evaluate the effect of AFR on survival prognosis in patients with AML. We analyzed 227 patients newly diagnosed with non-M3 AML. AFR was calculated as albumin divided by fibrinogen. Based on the cutoff point from X-tile program, patients were divided into AFR-high (38.8%) and AFR-low (61.2%) groups. AFR-low group showed a poorer complete remission rate (P < 0.001) and median time to relapse (P = 0.026), while the mortality was higher (P = 0.009) than AFR-high ones. According to the log-rank test, AFR-low group had shorter OS (P < 0.001) and DFS (P = 0.034). Multivariate analysis identified AFR, ELN risk, bone marrow transplant, and hemoglobin as independent prognostic variables associated with OS. A visualized nomogram for predicting OS was performed. The C-index (0.75), calibration plots, and decision curve analyses of new model showed better discrimination, calibration, and net benefits than the ELN risk model. The time-dependent receiver operating characteristic (ROC) curve of 1-, 2-, and 3-year also functioned well (AUC, 0.81, 0.93 and 0.90, respectively). Our study provided a comprehensive view of AFR which could be an independent prognostic indicator in AML patients. The prognostic model utilized readily available information from ordinary clinical practice to improve predictive performance, identify risks, and assist in therapeutic decision-making., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

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.)

Objective: This study investigated the relationship between maternal gestational weight gain (GWG) and the risk of adverse pregnancy outcomes in gestational diabetes mellitus (GDM)-negative pregnant women., Methods: We did a retrospective cohort study between 1 July 2017, and 1 January 2020, at Women's Hospital, Zhejiang University School of Medicine. Firstly, pregnant women were divided into subgroups according to the entire GWG (inadequate GWG, adequate GWG, and excessive GWG) and GDM status (positive and negative) during pregnancy. Secondly, the whole population of pregnant women with GDM was used as a reference to evaluate the relationship between GWG and adverse pregnancy outcomes in GDM-negative pregnant women. Lastly, subgroup analysis was conducted based on pre-pregnancy body mass index (pp-BMI)., Results: A total of 30,910 pregnant women were analysed. Included pregnancy women were divided into three groups based on GWG: 7569 (24.49%) pregnancy women had inadequate GWG, 13088 (42.34%) had adequate GWG, and 10,253 (33.17%) had excessive GWG. In addition to preterm birth and small for gestational age (SGA), the incidence of macrosomia and large for gestational age (LGA) continues to increase from inadequate GWG to excessive GWG groups. Pregnant women without GDM who have excessive GWG are at higher risk of macrosomia and LGA than pregnant women with GDM. Moreover, this risk increased with increasing pp-BMI. Pregnant women without GDM with inadequate GWG were at risk of preterm birth regardless of pp-BMI. Only those with inadequate GWG and pp-BMI < 18.5 kg/m 2 had an increased risk of SGA., Conclusions: In conclusion, inappropriate GWG is strongly associated with adverse pregnancy outcomes, even if they do not have GDM. Therefore, this population should receive attention and management before and during pregnancy.Impact Statement What is already known on this subject? Several studies have focused on the GDM population and the risk of adverse pregnancy outcomes, but few have focused on GDM-negative populations. This is because GDM-negative women are perceived to be "safe," leading to less focus on themselves, which can lead to subsequent excessive weight gain during pregnancy. Whether this factor increases the risk of adverse pregnancy outcomes in this population remains unknown. What do the results of this study add? Our study found an inverse relationship between GWG and GDM. Therefore, our study focuses on this group of GDM-negative pregnant women. Their excessive weight gain increases the risk of adverse pregnancy outcomes, even higher than GDM pregnant women. What are the implications of these findings for clinical practice and/or further research? GWG is associated with adverse pregnancy outcomes. Therefore, pregnant women without GDM also need increased attention and management of their weight before and during pregnancy. Prenatal care providers can utilise tools such as diet, exercise counselling, weight tracking, and setting weight gain goals to reduce inappropriate weight gain and mitigate its adverse effects on pregnancy outcomes.

Engineered vector-based in vivo protein delivery platforms have made significant progress for both prophylactic and therapeutic applications. However, the lack of effective release strategies results in foreign cargo being trapped within the vector, restricting the provision of significant performance benefits and enhanced therapeutic results compared to traditional vaccines. Herein, the development of a Salmonella mRNA interferase regulation vector (SIRV) system is reported to overcome this challenge. The genetic circuits are engineered that (1) induce self-lysis to release foreign antigens into target cells and (2) activate the cytosolic surveillance cGAS-STING axis by releasing DNA into the cytoplasm. Delayed synthesis of the MazF interferase regulates differential mRNA cleavage, resulting in a 36-fold increase in the delivery of foreign antigens and modest activation of the inflammasome, which collectively contribute to the marked maturation of antigen-presenting cells (APCs). Bacteria delivering the protective antigen SaoA exhibits excellent immunogenicity and safety in mouse and pig models, significantly improving the survival rate of animals challenged with multiple serotypes of Streptococcus suis. Thus, the SIRV system enables the effective integration of various modular components and antigen cargos, allowing for the generation of an extensive range of intracellular protein delivery systems using multiple bacterial species in a highly efficient manner., (© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.)

By soaking microRNAs (miRNAs), long non-coding RNAs (lncRNAs) have the potential to regulate gene expression. Few methods have been created based on this mechanism to anticipate the lncRNA-gene relationship prediction. Hence, we present lncRNA-Top to forecast potential lncRNA-gene regulation relationships. Specifically, we constructed controlled deep-learning methods using 12417 lncRNAs and 16127 genes. We have provided retrospective and innovative views among negative sampling, random seeds, cross-validation, metrics, and independent datasets. The AUC, AUPR, and our defined precision@k were leveraged to evaluate performance. In-depth case studies demonstrate that 47 out of 100 projected top unknown pairings were recorded in publications, supporting the predictive power. Our additional software can annotate the scores with target candidates. The lncRNA-Top will be a helpful tool to uncover prospective lncRNA targets and better comprehend the regulatory processes of lncRNAs., Competing Interests: The authors declare no competing interest., (© 2023 The Author(s).)

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.)

Background: Bacterial strains under the same species can exhibit different biological properties, making strain-level composition analysis an important step in understanding the dynamics of microbial communities. Metagenomic sequencing has become the major means for probing the microbial composition in host-associated or environmental samples. Although there are a plethora of composition analysis tools, they are not optimized to address the challenges in strain-level analysis: highly similar strain genomes and the presence of multiple strains under one species in a sample. Thus, this work aims to provide a high-resolution and more accurate strain-level analysis tool for short reads., Results: In this work, we present a new strain-level composition analysis tool named StrainScan that employs a novel tree-based k-mers indexing structure to strike a balance between the strain identification accuracy and the computational complexity. We tested StrainScan extensively on a large number of simulated and real sequencing data and benchmarked StrainScan with popular strain-level analysis tools including Krakenuniq, StrainSeeker, Pathoscope2, Sigma, StrainGE, and StrainEst. The results show that StrainScan has higher accuracy and resolution than the state-of-the-art tools on strain-level composition analysis. It improves the F1 score by 20% in identifying multiple strains at the strain level., Conclusions: By using a novel k-mer indexing structure, StrainScan is able to provide strain-level analysis with higher resolution than existing tools, enabling it to return more informative strain composition analysis in one sample or across multiple samples. StrainScan takes short reads and a set of reference strains as input and its source codes are freely available at https://github.com/liaoherui/StrainScan . Video Abstract., (© 2023. BioMed Central Ltd., part of Springer Nature.)

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.)

Acute myeloid leukemia (AML) is a hematological malignancy with an alarming mortality rate. The development of novel therapeutic targets or drugs for AML is urgently needed. Ferroptosis is a form of regulated cell death driven by iron-dependent lipid peroxidation. Recently, ferroptosis has emerged as a novel method for targeting cancer, including AML. Epigenetic dysregulation is a hallmark of AML, and a growing body of evidence suggests that ferroptosis is subject to epigenetic regulation. Here, we identified protein arginine methyltransferase 1 (PRMT1) as a ferroptosis regulator in AML. The type I PRMT inhibitor GSK3368715 promoted ferroptosis sensitivity in vitro and in vivo. Moreover, PRMT1-knockout cells exhibited significantly increased sensitivity to ferroptosis, suggesting that PRMT1 is the primary target of GSK3368715 in AML. Mechanistically, both GSK3368715 and PRMT1 knockout upregulated acyl-CoA synthetase long-chain family member 1 (ACSL1), which acts as a ferroptosis promoter by increasing lipid peroxidation. Knockout ACSL1 reduced the ferroptosis sensitivity of AML cells following GSK3368715 treatment. Additionally, the GSK3368715 treatment reduced the abundance of H4R3me2a, the main histone methylation modification mediated by PRMT1, in both genome-wide and ACSL1 promoter regions. Overall, our results demonstrated a previously unknown role of the PRMT1/ACSL1 axis in ferroptosis and suggested the potential value and applications of the combination of PRMT1 inhibitor and ferroptosis inducers in AML treatment., (© 2023 Wiley Periodicals LLC.)

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.)

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.)

Background: Nirmatrelvir-ritonavir (Paxlovid) and ensitrelvir are 3-chymotrypsin-like cysteine protease (3CL pro ) inhibitors which have been approved for the treatment of COVID-19 in 2021 and 2022, respectively. Previous studies have identified 3CL pro mutations that are associated with reduced susceptibility to these antivirals. The aim of the current study was to estimate the global prevalence of 3CL pro inhibitor-resistant SARS-CoV-2 strains., Methods: We compiled a list of 3CL pro mutations which have been associated with nirmatrelvir or ensitrelvir resistance based on either viral replication or 3CL pro activity assays, and determined their prevalence among 13.4 million sequences deposited in GISAID as of December 14, 2022, about 1 year after the approval of nirmatrelvir-ritonavir. We analyzed the prevalence for different time periods, SARS-CoV-2 lineages and geographical locations., Findings: Overall, 0.5% (67,095/13,446,588) of the sequences contained at least one mutation that was shown to affect the inhibitory activity of nirmatrelvir or ensitrelvir on viral replication or 3CL pro activity. We did not observe any increasing trend of resistance after the widespread clinical use of nirmatrelvir-ritonavir. G15S (2070 per million) and T21I (1386 per million) were the most prevalent mutations, and these mutations were dominant in some SARS-CoV-2 lineages. E166V and S144E, previously shown to affect the inhibitory activity of nirmatrelvir on viral replication or protease activity by > 100-folds, were found in <1 per million sequences., Interpretation: Our data suggest that 3CL pro inhibitor resistance is currently rare. However, continuous global genotypic and phenotypic surveillance would be crucial in the early detection of resistant mutants., Funding: Richard and Carol Yu, May Tam Mak Mei Yin, The Shaw Foundation Hong Kong, Michael Tong, Marina Lee, Government Consultancy Service, the Emergency Key Program of Guangzhou Laboratory (See acknowledgements for full list)., Competing Interests: Declaration of interests KKWT report collaboration with Sinovac, Sinopharm and Wantai BioPharm. Other authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Given the high injection pressure and insufficient injection volume in the offshore oilfield, Bohai Oilfield has developed a bio-nano-depressurization and injection-increasing composite system solution (bio-nano-injection-increasing solution) composed of bio-surfactants, hydrophobic nano-polysilicon particles, and dispersant additives. In response to the current problems, a new type of bio-nano-depressurization and injection enhancement technology has been studied, which has multiple functions such as nano-scale inhibition and wetting reversal. The new technology has the technical advantages of efficient decompression, long-term injection, and wide adaptation. However, there is still a lack of optimization schemes and application effect prediction methods, which hinder the further popularization and application of the bio-nano-composite system solution. To solve this problem, this paper takes Well A1 in the Bohai Sea as an example to optimize the injection volume, concentration, and speed of the bio-nano-augmentation fluid and evaluates the application effect by using the proposed well testing, water absorption index, and numerical simulation methods. The research results show that the bio-nano-injection fluid can effectively improve the reservoir permeability and reduce the injection pressure. The application effect evaluation method proposed is reliable and can provide some reference for similar depressurization and injection-increasing technologies., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

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.)

Intrahepatic cholestasis of pregnancy (ICP) is a rare liver disease occurring during pregnancy that is characterized by disordered bile acid (BA) metabolism. It is related to adverse clinical outcomes in both the mother and fetus. Our aim was to evaluate the BA metabolism profiles in different types of ICP and investigate the association between specific BAs and perinatal complications in ICP patients. We consecutively evaluated 95 patients with ICP, in which 53 patients were diagnosed with early-onset ICP (EICP) and 42 patients were diagnosed with late-onset ICP (LICP). Concentrations of 15 BA components were detected using high-performance liquid chromatography tandem mass spectrometry. Clinical information was abstracted from the medical records. The percentage of conjugated bile acids increased in ICP patients. Specifically, taurocholic acid (TCA) accumulated in LICP patients, and glycocholic acid (GCA) predominated in EICP patients. A higher preterm birth incidence was observed among ICP patients. Albumin, total bile acids, total bilirubin and GCA percentage values at ICP diagnosis predicts 83.5% of preterm birth in EICP, and the percentage of TCA in total bile acids at ICP diagnosis predicts 93.2% of preterm birth in LICP. This analysis showed that the BA metabolism profiles of EICP and LICP were distinct. Increased hepatic load was positively correlated with preterm birth in EICP. An elevated TCA percentage in total bile acids provides a biomarker to predict preterm birth in LICP., (© 2023. The Author(s).)

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.)

Rice agriculture is both an important source of the potent greenhouse gas methane (CH 4 ) and a bioaccumulator of cadmium (Cd), which is hazardous to human health. Avoiding flooding during rice production is effective for reducing CH 4 emissions, but it increases rice Cd uptake. Although lime application decreases Cd concentration in rice grains, it is not clear whether combining appropriate water management with liming can simultaneously reduce CH 4 emissions and Cd uptake in rice paddies. Thus, a pot experiment was performed to investigate the interactive effects of water management (F: continuous flooding, FDF: flooding - midseason drainage - flooding, FDI: flooding - midseason drainage - intermittent irrigation) and lime application on CH 4 emissions and rice Cd uptake in an acid paddy soil spiked with Cd. Results showed that neither water management nor liming significantly affected grain yield. Overall, liming reduced CH 4 emissions by 42.2%. Compared to F, the FDF and FDI treatments reduced CH 4 emissions by 43.5% and 54.2%, respectively. Liming reduced CH 4 emissions by 32.6% under F, but with a greater decrease of 48.6% and 52.7% under FDF and FDI, respectively. Overall, liming reduced rice Cd uptake by an average of 47.3%. Compared to FDI, F and FDF significantly reduced Cd uptake by 84.0% and 75.1%, respectively, but there was no significant difference between F and FDF. Liming did not significantly affect Cd uptake under F and FDF, whereas liming reduced Cd uptake by 55.9% under FDI. These results suggest that maintaining flooding following midseason drainage can help in reducing rice Cd uptake, though slightly promoting CH 4 emissions. Therefore, we recommend FDF combined with liming to mitigate CH 4 emissions without increasing rice Cd uptake in acid paddy soils., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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.)

Viruses are the most abundant form of life on earth and play important roles in a broad range of ecosystems. Currently, two methods, whole genome shotgun metagenome (WGSM) and viral-like particle enriched metagenome (VLPM) sequencing, are widely applied to compare viruses in various environments. However, there is no critical assessment of their performance in recovering viruses and biological interpretation in comparative viral metagenomic studies. To fill this gap, we applied the two methods to investigate the stool virome in hepatocellular carcinoma (HCC) patients and healthy controls. Both WGSM and VLPM methods can capture the major diversity patterns of alpha and beta diversities and identify the altered viral profiles in the HCC stool samples compared with healthy controls. Viral signatures identified by both methods showed reductions of Faecalibacterium virus Taranis in HCC patients' stool. Ultra-deep sequencing recovered more viruses in both methods, however, generally, 3 or 5 Gb were sufficient to capture the non-fragmented long viral contigs. More lytic viruses were detected than lysogenetic viruses in both methods, and the VLPM can detect the RNA viruses. Using both methods would identify shared and specific viral signatures and would capture different parts of the total virome.

Bacteriophages, which are viruses infecting bacteria, are the most ubiquitous and diverse entities in the biosphere. There is accumulating evidence revealing their important roles in shaping the structure of various microbiomes. Thanks to (viral) metagenomic sequencing, a large number of new bacteriophages have been discovered. However, lacking a standard and automatic virus classification pipeline, the taxonomic characterization of new viruses seriously lag behind the sequencing efforts. In particular, according to the latest version of ICTV, several large phage families in the previous classification system are removed. Therefore, a comprehensive review and comparison of taxonomic classification tools under the new standard are needed to establish the state-of-the-art. In this work, we retrained and tested four recently published tools on newly labeled databases. We demonstrated their utilities and tested them on multiple datasets, including the RefSeq, short contigs, simulated metagenomic datasets, and low-similarity datasets. This study provides a comprehensive review of phage family classification in different scenarios and a practical guidance for choosing appropriate taxonomic classification pipelines. To our best knowledge, this is the first review conducted under the new ICTV classification framework. The results show that the new family classification framework overall leads to better conserved groups and thus makes family-level classification more feasible., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhu, Shang, Peng and Sun.)

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.)

Objective: To explore the effect of maternal body mass index (BMI) on steroid hormone profiles in women with gestational diabetes mellitus (GDM) and those with normal glucose tolerance (NGT)., Methods: We enrolled 79 women with NGT and 80 women with GDM who had a gestational age of 24-28 weeks. The participants were grouped according to their BMI. We quantified 11 steroid hormones profiles by liquid chromatography-tandem mass spectrometry and calculated the product-to-precursor ratios in the steroidogenic pathway., Results: Women with GDM and BMI<25kg/m 2 showed higher concentrations of dehydroepiandrosterone (DHEA) ( p <0.001), testosterone (T) ( p= 0.020), estrone (E1) ( p =0.010) and estradiol (E2) ( p =0.040) and lower Matsuda index and HOMA-β than women with NGT and BMI<25kg/m 2 . In women with GDM, concentrations of E1 ( p =0.006) and E2 ( p =0.009) declined, accompanied by reduced E2/T ( p =0.008) and E1/androstenedione (A4) ( p =0.010) in the BMI>25 kg/m 2 group, when compared to that in the BMI<25 kg/m 2 group. The values of E2/T and E1/A4 were used to evaluate the cytochrome P450 aromatase enzyme activity in the steroidogenic pathway. Both aromatase activities negatively correlated with the maternal BMI and positively correlated with the Matsuda index in women with GDM., Conclusions: NGT women and GDM women with normal weight presented with different steroid hormone profiles. Steroidogenic pathway profiling of sex hormones synthesis showed a significant increase in the production of DHEA, T, E1, and E2 in GDM women with normal weight. Additionally, the alteration of steroid hormone metabolism was related to maternal BMI in women with GDM, and GDM women with overweight showed reduced estrogen production and decreased insulin sensitivity compared with GDM women with normal weight., Competing Interests: Authors DZ and JX were employed by Hangzhou BIOZON Medical Laboratory Co. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Sun, Zhu, Meng, Yin, Wu, Liu, Zou, Xue, Sun, Zhang and Ma.)

At present, the water displacement recovery in some medium- and low-permeability reservoirs that cannot be injected and produced in offshore oil fields because of small pores and complex structures is less than 18%. This amount is far lower than 25-40%, which is obtained after water displacement and chemical displacement in medium- and high-permeability reservoirs. Given the current situation of water injection in offshore medium- and low-permeability reservoirs, a new green and environmentally friendly nano-oil displacement technology must be urgently developed to improve the sweep coefficient and oil displacement efficiency of injected water. In this study, the experimental laboratory investigation of a biological nano-oil displacement system suitable for medium- and low-permeability reservoirs is performed. The oil displacement effects, such as changing interfacial tension, viscosity reduction, and oil flushing ability, are also evaluated. The partial differential mathematical model of multicomponent isothermal multiphase seepage is deduced, the mechanism of biological nano-oil displacement technology is finely characterized, and a set of numerical simulation optimization charts of the biological nano-oil displacement process parameters is established. Results show that the biological nano-oil displacement system has adsorption characteristics in porous media, effective miscibility with crude oil, and a minimum contact angle reaching 14.3°. Its interfacial tension can be reduced to the 10 -3 level, the viscosity reduction efficiency can reach more than 90%, and the oil washing efficiency can reach more than 70%. Compared with the conventional water and chemical displacement systems, the displacement system in this study has a good oil rock flushing effect and improves oil recovery by 15%. When the injection-production ratio is comprehensively considered, the recommended injection cycle is 6000 ppm. The field test of the biological nano-oil displacement system has been completed, with a validity period of 1 year and a cumulative oil increase of 1.2 × 10 4 m 3 , which is still effective. This study provides environmentally friendly solutions for the new chemical displacement of offshore medium- and low-permeability reservoirs. The established process parameter optimization chart has important guiding relevance for the optimization of technical schemes and improvement of the oil increase effect in chemical displacement., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

Recent years have witnessed promising artificial intelligence (AI) applications in many disciplines, including optics, engineering, medicine, economics, and education. In particular, the synergy of AI and meta-optics has greatly benefited both fields. Meta-optics are advanced flat optics with novel functions and light-manipulation abilities. The optical properties can be engineered with a unique design to meet various optical demands. This review offers comprehensive coverage of meta-optics and artificial intelligence in synergy. After providing an overview of AI and meta-optics, we categorize and discuss the recent developments integrated by these two topics, namely AI for meta-optics and meta-optics for AI. The former describes how to apply AI to the research of meta-optics for design, simulation, optical information analysis, and application. The latter reports the development of the optical Al system and computation via meta-optics. This review will also provide an in-depth discussion of the challenges of this interdisciplinary field and indicate future directions. We expect that this review will inspire researchers in these fields and benefit the next generation of intelligent optical device design.

Sacred lotus (Nelumbo nucifera Gaertn.) is a basal eudicot plant with a unique lifestyle, physiological features, and evolutionary characteristics. Here we report the unique profile of transposable elements (TEs) in the genome, using a manually curated repeat library. TEs account for 59% of the genome, and hAT (Ac/Ds) elements alone represent 8%, more than in any other known plant genome. About 18% of the lotus genome is comprised of Copia LTR retrotransposons, and over 25% of them are associated with non-canonical termini (non-TGCA). Such high abundance of non-canonical LTR retrotransposons has not been reported for any other organism. TEs are very abundant in genic regions, with retrotransposons enriched in introns and DNA transposons primarily in flanking regions of genes. The recent insertion of TEs in introns has led to significant intron size expansion, with a total of 200 Mb in the 28 455 genes. This is accompanied by declining TE activity in intergenic regions, suggesting distinct control efficacy of TE amplification in different genomic compartments. Despite the prevalence of TEs in genic regions, some genes are associated with fewer TEs, such as those involved in fruit ripening and stress responses. Other genes are enriched with TEs, and genes in epigenetic pathways are the most associated with TEs in introns, indicating a dynamic interaction between TEs and the host surveillance machinery. The dramatic differential abundance of TEs with genes involved in different biological processes as well as the variation of target preference of different TEs suggests the composition and activity of TEs influence the path of evolution., (© 2022 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.)

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.)

The virus-like particles (VLPs) of porcine circovirus type 2 (PCV2) is an attractive vaccine candidate that retains the natural conformation of the virion but lacks the viral genome to replicate, thus balancing safety and immunogenicity. However, the assembly of VLPs requires cumbersome subsequent processes, hindering the development of related vaccines. In addition, as a subunit antigen, VLPs are defective in inducing cellular and mucosal immune responses. In this study, the capsid (Cap) protein of PCV2 was synthesized and self-assembled into VLPs in the recombinant attenuated S. Choleraesuis vector, rSC0016(pS-Cap). Furthermore, rSC0016(pS-Cap) induced a Cap-specific Th1-dominant immune response, mucosal immune responses, and neutralizing antibodies against PCV2. Finally, the virus genome copies in mice immunized with the rSC0016(pS-Cap) were significantly lower than those of the empty vector control group after challenge with PCV2. In conclusion, our study demonstrates the potential of using S. Choleraesuis vectors to delivery VLPs, providing new ideas for the development of PCV2 vaccines., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

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.)

A universal vaccine protecting against multiple serotypes of Streptococcus suis is urgently needed to improve animal welfare and reduce the consumption of antibiotics. In this study, a dual antigen expression cassette consisting of SS2-SaoA and SS9-Eno was delivered by a recombinant Salmonella Choleraesuis vector to form the vaccine candidate rSC0016(pS-SE). SaoA and Eno were simultaneously synthesized in rSC0016(pS-SE) without affecting the colonization of the recombinant vector in the lymphatic system. In addition, the antiserum of mice immunized with rSC0016(pS-SE) produced a broader and potent opsonophagocytic response against multiple serotypes of S. suis. Finally, rSC0016(pS-SE) provided mice with a 100% protection against a lethal dose of parent S. suis serotype 2 and serotype 9, and provided 90% and 80% protection against heterologous S. suis serotype 7 or 1/2. These values were significantly higher than those obtained with rSC0016(pS-SaoA) or rSC0016(pS-Eno). Together, this study serves as a foundation for developing a universal vaccine against multiple serotypes of S. suis., (© 2022. The Author(s).)

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.)

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.)

Short interspersed nuclear elements (SINEs) are a widespread type of small transposable element (TE). With increasing evidence for their impact on gene function and genome evolution in plants, accurate genome-scale SINE annotation becomes a fundamental step for studying the regulatory roles of SINEs and their relationship with other components in the genomes. Despite the overall promising progress made in TE annotation, SINE annotation remains a major challenge. Unlike some other TEs, SINEs are short and heterogeneous, and they usually lack well-conserved sequence or structural features. Thus, current SINE annotation tools have either low sensitivity or high false discovery rates. Given the demand and challenges, we aimed to provide a more accurate and efficient SINE annotation tool for plant genomes. The pipeline starts with maximizing the pool of SINE candidates via profile hidden Markov model-based homology search and de novo SINE search using structural features. Then, it excludes the false positives by integrating all known features of SINEs and the features of other types of TEs that can often be misannotated as SINEs. As a result, the pipeline substantially improves the tradeoff between sensitivity and accuracy, with both values close to or over 90%. We tested our tool in Arabidopsis thaliana and rice (Oryza sativa), and the results show that our tool competes favorably against existing SINE annotation tools. The simplicity and effectiveness of this tool would potentially be useful for generating more accurate SINE annotations for other plant species. The pipeline is freely available at https://github.com/yangli557/AnnoSINE., (© The Author(s) 2021. Published by Oxford University Press on behalf of American Society of Plant Biologists.)

Viruses change constantly during replication, leading to high intra-species diversity. Although many changes are neutral or deleterious, some can confer on the virus different biological properties such as better adaptability. In addition, viral genotypes often have associated metadata, such as host residence, which can help with inferring viral transmission during pandemics. Thus, subspecies analysis can provide important insights into virus characterization. Here, we present VirStrain, a tool taking short reads as input with viral strain composition as output. We rigorously test VirStrain on multiple simulated and real virus sequencing datasets. VirStrain outperforms the state-of-the-art tools in both sensitivity and accuracy., (© 2022. The Author(s).)