Accurate and efficient protein embedding using multi-teacher distillation learning.

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.

VirTAXA: enhancing RNA virus taxonomic classification with remote homology search and tree-based validation.

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.

A prediction model for gestational diabetes mellitus based on steroid hormonal changes in early and mid-down syndrome screening: A multicenter longitudinal study.

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.

VirID: Beyond Virus Discovery - An Integrated Platform for Comprehensive RNA Virus Characterization.

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.

[Comparison of the effects of using iRoot BP plus and MTA apical barrier surgery in young permanent teeth with chronic apical periodontitis].

PURPOSE: To explore the effect of using iRoot BP plus and MTA apical barrier surgery in young permanent teeth with chronic apical periodontitis. METHODS: A total of 122 patients with chronic periapical periodontitis with open root tips of permanent teeth were randomly divided into experimental group (n=61, 61 teeth) and a control group (n=61, 61 teeth). Patients in the experimental group received iRoot BP plus plus apical barrier surgery, while those in the control group received MTA apical barrier surgery. The old periapical index (O-PAI), apical transmission area, efficacy, treatment times, and inflammatory factor levels of the two groups of patients were compared at 3, 6, 9, and 12 months after surgery. SPSS 19.0 software package was used for statistical analysis. RESULTS: At 12 months after surgery, the O-PAI ratings of the experimental group and the control group were (1.48±0.36) and (1.71±0.42), respectively, and the apical transmission area was (0.51±0.14) and (1.09±0.31). There was a significant difference in the O-PAI ratings and apical transmission area between the two groups(P＜0.05). At 3 months, 6 months, and 12 months after surgery, the O-PAI scores of patients in both groups gradually decreased (P＜0.05). After 12 months of treatment, the success rates of the experimental group and the control group were 98.36% and 88.52%, respectively, with significant difference between the two groups (P＜0.05). The treatment frequency of patients in the experimental group and the control group was (3.64±0.58) times and (4.72±0.61) times, respectively, with a significant difference between the two groups(P＜0.05). After 3 months of treatment, the serum hs-CRP levels in the experimental group and the control group were (6.89±1.13) mg/L and (7.25±1.40) mg/L, respectively, with a significant difference compared to pre-treatment(P＜0.05). After 3 months of treatment, the serum IL-6 levels in the experimental group and the control group were (82.04±19.62) mg/L and (87.52±20.85) mg/L, respectively, with significant differences compared to pre-treatment (P＜0.05). There was no significant difference in serum IL-6 and hs-CRP levels between the two groups before and after treatment(P＞0.05). CONCLUSIONS: iRoot BP plus apical barrier surgery for the treatment of chronic apical periodontitis with open permanent teeth can reduce the O-PAI index, decrease the number of postoperative visits, and have a higher postoperative success rate.

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

BACKGROUND: The high-throughput sequencing technologies have revolutionized the identification of novel RNA viruses. Given that viruses are infectious agents, identifying hosts of these new viruses carries significant implications for public health and provides valuable insights into the dynamics of the microbiome. However, determining the hosts of these newly discovered viruses is not always straightforward, especially in the case of viruses detected in environmental samples. Even for host-associated samples, it is not always correct to assign the sample origin as the host of the identified viruses. The process of assigning hosts to RNA viruses remains challenging due to their high mutation rates and vast diversity. RESULTS: In this study, we introduce RNAVirHost, a machine learning-based tool that predicts the hosts of RNA viruses solely based on viral genomes. RNAVirHost is a hierarchical classification framework that predicts hosts at different taxonomic levels. We demonstrate the superior accuracy of RNAVirHost in predicting hosts of RNA viruses through comprehensive comparisons with various state-of-the-art techniques. When applying to viruses from novel genera, RNAVirHost achieved the highest accuracy of 84.3%, outperforming the alignment-based strategy by 12.1%. CONCLUSIONS: The application of machine learning models has proven beneficial in predicting hosts of RNA viruses. By integrating genomic traits and sequence homologies, RNAVirHost provides a cost-effective and efficient strategy for host prediction. We believe that RNAVirHost can greatly assist in RNA virus analyses and contribute to pandemic surveillance.

Quantum modeling simulates nutrient effect of bioplastic polyhydroxyalkanoate (PHA) production in Pseudomonas putida.

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 (kD, kN 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.

SegVir: Reconstruction of Complete Segmented RNA Viral Genomes from Metatranscriptomes.

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.

A novel LGALS1-depended and immune-associated fatty acid metabolism risk model in acute myeloid leukemia stem cells.

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.

Utilizing profile hidden Markov model databases for discovering viruses from metagenomic data: a comprehensive review.

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.

Towards more accurate microbial source tracking via non-negative matrix factorization (NMF).

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 clinical outcome of COVID-19 is strongly associated with microbiome dynamics in the upper respiratory tract.

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.

GDmicro: classifying host disease status with GCN and deep adaptation network based on the human gut microbiome data.

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.

Albumin-to-fibrinogen ratio is an independent prognostic parameter in de novo non-M3 acute myeloid leukemia.

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.

LncRNA-Top: Controlled deep learning approaches for lncRNA gene regulatory relationship annotations across different platforms.

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.

Salmonella enterica serovar Choleraesuis vector outperforms alum as an adjuvant, increasing a cross-protective immune response against Glaesserella parasuis.

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_06257 of Glaesserella parasuis (G. parasuis), generating a novel recombinant strain rSC0016(pS-HPS_06257). The rSC0016(pS-HPS_06257) can express and deliver the HPS_06257 protein to the lymphatic system of the host. In comparison to HPS_06257 adjuvanted with alum, rSC0016(pS-HPS_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_06257 in mice immunized with rSC0016(pS-HPS_06257) were significantly higher than those of HPS_06257 adjuvanted with alum. HPS_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_06257 adjuvanted with alum only causes opsonophagocytic activity (OPA) responses against a homologous strain (G. parasuis serotype 5, GPS5), whereas rSC0016(pS-HPS_06257) could generate cross-OPA responses against a homologous strain and a heterologous strain (G. parasuis serotype 12, GPS12). Ultimately, HPS_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_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.

PhaGenus: genus-level classification of bacteriophages using a Transformer model.

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.

A Bacterial mRNA-Lysis-Mediated Cargo Release Vaccine System for Regulated Cytosolic Surveillance and Optimized Antigen Delivery.

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.

Maternal gestational weight gain and adverse pregnancy outcomes in non-diabetic women.

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/m2 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 StatementWhat 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.

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

Motivation: There is accumulating evidence showing the important roles of bacteriophages (phages) in regulating the structure and functions of the microbiome. However, lacking an easy-to-use and integrated phage analysis software hampers microbiome-related research from incorporating phages in the analysis. Results: In this work, we developed a web server, PhaBOX, which can comprehensively identify and analyze phage contigs in metagenomic data. It supports integrated phage analysis, including phage contig identification from the metagenomic assembly, lifestyle prediction, taxonomic classification, and host prediction. Instead of treating the algorithms as a black box, PhaBOX also supports visualization of the essential features for making predictions. The web server is designed with a user-friendly graphical interface that enables both informatics-trained and nonspecialist users to analyze phages in microbiome data with ease. Availability and implementation: The web server of PhaBOX is available via: https://phage.ee.cityu.edu.hk. The source code of PhaBOX is available at: https://github.com/KennthShang/PhaBOX.