Global pattern and determinant for interaction of seasonal influenza viruses.

BACKGROUND: The prevalence of different types/subtypes varies across seasons and countries for seasonal influenza viruses, indicating underlying interactions between types/subtypes. The global interaction patterns and determinants for seasonal influenza types/subtypes need to be explored. METHODS: Influenza epidemiological surveillance data, as well as multidimensional data that include population-related, environment-related, and virus-related factors from 55 countries worldwide were used to explore type/subtype interactions based on Spearman correlation coefficient. The machine learning method Extreme Gradient Boosting (XGBoost) and interpretable framework SHapley Additive exPlanation (SHAP) were utilized to quantify contributing factors and their effects on interactions among influenza types/subtypes. Additionally, causal relationships between types/subtypes were also explored based on Convergent Cross-mapping (CCM). RESULTS: A consistent globally negative correlation exists between influenza A/H3N2 and A/H1N1. Meanwhile, interactions between influenza A (A/H3N2, A/H1N1) and B show significant differences across countries, primarily influenced by population-related factors. Influenza A has a stronger driving force than influenza B, and A/H3N2 has a stronger driving force than A/H1N1. CONCLUSION: The research elucidated the globally complex and heterogeneous interaction patterns among influenza type/subtypes, identifying key factors shaping their interactions. This sheds light on better seasonal influenza prediction and model construction, informing targeted prevention strategies and ultimately reducing the global burden of seasonal influenza.

Network-based approach for drug repurposing against mpox.

The current outbreak of mpox presents a significant threat to the global community. However, the lack of mpox-specific drugs necessitates the identification of additional candidates for clinical trials. In this study, a network medicine framework was used to investigate poxviruses-human interactions to identify potential drugs effective against the mpox virus (MPXV). The results indicated that poxviruses preferentially target hubs on the human interactome, and that these virally-targeted proteins (VTPs) tend to aggregate together within specific modules. Comorbidity analysis revealed that mpox is closely related to immune system diseases. Based on predicted drug-target interactions, 268 drugs were identified using the network proximity approach, among which 23 drugs displaying the least side-effects and significant proximity to MPXV were selected as the final candidates. Lastly, specific drugs were explored based on VTPs, differentially expressed proteins, and intermediate nodes, corresponding to different categories. These findings provide novel insights that can contribute to a deeper understanding of the pathogenesis of MPXV and development of ready-to-use treatment strategies based on drug repurposing.

Characterizing the antigenic evolution of pandemic influenza A (H1N1) pdm09 from 2009 to 2023.

The H1N1pdm09 virus has been a persistent threat to public health since the 2009 pandemic. Particularly, since the relaxation of COVID-19 pandemic mitigation measures, the influenza virus and SARS-CoV-2 have been concurrently prevalent worldwide. To determine the antigenic evolution pattern of H1N1pdm09 and develop preventive countermeasures, we collected influenza sequence data and immunological data to establish a new antigenic evolution analysis framework. A machine learning model (XGBoost, accuracy = 0.86, area under the receiver operating characteristic curve = 0.89) was constructed using epitopes, physicochemical properties, receptor binding sites, and glycosylation sites as features to predict the antigenic similarity relationships between influenza strains. An antigenic correlation network was constructed, and the Markov clustering algorithm was used to identify antigenic clusters. Subsequently, the antigenic evolution pattern of H1N1pdm09 was analyzed at the global and regional scales across three continents. We found that H1N1pdm09 evolved into around five antigenic clusters between 2009 and 2023 and that their antigenic evolution trajectories were characterized by cocirculation of multiple clusters, low-level persistence of former dominant clusters, and local heterogeneity of cluster circulations. Furthermore, compared with the seasonal H1N1 virus, the potential cluster-transition determining sites of H1N1pdm09 were restricted to epitopes Sa and Sb. This study demonstrated the effectiveness of machine learning methods for characterizing antigenic evolution of viruses, developed a specific model to rapidly identify H1N1pdm09 antigenic variants, and elucidated their evolutionary patterns. Our findings may provide valuable support for the implementation of effective surveillance strategies and targeted prevention efforts to mitigate the impact of H1N1pdm09.

Spatiotemporal genotype replacement of H5N8 avian influenza viruses contributed to H5N1 emergence in 2021/2022 panzootic.

Since 2020, clade 2.3.4.4b highly pathogenic avian influenza H5N8 and H5N1 viruses have swept through continents, posing serious threats to the world. Through comprehensive analyses of epidemiological, genetic, and bird migration data, we found that the dominant genotype replacement of the H5N8 viruses in 2020 contributed to the H5N1 outbreak in the 2021/2022 wave. The 2020 outbreak of the H5N8 G1 genotype instead of the G0 genotype produced reassortment opportunities and led to the emergence of a new H5N1 virus with G1's HA and MP genes. Despite extensive reassortments in the 2021/2022 wave, the H5N1 virus retained the HA and MP genes, causing a significant outbreak in Europe and North America. Furtherly, through the wild bird migration flyways investigation, we found that the temporal-spatial coincidence between the outbreak of the H5N8 G1 virus and the bird autumn migration may have expanded the H5 viral spread, which may be one of the main drivers of the emergence of the 2020-2022 H5 panzootic.IMPORTANCESince 2020, highly pathogenic avian influenza (HPAI) H5 subtype variants of clade 2.3.4.4b have spread across continents, posing unprecedented threats globally. However, the factors promoting the genesis and spread of H5 HPAI viruses remain unclear. Here, we found that the spatiotemporal genotype replacement of H5N8 HPAI viruses contributed to the emergence of the H5N1 variant that caused the 2021/2022 panzootic, and the viral evolution in poultry of Egypt and surrounding area and autumn bird migration from the Russia-Kazakhstan region to Europe are important drivers of the emergence of the 2020-2022 H5 panzootic. These findings provide important targets for early warning and could help control the current and future HPAI epidemics.

Characterizing the dynamics of BCR repertoire from repeated influenza vaccination.

Yearly epidemics of seasonal influenza cause an enormous disease burden around the globe. An understanding of the rules behind the immune response with repeated vaccination still presents a significant challenge, which would be helpful for optimizing the vaccination strategy. In this study, 34 healthy volunteers with 16 vaccinated were recruited, and the dynamics of the BCR repertoire for consecutive vaccinations in two seasons were tracked. In terms of diversity, length, network, V and J gene segments usage, somatic hypermutation (SHM) rate and isotype, it was found that the overall changes were stronger in the acute phase of the first vaccination than the second vaccination. However, the V gene segments of IGHV4-39, IGHV3-9, IGHV3-7 and IGHV1-69 were amplified in the acute phase of the first vaccination, with IGHV3-7 dominant. On the other hand, for the second vaccination, the changes were dominated by IGHV1-69, with potential for coding broad neutralizing antibody. Additional analysis indicates that the application of V gene segment for IGHV3-7 in the acute phase of the first vaccination was due to the elevated usage of isotypes IgM and IgG3. While for IGHV1-69 in the second vaccination, it was contributed by isotypes IgG1 and IgG2. Finally, 41 public BCR clusters were identified in the vaccine group, with both IGHV3-7 and IGHV1-69 were involved and representative complementarity determining region 3 (CDR3) motifs were characterized. This study provides insights into the immune response dynamics following repeated influenza vaccination in humans and can inform universal vaccine design and vaccine strategies in the future.

Preparation and application of a novel imine-linked covalent organic framework@silica composite for reversed-phase and hydrophilic interaction chromatographic separations.

The composites of covalent organic frameworks (COFs) and silica gel have been considered to be promising chromatographic separation materials due to the distinct advantages such as large specific surface area, good mechanical strength and high porosity. In the present study, a novel imine-linked COF@silica composite was prepared by in-situ growth of 2,4,6-tris(4-aminophenyl)-1,3,5-triazine (TAPT) and 2,5-dihydroxyterephthalaldehyde (DHTA) monomers on the surface of aminated silica gel (SiO2-NH2). The successful surface-modification of TAPT-DHTA-COF distinctly enhanced the separation selectivity and efficiency of SiO2-NH2. Multiple types of analyte-stationary phase interactions contributed to the selective retention of structurally similar analytes. The designed TAPT-DHTA-COF@SiO2 was observed to effectively separate hydrophobic phenyl ketones, phthalate esters and steroid hormones. Moreover, the polar amino and hydroxyl groups of TAPT-DHTA-COF facilitated the selective determination of hydrophilic nucleosides/bases. The kinetic performance and thermodynamic behavior of TAPT-DHTA-COF@SiO2 column were particularly explored. It was found that column efficiency was mainly affected by the mass transfer resistance, and the retention of nucleosides/bases on the TAPT-DHTA-COF@SiO2 column was temperature dependent. The developed versatile TAPT-DHTA-COF@SiO2 column was finally applied for detecting environmental hormones as well as water-soluble nicotinamide in real samples. In summary, the potential application of TAPT-DHTA-COF@SiO2 composite material for liquid chromatographic separations was first explored and verified. The TAPT-DHTA-COF@SiO2 was proved to be a promising chromatographic separation material.

[Biosynthesis of natural products by non-conventional yeasts].

Non-conventional yeasts such as Yarrowia lipolytica, Pichia pastoris, Kluyveromyces marxianus, Rhodosporidium toruloides and Hansenula polymorpha have proven to be efficient cell factories in producing a variety of natural products due to their wide substrate utilization spectrum, strong tolerance to environmental stresses and other merits. With the development of synthetic biology and gene editing technology, metabolic engineering tools and strategies for non-conventional yeasts are expanding. This review introduces the physiological characteristics, tool development and current application of several representative non-conventional yeasts, and summarizes the metabolic engineering strategies commonly used in the improvement of natural products biosynthesis. We also discuss the strengths and weaknesses of non-conventional yeasts as natural products cell factories at current stage, and prospects future research and development trends.

Chromatographic separation performance of silica microspheres surface-modified with triazine-containing imine-linked covalent organic frameworks.

In this work, 2,4,6-tris(4-aminophenyl)-1,3,5-triazine (TAPT) and 1,3,5-tris(4-formylphenyl)benzene (TFPB) were used as monomers to construct a triazine-containing imine-linked covalent organic framework (COF), which was then bonded onto the surface of aldehydized silica (SiO2-CHO), and finally a COF@silica composite material (TAPT-TFPB COF@SiO2) was successfully prepared. The chromatographic separation performance of SiO2-CHO, TAPT-TFPB COF@SiO2 and TAPT-TFPB COF@SiO2/SiO2-CHO (80/20, mass ratio) was evaluated and compared. It was found that separation efficiency was obviously enhanced by adding an appropriate amount of SiO2-CHO into TAPT-TFPB COF@SiO2. The obtained TAPT-TFPB COF@SiO2/SiO2-CHO showed more favorable separation ability than SiO2-CHO and TAPT-TFPB COF@SiO2. Various aromatic compounds including alkylbenzenes, polycyclic aromatic hydrocarbons, environmental endocrine disruptors, foodborne stimulants and phenyl ketones were effectively separated on the TAPT-TFPB COF@SiO2/SiO2-CHO column in reversed phase chromatography mode. The silica microspheres surface-modified with triazine-containing imine-linked COFs proved to be a new type of promising chromatographic packing materials.

Variation in synonymous evolutionary rates in the SARS-CoV-2 genome.

Introduction: Coronavirus disease 2019 is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Influential variants and mutants of this virus continue to emerge, and more effective virus-related information is urgently required for identifying and predicting new mutants. According to earlier reports, synonymous substitutions were considered phenotypically silent; thus, such mutations were frequently ignored in studies of viral mutations because they did not directly cause amino acid changes. However, recent studies have shown that synonymous substitutions are not completely silent, and their patterns and potential functional correlations should thus be delineated for better control of the pandemic. Methods: In this study, we estimated the synonymous evolutionary rate (SER) across the SARS-CoV-2 genome and used it to infer the relationship between the viral RNA and host protein. We also assessed the patterns of characteristic mutations found in different viral lineages. Results: We found that the SER varies across the genome and that the variation is primarily influenced by codon-related factors. Moreover, the conserved motifs identified based on the SER were found to be related to host RNA transport and regulation. Importantly, the majority of the existing fixed-characteristic mutations for five important virus lineages (Alpha, Beta, Gamma, Delta, and Omicron) were significantly enriched in partially constrained regions. Discussion: Taken together, our results provide unique information on the evolutionary and functional dynamics of SARS-CoV-2 based on synonymous mutations and offer potentially useful information for better control of the SARS-CoV-2 pandemic.

High-Strength Bio-Degradable Polymer Foams with Stable High Volume-Expansion Ratio Using Chain Extension and Green Supercritical Mixed-Gas Foaming.

The preparation of biodegradable polymer foams with a stable high volume-expansion ratio (VER) is challenging. For example, poly (butylene adipate-co-terephthalate) (PBAT) foams have a low melt strength and high shrinkage. In this study, polylactic acid (PLA), which has a high VER and crystallinity, was added to PBAT to reduce shrinkage during the supercritical molded-bead foaming process. The epoxy chain extender ADR4368 was used both as a chain extender and a compatibilizer to mitigate the linear chain structure and incompatibility and improve the foamability of PBAT. The branched-chain structure increased the energy-storage modulus (G') and complex viscosity (η*), which are the key factors for the growth of cells, by 1-2 orders of magnitude. Subsequently, we innovatively used the CO2 and N2 composite gas method. The foam-shrinkage performance was further inhibited; the final foam had a VER of 23.39 and a stable cell was obtained. Finally, after steam forming, the results showed that the mechanical strength of the PBAT/PLA blended composite foam was considerably improved by the addition of PLA. The compressive strength (50%), bending strength, and fracture load by bending reached 270.23 kPa, 0.36 MPa, and 23.32 N, respectively. This study provides a potential strategy for the development of PBAT-based foam packaging materials with stable cell structure, high VER, and excellent mechanical strength.

A data set of distributed global population and water withdrawal from 1960 to 2020.

Population and water withdrawal data sets are currently faced with difficulties in collecting, processing and verifying multi-source time series, and the spatial distribution characteristics of long series are also relatively lacking. Time series is the basic guarantee for the accuracy of data sets, and the production of long series spatial distribution is a realistic requirement to expand the application scope of data sets. Through the time-consuming and laborious basic processing work, this research focuses on the population and water intake time series, and interpolates and extends them to specific land uses to ensure the accuracy of the time series and the demand of spatially distributed data sets. This research provides a set of population density and water intensity products from 1960 to 2020 distributed to the administrative units or the corresponding regions. The data set fills the gaps in the multi-year data set for the accuracy of population density and the intensity of water withdrawal.

Inflammation-Regulated Nanodrug Sensitizes Hepatocellular Carcinoma to Checkpoint Blockade Therapy by Reprogramming the Tumor Microenvironment.

Immune checkpoint blockade (ICB) utilizing programmed death ligand-1 (PD-L1) antibody is a promising treatment strategy in solid tumors. However, in fact, more than half of hepatocellular carcinoma (HCC) patients are unresponsive to PD-L1-based ICB treatment due to multiple immune evasion mechanisms such as the hyperactivation of inflammation pathway, excessive tumor-associated macrophages (TAMs) infiltration, and insufficient infiltration of T cells. Herein, an inflammation-regulated nanodrug was designed to codeliver NF-κB inhibitor curcumin and PD-L1 antibody to reprogram the tumor microenvironment (TME) and activate antitumor immunity. The nanodrug accumulated in TME by an enhanced permeability and retention effect, where it left antibody to block PD-L1 on the membrane of tumor cells and TAMs due to pH-responsiveness. Simultaneously, a new curcumin-encapsulated nanodrug was generated, which was easily absorbed by either tumor cells or TAMs to inhibit the nuclear factor kappa-B (NF-κB) signal and related immunosuppressive genes. The inflammation-regulated nanodrug possessed good biocompatibility. Simultaneously, it reprogrammed TME effectively and exhibited an effective anticancer effect in immunocompetent mice. Overall, this study provided a potent strategy to improve the efficiency of ICB-based treatment for HCC.

Improved H-adsorption ability of Cu in CuNi alloy nanodots toward the efficient photocatalytic H2-evolution activity of TiO2.

Compared with the noble metal Pt, the non-noble metal Cu as a cocatalyst exhibits a low hydrogen-evolution activity owing to its weak Cu-H bond (11 kcal mol-1), which inhibits hydrogen adsorption on Cu atoms for the hydrogen-evolution reaction of photocatalysts. Considering that the introduction of Ni with a strong Ni-H bond into Cu is beneficial for strengthening the H-adsorption ability of Cu, in this paper, the low-cost transition-metal Ni was directly introduced into Cu to form CuNi alloy nanodots as photocatalytic cocatalysts to enhance the hydrogen-evolution rate of TiO2. The CuNi alloy nanodots (2-3 nm) were photodeposited on the surface of a reduced graphene oxide (rGO)-modified TiO2 photocatalyst to generate CuNi-rGO/TiO2 by the pre-adsorption of Cu2+ and Ni2+ ions on graphene oxide (GO). Photocatalytic hydrogen-production data manifested that the CuNi-rGO/TiO2 photocatalyst achieved the highest hydrogen-production rate (10 411 µmol h-1 g-1), which was 53.7, 38.7, 1.8, and 2.2 times higher than that of pure TiO2, rGO/TiO2, Cu-rGO/TiO2, and Ni-rGO/TiO2, respectively. Density-functional-theory (DFT) calculations and mechanistic investigation showed that the introduction of Ni into Cu to form CuNi alloy nanodots improved the H-adsorption ability of Cu and optimized the H-adsorption free energy close to zero (0.046 eV) for boosting the hydrogen production rate of TiO2. This research presents a promising design of bimetallic alloy structures as H2-production cocatalysts for efficient photocatalysts.

Microbial and human transcriptional profiling of coronavirus disease 2019 patients: Potential predictors of disease severity.

The high morbidity of patients with coronavirus disease 2019 (COVID-19) brings on a panic around the world. COVID-19 is associated with sex bias, immune system, and preexisting chronic diseases. We analyzed the gene expression in patients with COVID-19 and in their microbiota in order to identify potential biomarkers to aid in disease management. A total of 129 RNA samples from nasopharyngeal, oropharyngeal, and anal swabs were collected and sequenced in a high-throughput manner. Several microbial strains differed in abundance between patients with mild or severe COVID-19. Microbial genera were more abundant in oropharyngeal swabs than in nasopharyngeal or anal swabs. Oropharyngeal swabs allowed more sensitive detection of the causative SARS-CoV-2. Microbial and human transcriptomes in swabs from patients with mild disease showed enrichment of genes involved in amino acid metabolism, or protein modification via small protein removal, and antibacterial defense responses, respectively, whereas swabs from patients with severe disease showed enrichment of genes involved in drug metabolism, or negative regulation of apoptosis execution, spermatogenesis, and immune system, respectively. Microbial abundance and diversity did not differ significantly between males and females. The expression of several host genes on the X chromosome correlated negatively with disease severity. In this way, our analyses identify host genes whose differential expression could aid in the diagnosis of COVID-19 and prediction of its severity via non-invasive assay.

DDX24 promotes metastasis by regulating RPL5 in non-small cell lung cancer.

PURPOSE: Non-small cell lung cancer (NSCLC) is a leading cause of cancer death, and metastasis is a crucial determinant of increased cancer mortality. DDX24 has garnered increased attention due to its correlation with tumorigenesis and malignant progression. However, the correlation between DDX24 and NSCLC remains unclear. METHODS: DDX24 expression in NSCLC tissues and survival rate of patients was analyzed using bioinformatic analysis. Transwell assays, wound-healing assays, and tail vein lung colonization models were employed to determine the role of DDX24 in migration and invasion in vitro and in vivo. We searched for DDX24-interacting proteins using co-immunoprecipitation followed by mass spectroscopy and verified the interaction. The influence of DDX24 on RPL5 expression and ubiquitination was examined using protein stability assays. RESULTS: DDX24 expression was upregulated in NSCLC cell lines and tumors of patients, particularly those with high tumor grades. A high DDX24 level was also correlated with a poor prognosis. DDX24 upregulation enhanced the migration and invasion ability of NSCLC cells, whereas its downregulation had the opposite effects. In vivo xenograft experiments confirmed that tumors with high DDX24 expression had higher metastatic abilities. The interaction between DDX24 and RPL5 promoted its ubiquitination and destabilized it. CONCLUSIONS: DDX24 acted as a pro-tumorigenic factor and promoted metastasis in NSCLC. DDX24 interacted with RPL5 to promote its ubiquitination and degradation. As a result, targeting DDX24/RPL5 axis may provide a novel potential therapeutic strategy for NSCLC.

RNA Helicase DDX24 Stabilizes LAMB1 to Promote Hepatocellular Carcinoma Progression.

Hepatocellular carcinoma (HCC) is one of the most aggressive malignancies. Elucidating the underlying mechanisms of this disease could provide new therapeutic strategies for treating HCC. Here, we identified a novel role of DEAD-box helicase 24 (DDX24), a member of the DEAD-box protein family, in promoting HCC progression. DDX24 levels were significantly elevated in HCC tissues and were associated with poor prognosis of HCC. Overexpression of DDX24 promoted HCC migration and proliferation in vitro and in vivo, whereas suppression of DDX24 inhibited both functions. Mechanistically, DDX24 bound the mRNA618-624nt of laminin subunit beta 1 (LAMB1) and increased its stability in a manner dependent upon the interaction between nucleolin and the C-terminal region of DDX24. Moreover, regulatory factor X8 (RFX8) was identified as a DDX24 promoter-binding protein that transcriptionally upregulated DDX24 expression. Collectively, these findings demonstrate that the RFX8/DDX24/LAMB1 axis promotes HCC progression, providing potential therapeutic targets for HCC. SIGNIFICANCE: The identification of a tumor-promoting role of DDX24 and the elucidation of the underlying regulatory mechanism provide potential prognostic indicators and therapeutic approaches to help improve the outcome of patients with hepatocellular carcinoma.

Drivers and forecasts of multiple waves of the coronavirus disease 2019 pandemic: A systematic analysis based on an interpretable machine learning framework.

Coronavirus disease 2019 (COVID-19) has become a global pandemic and continues to prevail with multiple rebound waves in many countries. The driving factors for the spread of COVID-19 and their quantitative contributions, especially to rebound waves, are not well studied. Multidimensional time-series data, including policy, travel, medical, socioeconomic, environmental, mutant and vaccine-related data, were collected from 39 countries up to 30 June 2021, and an interpretable machine learning framework (XGBoost model with Shapley Additive explanation interpretation) was used to systematically analyze the effect of multiple factors on the spread of COVID-19, using the daily effective reproduction number as an indicator. Based on a model of the pre-vaccine era, policy-related factors were shown to be the main drivers of the spread of COVID-19, with a contribution of 60.81%. In the post-vaccine era, the contribution of policy-related factors decreased to 28.34%, accompanied by an increase in the contribution of travel-related factors, such as domestic flights, and contributions emerged for mutant-related (16.49%) and vaccine-related (7.06%) factors. For single-peak countries, the dominant ones were policy-related factors during both the rising and fading stages, with overall contributions of 33.7% and 37.7%, respectively. For double-peak countries, factors from the rebound stage contributed 45.8% and policy-related factors showed the greatest contribution in both the rebound (32.6%) and fading (25.0%) stages. For multiple-peak countries, the Delta variant, domestic flights (current month) and the daily vaccination population are the three greatest contributors (8.12%, 7.59% and 7.26%, respectively). Forecasting models to predict the rebound risk were built based on these findings, with accuracies of 0.78 and 0.81 for the pre- and post-vaccine eras, respectively. These findings quantitatively demonstrate the systematic drivers of the spread of COVID-19, and the framework proposed in this study will facilitate the targeted prevention and control of the ongoing COVID-19 pandemic.

The NRF2-dependent transcriptional axis, XRCC5/hTERT drives tumor progression and 5-Fu insensitivity in hepatocellular carcinoma.

Human telomerase reverse transcriptase (hTERT) is highly expressed in many tumors and is essential for tumorigenesis and metastasis in multiple cancers. However, the molecular mechanisms underlying its high expression level in hepatocellular carcinoma (HCC) remain unclear. In this study, we identified X-ray repair cross-complementing 5 (XRCC5), a novel hTERT promoter-binding protein in HCC cells, using biotin-streptavidin-agarose pull-down assay. We found that XRCC5 was highly expressed in HCC cells, in which it transcriptionally upregulated hTERT. Functionally, the transgenic expression of XRCC5 promoted HCC progression and 5-fluorouracil resistance, whereas short hairpin RNA knockdown of XRCC5 had converse effects in vitro and in vivo. Moreover, hTERT overexpression reversed XRCC5 knockdown- or 5-fluorouracil (5-Fu)-mediated HCC inhibition. Mechanistically, nuclear-factor-erythroid-2-related factor 2 (NRF2) interacted with XRCC5, which in turn upregulated hTERT. However, the upregulation was insignificant when NRF2 was reduced, suggesting that the XRCC5-mediated hTERT expression was NRF2 dependent. The HCC patients with high expression levels of XRCC5 and hTERT had shorter overall survival times compared with those with low XRCC5 and hTERT levels in their tumor tissues. Collectively, our study demonstrates the molecular mechanisms of the XRCC5/NRF2/hTERT signaling in HCC metastasis, which will aid in the identification of novel strategies for the diagnosis and treatment of HCC.

Assessment of the global circulation and endemicity of dengue.

Dengue is a significant public health issue, affecting hundreds of millions of people worldwide. As it is spreading from tropical and subtropical zones, some regions previously recognised as non-endemic are at risk of becoming endemic. However, the global circulation of dengue is not fully understood and quantitative measurements of endemicity levels are lacking, posing an obstacle in the precise control of dengue spread. In this study, a sequence-based pipeline was designed based on random sampling to study the transmission of dengue. The limited intercontinental transmission was identified, while regional circulation of dengue was quantified in terms of importation, local circulation and exportation. Additionally, hypo- and hyper-endemic regions were identified using a new metric, with the former characterised by low local circulation and increased importation, whereas the latter by high local circulation and reduced importation. In this study, the global circulation pattern of dengue was examined and a sequence-based endemicity measurement was proposed, which will be helpful for future surveillance and targeted control of dengue.

Heavy chain sequence-based classifier for the specificity of human antibodies.

Antibodies specifically bind to antigens and are an essential part of the immune system. Hence, antibodies are powerful tools in research and diagnostics. High-throughput sequencing technologies have promoted comprehensive profiling of the immune repertoire, which has resulted in large amounts of antibody sequences that remain to be further analyzed. In this study, antibodies were downloaded from IMGT/LIGM-DB and Sequence Read Archive databases. Contributing features from antibody heavy chains were formulated as numerical inputs and fed into an ensemble machine learning classifier to classify the antigen specificity of six classes of antibodies, namely anti-HIV-1, anti-influenza virus, anti-pneumococcal polysaccharide, anti-citrullinated protein, anti-tetanus toxoid and anti-hepatitis B virus. The classifier was validated using cross-validation and a testing dataset. The ensemble classifier achieved a macro-average area under the receiver operating characteristic curve (AUC) of 0.9246 from the 10-fold cross-validation, and 0.9264 for the testing dataset. Among the contributing features, the contribution of the complementarity-determining regions was 53.1% and that of framework regions was 46.9%, and the amino acid mutation rates occupied the first and second ranks among the top five contributing features. The classifier and insights provided in this study could promote the mechanistic study, isolation and utilization of potential therapeutic antibodies.