ViPal: A framework for virulence prediction of influenza viruses with prior viral knowledge using genomic sequences.

Influenza viruses pose great threats to public health and cause enormous economic losses every year. Previous work has revealed the viral factors associated with the virulence of influenza viruses in mammals. However, taking prior viral knowledge represented by heterogeneous categorical and discrete information into account to explore virus virulence is scarce in the existing work. How to make full use of the preceding domain knowledge in virulence study is challenging but beneficial. This paper proposes a general framework named ViPal for virulence prediction in mice that incorporates discrete prior viral mutation and reassortment information based on all eight influenza segments. The posterior regularization technique is leveraged to transform prior viral knowledge into constraint features and integrated into the machine learning models. Experimental results on influenza genomic datasets validate that our proposed framework can improve virulence prediction performance over baselines. The comparison between ViPal and other existing methods shows the computational efficiency of our framework with comparable or superior performance. Moreover, the interpretable analysis through SHAP (SHapley Additive exPlanations) identifies the scores of constraint features contributing to the prediction. We hope this framework could provide assistance for the accurate detection of influenza virulence and facilitate flu surveillance.

VirPreNet: a weighted ensemble convolutional neural network for the virulence prediction of influenza A virus using all eight segments.

MOTIVATION: Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics. The evolution of influenza viruses remains to be the main obstacle in the effectiveness of antiviral treatments due to rapid mutations. Previous work has been investigated to reveal the determinants of virulence of the influenza A virus. To further facilitate flu surveillance, explicit detection of influenza virulence is crucial to protect public health from potential future pandemics. RESULTS: In this article, we propose a weighted ensemble convolutional neural network (CNN) for the virulence prediction of influenza A viruses named VirPreNet that uses all eight segments. Firstly, mouse lethal dose 50 is exerted to label the virulence of infections into two classes, namely avirulent and virulent. A numerical representation of amino acids named ProtVec is applied to the eight-segments in a distributed manner to encode the biological sequences. After splittings and embeddings of influenza strains, the ensemble CNN is constructed as the base model on the influenza dataset of each segment, which serves as the VirPreNet's main part. Followed by a linear layer, the initial predictive outcomes are integrated and assigned with different weights for the final prediction. The experimental results on the collected influenza dataset indicate that VirPreNet achieves state-of-the-art performance combining ProtVec with our proposed architecture. It outperforms baseline methods on the independent testing data. Moreover, our proposed model reveals the importance of PB2 and HA segments on the virulence prediction. We believe that our model may provide new insights into the investigation of influenza virulence. AVAILABILITY AND IMPLEMENTATION: Codes and data to generate the VirPreNet are publicly available at https://github.com/Rayin-saber/VirPreNet. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PEGylated Poly-HDACi: A Designer Polyprodrug from Optimized Drug Units.

We designed, synthesized, and characterized a tri-block copolymer. Its hydrophobic part, a chain of histone deacetylase inhibitor (HDACi) prodrug, was symmetrically flanked by two identical PEG blocks, whereas the built-in HDACi was a linear molecule, terminated with a thiol at one end, and a hydroxyl group at the other. Such a feature facilitated end-to-end linkage of prodrugs through alternatively aligned disulfides and carbonates. The disulfides served dual roles: redox sensors of smart nanomedicine, and warheads of masked HDACi drugs. This approach, carefully designed to benefit both control-release and efficacy, is conceptually novel for optimizing drug units in nanomedicine. Micelles from this designer polyprodrug released only PEG, CO2 and HDACi, and synergized with DOX against HCT116 cells, demonstrating its widespread potential in combination therapy. Our work highlights, for the first time, the unique advantage of thiol-based drug molecules in nanomedicine design.

Exploring the Lethality of Human-Adapted Coronavirus Through Alignment-Free Machine Learning Approaches Using Genomic Sequences.

Background: A newly emerging novel coronavirus appeared and rapidly spread worldwide and World Health Organization declared a pandemic on March 11, 2020. The roles and characteristics of coronavirus have captured much attention due to its power of causing a wide variety of infectious diseases, from mild to severe, on humans. The detection of the lethality of human coronavirus is key to estimate the viral toxicity and provide perspectives for treatment. Methods: We developed an alignment-free framework that utilizes machine learning approaches for an ultra-fast and highly accurate prediction of the lethality of human-adapted coronavirus using genomic sequences. We performed extensive experiments through six different feature transformation and machine learning algorithms combining digital signal processing to identify the lethality of possible future novel coronaviruses using existing strains. Results: The results tested on SARS-CoV, MERS-CoV and SARS-CoV-2 datasets show an average 96.7% prediction accuracy. We also provide preliminary analysis validating the effectiveness of our models through other human coronaviruses. Our framework achieves high levels of prediction performance that is alignment-free and based on RNA sequences alone without genome annotations and specialized biological knowledge. Conclusion: The results demonstrate that, for any novel human coronavirus strains, this study can offer a reliable real-time estimation for its viral lethality.

Cell-penetrating peptides noncovalently modified red phosphorescent nanoparticles for high-efficiency imaging.

The application of long-lived phosphorescence probes in time-resolved luminescence imaging is limited by their low quantum yield in aqueous solutions. However, sensitization of thermally activated delayed fluorescence (TADF) materials can compensate for this limitation while addressing the issue of insufficient proportion of their own long lifetime. In this study, we utilized the characteristics of phosphorescence and TADF materials simultaneously by doping the receptor iridium complex PMD-Ir into the donor TADF polymer PCzDP-20 through donor-receptor doping method, and successfully prepared highly efficient red phosphorescent nanoparticles. The quantum yield of the nanoparticles obtained by this method reaches up to 30%, and the luminescence lifetime can reach several thousand nanoseconds. Additionally, due to the low concentration doping of PMD-Ir, the risk of transition metal toxicity is greatly reduced. Furthermore, we used non-covalent modification with amphiphilic cell-penetrating peptides (CPPs) to increase the cell membrane permeability of the nanoparticles. The CPPs modified nanoparticles achieve in vivo confocal imaging of zebrafish and intracellular time-resolved imaging by its significantly improved bioimaging capabilities. The functional nanoparticles designing method fully utilizes the characteristics of PMD-Ir, PCzDP-20, and CPPs, solving the problems of low quantum yield and poor membrane permeability of Ir-complex nanoparticles. This will greatly promote the development of time-resolved luminescence imaging.

Alleviation of metal stress in rape seedlings (Brassica napus L.) using the antimony-resistant plant growth-promoting rhizobacteria Cupriavidus sp. S-8-2.

This study investigated an effective strategy for remediating antimony (Sb)-contaminated soil using the bacterial strain screened from Sb-contaminated fern rhizospheres due to its superior growth-promoting, heavy-metal(loid) resistant, and antibiotic-tolerant characteristics. The strain that belongs to Cupriavidus sp. was determined by 16S rRNA sequencing and showed no morphological changes when grown with high concentrations of Sb (608.8 mg/L). The strain showed prominent indole acetic acid (IAA), phosphate-solubilizing abilities, and ACC deaminase activity under Sb stress. Moreover, IAA and soluble phosphate levels increased in the presence of 608.8 mg/L Sb. Inoculation of rape seedlings with Cupriavidus sp. S-8-2 enhanced several morphological and biochemical growth features compared to untreated seedlings grown under Sb stress. Inoculation of Cupriavidus sp. S-8-2 increased root weight by more than four-fold for fresh weight and over two-fold for dry weight, despite high environmental Sb. The strain also reduced Sb-mediated oxidative stress and malondialdehyde contents by reducing Sb absorption, thus alleviating Sb-induced toxicity. Environmental Scanning Electron Microscope (ESEM) imaging and dilution plating technique revealed Cupriavidus sp. S-8-2 is localized on the surface of roots. Identifying the Sb-resistant plant growth-promoting bacterium suggested its usefulness in the remediation of contaminated agricultural soil and for the promotion of crop growth. We highly recommend the strain for further implementation in field experiments.

The cis configuration of amino-modified tetraphenylethene for selective detection of single-stranded guanine-rich DNA.

The selectivity and sensitivity of amino-functionalized tetraphenylethene probes (namely Z-N2TPE and E-N2TPE) for ssDNA detection in aqueous solution were investigated. Both Z-N2TPE and E-N2TPE showed high selectivity to guanine-rich ssDNA. The sensitivity was found to be positively related to the DNA length, indicating the longer DNA could binding more probes to cause aggregation induced fluorescence. Z-N2TPE and E-N2TPE could detect guanine-rich ssDNA as short as 5 nt and 10 nt respectively. Theoretical simulation calculation shows that the amino group of the probe could simultaneously bind with guanine and phosphate ester, which contribute to high selectivity. And the cis probe could bind DNA with higher affinity than the trans one, since the two amino groups of Z-N2TPE could synergistically bind DNA while E-N2TPE could bind DNA with only one amino group.