Molecular Insights into the Variability in Infection and Immune Evasion Capabilities of SARS-CoV-2 Variants: A Sequence and Structural Investigation of the RBD Domain.

As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continuously emerge, an increasing number of mutations are accumulating in the Spike protein receptor-binding domain (RBD) region. Through sequence analysis of various Variants of Concern (VOC), we identified that they predominantly fall within the ο lineage although recent variants introduce any novel mutations in the RBD. Molecular dynamics simulations were employed to compute the binding free energy of these variants with human Angiotensin-converting enzyme 2 (ACE2). Structurally, the binding interface of the ο RBD displays a strong positive charge, complementing the negatively charged binding interface of ACE2, resulting in a significant enhancement in the electrostatic potential energy for the ο variants. Although the increased potential energy is partially offset by the rise in polar solvation free energy, enhanced electrostatic potential contributes to the long-range recognition between the ο variant's RBD and ACE2. We also conducted simulations of glycosylated ACE2-RBD proteins. The newly emerged ο (JN.1) variant has added a glycosylation site at N-354@RBD, which significantly weakened its binding affinity with ACE2. Further, our interaction studies with three monoclonal antibodies across multiple SARS-CoV-2 strains revealed a diminished neutralization efficacy against the ο variants, primarily attributed to the electrostatic repulsion between the antibodies and RBD interface. Considering the characteristics of the ο variant and the trajectory of emerging strains, we propose that newly developed antibodies against SARS-CoV-2 RBD should have surfaces rich in negative potential and, postbinding, exhibit strong van der Waals interactions. These findings provide invaluable guidance for the formulation of future therapeutic strategies.

Discovery of inhibitors against SARS-CoV-2 main protease using fragment-based drug design.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiological agent of coronavirus disease 2019 (COVID-19), in which the main protease (Mpro) plays an important role in the virus's life cycle. In this work, two representative peptide inhibitors (11a and PF-07321332) were selected, and their interaction mechanisms of non-covalently bound with Mpro were firstly investigated by means of molecular dynamical simulation. Then, using the fragment-based drug design method, some fragments from the existing SARS-CoV and SARS-CoV-2 inhibitors were selected to replace the original P2 and P3 fragments, resulting in some new molecules. Among them, two molecules (O-74 and N-98) were confirmed by molecular docking and molecular dynamics simulation, and ADMET properties prediction was employed for further verification. The results shown that they presented excellent activity and physicochemical properties, and had the potential to be new inhibitors for SARS-CoV-2 main protease.

Rapid determination of trace homogeneous catalyst in chemical production.

As one of the important factors in chemical production, catalyst content directly affects the process of reaction and the quality of products. The quantitative analysis of trace catalyst in homogeneous reaction system is still faced with great challenges. In this work, a simple and effective approach to the rapid determination of trace homogeneous catalyst (THC) was proposed based on UV-vis spectrophotometry. Wavelet transform and Tchebichef curve moment methods were combined with gray wolf algorithm to extract the feature information from the original UV-vis spectra of samples. Then the partial least-squares model was established. The predictive correlation coefficient (Rp2) was 0.9842, and the limit of quantification was 0.07 ‰. The intra-day and inter-day precision were 3.97 % and 4.36 %, respectively. The spiked recoveries of three different concentrations in actual samples were between 97.6 and 101.9 %. The results indicated that the obtained model was satisfactory and could be used in practical measurement. Compared with the conventional modeling methods, the proposed approach was more accurate and reliable, which provided a feasible new pathway for enterprise product quality control.

Chemometrics-assisted simultaneous voltammetric determination of multiple neurotransmitters in human serum.

An electrochemical method combining chemometrics was developed for simultaneous quantification of multiple neurotransmitters including Dopamine (DA), Epinephrine (EP), Norepinephrine (NE) and serotonin (5-hydroxytryptamine, 5-HT) in human blood serum. A reduced graphene oxide modified glassy carbon electrode (RGO/GCE) was prepared via electrodeposition method. Differential pulse voltammetry (DPV) measurement of the four neurotransmitters showed that the voltammetric signals of the four targets overlapped significantly. To facilitate the simultaneous determination of the neurotransmitters, a chemometric tool of Tchebichef curve moment (TcM) method was proposed. The TcMs calculated from the voltammograms were used to establish the quantitative models by stepwise regression. The intra-day and inter-day precisions of the proposed method were less than 3.5% and 8.1%, respectively, and the recoveries were from 87.4% to 124%. The limit of detection (LOD) for DA, EP, NE and 5-HT were 74 nM, 104 nM, 84 nM and 97 nM, respectively. The above results indicated that the proposed approach is simple and reliable for the simultaneous determination of multiple neurotransmitters in human serum.

An effective approach to the early diagnosis of colorectal cancer based on three-dimensional fluorescence spectra of human blood plasma.

Colorectal cancer (CRC) is a common malignancy in the gastrointestinal tract, and its screening rates remain relatively low in the general population due to the lack of specific symptoms and effective methods. It is still in urgent need to develop rapid and reliable approach to the early diagnosis of CRC. Herein, based on the three-dimensional (3D) fluorescence spectra of human blood plasma, a combination strategy of Tchebichef image moments coupled with partial least squares-discriminate analysis (TM-PLS-DA) was proposed for the detection of CRC from three classes (CRC samples, adenomas samples and non-malignant findings). The established TM-PLS-DA classification model provided an 84 % correct classification for CRC prediction. Venetian blinds 10-fold cross validation was carried out. The error rates both in cross validation and test sets were less than 0.16. Sensitivity and specificity for CRC prediction were 0.95 and 0.88, respectively. At the same time, the diagnostic capacity of the proposed method was tested by receiver operating characteristics (ROC) analysis with area under the curve (AUC) of 0.94 for CRC diagnosis. These results demonstrate that the proposed TM-PLS-DA method based on the 3D fluorescence spectra of blood plasma has great advantage for the accurate CRC detection, which will provide a potential alternative approach for cancer diagnostics.

Novel Approach to the Analysis of Chemical Third-Order Data.

For the more complex samples, chemical higher-order data can be collected from various information sources, which become the necessary foundation of accurate analysis. In this article, the Tchebichef cubic moment (TCM) was developed for the analysis of chemical third-order data for the first time. Then, the proposed TCM approach was applied to the fluorescence excitation-emission time data for the analysis of adrenaline and noradrenaline in urinary samples (Data I) and the data fusion of the excitation-emission matrix (EEM), NMR, and liquid chromatography-mass spectrometry (LC-MS) spectra for the determination of the five target components (Data II). For Data I, all of the cross-validation correlation coefficients (Rcv2) of the obtained linear models on the calibration set were more than 0.9937 and the prediction root-mean-square errors (RMSEp) of the external independent test samples were less than 0.0250 µM. For Data II, all of the Rcv2 were higher than 0.9846 and RMSEp were less than 0.2267 µM. Compared with several conventional methods, the proposed method was more convenient and accurate. This study provides another effective approach to the analysis of complex samples based on their chemical third-order data.

Long-term fertilization alters soil properties and fungal community composition in fluvo-aquic soil of the North China Plain.

Different fertilization regimes can substantially influence soil fungal community composition, yet fewer studies try to control for the effects of nitrogen input. Here, we investigated the impact of fertilization with equal nitrogen upon soil properties and soil fungal diversity and community composition in the North China Plain in a long-term field experiment. Long-term (32 years) fertilization regimes were applied with equal amounts of nitrogen: no chemical fertilizer or organic manure; chemical fertilization only; organic manure fertilization only, and; combination of 1/2 chemical fertilizer and 1/2 organic manure. Then we investigated the influence of these four fertilization regimes to soil properties, fungal diversity and community composition. The results showed that applying organic manure significantly influenced soil properties. Illumina MiSeq sequencing and its analysis revealed that organic manure fertilization significantly changed soil fungal alpha diversity, but chemical fertilization did not. Although soil fungal community composition did not differ significantly among all the fertilization regimes at the phylum and class levels, they did show differences in the abundance of dominant fungi. Yet at the genus level, soil fungal community composition, abundance, and beta diversity was affected by all fertilization regimes. Application of organic manure also reduced the abundance of soil-born fungal pathogens such as Fusarium. Our results suggest that long-term application of organic manure could markedly improve soil properties, altering soil fungal community composition and its diversity. Moreover, organic manure fertilization could limit soil-born fungal diseases, to further contribute to soil ecosystem sustainability.

Paenibacillus salinicaeni sp. nov., isolated from saline silt sample.

A novel facultatively anaerobic bacterium, designated strain LAM0A28(T), was isolated from a saline silt sample collected from the Chinese Sea of Death located in Suining city, Sichuan province, China. Cells of strain LAM0A28(T) were observed to be Gram-stain positive, motile, endospore-forming and straight-rod shaped. Strain LAM0A28(T) was found to be able to grow at 15-45 °C (optimum: 30-35 °C), pH 5.0-10.0 (optimum: 7.5) and 0-5 % NaCl (w/v) (optimum: 0.5 %). The 16S rRNA gene sequence similarity analysis showed that strain LAM0A28(T) is closely related to Paenibacillus jilunlii DSM 23019(T) (97.5 %) and Paenibacillus graminis DSM 15220(T) (97.2 %). The DNA-DNA hybridization values between the isolate and P. jilunlii DSM 23019(T), P. graminis DSM 15220(T) were 30.2 ± 1.6 % and 44.7 ± 2.1 %, respectively. The DNA G+C content was found to be 51.2 mol% as determined by the T m method. The major cellular fatty acids were identified as anteiso-C15:0, C16:0, iso-C16:0 and C14:0. The major isoprenoid quinone was identified as MK-7. The cell wall peptidoglycan was found to contain meso-diaminopimelic acid. The major polar lipids were found to be diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine, two aminophospholipids and six unidentified lipids. Based on the phylogenetic, phenotypic and chemotaxonomic characteristics, strain LAM0A28(T) is concluded to represent a novel species within the genus Paenibacillus, for which the name Paenibacillus salinicaeni sp. nov. is proposed. The type strain is LAM0A28(T) (=ACCC 00741(T) = JCM 30850(T)).