Solution structure of a Plasmodium falciparum AMA-1/MSP 1 chimeric protein vaccine candidate (PfCP-2.9) for malaria

Background: The Plasmodium falciparum chimeric protein PfCP-2.9 is a promising asexual-stage malaria vaccine evaluated in clinical trials. This chimeric protein consists of two cysteine-rich domains: domain III of the apical membrane antigen 1 (AMA-1 [III]) and the C-terminal region of the merozoite surface protein 1 (MSP1-19). It has been reported that the fusion of these two antigens enhanced their immunogenicity and antibody-mediated inhibition of parasite growth in vitro. Methods: The N-15-labeled and C-13/N-15-labeled PfCP-2.9 was produced in Pichia pastoris for nuclear magnetic resonance (NMR) structure analysis. The chemical shift assignments of PfCP-2.9 were compared with those previously reported for the individual domains (i.e., PfAMA-1(III) or PfMSP 1-19). The two-dimensional spectra and transverse relaxation rates (R-2) of the PfMSP1-19 alone were compared with that of the PfCP-2.9. Results: Confident backbone assignments were obtained for 122 out of 241 residues of PfCP-2.9. The assigned residues in PfCP-2.9 were very similar to those previously reported for the individual domains. The conformation of the PfMSP1-19 in different constructs is essentially the same. Comparison of transverse relaxation rates (R-2) strongly suggests no weak interaction between the domains. Conclusions: These data indicate that the fusion of AMA-1(III) and MSP1-19 as chimeric protein did not change their structures, supporting the use of the chimeric protein as a potential malaria vaccine.Infectious DiseasesParasitologyTropical MedicineSCI(E)5ARTICLEnull

Isolation of starch and protein degrading strain Bacillus subtilis FYZ1-3 from tobacco waste and genomic analysis of its tolerance to nicotine and inhibition of fungal growth

Aerobic fermentation is an effective technique for the large-scale processing of tobacco waste. However, the specificity of the structure and composition of tobacco-derived organic matter and the toxic alkaloids in the material make it currently difficult to directly use microbial agents. In this study, a functional strain FYZ1-3 was isolated and screened from thermophilic phase samples of tobacco waste composting. This strain could withstand temperatures as high as 80°C and grow normally at 0.6% nicotine content. Furthermore, it had a strong decomposition capacity of tobacco-derived starch and protein, with amylase activity of 122.3  U/mL and protease activity and 52.3  U/mL, respectively. To further understand the mechanism of the metabolic transformation of the target, whole genome sequencing was used and the secondary metabolite gene cluster was predicted. The inhibitory effect of the strain on common tobacco fungi was verified using the plate confrontation and agar column methods. The results showed that the strain FYZ1-3 was Bacillus subtilis, with a genome size of 4.17  Mb and GC content of 43.68%; 4,338 coding genes were predicted. The genome was annotated and analyzed using multiple databases to determine its ability to efficiently degrade starch proteins at the molecular level. Moreover, 14 functional genes related to nicotine metabolism were identified, primarily located on the distinct genomic island of FYZ1-3, giving a speculation for its nicotine tolerance capability on the molecular mechanism. By mining the secondary metabolite gene cluster prediction, we found potential synthetic bacteriocin, antimicrobial peptide, and other gene clusters on its chromosome, which may have certain antibacterial properties. Further experiments confirmed that the FYZ1-3 strain was a potent growth inhibitor of Penicillium chrysogenum, Aspergillus sydowii, A. fumigatus, and Talaromyces funiculosus. The creation and industrial use of the functional strains obtained in this study provide a theoretical basis for its industrial use, where it would be of great significance to improve the utilization rate of tobacco waste

Screening high potassium efficiency potato genotypes and physiological responses at different potassium levels

Potato (Solanum tuberosum L.) growth and production is highly dependent on potassium (K) levels in the soil. Southwest China is the largest potato production region but it has low availability of soil potassium. To assess the genetic variation in K use efficiency, 20 potato genotypes were collected to compare the yield and K content in a pot experiment. Moreover, ‘Huayu-5’ and ‘Zhongshu-19’ were cultivated in five K applications to investigate the K distribution and sucrose in different organs. The results indicated that there were highly significant effects of K, genotype and K×G interactions on tuber yield, plant and tuber K content, plant K uptake efficiency and K harvest index. Cluster analysis classified 20 potato genotypes into four types: DH (high efficiency at low and high K application), LKH (high efficiency at low K application), HKH (high efficiency at high K application) and DL (low efficiency at low and high K application). The potassium distribution percentage in the tubers of the potassium-efficient genotype was higher than that of the potassium-inefficient genotype under low potassium application. The sucrose content in the tuber gently declined as the application of K rose in both cultivars, and that in the tuber of ‘Huayu-5’ was higher than that in ‘Zhongshu-19’. ‘Huayu-5’ reached the highest yield when the potassium application was 159.45 kg ha-1, and ‘Zhongshu-19’ reached the highest yield when the potassium application was 281.4 kg ha-1. This study indicated that genetic variation for K utilization efficiency existed among 20 genotypes, and yield in low K application and relative yield were suitable criteria for screening K utilization efficiency genotypes

Rethinking Causal Relationships Learning in Graph Neural Networks

Graph Neural Networks (GNNs) demonstrate their significance by effectively modeling complex interrelationships within graph-structured data. To enhance the credibility and robustness of GNNs, it becomes exceptionally crucial to bolster their ability to capture causal relationships. However, despite recent advancements that have indeed strengthened GNNs from a causal learning perspective, conducting an in-depth analysis specifically targeting the causal modeling prowess of GNNs remains an unresolved issue. In order to comprehensively analyze various GNN models from a causal learning perspective, we constructed an artificially synthesized dataset with known and controllable causal relationships between data and labels. The rationality of the generated data is further ensured through theoretical foundations. Drawing insights from analyses conducted using our dataset, we introduce a lightweight and highly adaptable GNN module designed to strengthen GNNs' causal learning capabilities across a diverse range of tasks. Through a series of experiments conducted on both synthetic datasets and other real-world datasets, we empirically validate the effectiveness of the proposed module

Automics: an integrated platform for NMR-based metabonomics spectral processing and data analysis

<p>Abstract</p> <p>Background</p> <p>Spectral processing and post-experimental data analysis are the major tasks in NMR-based metabonomics studies. While there are commercial and free licensed software tools available to assist these tasks, researchers usually have to use multiple software packages for their studies because software packages generally focus on specific tasks. It would be beneficial to have a highly integrated platform, in which these tasks can be completed within one package. Moreover, with open source architecture, newly proposed algorithms or methods for spectral processing and data analysis can be implemented much more easily and accessed freely by the public.</p> <p>Results</p> <p>In this paper, we report an open source software tool, Automics, which is specifically designed for NMR-based metabonomics studies. Automics is a highly integrated platform that provides functions covering almost all the stages of NMR-based metabonomics studies. Automics provides high throughput automatic modules with most recently proposed algorithms and powerful manual modules for 1D NMR spectral processing. In addition to spectral processing functions, powerful features for data organization, data pre-processing, and data analysis have been implemented. Nine statistical methods can be applied to analyses including: feature selection (Fisher's criterion), data reduction (PCA, LDA, ULDA), unsupervised clustering (K-Mean) and supervised regression and classification (PLS/PLS-DA, KNN, SIMCA, SVM). Moreover, Automics has a user-friendly graphical interface for visualizing NMR spectra and data analysis results. The functional ability of Automics is demonstrated with an analysis of a type 2 diabetes metabolic profile.</p> <p>Conclusion</p> <p>Automics facilitates high throughput 1D NMR spectral processing and high dimensional data analysis for NMR-based metabonomics applications. Using Automics, users can complete spectral processing and data analysis within one software package in most cases. Moreover, with its open source architecture, interested researchers can further develop and extend this software based on the existing infrastructure.</p

Streptococcus suis Sequence Type 7 Outbreak, Sichuan, China

An outbreak of Streptococcus suis serotype 2 emerged in the summer of 2005 in Sichuan Province, and sporadic infections occurred in 4 additional provinces of China. In total, 99 S. suis strains were isolated and analyzed in this study: 88 isolates from human patients and 11 from diseased pigs. We defined 98 of 99 isolates as pulse type I by using pulsed-field gel electrophoresis analysis of SmaI-digested chromosomal DNA. Furthermore, multilocus sequence typing classified 97 of 98 members of the pulse type I in the same sequence type (ST), ST-7. Isolates of ST-7 were more toxic to peripheral blood mononuclear cells than ST-1 strains. S. suis ST-7, the causative agent, was a single-locus variant of ST-1 with increased virulence. These findings strongly suggest that ST-7 is an emerging, highly virulent S. suis clone that caused the largest S. suis outbreak ever described

NMR Characterizations of the Ice Binding Surface of an Antifreeze Protein

Antifreeze protein (AFP) has a unique function of reducing solution freezing temperature to protect organisms from ice damage. However, its functional mechanism is not well understood. An intriguing question concerning AFP function is how the high selectivity for ice ligand is achieved in the presence of free water of much higher concentration which likely imposes a large kinetic barrier for protein-ice recognition. In this study, we explore this question by investigating the property of the ice binding surface of an antifreeze protein using NMR spectroscopy. An investigation of the temperature gradient of amide proton chemical shift and its correlation with chemical shift deviation from random coil was performed for CfAFP-501, a hyperactive insect AFP. A good correlation between the two parameters was observed for one of the two Thr rows on the ice binding surface. A significant temperature-dependent protein-solvent interaction is found to be the most probable origin for this correlation, which is consistent with a scenario of hydrophobic hydration on the ice binding surface. In accordance with this finding, rotational correlation time analyses combined with relaxation dispersion measurements reveals a weak dimer formation through ice binding surface at room temperature and a population shift of dimer to monomer at low temperature, suggesting hydrophobic effect involved in dimer formation and hence hydrophobic hydration on the ice binding surface of the protein. Our finding of hydrophobic hydration on the ice binding surface provides a test for existing simulation studies. The occurrence of hydrophobic hydration on the ice binding surface is likely unnecessary for enhancing protein-ice binding affinity which is achieved by a tight H-bonding network. Subsequently, we speculate that the hydrophobic hydration occurring on the ice binding surface plays a role in facilitating protein-ice recognition by lowering the kinetic barrier as suggested by some simulation studies

Structure and Novel Functional Mechanism of Drosophila SNF in Sex-Lethal Splicing

Sans-fille (SNF) is the Drosophila homologue of mammalian general splicing factors U1A and U2B″, and it is essential in Drosophila sex determination. We found that, besides its ability to bind U1 snRNA, SNF can also bind polyuridine RNA tracts flanking the male-specific exon of the master switch gene Sex-lethal (Sxl) pre-mRNA specifically, similar to Sex-lethal protein (SXL). The polyuridine RNA binding enables SNF directly inhibit Sxl exon 3 splicing, as the dominant negative mutant SNF1621 binds U1 snRNA but not polyuridine RNA. Unlike U1A, both RNA recognition motifs (RRMs) of SNF can recognize polyuridine RNA tracts independently, even though SNF and U1A share very high sequence identity and overall structure similarity. As SNF RRM1 tends to self-associate on the opposite side of the RNA binding surface, it is possible for SNF to bridge the formation of super-complexes between two introns flanking Sxl exon 3 or between a intron and U1 snRNP, which serves the molecular basis for SNF to directly regulate Sxl splicing. Taken together, a new functional model for SNF in Drosophila sex determination is proposed. The key of the new model is that SXL and SNF function similarly in promoting Sxl male-specific exon skipping with SNF being an auxiliary or backup to SXL, and it is the combined dose of SXL and SNF governs Drosophila sex determination

Cross-correlation suppressed T1 and NOE experiments for protein side-chain 13CH2 groups

Relaxation measurements of side-chain 13CH2-groups of uniformly 13C labeled human ubiquitin were performed at 600 MHz and 800 MHz magnetic field strength at 30°C. Dipole-dipole cross-correlated relaxation effects in T1 experiments were suppressed by the combination of radio-frequency pulses and pulsed field gradients during the relaxation delay leading to monoexponential relaxation decays that allow a more accurate extraction of the 13C T1 relaxation times. Heteronuclear 1H-13C NOEs obtained by using different proton saturation schemes indicate that the influence of cross-correlation is small. The experimental T1 and NOE data were interpreted in a model-free way in terms of a generalized order parameter and an internal correlation time

Solution Structure of a Low-Molecular-Weight Protein Tyrosine Phosphatase from Bacillus subtilis

The low-molecular-weight (LMW) protein tyrosine phosphatases (PTPs) exist ubiquitously in prokaryotes and eukaryotes and play important roles in cellular processes. We report here the solution structure of YwlE, an LMW PTP identified from the gram-positive bacteria Bacillus subtilis. YwlE consists of a twisted central four-stranded parallel β-sheet with seven α-helices packing on both sides. Similar to LMW PTPs from other organisms, the conformation of the YwlE active site is favorable for phosphotyrosine binding, indicating that it may share a common catalytic mechanism in the hydrolysis of phosphate on tyrosine residue in proteins. Though the overall structure resembles that of the eukaryotic LMW PTPs, significant differences were observed around the active site. Residue Asp115 is likely interacting with residue Arg13 through electrostatic interaction or hydrogen bond interaction to stabilize the conformation of the active cavity, which may be a unique character of bacterial LMW PTPs. Residues in the loop region from Phe40 to Thr48 forming a wall of the active cavity are more flexible than those in other regions. Ala41 and Gly45 are located near the active cavity and form a noncharged surface around it. These unique properties demonstrate that this loop may be involved in interaction with specific substrates. In addition, the results from spin relaxation experiments elucidate further insights into the mobility of the active site. The solution structure in combination with the backbone dynamics provides insights into the mechanism of substrate specificity of bacterial LMW PTPs