Cellular specificity of lactate metabolism and a novel lactate-related gene pair index for frontline treatment in clear cell renal cell carcinoma

BackgroundAlthough lactate metabolism-related genes (LMRGs) have attracted attention for their effects on cancer immunity, little is known about their function in clear cell renal cell carcinoma (ccRCC). The aim of this study was to examine the cellular specificity of lactate metabolism and how it affected the first-line treatment outcomes in ccRCC.MethodsGSE159115 was used to examine the features of lactate metabolism at the single-cell level. Utilizing the transcriptome, methylation profile, and genomic data from TCGA-KIRC, a multi-omics study of LMRG expression characteristics was performed. A prognostic index based on a gene-pair algorithm was created to assess how LMRGs affected patients’ clinical outcomes. To simulate the relationship between the prognostic index and the frontline treatment, pRRophetic and Subclass Mapping were used. E-MTAB-1980, E-MTAB-3267, Checkmate, and Javelin-101 were used for external validation.ResultsThe variable expression of some LMRGs in ccRCC can be linked to variations in DNA copy number or promoter methylation levels. Lactate metabolism was active in tumor cells and vSMCs, and LDHA, MCT1, and MCT4 were substantially expressed in tumor cells, according to single-cell analysis. The high-risk patients would benefit from immune checkpoint blockade monotherapy (ICB) and ICB plus tyrosine kinase inhibitors (TKI) therapy, whereas the low-risk individuals responded to mTOR-targeted therapy.ConclusionsAt the single-cell level, our investigation demonstrated the cellular specificity of lactate metabolism in ccRCC. We proposed that the lactate-related gene pair index might be utilized to identify frontline therapy responders in ccRCC patients as well as predict prognosis

Who Is the Rightful Owner? Young Children’s Ownership Judgments in Different Transfer Contexts

This study aimed to examine whether Chinese preschoolers understand that ownership can be transferred in different contexts. The study participants were 3- to 5-year-old Chinese children (n = 96) and adults (n = 34). With four scenarios that contained different transfer types (giving, stealing, losing, and abandoning), participants were asked four questions about ownership. The results indicated that preschoolers’ ability to distinguish legitimate ownership transfers from illegitimate ownership transfers improved with age. Three-year-olds understood that ownership cannot be transferred in a stealing context, but the appropriate understanding of ownership was not attained until 4 years old in a giving context and 5 years old in losing and abandoning contexts, which is similar to the adults’ performance. In addition to the first possessor bias (a tendency to judge the first possessor as the owner) found in previous studies, 3-year-olds also displayed a loan bias (a tendency to believe everything that is transferred should be returned) in the study. The findings suggest that the developmental trajectories of preschoolers’ understanding of ownership transfers varied across different contexts, which may relate to children’s ability to consider the role of intent in determining ownership and parents’ disciplinary behavior. Both cross-cultural similarities and differences are discussed

PSSP-RFE: Accurate Prediction of Protein Structural Class by Recursive Feature Extraction from PSI-BLAST Profile, Physical-Chemical Property and Functional Annotations

Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets

Complete Sequence of a F33:A-:B- Conjugative Plasmid Carrying the oqxAB, fosA3, and blaCTX-M-55 Elements from a Foodborne Escherichia coli Strain

This study reports the complete sequence of pE80, a conjugative IncFII plasmid recovered from an Escherichia coli strain isolated from chicken meat. This plasmid harbors multiple resistance determinants including oqxAB, fosA3, blaCTX-M-55, and blaTEM-1, and is a close variant of the recently reported p42-2 element, which was recovered from E. coli of veterinary source. Recovery of pE80 constitutes evidence that evolution or genetic re-arrangement of IncFII type plasmids residing in animal-borne organisms is an active event, which involves acquisition and integration of foreign resistance elements into the plasmid backbone. Dissemination of these plasmids may further compromise the effectiveness of current antimicrobial strategies.Department of Applied Biology and Chemical Technolog

Identification of copper death-associated molecular clusters and immunological profiles in rheumatoid arthritis

Objective: An analysis of the relationship between rheumatoid arthritis (RA) and copper death-related genes (CRG) was explored based on the GEO dataset. / Methods: Based on the differential gene expression profiles in the GSE93272 dataset, their relationship to CRG and immune signature were analysed. Using 232 RA samples, molecular clusters with CRG were delineated and analysed for expression and immune infiltration. Genes specific to the CRGcluster were identified by the WGCNA algorithm. Four machine learning models were then built and validated after selecting the optimal model to obtain the significant predicted genes, and validated by constructing RA rat models. / Results: The location of the 13 CRGs on the chromosome was determined and, except for GCSH. LIPT1, FDX1, DLD, DBT, LIAS and ATP7A were expressed at significantly higher levels in RA samples than in non-RA, and DLST was significantly lower. RA samples were significantly expressed in immune cells such as B cells memory and differentially expressed genes such as LIPT1 were also strongly associated with the presence of immune infiltration. Two copper death-related molecular clusters were identified in RA samples. A higher level of immune infiltration and expression of CRGcluster C2 was found in the RA population. There were 314 crossover genes between the 2 molecular clusters, which were further divided into two molecular clusters. A significant difference in immune infiltration and expression levels was found between the two. Based on the five genes obtained from the RF model (AUC = 0.843), the Nomogram model, calibration curve and DCA also demonstrated their accuracy in predicting RA subtypes. The expression levels of the five genes were significantly higher in RA samples than in non-RA, and the ROC curves demonstrated their better predictive effect. Identification of predictive genes by RA animal model experiments was also confirmed. / Conclusion: This study provides some insight into the correlation between rheumatoid arthritis and copper mortality, as well as a predictive model that is expected to support the development of targeted treatment options in the future