Identification of novel single nucleotide variants in the drug resistance mechanism of Mycobacterium tuberculosis isolates by whole-genome analysis.

BACKGROUND: Tuberculosis (TB) represents a major global health challenge. Drug resistance in Mycobacterium tuberculosis (MTB) poses a substantial obstacle to effective TB treatment. Identifying genomic mutations in MTB isolates holds promise for unraveling the underlying mechanisms of drug resistance in this bacterium. METHODS: In this study, we investigated the roles of single nucleotide variants (SNVs) in MTB isolates resistant to four antibiotics (moxifloxacin, ofloxacin, amikacin, and capreomycin) through whole-genome analysis. We identified the drug-resistance-associated SNVs by comparing the genomes of MTB isolates with reference genomes using the MuMmer4 tool. RESULTS: We observed a strikingly high proportion (94.2%) of MTB isolates resistant to ofloxacin, underscoring the current prevalence of drug resistance in MTB. An average of 3529 SNVs were detected in a single ofloxacin-resistant isolate, indicating a mutation rate of approximately 0.08% under the selective pressure of ofloxacin exposure. We identified a set of 60 SNVs associated with extensively drug-resistant tuberculosis (XDR-TB), among which 42 SNVs were non-synonymous mutations located in the coding regions of nine key genes (ctpI, desA3, mce1R, moeB1, ndhA, PE_PGRS4, PPE18, rpsA, secF). Protein structure modeling revealed that SNVs of three genes (PE_PGRS4, desA3, secF) are close to the critical catalytic active sites in the three-dimensional structure of the coding proteins. CONCLUSION: This comprehensive study elucidates novel resistance mechanisms in MTB against antibiotics, paving the way for future design and development of anti-tuberculosis drugs.

Identification of cell-type-specific genes in multimodal single-cell data using deep neural network algorithm.

The emergence of single-cell RNA sequencing (scRNA-seq) technology makes it possible to measure DNA, RNA, and protein in a single cell. Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) is a powerful multimodal single-cell research innovation, allowing researchers to capture RNA and surface protein expression on the same cells. Currently, identification of cell-type-specific genes in CITE-seq data is still challenging. In this study, we obtained a set of CITE-seq datasets from Kaggle database, which included the sequencing dataset of seven cell types during bone marrow stem cell differentiation. We used Student's t-test to analyze these transcription RNAs and pick out 133 significantly differentially expressed genes (DEGs) among all cell types. Functional enrichment revealed that these DEGs were strongly associated with blood-related diseases, providing important insights into the cellular heterogeneity within bone marrow stem cells. The relation between RNA and protein levels was performed by deep neural network (DNN) model and achieved a high prediction score of 0.867. Based on their coefficients in the DNN model, three genes (LGALS1, CENPV, TRIM24) were identified as cell-type-specific genes in erythrocyte progenitor. Our works provide a novel perspective regarding the differentiation of stem cells in the bone marrow and provide valuable insights for further research in this field.