Klebsiella pneumoniae is an important gram-negative opportunistic pathogen that causes a variety of infectious diseases, including urinary tract infections, bacteremia, pneumonia, and liver abscesses. With the emergence of multidrug-resistant (MDR) and hypervirulent K. pneumoniae (hvKP) strains, the rapid spread of these clinical strains in geography is particularly worrying. However, the detailed mechanisms of virulence and antibiotic resistance in K. pneumoniae are still not very clear. Therefore, studying and elucidating the pathogenic mechanisms and drug resistance mechanism of K. pneumoniae infection are important parts of current medical research. In this paper, we systematically summarized the virulence, biofilm, and antibiotic tolerance mechanisms of K. pneumoniae, and explored the application of whole genome sequencing and global proteomics, which will provide new clues for clinical treatment of K. pneumoniae.

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Tuberculosis (TB), one major threat to humans, can infect one third of the worldwide population, and cause more than one million deaths each year. This study aimed to identify the effective diagnosis and therapy biomarkers of TB. Hence, we analyzed two microarray datasets (GSE54992 and GSE62525) derived from the Gene Expression Omnibus (GEO) database to find the differentially expressed genes (DEGs) of peripheral blood mononuclear cell (PBMC) between TB patients and healthy specimens. Functional and pathway enrichment of the DEGs were analyzed by Metascape database. Protein-protein interaction (PPI) network among the DEGs were constructed by STRING databases and visualized in Cytoscape software. The related transcription factors regulatory network of the DEGs was also constructed. A total of 190 DEGs including 36 up-regulated genes and 154 down-regulated genes were obtained in TB samples. Gene functional enrichment analysis showed that these DEGs were enriched in T cell activation, chemotaxis, leukocyte activation involved in immune response, cytokine secretion, head development, etc. The top six hub genes (namely, LRRK2, FYN, GART, CCR7, CXCR5, and FASLG) and two significant modules were got from PPI network of DEGs. Vital transcriptional factors, such as FoxC1 and GATA2, were discovered with close interaction with these six hub DEGs. By systemic bioinformatic analysis, many DEGs associated with TB were screened, and these identified hub DEGs may be potential biomarkers for diagnosis and treatment of TB in the future.

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Clear cell renal cell carcinoma (ccRCC) is the most common histological type of renal carcinoma and has a high recurrence rate and poor outcome. Accurate patient risk stratification based on genetic markers can help to identify the high-risk patient for early and further treatments and would promote patient survival. Long non-coding RNAs (lncRNAs) have attracted widespread attention as biomarkers for early diagnosis, treatment, and prognosis because of their high specificity and sensitivity. Here, we performed a systematic search in NCBI PubMed and found 44 lncRNAs as oncogenes, 18 lncRNAs as tumor suppressors, 199 lncRNAs as diagnostic biomarkers, 62 lncRNAs as prognostic biomarkers, and 3 lncRNAs as predictive biomarkers for ccRCC. We also comprehensively discuss the biological functions and molecular regulatory mechanisms of lncRNAs in ccRCC. Overall, the present study is a systemic analysis to assess the expression and clinical value of lncRNAs in ccRCC, and lncRNAs hold promise to be diagnostic, prognostic, and predictive biomarkers.