Current advances of CRISPR/Cas system in antibacterial field / 药学学报

A breakthrough in molecular biology for the twenty-first century is CRISPR/Cas gene editing, which has been used in a variety of fields due to its simplicity, adaptability, and targeting. Given the current global challenge of severe bacterial resistance, difficulties in detecting antimicrobial resistance, and slow development of antimicrobial drugs, CRISPR/Cas gene-editing technology offers a promising avenue for the development of antibacterial treatments. On the one hand, CRISPR/Cas gene editing technology helps advance the study of bacterial functions and serves as a toolbox. For instance, Cas proteins and exogenous repair systems enable efficient and precise gene editing, nCas proteins and deaminase systems facilitate template-free and single base precision editing, dCas proteins and reverse transcriptase allow for repair-free gene editing, and dCas proteins and modified sgRNA enable gene expression level regulation and gene function analysis. On the other hand, its specific gene recognition and targeted DNA cleavage characteristics can be used for pathogen detection, elimination of drug-resistant bacteria and genes, and hold promise as a new strategy for clinical diagnosis and treatment.

Polymyxin resistance caused by large-scale genomic inversion due to IS26 intramolecular translocation in Klebsiella pneumoniae

Polymyxin B and polymyxin E (colistin) are presently considered the last line of defense against human infections caused by multidrug-resistant Gram-negative organisms such as carbapenemase-producer Enterobacterales, Acinetobacter baumannii, and Klebsiella pneumoniae. Yet resistance to this last-line drugs is a major public health threat and is rapidly increasing. Polymyxin S2 (S2) is a polymyxin B analogue previously synthesized in our institute with obviously high antibacterial activity and lower toxicity than polymyxin B and colistin. To predict the possible resistant mechanism of S2 for wide clinical application, we experimentally induced bacterial resistant mutants and studied the preliminary resistance mechanisms. Mut-S, a resistant mutant of K. pneumoniae ATCC BAA-2146 (Kpn2146) induced by S2, was analyzed by whole genome sequencing, transcriptomics, mass spectrometry and complementation experiment. Surprisingly, large-scale genomic inversion (LSGI) of approximately 1.1 Mbp in the chromosome caused by IS26 mediated intramolecular transposition was found in Mut-S, which led to mgrB truncation, lipid A modification and hence S2 resistance. The resistance can be complemented by plasmid carrying intact mgrB. The same mechanism was also found in polymyxin B and colistin induced drug-resistant mutants of Kpn2146 (Mut-B and Mut-E, respectively). This is the first report of polymyxin resistance caused by IS26 intramolecular transposition mediated mgrB truncation in chromosome in K. pneumoniae. The findings broaden our scope of knowledge for polymyxin resistance and enriched our understanding of how bacteria can manage to survive in the presence of antibiotics.

The role of cytokeratin 7/20 coordination revisited-Machine learning identifies improved interpretative algorithms for cell block immunohistochemistry in aspirates of metastatic carcinoma.

BACKGROUND: Fine-needle aspiration (FNA) is a robust diagnostic technique often used for tissue diagnosis of metastatic carcinoma. For interpretation of FNA cytology, cell block immunohistochemistry (IHC) and clinicocytologic parameters are indispensable. In this review of a large cohort, the current report: 1) describes clinicocytologic parameters and immunoprofiles of aspirates of metastatic carcinoma, 2) compares the predictivity of immunostains and classical approaches for IHC interpretation, and 3) describes machine learning-based algorithms for IHC interpretation. METHODS: Aspirates of metastatic carcinoma that had IHC performed were retrieved. Clinicocytologic parameters, IHC results, the corresponding primary site, and histologic diagnoses were recorded. By using machine learning, decision trees for predicting the primary site were generated, their performance was compared with 2 human-designed algorithms, and the primary site was suggested in the historical diagnosis. RESULTS: In total, 1145 cases were identified. The 6 most populated groups were selected for machine learning and predictive analysis. With IHC input, the decision tree achieved a concordance rate of 94.5% and overall accuracy of 83.6%, which improved to 95.3% and 85.8%, respectively, when clinical data were incorporated and exceeded the human-designed IHC algorithms (P < .001). The historical diagnosis was more accurate unless indeterminate diagnoses were regarded as discordant (P < .001). CDX2 and TTF-1 immunostains had the highest weight in model accuracy, occupied the root of the decision trees, scored higher as features of importance, and outperformed the predictive power of cytokeratins 7 and 20. CONCLUSIONS: Cytokeratins 7 and 20 may be superseded in immunostaining panels, including organ-specific immunostains such as CDX2 and TTF-1. Machine learning generates algorithms that surpasses human-designed algorithms but is inferior to expert assessment integrating clinical and cytologic assessment.

Deep learning model for prediction of extended-spectrum beta-lactamase (ESBL) production in community-onset Enterobacteriaceae bacteraemia from a high ESBL prevalence multi-centre cohort.

Adequate empirical antimicrobial coverage is instrumental in clinical management of community-onset Enterobacteriaceae bacteraemia in areas with high ESBL prevalence, while balancing the risk of carbapenem overuse and emergence of carbapenem-resistant organisms. It is unknown whether machine learning offers additional advantages to conventional statistical methods in prediction of ESBL production. To develop a validated model to predict ESBL production in Enterobacteriaceae causing community-onset bacteraemia. 5625 patients with community-onset bacteraemia caused by Escherichia coli, Klebsiella species and Proteus mirabilis during 1 January 2015-31 December 2019 from three regional hospitals in Hong Kong were included in the analysis, after exclusion of blood cultures obtained beyond 48 h of admission. The prevalence of ESBL-producing Enterobacteriaceae was 23.7% (1335/5625). Deep neural network and other machine learning algorithms were compared against conventional statistical model via multivariable logistic regression. Primary outcomes compared consisted of predictive model area under curve of receiver-operator characteristic curve (AUC), and macro-averaged F1 score. Secondary outcomes included sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Deep neural network yielded an AUC of 0.761 (95% CI 0.725-0.797) and F1 score of 0.661 (95% CI 0.633-0.689), which was superior to logistic regression (AUC 0.667 (95% CI 0.627-0.707), F1 score 0.596 (95% CI 0.567-0.625)). Deep neural network had a specificity of 91.5%, sensitivity of 37.5%, NPV of 82.5%, and PPV of 57.9%. Deep neural network is superior to logistic regression in predicting ESBL production in Enterobacteriaceae causing community-onset bacteraemia in high-ESBL prevalence area. Machine learning offers clinical utility in guiding judicious empirical antibiotics use.

Non-targeted metabolomics in septic mice infected with Klebsiella pneumoniae / 药学学报

UHPLC-QTOF-MS was applied to non-targeted metabolomics study of mice infected with K. pneumoniae ATCC® BAA 2146 to discover potential biomarkers and metabolic pathways that are associated with sepsis. Fifty-eight metabolites were identified by principal components analysis (PCA) and partial least-squares discriminant analysis (OPLS-DA), which was combined with variable projection importance (VIP) and nonparametric test. Eighteen of the 58 metabolites were further found to be involved in 8 metabolic pathways, including nicotinate and nicotinamide metabolism, pyrimidine metabolism, vitamin B6 metabolism, taurine and hypotaurine metabolism, arginine and proline metabolism, alanine, aspartate and glutamate metabolism, D-glutamine and D-glutamate metabolism and glycerophospholipid metabolism.

Study on Quality Standards for Lingyangqingfei Capsules / 中成药

Objective: To establish the quality standards for Lingyangqingfei capsules. Methods: Microscopic identification of products, TLC identifications of Fructus Gardeniae and Cortex Moutan were carried out.Baicalin was determined by HPLC. Results: Baicalin in Lingyangqingfei capsules was about 1.5mg/capsule. The average recovery was 99.82% and RSD was 0.96%. Conclusion: The result is accurate and the reproducibility is good. This method can be used as the quality control of this preparation.