Single-cell dissection of cervical cancer reveals key subsets of the tumor immune microenvironment.

The tumor microenvironment (TME) directly determines patients' outcomes and therapeutic efficiencies. An in-depth understanding of the TME is required to improve the prognosis of patients with cervical cancer (CC). This study conducted single-cell RNA and TCR sequencing of six-paired tumors and adjacent normal tissues to map the CC immune landscape. T and NK cells were highly enriched in the tumor area and transitioned from cytotoxic to exhaustion phenotypes. Our analyses suggest that cytotoxic large-clone T cells are critical effectors in the antitumor response. This study also revealed tumor-specific germinal center B cells associated with tertiary lymphoid structures. A high-germinal center B cell proportion in patients with CC is predictive of improved clinical outcomes and is associated with elevated hormonal immune responses. We depicted an immune-excluded stromal landscape and established a joint model of tumor and stromal cells to predict CC patients' prognosis. The study revealed tumor ecosystem subsets linked to antitumor response or prognosis in the TME and provides information for future combinational immunotherapy.

TISCH2: expanded datasets and new tools for single-cell transcriptome analyses of the tumor microenvironment.

The Tumor Immune Single Cell Hub 2 (TISCH2) is a resource of single-cell RNA-seq (scRNA-seq) data from human and mouse tumors, which enables comprehensive characterization of gene expression in the tumor microenvironment (TME) across multiple cancer types. As an increasing number of datasets are generated in the public domain, in this update, TISCH2 has included 190 tumor scRNA-seq datasets covering 6 million cells in 50 cancer types, with 110 newly collected datasets and almost tripling the number of cells compared with the previous release. Furthermore, TISCH2 includes several new functions that allow users to better utilize the large-scale scRNA-seq datasets. First, in the Dataset module, TISCH2 provides the cell-cell communication results in each dataset, facilitating the analyses of interacted cell types and the discovery of significant ligand-receptor pairs between cell types. TISCH2 also includes the transcription factor analyses for each dataset and visualization of the top enriched transcription factors of each cell type. Second, in the Gene module, TISCH2 adds functions for identifying correlated genes and providing survival information for the input genes. In summary, TISCH2 is a user-friendly, up-to-date and well-maintained data resource for gene expression analyses in the TME. TISCH2 is freely available at http://tisch.comp-genomics.org/.

Metabolomics reveals the mechanism of action of meropenem and amikacin combined in the treatment of Pseudomonas aeruginosa.

The treatment of Pseudomonas aeruginosa infection often involves the combined use of ß-lactam and aminoglycoside antibiotics. In this study, we employed metabolomic analysis to investigate the mechanism responsible for the synergistic activities of meropenem/amikacin combination therapy against multidrug-resistant P. aeruginosa strains harboring OXA-50 and PAO genes. Antibiotic concentrations for meropenem (2 mg/L) monotherapy, amikacin (16 mg/L) monotherapy, and meropenem/amikacin (2/16 mg/L) combination therapy were selected based on clinical breakpoint considerations. Metabolomic analysis revealed significant alterations in relevant metabolites involved in bacterial cell membrane and cell wall synthesis within 15 min of combined drug administration. These alterations encompassed various metabolic pathways, including fatty acid metabolism, peptidoglycan synthesis, and lipopolysaccharide metabolism. Furthermore, at 1 h and 4 h, the combination therapy exhibited significant interference with amino acid metabolism, nucleotide metabolism, and central carbon metabolism pathways, including the tricarboxylic acid cycle and pentose phosphate pathway. In contrast, the substances affected by single drug administration at 1 h and 4 h demonstrated a noticeable reduction. Meropenem/amikacin combination resulted in notable perturbations of metabolic pathways essential for survival of P. aeruginosa, whereas monotherapies had comparatively diminished impacts.

Metabolomics revealed mechanism for the synergistic effect of sulbactam, polymyxin-B and amikacin combination against Acinetobacter baumannii.

Introduction: The emergence of multidrug-resistant (MDR) Acinetobacter baumannii prompts clinicians to consider treating these infections with polymyxin combination. Methods: Metabolomic analysis was applied to investigate the synergistic effects of polymyxin-B, amikacin and sulbactam combination therapy against MDR A. baumannii harboring OXA-23 and other drug resistant genes. The drug concentrations tested were based on their clinical breakpoints: polymyxin-B (2 mg/L), amikacin (16 mg/L), polymyxin-B/amikacin (2/16 mg/L), and polymyxin-B/amikacin/sulbactam (2/16/4 mg/L). Results: The triple antibiotic combination significantly disrupted levels of metabolites involved in cell outer membrane structure including fatty acids, glycerophospholipids, nucleotides, amino acids and peptides as early as 15 min after administration. Amikacin and polymyxin-B alone perturbed a large number of metabolites at 15 min and 1 h, respectively, but the changes in metabolites were short-lived lasting for less than 4 h. In contrast, the combination treatment disrupted a large amount of metabolites beyond 4 h. Compared to the double-combination, the addition of sulbactam to polymyxin-B/amikacin combination produce a greater disorder in A. baumannii metabolome that further confer susceptibility of bacteria to the antibiotics. Conclusion: The metabolomic analysis identified mechanisms responsible for the synergistic activities of polymyxin-B/amikacin/sulbactam against MDR A. baumannii.

Pharmacokinetic/Pharmacodynamic Evaluation of Aztreonam/Amoxicillin/Clavulanate Combination against New Delhi Metallo-ß-Lactamase and Serine-ß-Lactamase Co-Producing Escherichia coli and Klebsiella pneumoniae.

This study aimed to examine specific niches and usage for the aztreonam/amoxicillin/clavulanate combination and to use population pharmacokinetic simulations of clinical dosing regimens to predict the impact of this combination on restricting mutant selection. The in vitro susceptibility of 19 New-Delhi metallo-ß-lactamase (NDM)-producing clinical isolates to amoxicillin/clavulanate and aztreonam alone and in co-administration was determined based on the minimum inhibitory concentration (MIC) and mutant prevention concentration (MPC). The fractions of a 24-h duration that the free drug concentration was within the mutant selection window (fTMSW) and above the MPC (fT>MPC) in both plasma and epithelial lining fluid were determined from simulations of 10,000 subject profiles based on regimens by renal function categories. This combination reduced the MIC of aztreonam and amoxicillin/clavulanate to values below their clinical breakpoint in 7/9 K. pneumoniae and 8/9 E. coli, depending on the ß-lactamase genes detected in the isolate. In the majority of the tested isolates, the combination resulted in fT>MPC > 90% and fTMSW < 10% for both aztreonam and amoxicillin/clavulanate. Clinical dosing regimens of aztreonam and amoxicillin/clavulanate were sufficient to provide mutant restriction coverage for MPC and MIC ≤ 4 mg/L. This combination has limited coverage against NDM- and extended-spectrum ß-lactamase co-producing E. coli and K. pneumoniae and is not effective against isolates carrying plasmid-mediated AmpC and KPC-2. This study offers a potential scope and limitations as to where the aztreonam/amoxicillin/clavulanate combination may succeed or fail.

Physiologically-based pharmacokinetic modeling to inform dosing regimens and routes of administration of rifampicin and colistin combination against Acinetobacter baumannii.

BACKGROUND: Carbapenem-resistant Acinetobacter baumannii (CRAB) is resistant to major antibiotics such as penicillin, cephalosporin, fluoroquinolone and aminoglycoside, and has become a significant nosocomial pathogen. The efficacy of rifampicin and colistin combination against CRAB could be dependent on the administration routes and drug concentrations at the site of infection. OBJECTIVE: The objective is to predict drug disposition in biological tissues. Treatment efficacy is extrapolated by assessing respective pharmacodynamic (PD) indices, as well as parameters associated with the emergence of resistance. METHODS: Physiologically-based pharmacokinetic models of rifampicin and colistin were utilized to predict tissue exposures. Dosing regimens and administration routes for combination therapy were evaluated in terms of in vitro antimicrobial susceptibility of A. baumannii associated with targeted PD indices and resistance parameters. RESULTS: Simulated exposures in blood, heart, lung, skin and brain were consistent with reported penetration rates. The results demonstrated that a combination of colistin and rifampicin using conventional intravenous (i.v.) doses could achieve effective exposures in the blood and skin. However, for lung infections, colistin by inhalation would be required due to low lung penetration from intravenous route. Inhaled colistin alone provided good PD coverage but this practice could encourage the emergence of additional resistance which may be overcome by a combination regimen that includes inhaled rifampicin. CONCLUSION: This in silico extrapolation provides valuable information on dosing regimens and routes of administration against CRAB infections in specific tissues. The PBPK modeling approach could be a non-invasive way to inform therapeutic benefits of combination antimicrobial therapy.