GraphADT: Empowering interpretable predictions of acute dermal toxicity with Multi-View graph pooling and structure remapping.

MOTIVATION: Accurate prediction of acute dermal toxicity (ADT) is essential for the safe and effective development of contact drugs. Currently, graph neural networks (GNNs), a form of deep learning technology, accurately model the structure of compound molecules, enhancing predictions of their ADT. However, many existing methods emphasize atom-level information transfer and overlook crucial data conveyed by molecular bonds and their interrelationships. Additionally, these methods often generate" equal" node representations across the entire graph, failing to accentuate" important" substructures like functional groups, pharmacophores, and toxicophores, thereby reducing interpretability. RESULTS: We introduce a novel model, GraphADT, utilizing structure remapping and multi-view graph pooling technologies to accurately predict compound ADT. Initially, our model applies structure remapping to better delineate bonds, transforming" bonds" into new nodes and" bond-atom-bond" interactions into new edges, thereby reconstructing the compound molecular graph. Subsequently, we employ multi-view graph pooling to amalgamate data from various perspectives, minimizing biases inherent to single-view analyses. Following this, the model generates a robust node ranking collaboratively, emphasizing critical nodes or substructures to enhance model interpretability. Lastly, we apply a graph comparison learning strategy to train both the original and structure remapped molecular graphs, deriving the final molecular representation. Experimental results on public datasets indicate that the GraphADT model outperforms existing state-of-the-art models. The GraphADT model has been demonstrated to effectively predict compound ADT, offering potential guidance for the development of contact drugs and related treatments. AVAILABILITY AND IMPLEMENTATION: Our code and data are accessible at: https://github.com/mxqmxqmxq/GraphADT.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Advancing cancer driver gene detection via Schur complement graph augmentation and independent subspace feature extraction.

Accurately identifying cancer driver genes (CDGs) is crucial for guiding cancer treatment and has recently received great attention from researchers. However, the high complexity and heterogeneity of cancer gene regulatory networks limit the precition accuracy of existing deep learning models. To address this, we introduce a model called SCIS-CDG that utilizes Schur complement graph augmentation and independent subspace feature extraction techniques to effectively predict potential CDGs. Firstly, a random Schur complement strategy is adopted to generate two augmented views of gene network within a graph contrastive learning framework. Rapid randomization of the random Schur complement strategy enhances the model's generalization and its ability to handle complex networks effectively. Upholding the Schur complement principle in expectations promotes the preservation of the original gene network's vital structure in the augmented views. Subsequently, we employ feature extraction technology using multiple independent subspaces, each trained with independent weights to reduce inter-subspace dependence and improve the model's expressiveness. Concurrently, we introduced a feature expansion component based on the structure of the gene network to address issues arising from the limited dimensionality of node features. Moreover, it can alleviate the challenges posed by the heterogeneity of cancer gene networks to some extent. Finally, we integrate a learnable attention weight mechanism into the graph neural network (GNN) encoder, utilizing feature expansion technology to optimize the significance of various feature levels in the prediction task. Following extensive experimental validation, the SCIS-CDG model has exhibited high efficiency in identifying known CDGs and uncovering potential unknown CDGs in external datasets. Particularly when compared to previous conventional GNN models, its performance has seen significant improved. The code and data are publicly available at: https://github.com/mxqmxqmxq/SCIS-CDG.

Plasma proteome analysis and validation of patients with community-acquired pneumonia: A cohort study.

PURPOSE: This study aimed to investigate the diagnostic potential of plasma biomarkers of community-acquired pneumonia (CAP) and their severity grading. EXPERIMENTAL DESIGN: Plasma proteomes from cohort I (n = 32) with CAP were analyzed by data-independent acquisition mass spectrometry (MS). MetaboAnalyst 5.0 was used to statistically evaluate significant differences in proteins from different samples, and demographic and clinical data were recorded for all enrolled patients. Cohort II (n = 80) was used to validate candidate biomarkers. Plasma protein levels were determined using quantitative enzyme-linked immunosorbent assay (ELISA). Correlations were assessed using Pearson's correlation coefficient. A receiver operating characteristic curve was used to verify the association between the variables, CAP diagnosis, and prognosis. RESULTS: 121 differentially expressed proteins (DEPs) were obtained between CAP and controls. These DEPs were mainly aggregated in pathways of phagosome(hsa04145) and complement and coagulation cascades (hsa04610). No significant differential proteins were detected in bacterial, viral, and mixed infection groups. The plasma levels of fetuin-A, alpha-1-antichymotrypsin (AACT), α1-acid glycoprotein (A1AG), and S100A8/S100A9 heterodimers detected by ELISA were consistent with those of MS. AACT, A1AG, S100A8/S100A9 heterodimer, and fetuin-A can potentially be used as diagnostic predictors, and fetuin-A and AACT are potential predictors of SCAP. CONCLUSIONS AND CLINICAL RELEVANCE: Plasma protein profiling can successfully identify potential biomarkers for CAP diagnosis and disease severity assessment. These biomarkers should be further studied for their clinical application.

Clinical performance of BALF droplet digital PCR for differential diagnosis of Pneumocystis jirovecii pneumonia and Pneumocystis jirovecii colonization.

BACKGROUND: Accurate differentiation between Pneumocystis jirovecii (Pj) infection and colonization is crucial for effective treatment. METHODS: From September 2016 to June 2022, 89 immunocompromised patients with unexplained lung infiltrates and clinical suspicion of Pj pneumonia were enrolled at Peking University People's Hospital. Bronchoalveolar lavage fluid (BALF) of these patients were detected by quantitative PCR (qPCR) and droplet digital PCR (ddPCR). RESULTS: The performance of ddPCR was superior to qPCR in detecting Pj infection. Area under the curve was 0.97 (95 %CI: 0.94-1) for ddPCR of the BALF in all patients. The optimal threshold value for discriminating Pj infection from colonization by ddPCR was 13.98 copies/test, with a sensitivity of 97.96 %, specificity of 85.71 %. No obvious correlation between ddPCR copy number and disease severity was observed. CONCLUSION: BALF ddPCR exhibits robust potential in detecting Pj and effectively discriminating colonization and infection.

Relationship between clinical features and droplet digital PCR copy number in non-HIV patients with pneumocystis pneumonia.

OBJECTIVE: Droplet digital PCR (ddPCR) is a novel assay to detect pneumocystis jjrovecii (Pj) which has been defined to be more sensitive than qPCR in recent studies. We aimed to explore whether clinical features of pneumocystis pneumonia (PCP) were associated with ddPCR copy numbers of Pj. METHODS: A total of 48 PCP patients were retrospectively included. Pj detection was implemented by ddPCR assay within 4 h. Bronchoalveolar fluid (BALF) samples were collected from 48 patients with molecular diagnosis as PCP via metagenomic next generation sequencing (mNGS) or quantitative PCR detection. Univariate and multivariate logistic regression were performed to screen out possible indicators for the severity of PCP. The patients were divided into two groups according to ddPCR copy numbers, and their clinical features were further analyzed. RESULTS: Pj loading was a pro rata increase with serum (1,3)-beta-D glucan, D-dimmer, neutrophil percentage, procalcitonin and BALF polymorphonuclear leucocyte percentage, while negative correlation with albumin, PaO2/FiO2, BALF cell count, and BALF lymphocyte percentage. D-dimmer and ddPCR copy number of Pj were independent indicators for moderate/severe PCP patients with PaO2/FiO2 lower than 300. We made a ROC analysis of ddPCR copy number of Pj for PaO2/FiO2 index and grouped the patients according to the cut-off value (2.75). The high copy numbers group was characterized by higher level of inflammatory markers. Compared to low copy number group, there was lower level of the total cell count while higher level of polymorphonuclear leucocyte percentage in BALF in the high copy numbers group. Different from patients with high copy numbers, those with high copy numbers had a tendency to develop more severe complications and required advanced respiratory support. CONCLUSION: The scenarios of patients infected with high ddPCR copy numbers of Pj showed more adverse clinical conditions. Pj loading could reflect the severity of PCP to some extent.

T2T-YAO: A Telomere-to-telomere Assembled Diploid Reference Genome for Han Chinese.

Since its initial release in 2001, the human reference genome has undergone continuous improvement in quality, and the recently released telomere-to-telomere (T2T) version - T2T-CHM13 - reaches its highest level of continuity and accuracy after 20 years of effort by working on a simplified, nearly homozygous genome of a hydatidiform mole cell line. Here, to provide an authentic complete diploid human genome reference for the Han Chinese, the largest population in the world, we assembled the genome of a male Han Chinese individual, T2T-YAO, which includes T2T assemblies of all the 22 + X + M and 22 + Y chromosomes in both haploid. The quality of T2T-YAO is much better than all currently available diploid assemblies, and its haploid version, T2T-YAO-hp, generated by selecting the better assembly for each autosome, reaches the top quality of fewer than one error per 29.5 Mb, even higher than that of T2T-CHM13. Derived from an individual living in the aboriginal region of the Han population, T2T-YAO shows clear ancestry and potential genetic continuity from the ancient ancestors. Each haplotype of T2T-YAO possesses ∼ 330-Mb exclusive sequences, ∼ 3100 unique genes, and tens of thousands of nucleotide and structural variations as compared with CHM13, highlighting the necessity of a population-stratified reference genome. The construction of T2T-YAO, a truly accurate and authentic representative of the Chinese population, would enable precise delineation of genomic variations and advance our understandings in the hereditability of diseases and phenotypes, especially within the context of the unique variations of the Chinese population.

Collateral sensitivity between tetracyclines and aminoglycosides constrains resistance evolution in carbapenem-resistant Klebsiella pneumoniae.

AIMS: The acquisition of resistance to one antibiotic may confer an increased sensitivity to another antibiotic in bacteria, which is an evolutionary trade-off between different resistance mechanisms, defined as collateral sensitivity (CS). Exploiting the role of CS in treatment design could be an effective method to suppress or even reverse resistance evolution. METHODS: Using experimental evolution, we systematically studied the CS between aminoglycosides and tetracyclines in carbapenem-resistant Klebsiella pneumoniae (CRKP) and explored the underlying mechanisms through genomic and transcriptome analyses. The application of CS-based therapies for resistance suppression, including combination therapy and alternating antibiotic therapy, was further evaluated in vitro and in vivo. RESULTS: Reciprocal CS existed between tetracyclines and aminoglycosides in CRKP. The increased sensitivity of aminoglycoside-resistant strains to tetracyclines was associated with the alteration of bacterial membrane potential, whereas the unbalanced oxidation-reduction process of tetracycline-resistant strains may lead to an increased bacterial sensitivity to aminoglycosides. CS-based combination therapy could efficiently constrain the evolution of CRKP resistance in vitro and in vivo. In addition, alternating antibiotic therapy can re-sensitize CRKP to previously resistant drugs, thereby maintaining the trade-off. CONCLUSIONS: These results provide new insights into constraining the evolution of CRKP resistance through CS-based therapies.

Oral microbial dysbiosis in patients with periodontitis and chronic obstructive pulmonary disease.

Background: Oral microbiota is closely related to the homeostasis of the oral cavity and lungs. To provide potential information for the prediction, screening, and treatment strategies of individuals, this study compared and investigated the bacterial signatures in periodontitis and chronic obstructive pulmonary disease (COPD). Materials and methods: We collected subgingival plaque and gingival crevicular fluid samples from 112 individuals (31 healthy controls, 24 patients with periodontitis, 28 patients with COPD, and 29 patients with both periodontitis and COPD). The oral microbiota was analyzed using 16S rRNA gene sequencing and diversity and functional prediction analysis were performed. Results: We observed higher bacterial richness in individuals with periodontitis in both types of oral samples. Using LEfSe and DESeq2 analyses, we found differentially abundant genera that may be potential biomarkers for each group. Mogibacterium is the predominant genus in COPD. Ten genera, including Desulfovibrio, Filifactor, Fretibacterium, Moraxella, Odoribacter, Pseudoramibacter Pyramidobacter, Scardovia, Shuttleworthia and Treponema were predominant in periodontitis. Bergeyella, Lautropia, Rothia, Propionibacterium and Cardiobacterium were the signature of the healthy controls. The significantly different pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) between healthy controls and other groups were concentrated in genetic information processing, translation, replication and repair, and metabolism of cofactors and vitamins. Conclusions: We found the significant differences in the bacterial community and functional characterization of oral microbiota in periodontitis, COPD and comorbid diseases. Compared to gingival crevicular fluid, subgingival plaque may be more appropriate for reflecting the difference of subgingival microbiota in periodontitis patients with COPD. These results may provide potentials for predicting, screening, and treatment strategies for individuals with periodontitis and COPD.

Metabolic landscape dysregulation in bronchoalveolar lavage fluid of checkpoint inhibitor pneumonitis.

BACKGROUND: Checkpoint inhibitor pneumonitis (CIP) is a potentially fatal adverse event resulting from immunotherapy in patients with malignant tumors. However, the pathogenesis of CIP remains poorly understood. METHODS: We collected bronchoalveolar lavage fluid (BALF) from cohorts of patients with CIP, new-onset lung cancer (LC), and idiopathic pulmonary fibrosis (IPF). Non-targeted metabolomics analysis was conducted to analyze metabolic signatures. Flow cytometry was used to evaluate immune cell subsets. RESULTS: Lymphocytes were predominant in the BALF of patients with CIP. A total of 903 metabolites were identified, among which lipid compounds were the most abundant. In a comparison between patients with CIP and LC, enrichment analysis of the altered metabolites showed suppressed amino sugar metabolism, and spermidine and spermine biosynthesis in the CIP group. Metabolism of alpha linolenic acid, linoleic acid, and their fatty acid derivatives was enriched in the CIP group relative to the IPF group. The twelve metabolites found to be enriched in the CIP group were positively correlated with the proportion of CD8+ T cells. One cluster of BALF metabolites, 57.14% of which were lipid molecules, was inversely correlated with the proportion of natural killer cells. CONCLUSIONS: In this study, the metabolomic landscape of BALF in patients with CIP was determined. We elucidated suppressed tumor metabolic signatures, enhanced pulmonary inflammatory signaling, and the characteristics of responsible immune cells, which helps to understand the pathogenesis of CIP.

Lysophosphatidylcholine acyltransferase level predicts the severity and prognosis of patients with community-acquired pneumonia: a prospective multicenter study.

Background: Identifying the diagnosis as well as prognosis for patients presented with community-acquired pneumonia (CAP) remains challenging. We aimed to identify the role of lysophosphatidylcholine acyl-transferase (LPCAT) for CAP along with assessing this protein's effectiveness as a biomarker for severity of disease and mortality. Methods: Prospective multicenter research study was carried out among hospitalized patients. A total of 299 CAP patients (including 97 severe CAP patients [SCAP]) and 20 healthy controls (HC) were included. A quantitative enzyme-linked immunosorbent test kit was employed for detecting the LPCAT level in plasma. We developed a deep-learning-based binary classification (SCAP or non-severe CAP [NSCAP]) model to process LPCAT levels and other laboratory test results. Results: The level of LPCAT in patients with SCAP and death outcome was significantly higher than that in other patients. LPCAT showed the highest predictive value for SCAP. LPCAT was able to predict 30-day mortality among CAP patients, combining LPCAT values with PSI scores or CURB-65 further enhance mortality prediction accuracy. Conclusion: The on admission level of LPCAT found significantly raised among SCAP patients and strongly predicted SCAP patients but with no correlation to etiology. Combining the LPCAT value with CURB-65 or PSI improved the 30-day mortality forecast significantly. Trial registration: NCT03093220 Registered on March 28th, 2017.

Resistance to Cefiderocol Involved Expression of PER-1 ß-Lactamase and Downregulation of Iron Transporter System in Carbapenem-Resistant Acinetobacter baumannii.

Background: Cefiderocol (CFDC) is a promising antimicrobial agent against multidrug resistant Gram-negative bacteria. However, CFDC resistance has emerged in carbapenem-resistant Acinetobacter baumannii (CR-AB) but the underlying mechanisms remain unclear. Methods: Whole-genome sequencing and transcriptome sequencing were performed on CFDC-non-susceptible and CFDC-susceptible isolates. Two different recombinant plasmids was electro-transformed into the E. coli BL21 strain to determine the impact of blaPER and the combined impact of blaPER-1 and blaOXA-23 on CFDC resistance. Results: Fifty-five CR-AB isolates with minimum inhibitory concentrations (MICs) ranged from 0.06 mg/L to >256 mg/L were sequenced, including 47 CFDC-non-susceptible and eight CFDC-susceptible isolates. Two CFDC-non-susceptible isolates belonged to ST104 whereas the remaining isolates belonged to ST2, and blaPER-1 was present only in CFDC-non-susceptible isolates. Amino acid substitutions were noted in penicillin-binding proteins (PBPs) in four CFDC-susceptible isolates, with slightly elevated MICs. The MICs of recombinant E. coli BL21 carrying the blaPER-1 gene increased 64-fold and recombinant E. coli BL21 carrying both the blaPER-1 and blaOXA-23 genes increased 8-fold but both remained within the susceptibility range. Transcriptome sequencing of 17 CFDC-non-susceptible isolates and eight CFDC-susceptible isolates revealed that transcriptional levels of various iron transport proteins, such as fiu, feoA, and feoB, and the energy transduction system, TonB-ExbB-ExbD, were relatively downregulated in CFDC-non-susceptible isolates. GO enrichment analysis revealed that the upregulated genes in CFDC-non-susceptible isolates were mainly associated with redox homeostasis and stress response. Besides, the expression levels of the blaOXA-23 and exbD genes were negatively correlated with the MICs. Conclusion: PER-1 production, iron transport system downregulation, and mutations in PBPs may synergistically impart high-level resistance to CFDC in CR-AB.

Characteristics of oral microbiome of healthcare workers in different clinical scenarios: a cross-sectional analysis.

The environment of healthcare institutes (HCIs) potentially affects the internal microecology of medical workers, which is reflected not only in the well-studied gut microbiome but also in the more susceptible oral microbiome. We conducted a prospective cross-sectional cohort study in four hospital departments in Central China. Oropharyngeal swabs from 65 healthcare workers were collected and analyzed using 16S rRNA gene amplicon sequencing. The oral microbiome of healthcare workers exhibited prominent deviations in diversity, microbial structure, and predicted function. The coronary care unit (CCU) samples exhibited robust features and stability, with significantly higher abundances of genera such as Haemophilus, Fusobacterium, and Streptococcus, and a lower abundance of Prevotella. Functional prediction analysis showed that vitamin, nucleotide, and amino acid metabolisms were significantly different among the four departments. The CCU group was at a potential risk of developing periodontal disease owing to the increased abundance of F. nucleatum. Additionally, oral microbial diversification of healthcare workers was related to seniority. We described the oral microbiome profile of healthcare workers in different clinical scenarios and demonstrated that community diversity, structure, and potential functions differed markedly among departments. Intense modulation of the oral microbiome of healthcare workers occurs because of their original departments, especially in the CCU.

In vitro and in vivo efficacy of cefiderocol plus tigecycline, colistin, or meropenem against carbapenem-resistant Acinetobacter baumannii.

We investigated activities of cefiderocol combination therapy against carbapenem-resistant Acinetobacter baumannii (CR-AB). A total of 123 clinical isolates of CR-AB, including 44 cefiderocol-resistant isolates were tested. Cefiderocol functioned synergistically with tigecycline in most cefiderocol-susceptible isolates (84.8%, 67/79), but not with colistin or meropenem by checkerboard method. Cefiderocol functioned synergistically with tigecycline, colistin, and meropenem in 90.9% (40/44), 47.7% (21/44), and 79.5% (35/44) cefiderocol-resistant isolates, respectively. The time-kill assay and the in vivo Galleria mellonella model confirmed these observations. In summary, cefiderocol combined with tigecycline showed synergistic effects against both cefiderocol-susceptible and -resistant CR-AB, suggesting a potentially valuable combination regimen.

Characteristics of lower respiratory tract microbiota in the patients with post-hematopoietic stem cell transplantation pneumonia.

Background: Pneumonia is a leading cause of non-relapse mortality after hematopoietic stem cell transplantation (HSCT), and the lower respiratory tract (LRT) microbiome has been proven to be associated with various respiratory diseases. However, little is known about the characteristics of the LRT microbiome in patients with post-HSCT compared to healthy controls (HC) and community-acquired pneumonia (CAP). Methods: Bronchoalveolar lavage samples from 55 patients with post-HSCT pneumonia, 44 patients with CAP, and 30 healthy volunteers were used to detect microbiota using 16S rRNA gene sequencing. Results: The diversity of the LRT microbiome significantly decreased in patients with post-HSCT pneumonia, and the overall community was different from the CAP and HC groups. At the phylum level, post-HSCT pneumonia samples had a high abundance of Actinobacteria and a relatively low abundance of Bacteroidetes. The same is true for non-survivors compared with survivors in patients with post-HSCT pneumonia. At the genus level, the abundances of Pseudomonas, Acinetobacter, Burkholderia, and Mycobacterium were prominent in the pneumonia group after HSCT. On the other hand, gut-associated bacteria, Enterococcus were more abundant in the non-survivors. Some pathways concerning amino acid and lipid metabolism were predicted to be altered in patients with post-HSCT pneumonia. Conclusions: Our results reveal that the LRT microbiome in patients with post-HSCT pneumonia differs from CAP patients and healthy controls, which could be associated with the outcome. The LRT microbiota could be a target for intervention during post-HSCT pneumonia.

Decreased plasma fetuin-A level as a novel bioindicator of poor prognosis in community-acquired pneumonia: A multi-center cohort study.

Background: Community-acquired pneumonia (CAP) is a respiratory disease that frequently requires hospital admission, and is a significant cause of death worldwide. Plasma fetuin-A levels were significantly lower in patients with sepsis, but data regarding CAP are scarce. This study aimed to evaluate the usefulness of fetuin-A as a prognostic biomarker of CAP. Methods: A multicenter cohort study on CAP was conducted between January 2017 and December 2018. Demographic and clinical data were recorded for all enrolled patients. Plasma fetuin-A levels were determined using a quantitative enzyme-linked immunosorbent assay. A Cox proportional hazards regression analysis was used to analyse the effect of variables on 30-day mortality. A logistic regression analysis was performed to assess risk factors associated with severe CAP (SCAP) and 30-day mortality. A receiver operating characteristic (ROC) curve was used to verify the association between variables and CAP prognosis. Correlations were assessed using Spearman's test. Survival curves were constructed and compared using the log-rank test. Results: A total of 283 patients with CAP were enrolled in this study. Fetuin-A levels were decreased in patients with CAP, especially in SCAP and non-survivors. A cox regression analysis showed that CURB-65 and fetuin-A levels were independent prognostic indicators of 30-day mortality. Via a multiple logistic regression analysis, plasma level of fetuin-A (<202.86 mg/L) was determined to be the strongest independent predictor of 30-day mortality considered (odds ratio, 57.365), and also was also determined to be an independent predictor of SCAP. The area under the curve (AUC) of fetuin-A for predicting 30-day mortality was 0.871, and accuracy was high (P < 0.05). Plasma fetuin-A levels were negatively correlated with WBC, NE%, Glu, CRP, PCT, CURB-65, and pneumonia severity index scores and positively correlated with albumin level. Kaplan-Meier curves showed that lower plasma levels of fetuin-A levels were associated with increased 30-day mortality levels (P < 0.0001). Conclusion: Plasma fetuin-A levels were decreased in patients with CAP. Fetuin-A can reliably predict mortality in patients with CAP, and is a useful diagnostic indicator of SCAP.

Proteomics Study of the Synergistic Killing of Tigecycline in Combination With Aminoglycosides Against Carbapenem-Resistant Klebsiella pneumoniae.

Co-administration of antibiotics with synergistic effects is one method to combat carbapenem-resistant organisms. Although the synergistic effects of tigecycline combined with aminoglycosides against carbapenem-resistant Klebsiella pneumoniae (CRKP) have been demonstrated in vitro and in animal models, the underlying mechanism remains elusive. Here we used proteomics analysis to assess the short-term bacterial responses to tigecycline and aminoglycosides alone or in combination. Emergence of tigecycline resistance during treatment and the susceptibility of tigecycline-resistant strains to aminoglycosides was further evaluated. The proteomic responses to tigecycline and aminoglycosides were divergent in monotherapy, with proteomic alterations to combination therapy dominated by tigecycline. Adaptive responses to tigecycline were associated with the upregulation of oxidative phosphorylation and translation-related proteins. These responses might confer CRKP hypersensitivity towards aminoglycosides by increasing the drug uptake and binding targets. Meanwhile, tigecycline might perturb adaptive responses to aminoglycosides through inhibition of heat shock response. Tigecycline-resistant strains could be isolated within 24 h exposure even in strains without heteroresistance, and the sensitivity to aminoglycosides significantly increased in resistant strains. Overall, these findings demonstrated that adaption to tigecycline in CRKP was a double-edged sword associated with the synergistic killing in tigecycline-aminoglycoside combination. Evolutionary hypersensitivity can provide novel insight into the mechanisms of antibiotic synergistic effects.

Targeted lipidomics reveals phospholipids and lysophospholipids as biomarkers for evaluating community-acquired pneumonia.

Background: Community-acquired pneumonia (CAP) is often accompanied by changes in lipid metabolism. This study aimed to examine the changes in serum phospholipids (PLs) that may be useful for early disease stratification and as potential therapeutic targets in patients with CAP. Methods: Serum samples from 58 patients hospitalized with CAP and 11 control samples were collected during admission between January 2017 and October 2018. Targeted lipidomic analysis was used to determine the concentrations of phosphatidylcholine (PC), lysophosphatidylcholine (LPC), phosphatidylethanolamine (PE), and lysophosphatidylethanolamine (LPE). The Gene Expression Omnibus (GEO) database was used to evaluate the gene expression levels of key enzymes in the Lands cycle, and quantitative real-time polymerase chain reaction (qRT-PCR) was used for further verification. Results: A significant decrease in LPC levels and an increase in PE levels, PC/LPC and PE/LPE ratios were observed in patients with CAP (P<0.05). The area under the curve (AUC) of PE serum concentrations combined with CURB-65 scores (confusion, uremia, respiratory rate, blood pressure, and age ≥65 years) was 0.848 for discriminating disease severity, which was significantly higher than the discriminating disease severity of CURB-65 (P<0.05). The efficiency of predicting 30-day mortality using PC, LPC, or PC/LPC ratio combined with CURB-65 scores (AUC =0.811, AUC =0.854, AUC =0.838, respectively) was better than CURB-65 alone (P<0.05). Gene expression analysis revealed the upregulation of LPC acyltransferase 2. Conclusions: LPC or PE serum levels as well as PC/LPC ratios combined with CURB-65 are effective biomarkers for predicting the disease severity and 30-day mortality of patients with CAP. Further investigations of phospholipid metabolism will improve our understanding and treatment of CAP.