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.

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.

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.

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.

Enhanced DNA and RNA pathogen detection via metagenomic sequencing in patients with pneumonia.

BACKGROUND: Metagenomic next-generation sequencing (mNGS) is an important supplement to conventional tests for pathogen detections of pneumonia. However, mNGS pipelines were limited by irregularities, high proportion of host nucleic acids, and lack of RNA virus detection. Thus, a regulated pipeline based on mNGS for DNA and RNA pathogen detection of pneumonia is essential. METHODS: We performed a retrospective study of 151 patients with pneumonia. Three conventional tests, culture, loop-mediated isothermal amplification (LAMP) and viral quantitative real-time polymerase chain reaction (qPCR) were conducted according to clinical needs, and all samples were detected using our optimized pipeline based on the mNGS (DNA and RNA) method. The performances of mNGS and three other tests were compared. Human DNA depletion was achieved respectively by MolYsis kit and pre-treatment using saponin and Turbo DNase. Three RNA library preparation methods were used to compare the detection performance of RNA viruses. RESULTS: An optimized mNGS workflow was built, which had only 1-working-day turnaround time. The proportion of host DNA in the pre-treated samples decreased from 99 to 90% and microbiome reads achieved an approximately 20-fold enrichment compared with those without host removal. Meanwhile, saponin and Turbo DNase pre-treatment exhibited an advantage for DNA virus detection compared with MolYsis. Besides, our in-house RNA library preparation procedure showed a more robust RNA virus detection ability. Combining three conventional methods, 76 (76/151, 50.3%) cases had no clear causative pathogen, but 24 probable pathogens were successfully detected in 31 (31/76 = 40.8%) unclear cases using mNGS. The agreement of the mNGS with the culture, LAMP, and viral qPCR was 60%, 82%, and 80%, respectively. Compared with all conventional tests, mNGS had a sensitivity of 70.4%, a specificity of 72.7%, and an overall agreement of 71.5%. CONCLUSIONS: A complete and effective mNGS workflow was built to provide timely DNA and RNA pathogen detection for pneumonia, which could effectively remove the host sequence, had a higher microbial detection rate and a broader spectrum of pathogens (especially for viruses and some pathogens that are difficult to culture). Despite the advantages, there are many challenges in the clinical application of mNGS, and the mNGS report should be interpreted with caution.

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 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.

Nosocomial outbreak of COVID-19 pneumonia in Wuhan, China.

BACKGROUND: The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), infected over 3300 healthcare workers in early 2020 in China. Little information is known about nosocomial infections of healthcare workers in the initial period. We analysed data from healthcare workers with nosocomial infections in Wuhan Union Hospital (Wuhan, China) and their family members. METHODS: We collected and analysed data on exposure history, illness timelines and epidemiological characteristics from 25 healthcare workers with laboratory-confirmed coronavirus disease 2019 (COVID-19) and two healthcare workers in whom COVID-19 was highly suspected, as well as 10 of their family members with COVID-19, between 5 January and 12 February 2020. The demographics and clinical features of the 35 laboratory-confirmed cases were investigated and viral RNA of 12 cases was sequenced and analysed. RESULTS: Nine clusters were found among the patients. All patients showed mild to moderate clinical manifestation and recovered without deterioration. The mean period of incubation was 4.5 days, the mean±sd clinical onset serial interval (COSI) was 5.2±3.2 days, and the median virus shedding time was 18.5 days. Complete genomic sequences of 12 different coronavirus strains demonstrated that the viral structure, with small irrelevant mutations, was stable in the transmission chains and showed remarkable traits of infectious traceability. CONCLUSIONS: SARS-CoV-2 can be rapidly transmitted from person to person, regardless of whether they have symptoms, in both hospital settings and social activities, based on the short period of incubation and COSI. The public health service should take practical measures to curb the spread, including isolation of cases, tracing close contacts, and containment of severe epidemic areas. Besides this, healthcare workers should be alert during the epidemic and self-quarantine if self-suspected of infection.

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.

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.

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.

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.

Heteroresistance Is Associated With in vitro Regrowth During Colistin Treatment in Carbapenem-Resistant Klebsiella pneumoniae.

Polymyxins including polymyxin B and colistin (polymyxin E) are considered the last resort for treating infections caused by carbapenem-resistant gram-negative bacteria. However, in vitro regrowth with the emergence of resistance during treatment is common. Polymyxin heteroresistance, particularly in Acinetobacter baumannii and Klebsiella pneumoniae, has been widely reported. This study was primarily performed to evaluate the prevalence of colistin heteroresistance in carbapenem-resistant K. pneumoniae (CR-KP) and the association between in vitro regrowth and heteroresistance. The mechanisms of colistin resistance and the ability of combination therapies to suppress resistance selection were further investigated. A population analysis profile (PAP) analysis showed that 69 (71.9%) of 96 CR-KP strains had colistin heteroresistance. Time-kill assays revealed that the colistin monotherapy could quickly eliminate the bacterial cells in strains without heteroresistance within the first 6 h. Conversely, it could initially reduce the number of cells in heteroresistant strains, but then regrowth occurred rapidly. Resistance screening at 12 and 24 h in the time-kill assays indicated that susceptible populations were killed, and regrowth was the exact result of the continued growth of resistant subpopulations. Colistin resistance in the regrowth subpopulations was mainly due to the overexpression of phoPQ and pmrD. Colistin combined with tetracyclines (tigecycline or minocycline) or aminoglycosides (amikacin or gentamicin) could effectively suppress the resistance selection and significantly elicit in vitro synergistic effects. These findings suggested that the combination therapy can be used to treat infections caused by CR-KP with colistin heteroresistance. Nevertheless, further in vivo studies considering drugs pharmacokinetics/pharmacodynamics are needed to confirm these findings.

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.

Shared and Specific Lung Microbiota with Metabolic Profiles in Bronchoalveolar Lavage Fluid Between Infectious and Inflammatory Respiratory Diseases.

BACKGROUND: Infiltration of the lower respiratory tract (LRT) microenvironment could be significantly associated with respiratory diseases. However, alterations in the LRT microbiome and metabolome in infectious and inflammatory respiratory diseases and their correlation with inflammation still need to be explored. METHODS: Bronchoalveolar lavage samples from 44 community-acquired pneumonia (CAP) patients, 29 connective tissue disease-associated interstitial disease (CTD-ILD) patients, and 30 healthy volunteers were used to detect microbiota and metabolites through 16S rRNA gene sequencing and untargeted high-performance liquid chromatography with mass spectrometry. RESULTS: The composition of the LRT microbial communities and metabolites differed in disease states. CAP patients showed a significantly low abundance and both diseases presented a depletion of some genera of the phylum Bacteroidetes, including Prevotella, Porphyromonas, and health-associated metabolites, such as sphingosine (d16:1), which were negatively correlated with infectious indicators. In contrast, Bacillus and Mycoplasma were both enriched in the disease groups. Streptococcus was specifically increased in CTD-ILD. In addition, co-elevated metabolites such as FA (22:4) and pyruvic acid represented hypoxia and inflammation in the diseases. Significantly increased levels of amino acids and succinate, as well as decreased itaconic acid levels, were observed in CAP patients, whereas CTD-ILD patients showed only a handful of specific metabolic alterations. Functions related to microbial lipid and amino acid metabolism were significantly altered, indicating the possible contributions of microbial metabolism. Dual omics analysis showed a moderate positive correlation between the microbiome and metabolome. The levels of L-isoleucine and L-arginine were negatively correlated with Streptococcus, and itaconic acid positively correlated with Streptococcus. CONCLUSION: In the LRT microenvironment, shared and specific alterations occurred in CAP and CTD-ILD patients, which were associated with inflammatory and immune reactions, which may provide a new direction for future studies aiming to elucidate the mechanism, improve the diagnosis, and develop therapies for different respiratory diseases.