Neutrophil and Macrophage Response in Acinetobacter Baumannii Infection and Their Relationship to Lung Injury

A chronic murine model of pulmonary Acinetobacter baumannii infection enabling the investigation of late virulence factors, long-term antibiotic treatments, and polymicrobial infections

Acinetobacter baumannii can cause prolonged infections that disproportionately affect immunocompromised populations. Our understanding of A. baumannii respiratory pathogenesis relies on an acute murine infection model with limited clinical relevance that employs an unnaturally high number of bacteria and requires the assessment of bacterial load at 24-36 hours post-infection. Here, we demonstrate that low intranasal inoculums in immunocompromised mice with a tlr4 mutation leads to reduced inflammation, allowing for persistent infections lasting at least 3 weeks. Using this "chronic infection model," we determined the adhesin InvL is an imperative virulence factor required during later stages of infection, despite being dispensable in the early phase. We also demonstrate that the chronic model enables the distinction between antibiotics that, although initially reduce bacterial burden, either lead to complete clearance or result in the formation of bacterial persisters. To illustrate how our model can be applied to study polymicrobial infections, we inoculated mice with an active A. baumannii infection with Staphylococcus aureus or Klebsiella pneumoniae. We found that S. aureus exacerbates the infection, while K. pneumoniae enhances A. baumannii clearance. In all, the chronic model overcomes some limitations of the acute pulmonary model, expanding our capabilities to study of A. baumannii pathogenesis and lays the groundwork for the development of similar models for other important opportunistic pathogens.

Bacterial contamination in the different parts of household air conditioners: a comprehensive evaluation from Chengdu, Southwest China

Introduction

Air flow driven by air-conditioner has a significant impact on the indoor environment, however, the bacterial contamination conditions in the different parts of air-conditioners have not been fully elucidated.

Methods

In this study, we assessed the bacterial pollution in the four parts, including air outlet, filter net, cooling fin and water sink, of ten household air-conditioners quantitatively and qualitatively from Chengdu, southwestern China.

Results

The microbial cultivation results showed the large total bacterial counts of 5042.0, 9127.6, 6595.1, and 12296.2 CFU/cm2 in air outlet, filter net, cooling fin, and water sink. Furthermore, the sequencing data showed that these four parts displayed different bacterial characteristics. At the level of genus, Caproiciproducens and Acidipropionibacterium were predominant in air outlet. Bacillus, Acinetobacter, Paracoccus, and Corynebacterium were detected as the characteristic bacteria in filter net. For cooling fin, Rhodococcus, Achromobacter, and Nocardioides were the dominant bacteria. The genera of Methylobacterium-Methylorubrum, Brevibacterium, Stenotrophomonas, and Psychrobacter were identified as the bioindicators in water sink. The bioinformatic analysis on the sequencing data illustrated that the bacteria from air-conditioners were associated with metabolic disturbance.

Discussion

This study reveals the distinct bacterial compositions in the different parts of air-conditioner, and provides new clues for the non-negligible bacterial pollution in this common appliance from Chinese households.

Using time-series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of Acinetobacter baumannii

BACKGROUND

Accurately distinguishing between pulmonary infection and colonization in patients with Acinetobacter baumannii is of utmost importance to optimize treatment and prevent antibiotic abuse or inadequate therapy. An efficient automated sorting tool could prompt individualized interventions and enhance overall patient outcomes. This study aims to develop a robust machine learning classification model using a combination of time-series chest radiographs and laboratory data to accurately classify pulmonary status caused by Acinetobacter baumannii.

METHODS

We proposed nested logistic regression models based on different time-series data to automatically classify the pulmonary status of patients with Acinetobacter baumannii. Advanced features were extracted from the time-series data of hospitalized patients, encompassing dynamic pneumonia indicators observed on chest radiographs and laboratory indicator values recorded at three specific time points.

RESULTS

Data of 152 patients with Acinetobacter baumannii cultured from sputum or alveolar lavage fluid were retrospectively analyzed. Our model with multiple time-series data demonstrated a higher performance of AUC (0.850, with a 95% confidence interval of [0.638-0.873]), an accuracy of 0.761, a sensitivity of 0.833. The model, which only incorporated a single time point feature, achieved an AUC of 0.741. The influential model variables included difference in the chest radiograph pneumonia score.

CONCLUSION

Dynamic assessment of time-series chest radiographs and laboratory data using machine learning allowed for accurate classification of colonization and infection with Acinetobacter baumannii. This demonstrates the potential to help clinicians provide individualized treatment through early detection.

Multiomics reveal human umbilical cord mesenchymal stem cells improving acute lung injury via the lung-gut axis

BACKGROUND

Acute lung injury (ALI) and its final severe stage, acute respiratory distress syndrome, are associated with high morbidity and mortality rates in patients due to the lack of effective specific treatments. Gut microbiota homeostasis, including that in ALI, is important for human health. Evidence suggests that the gut microbiota improves lung injury through the lung-gut axis. Human umbilical cord mesenchymal cells (HUC-MSCs) have attractive prospects for ALI treatment. This study hypothesized that HUC-MSCs improve ALI via the lung-gut microflora.

AIM

To explore the effects of HUC-MSCs on lipopolysaccharide (LPS)-induced ALI in mice and the involvement of the lung-gut axis in this process.

METHODS

C57BL/6 mice were randomly divided into four groups (18 rats per group): Sham, sham + HUC-MSCs, LPS, and LPS + HUC-MSCs. ALI was induced in mice by intraperitoneal injections of LPS (10 mg/kg). After 6 h, mice were intervened with 0.5 mL phosphate buffered saline (PBS) containing 1 × 106 HUC-MSCs by intraperitoneal injections. For the negative control, 100 mL 0.9% NaCl and 0.5 mL PBS were used. Bronchoalveolar lavage fluid (BALF) was obtained from anesthetized mice, and their blood, lungs, ileum, and feces were obtained by an aseptic technique following CO2 euthanasia. Wright's staining, enzyme-linked immunosorbent assay, hematoxylin-eosin staining, Evans blue dye leakage assay, immunohistochemistry, fluorescence in situ hybridization, western blot, 16S rDNA sequencing, and non-targeted metabolomics were used to observe the effect of HUC-MSCs on ALI mice, and the involvement of the lung-gut axis in this process was explored. One-way analysis of variance with post-hoc Tukey's test, independent-sample Student's t-test, Wilcoxon rank-sum test, and Pearson correlation analysis were used for statistical analyses.

RESULTS

HUC-MSCs were observed to improve pulmonary edema and lung and ileal injury, and decrease mononuclear cell and neutrophil counts, protein concentrations in BALF and inflammatory cytokine levels in the serum, lung, and ileum of ALI mice. Especially, HUC-MSCs decreased Evans blue concentration and Toll-like receptor 4, myeloid differentiation factor 88, p-nuclear factor kappa-B (NF-κB)/NF-κB, and p-inhibitor α of NF-κB (p-IκBα)/IκBα expression levels in the lung, and raised the pulmonary vascular endothelial-cadherin, zonula occludens-1 (ZO-1), and occludin levels and ileal ZO-1, claudin-1, and occludin expression levels. HUC-MSCs improved gut and BALF microbial homeostases. The number of pathogenic bacteria decreased in the BALF of ALI mice treated with HUC-MSCs. Concurrently, the abundances of Oscillospira and Coprococcus in the feces of HUS-MSC-treated ALI mice were significantly increased. In addition, Lactobacillus, Bacteroides, and unidentified_Rikenellaceae genera appeared in both feces and BALF. Moreover, this study performed metabolomic analysis on the lung tissue and identified five upregulated metabolites and 11 downregulated metabolites in the LPS + MSC group compared to the LPS group, which were related to the purine metabolism and the taste transduction signaling pathways. Therefore, an intrinsic link between lung metabolite levels and BALF flora homeostasis was established.

CONCLUSION

This study suggests that HUM-MSCs attenuate ALI by redefining the gut and lung microbiota.