Loss of cytochrome P450 (CYP)1B1 mitigates hyperoxia response in adult mouse lung by reprogramming metabolism and translation.

Oxygen supplementation is life saving for premature infants and for COVID-19 patients but can induce long-term pulmonary injury by triggering inflammation, with xenobiotic-metabolizing CYP enzymes playing a critical role. Murine studies showed that CYP1B1 enhances, while CYP1A1 and CYP1A2 protect from, hyperoxic lung injury. In this study we tested the hypothesis that Cyp1b1-null mice would revert hyperoxia-induced transcriptomic changes observed in WT mice at the transcript and pathway level. Wild type (WT) C57BL/6J and Cyp1b1-null mice aged 8-10 weeks were maintained in room air (21% O2) or exposed to hyperoxia (>95% O2) for 48h. Transcriptomic profiling was conducted using the Illumina microarray platform. Hyperoxia exposure led to robust changes in gene expression and in the same direction in WT, Cyp1a1-, Cyp1a2-, and Cyp1b1-null mice, but to different extents for each mouse genotype. At the transcriptome level, all Cyp1-null murine models reversed hyperoxia effects. Gene Set Enrichment Analysis identified 118 hyperoxia-affected pathways mitigated only in Cyp1b1-null mice, including lipid, glutamate, and amino acid metabolism. Cell cycle genes Cdkn1a and Ccnd1 were induced by hyperoxia in both WT and Cyp1b1-null mice but mitigated in Cyp1b1-null O2 compared to WT O2 mice. Hyperoxia gene signatures associated positively with bronchopulmonary dysplasia (BPD), which occurs in premature infants (with supplemental oxygen being one of the risk factors), but only in the Cyp1b1-null mice did the gene profile after hyperoxia exposure show a partial rescue of BPD-associated transcriptome. Our study suggests that CYP1B1 plays a pro-oxidant role in hyperoxia-induced lung injury.

A modular metagenomics analysis system for integrated multi-step data exploration.

Motivation: Computational analysis of large-scale metagenomics sequencing datasets has proved to be both incredibly valuable for extracting isolate-level taxonomic and functional insights from complex microbial communities. However, thanks to an ever-expanding ecosystem of metagenomics-specific algorithms and file formats, designing studies, implementing seamless and scalable end-to-end workflows, and exploring the massive amounts of output data have become studies unto themselves. Furthermore, there is little inter-communication between output data of different analytic purposes, such as short-read classification and metagenome assembled genomes (MAG) reconstruction. One-click pipelines have helped to organize these tools into targeted workflows, but they suffer from general compatibility and maintainability issues. Results: To address the gap in easily extensible yet robustly distributable metagenomics workflows, we have developed a module-based metagenomics analysis system written in Snakemake, a popular workflow management system, along with a standardized module and working directory architecture. Each module can be run independently or conjointly with a series of others to produce the target data format (ex. short-read preprocessing alone, or short-read preprocessing followed by de novo assembly), and outputs aggregated summary statistics reports and semi-guided Jupyter notebook-based visualizations, The module system is a bioinformatics-optimzied scaffold designed to be rapidly iterated upon by the research community at large. Availability: The module template as well as the modules described below can be found at https://github.com/MetaSUB-CAMP.

Differential co-expression networks of the gut microbiota are associated with depression and anxiety treatment resistance among psychiatric inpatients.

BACKGROUND: Comorbid anxiety and depression are common and are associated with greater disease burden than either alone. Our recent efforts have identified an association between gut microbiota dysfunction and severity of anxiety and depression. In this follow-up, we applied Differential Co-Expression Analysis (DiffCoEx) to identify potential gut microbiota biomarker(s) candidates of treatment resistance among psychiatric inpatients. METHODS: In a sample of convenience, 100 psychiatric inpatients provided clinical data at admission and discharge; fecal samples were collected early during the hospitalization. Whole genome shotgun sequencing methods were used to process samples. DiffCoEx was used to identify clusters of microbial features significantly different based on treatment resistance status. Once overlapping features were identified, a knowledge-mining tool was used to review the literature using a list of microbial species/pathways and a select number of medical subject headlines (MeSH) terms relevant for depression, anxiety, and brain-gut-axis dysregulation. Network analysis used overlapping features to identify microbial interactions that could impact treatment resistance. RESULTS: DiffCoEx analyzed 10,403 bacterial features: 43/44 microbial features associated with depression treatment resistance overlapped with 43/114 microbial features associated with anxiety treatment resistance. Network analysis resulted in 8 biological interactions between 16 bacterial species. Clostridium perfringens evidenced the highest connection strength (0.95). Erysipelotrichaceae bacterium 6_1_45 has been most widely examined, is associated with inflammation and dysbiosis, but has not been associated with depression or anxiety. CONCLUSION: DiffCoEx potentially identified gut bacteria biomarker candidates of depression and anxiety treatment-resistance. Future efforts in psychiatric microbiology should examine the mechanistic relationship of identified pro-inflammatory species, potentially contributing to a biomarker-based algorithm for treatment resistance.

Early Systemic Glycolytic Shift After Aneurysmal Subarachnoid Hemorrhage is Associated with Functional Outcomes.

BACKGROUND: Aneurysmal subarachnoid hemorrhage (aSAH) leads to a robust systemic inflammatory response. We hypothesized that an early systemic glycolytic shift occurs after aSAH, resulting in a unique metabolic signature and affecting systemic inflammation. METHODS: Control patients and patients with aSAH were analyzed. Samples from patients with aSAH were collected within 24 h of aneurysmal rupture. Mass spectrometry-based metabolomics was performed to assess relative abundance of 16 metabolites involved in the tricarboxylic acid cycle, glycolysis, and pentose phosphate pathway. Principal component analysis was used to segregate control patients from patients with aSAH. Dendrograms were developed to depict correlations between metabolites and cytokines. Analytic models predicting functional outcomes were developed, and receiver operating curves were compared. RESULTS: A total of 122 patients with aSAH and 38 control patients were included. Patients with aSAH had higher levels of glycolytic metabolites (3-phosphoglycerate/2-phosphoglycerate, lactate) but lower levels of oxidative metabolites (succinate, malate, fumarate, and oxalate). Patients with higher clinical severity (Hunt-Hess Scale score ≥ 4) had higher levels of glyceraldehyde 3-phosphate and citrate but lower levels of α-ketoglutarate and glutamine. Principal component analysis readily segregated control patients from patients with aSAH. Correlation analysis revealed distinct clusters in control patients that were not observed in patients with aSAH. Higher levels of fumarate were associated with good functional outcomes at discharge (odds ratio [OR] 1.76, 95% confidence interval [CI] 1.15-2.82) in multivariable models, whereas higher levels of citrate were associated with poor functional outcomes at discharge (OR 0.36, 95% CI 0.16-0.73) and at 3 months (OR 0.35, 95% CI 0.14-0.81). No associations were found with delayed cerebral ischemia. Levels of α-ketoglutarate and glutamine correlated with lower levels of interleukin-8, whereas fumarate was associated with lower levels of tumor necrosis factor alpha. CONCLUSIONS: Aneurysmal subarachnoid hemorrhage results in a unique pattern of plasma metabolites, indicating a shift toward glycolysis. Higher levels of fumarate and lower levels of citrate were associated with better functional outcomes. These metabolites may represent targets to improve metabolism after aSAH.

Metabolome and microbiome multi-omics integration from a murine lung inflammation model of bronchopulmonary dysplasia.

BACKGROUND: Respiratory tract microbial dysbiosis can exacerbate inflammation and conversely inflammation may cause dysbiosis. Dysbiotic microbiome metabolites may lead to bronchopulmonary dysplasia (BPD). Hyperoxia and lipopolysaccharide (LPS) interaction alters lung microbiome and metabolome, mediating BPD lung injury sequence. METHODS: C57BL6/J mice were exposed to 21% (normoxia) or 70% (hyperoxia) oxygen during postnatal days (PND) 1-14. Pups were injected with LPS (6 mg/kg) or equal PBS volume, intraperitoneally on PND 3, 5, and 7. At PND14, the lungs were collected for microbiome and metabolomic analyses (n = 5/group). RESULTS: Microbiome alpha and beta diversity were similar between groups. Metabolic changes included hyperoxia 31 up/18 down, LPS 7 up/4 down, exposure interaction 8. Hyperoxia increased Intestinimonas abundance, whereas LPS decreased Clostridiales, Dorea, and Intestinimonas; exposure interaction affected Blautia. Differential co-expression analysis on multi-omics data identified exposure-altered modules. Hyperoxia metabolomics response was integrated with a published matching transcriptome, identifying four induced genes (ALDOA, GAA, NEU1, RENBP), which positively correlated with BPD severity in a published human newborn cohort. CONCLUSIONS: We report hyperoxia and LPS lung microbiome and metabolome signatures in a clinically relevant BPD model. We identified four genes correlating with BPD status in preterm infants that are promising targets for therapy and prevention. IMPACT: Using multi-omics, we identified and correlated key biomarkers of hyperoxia and LPS on murine lung micro-landscape and examined their potential clinical implication, which shows strong clinical relevance for future research. Using a double-hit model of clinical relevance to bronchopulmonary dysplasia, we are the first to report integrated metabolomic/microbiome landscape changes and identify novel disease biomarker candidates.