Transcriptomic and proteomic profiling of young and old mice in the bleomycin model reveals high similarity.

The most common preclinical, in vivo model to study lung fibrosis is the bleomycin-induced lung fibrosis model in 2- to 3-mo-old mice. Although this model resembles key aspects of idiopathic pulmonary fibrosis (IPF), there are limitations in its predictability for the human disease. One of the main differences is the juvenile age of animals that are commonly used in experiments, resembling humans of around 20 yr. Because IPF patients are usually older than 60 yr, aging appears to play an important role in the pathogenesis of lung fibrosis. Therefore, we compared young (3 months) and old mice (21 months) 21 days after intratracheal bleomycin instillation. Analyzing lung transcriptomics (mRNAs and miRNAs) and proteomics, we found most pathways to be similarly regulated in young and old mice. However, old mice show imbalanced protein homeostasis as well as an increased inflammatory state in the fibrotic phase compared to young mice. Comparisons with published human transcriptomic data sets (GSE47460, GSE32537, and GSE24206) revealed that the gene signature of old animals correlates significantly better with IPF patients, and it also turned human healthy individuals better into "IPF patients" using an approach based on predictive disease modeling. Both young and old animals show similar molecular hallmarks of IPF in the bleomycin-induced lung fibrosis model, although old mice more closely resemble several features associated with IPF in comparison to young animals.

Transcriptome analysis of AAV-induced retinopathy models expressing human VEGF, TNF-α, and IL-6 in murine eyes.

Retinopathies are multifactorial diseases with complex pathologies that eventually lead to vision loss. Animal models facilitate the understanding of the pathophysiology and identification of novel treatment options. However, each animal model reflects only specific disease aspects and understanding of the specific molecular changes in most disease models is limited. Here, we conducted transcriptome analysis of murine ocular tissue transduced with recombinant Adeno-associated viruses (AAVs) expressing either human VEGF-A, TNF-α, or IL-6. VEGF expression led to a distinct regulation of extracellular matrix (ECM)-associated genes. In contrast, both TNF-α and IL-6 led to more comparable gene expression changes in interleukin signaling, and the complement cascade, with TNF-α-induced changes being more pronounced. Furthermore, integration of single cell RNA-Sequencing data suggested an increase of endothelial cell-specific marker genes by VEGF, while TNF-α expression increased the expression T-cell markers. Both TNF-α and IL-6 expression led to an increase in macrophage markers. Finally, transcriptomic changes in AAV-VEGF treated mice largely overlapped with gene expression changes observed in the oxygen-induced retinopathy model, especially regarding ECM components and endothelial cell-specific gene expression. Altogether, our study represents a valuable investigation of gene expression changes induced by VEGF, TNF-α, and IL-6 and will aid researchers in selecting appropriate animal models for retinopathies based on their agreement with the human pathophysiology.

Mapping the metabolomic and lipidomic changes in the bleomycin model of pulmonary fibrosis in young and aged mice.

Alterations in metabolic pathways were recently recognized as potential underlying drivers of idiopathic pulmonary fibrosis (IPF), translating into novel therapeutic targets. However, knowledge of metabolic and lipid regulation in fibrotic lungs is limited. To comprehensively characterize metabolic perturbations in the bleomycin mouse model of IPF, we analyzed the metabolome and lipidome by mass spectrometry. We identified increased tissue turnover and repair, evident by enhanced breakdown of proteins, nucleic acids and lipids and extracellular matrix turnover. Energy production was upregulated, including glycolysis, the tricarboxylic acid cycle, glutaminolysis, lactate production and fatty acid oxidation. Higher eicosanoid synthesis indicated inflammatory processes. Because the risk of IPF increases with age, we investigated how age influences metabolomic and lipidomic changes in the bleomycin-induced pulmonary fibrosis model. Surprisingly, except for cytidine, we did not detect any significantly differential metabolites or lipids between old and young bleomycin-treated lungs. Together, we identified metabolomic and lipidomic changes in fibrosis that reflect higher energy demand, proliferation, tissue remodeling, collagen deposition and inflammation, which might serve to improve diagnostic and therapeutic options for fibrotic lung diseases in the future.

FELLA: an R package to enrich metabolomics data.

BACKGROUND: Pathway enrichment techniques are useful for understanding experimental metabolomics data. Their purpose is to give context to the affected metabolites in terms of the prior knowledge contained in metabolic pathways. However, the interpretation of a prioritized pathway list is still challenging, as pathways show overlap and cross talk effects. RESULTS: We introduce FELLA, an R package to perform a network-based enrichment of a list of affected metabolites. FELLA builds a hierarchical representation of an organism biochemistry from the Kyoto Encyclopedia of Genes and Genomes (KEGG), containing pathways, modules, enzymes, reactions and metabolites. In addition to providing a list of pathways, FELLA reports intermediate entities (modules, enzymes, reactions) that link the input metabolites to them. This sheds light on pathway cross talk and potential enzymes or metabolites as targets for the condition under study. FELLA has been applied to six public datasets -three from Homo sapiens, two from Danio rerio and one from Mus musculus- and has reproduced findings from the original studies and from independent literature. CONCLUSIONS: The R package FELLA offers an innovative enrichment concept starting from a list of metabolites, based on a knowledge graph representation of the KEGG database that focuses on interpretability. Besides reporting a list of pathways, FELLA suggests intermediate entities that are of interest per se. Its usefulness has been shown at several molecular levels on six public datasets, including human and animal models. The user can run the enrichment analysis through a simple interactive graphical interface or programmatically. FELLA is publicly available in Bioconductor under the GPL-3 license.

Peak aggregation as an innovative strategy for improving the predictive power of LC-MS metabolomic profiles.

Liquid chromatography-mass spectrometry (LC-MS)-based metabolomic datasets consist of different features including (de)protonated molecules, fragments, adducts, and isotopes that may show high correlation values related to a high level of collinearity. There have been described several sources of these high correlation patterns regarding metabolomic datasets. Among these sources, it should be highlighted the high level of correlation computed between features coming from the same metabolite. It is well-known that soft ionization methods (such as electrospray) produce several mass features from a particular compound (i.e., metabolite spectrum). Typically, the statistical methods used in metabolomics consider spectral peaks as variables. However, it has been reported that a high collinearity between variables might be the responsible for high uncertainty values in the predictors of a regression. In this context, this technical note proposes a new strategy based on the application of the so-called peak aggregation methods (NMF Reduction, PCA Decomposition, Maximum Peak, and Spectrum Mean) to take advantage of the variable collinearity and solve the issue of high variable collinearity. A set of real samples obtained after human nutritional intervention with placebo or polyphenol-rich beverages was used to test this methodology. The results showed that applying any peak aggregation method (especially NMF and PCA) improves the statistical prediction power of class pertinence independently of the nature of the classifier (linear PLS-DA or nonlinear SVM). Overall, the introduction of this new approach resulted in a reduction of the dimensionality of the data and, in addition, in a significant increase in the overall predictive power of the data.