MaREA4Galaxy: Metabolic reaction enrichment analysis and visualization of RNA-seq data within Galaxy

Comparative Transcriptomic Analyses of a Vero Cell Line in Suspension versus Adherent Culture Conditions

The Vero cell line is the most used continuous cell line for viral vaccine manufacturing. Its anchorage-dependent use renders scaling up challenging and operations very labor-intensive which affects cost effectiveness. Thus, efforts to adapt Vero cells to suspension cultures have been invested, but hurdles such as the long doubling time and low cell viability remain to be addressed. In this study, building on the recently published Vero cell line annotated genome, a functional genomics analysis of the Vero cells adapted to suspension is performed to better understand the genetic and phenotypic switches at play during the adaptation of Vero cells from anchorage-dependent to suspension cultures. Results show downregulation of the epithelial-to-mesenchymal transition (EMT) pathway, highlighting the dissociation between the adaptation to suspension process and EMT. Surprisingly, an upregulation of cell adhesion components is observed, notably the CDH18 gene, the cytoskeleton pathway, and the extracellular pathway. Moreover, a downregulation of the glycolytic pathway is balanced by an upregulation of the asparagine metabolism pathway, promoting cell adaptation to nutrient deprivation. A downregulation of the adherens junctions and the folate pathways alongside with the FYN gene are possible explanations behind the currently observed low-cell viability and long doubling time.

Dystrophin deficiency affects human astrocyte properties and response to damage

In addition to progressive muscular degeneration due to dystrophin mutations, 1/3 of Duchenne muscular dystrophy (DMD) patients present cognitive deficits. However, there is currently an incomplete understanding about the function of the multiple dystrophin isoforms in human brains. Here, we tested the hypothesis that dystrophin deficiency affects glial function in DMD and could therefore contribute to neural impairment. We investigated human dystrophin isoform expression with development and differentiation and response to damage in human astrocytes from control and induced pluripotent stem cells from DMD patients. In control cells, short dystrophin isoforms were up-regulated with development and their expression levels changed differently upon neuronal and astrocytic differentiation, as well as in 2-dimensional versus 3-dimensional astrocyte cultures. All DMD-astrocytes tested displayed altered morphology, proliferative activity and AQP4 expression. Furthermore, they did not show any morphological change in response to inflammatory stimuli and their number was significantly lower as compared to stimulated healthy astrocytes. Finally, DMD-astrocytes appeared to be more sensitive than controls to oxidative damage as shown by their increased cell death. Behavioral and metabolic defects in DMD-astrocytes were consistent with gene pathway dysregulation shared by lines with different mutations as demonstrated by bulk RNA-seq analysis. Together, our DMD model provides evidence for altered astrocyte function in DMD suggesting that defective astrocyte responses may contribute to neural impairment and might provide additional potential therapeutic targets.

On the Use of Topological Features of Metabolic Networks for the Classification of Cancer Samples

BACKGROUND

The increasing availability of omics data collected from patients affected by severe pathologies, such as cancer, is fostering the development of data science methods for their analysis.

INTRODUCTION

The combination of data integration and machine learning approaches can provide new powerful instruments to tackle the complexity of cancer development and deliver effective diagnostic and prognostic strategies.

METHODS

We explore the possibility of exploiting the topological properties of sample-specific metabolic networks as features in a supervised classification task. Such networks are obtained by projecting transcriptomic data from RNA-seq experiments on genome-wide metabolic models to define weighted networks modeling the overall metabolic activity of a given sample.

RESULTS

We show the classification results on a labeled breast cancer dataset from the TCGA database, including 210 samples (cancer vs. normal). In particular, we investigate how the performance is affected by a threshold-based pruning of the networks by comparing Artificial Neural Networks, Support Vector Machines and Random Forests. Interestingly, the best classification performance is achieved within a small threshold range for all methods, suggesting that it might represent an effective choice to recover useful information while filtering out noise from data. Overall, the best accuracy is achieved with SVMs, which exhibit performances similar to those obtained when gene expression profiles are used as features.

CONCLUSION

These findings demonstrate that the topological properties of sample-specific metabolic networks are effective in classifying cancer and normal samples, suggesting that useful information can be extracted from a relatively limited number of features.

Host metabolism dysregulation and cell tropism identification in human airway and alveolar organoids upon SARS-CoV-2 infection

The coronavirus disease 2019 (COVID-19) pandemic is caused by infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is spread primary via respiratory droplets and infects the lungs. Currently widely used cell lines and animals are unable to accurately mimic human physiological conditions because of the abnormal status of cell lines (transformed or cancer cells) and species differences between animals and humans. Organoids are stem cell-derived self-organized three-dimensional culture in vitro and model the physiological conditions of natural organs. Here we showed that SARS-CoV-2 infected and extensively replicated in human embryonic stem cells (hESCs)-derived lung organoids, including airway and alveolar organoids which covered the complete infection and spread route for SARS-CoV-2 within lungs. The infected cells were ciliated, club, and alveolar type 2 (AT2) cells, which were sequentially located from the proximal to the distal airway and terminal alveoli, respectively. Additionally, RNA-seq revealed early cell response to virus infection including an unexpected downregulation of the metabolic processes, especially lipid metabolism, in addition to the well-known upregulation of immune response. Further, Remdesivir and a human neutralizing antibody potently inhibited SARS-CoV-2 replication in lung organoids. Therefore, human lung organoids can serve as a pathophysiological model to investigate the underlying mechanism of SARS-CoV-2 infection and to discover and test therapeutic drugs for COVID-19.