Biology subjects

Saez, J.

Publications and source records attributed to Saez, J..

3 recordsLinked to original sources

bioRxiv2021

The spatial transcriptomic landscape of the healing intestine following damage

The intestinal barrier is composed of a complex cell network defining highly compartmentalized and specialized structures. Here, we use spatial transcriptomics (ST) to define how the transcriptomic landscape is spatially organized in the steady state and healing murine colon. At steady state conditions, we demonstrate a previously unappreciated molecular regionalization of the colon, which dramatically changes during mucosal healing. Here, we identified spatially-organized transcriptional programs defining compartmentalized mucosal healing, and regions with dominant wired pathways. Furthermore, we showed that decreased p53 activation defined areas with increased presence of proliferating epithelial stem cells. Finally, we used our resource to map transcriptomics modules associated with human diseases demonstrating that ST can be used to inform clinical practice. Overall, we provide a publicly available resource defining principles of transcriptomic regionalization of the colon during mucosal healing and a framework to develop and progress further hypotheses.

systems biology↗

bioRxiv2021

Conditional deletion of HIF-1α provides new insight regarding the murine response to gastrointestinal infection with Salmonella Typhimurium

The hypoxia-inducible transcription factor 1 (HIF-1) has been shown to ameliorate different bacterial infections through enhancement of microbial killing. While the impact of HIF-1 on inflammatory diseases of the gut has been studied intensively, its function in bacterial infections of the intestine remains largely elusive. With the help of a publicly available gene expression data set, we could infer significant activation of the HIF-1 transcription factor after oral infection of mice with Salmonella Typhimurium. This prompted us to apply lineage-restricted deletion of the Hif1a locus in mice to examine cell type-specific functions of HIF-1 in this model. We show hypoxia-independent induction of HIF-1 activity upon Salmonella infection in the intestinal epithelium as well as in macrophages. Surprisingly, Hif1a deletion in intestinal epithelial cells impacted neither disease outcome nor inflammatory activity. The conditional knockout of Hif1a in myeloid cells enhanced the mRNA expression of the largely pro-inflammatory chemokine Cxcl2, revealing a potentially inflammatory effect of HIF-1 deficiency in myeloid cells in the gut in vivo. Again, the disease outcome was not affected. In vitro HIF-1-deficient macrophages showed an overall impaired transcription of pro-inflammatory factors, however, Salmonella bypassed direct intracellular, bactericidal HIF-1-dependent mechanisms in a Salmonella pathogenicity island (SPI)-2 independent manner. Taken together, our data suggest that HIF-1 in intestinal epithelial and myeloid cells is either dispensable or compensable in the immune defense against Salmonella Typhimurium.

microbiology↗

bioRxiv2020

DreamAI: algorithm for the imputation of proteomics data

Deep proteomics profiling using labeled LC-MS/MS experiments has been proven to be powerful to study complex diseases. However, due to the dynamic nature of the discovery mass spectrometry, the generated data contain a substantial fraction of missing values. This poses great challenges for data analyses, as many tools, especially those for high dimensional data, cannot deal with missing values directly. To address this problem, the NCI-CPTAC Proteogenomics DREAM Challenge was carried out to develop effective imputation algorithms for labeled LC-MS/MS proteomics data through crowd learning. The final resulting algorithm, DreamAI, is based on an ensemble of six different imputation methods. The imputation accuracy of DreamAI, as measured by Pearson correlation, is about 15%-50% greater than existing tools among less abundant proteins, which are more vulnerable to be missed in proteomics data sets. This new tool notably enhances data analysis capabilities in proteomics research.

bioinformatics↗