Biology subjects

Santos, F.

Publications and source records attributed to Santos, F..

3 recordsLinked to original sources

bioRxiv2019

A single-cell transcriptomics CRISPR-activation screen identifies new epigenetic regulators of zygotic genome activation

Zygotic genome activation (ZGA) is a crucial developmental milestone that remains poorly understood. This first essential transcriptional event in embryonic development coincides with extensive epigenetic reprogramming processes and is orchestrated, in part, by the interplay of transcriptional and epigenetic regulators. Here, we developed a novel high-throughput screening method that combines pooled CRISPR-activation (CRISPRa) with single-cell transcriptomics to systematically probe candidate regulators of ZGA. We screened 230 epigenetic and transcriptional regulators by upregulating their expression with CRISPRa in mouse embryonic stem cells (ESCs). Through single-cell RNA-sequencing (scRNA-seq) of CRISPRa-perturbed cells, we generated approximately 200,000 single-cell transcriptomes, each transduced with a unique short-guide RNA (sgRNA) targeting a specific candidate gene promoter. Using integrative dimensionality reduction of the perturbation scRNA-seq profiles, we characterized molecular signatures of ZGA and uncovered 44 factors that promote a ZGA-like response in ESCs, both in the coding and non-coding transcriptome. Upon upregulation of these factors, including the DNA binding protein Dppa2, the chromatin remodeller Smarca5 and the transcription factor Patz1, ESCs adopt an early embryonic-like state. Supporting their roles as ZGA regulators, Dppa2 and Smarca5 knock-out ESCs lose expression of ZGA genes, however, overexpression of Dppa2 in Smarca5 knock-out ESCs, but not vice versa, rescues ZGA-like expression, suggesting that Smarca5 regulates ZGA upstream and via Dppa2. Together, our single-cell transcriptomic profiling of CRISPRa-perturbed cells provides comprehensive system-level insights into the molecular mechanisms that orchestrate ZGA.\n\nHighlightsO_LIFirst large-scale screen combining pooled CRISPRa with scRNA-seq.\nC_LIO_LIMulti-omics factor analysis identifies a ZGA-like signature for 44 of the candidate regulators.\nC_LIO_LIDppa2, Smarca5 and Patz1 were validated as strong inducers of ZGA gene expression.\nC_LIO_LISmarca5 regulates zygotic genome activation in a Dppa2-dependent manner.\nC_LI

developmental biology↗

bioRxiv2019

A metabolic reconstruction of Lactobacillus reuteri JCM 1112 and analysis of its potential as a cell factory

BackgroundLactobacillus reuteri is a heterofermentative Lactic Acid Bacterium (LAB) that is commonly used for food fermentations and probiotic purposes. Due to its robust properties, it is also increasingly considered for use as a cell factory. It produces several industrially important compounds such as 1,3-propanediol and reuterin natively, but for cell factory purposes, developing improved strategies for engineering and fermentation optimization is crucial. Genome-scale metabolic models can be highly beneficial in guiding rational metabolic engineering. Reconstructing a reliable and a quantitatively accurate metabolic model requires extensive manual curation and incorporation of experimental data.\n\nResultsA genome-scale metabolic model of L. reuteri JCM 1112T was reconstructed and the resulting model, Lreuteri_530, was validated and tested with experimental data. Several knowledge gaps in the metabolism were identified and resolved during this process, including presence/absence of glycolytic genes. Flux distribution between the two glycolytic pathways, the phosphoketolase and Embden-Meyerhof-Parnas pathways, varies considerably between LAB species and strains. As these pathways result in different energy yields, it is important to include strain-specific utilization of these pathways in the model. We determined experimentally that the Embden-Meyerhof-Parnas pathway carried at most 7% of the total glycolytic flux. Predicted growth rates from Lreuteri_530 were in good agreement with experimentally determined values. To further validate the prediction accuracy of Lreuteri_530, the predicted effects of glycerol addition and adhE gene knock-out, which results in impaired ethanol production, were compared to in vivo data. Examination of both growth rates and uptake- and secretion rates of the main metabolites in central metabolism demonstrated that the model was able to accurately predict the experimentally observed effects. Lastly, the potential of L. reuteri as a cell factory was investigated, resulting in a number of general metabolic engineering strategies.\n\nConclusionWe have constructed a manually curated genome-scale metabolic model of L. reuteri JCM 1112T that has been experimentally parameterized and validated and can accurately predict metabolic behavior of this important platform cell factory.

systems biology↗

bioRxiv2019

Fetus-derived IGF2 matches placental development to fetal demand

In all eutherian mammals, growth of the fetus is dependent upon a functional placenta, but whether and how the latter adapts to putative fetal signals is currently unknown. Here we demonstrate, through fetal, endothelial, hematopoietic and trophoblast-specific genetic manipulations in the mouse, that endothelial and fetus-derived IGF2 is required for the continuous expansion of the feto-placental microvasculature in late pregnancy. The effects of IGF2 on placental microvasculature expansion are mediated, in part, through IGF2R and angiopoietin-Tie2/TEK signalling. Additionally, IGF2 exerts IGF2R-ERK1/2-dependent pro-proliferative and angiogenic effects on primary feto-placental endothelial cells ex vivo. Endothelial and fetus-derived IGF2 also plays an important role in trophoblast morphogenesis, acting through Gcm1 and Synb. Thus, our study reveals a direct role for the imprinted Igf2-Igf2r axis on matching placental development to fetal growth and establishes the principle that hormone-like signals from the fetus play important roles in controlling placental microvasculature and trophoblast morphogenesis.

developmental biology↗