Smad4 and Trim33/Tif1γ redundantly regulate neural stem cells in the developing cortex.

During central nervous system (CNS) development, proliferation and differentiation of neural stem cells (NSCs) have to be regulated in a spatio-temporal fashion. Here, we report different branches of the transforming growth factor ß (TGFß) signaling pathway to be required for the brain area-specific control of NSCs. In the midbrain, canonical TGFß signaling via Smad4 regulates the balance between proliferation and differentiation of NSCs. Accordingly, Smad4 deletion resulted in horizontal expansion of NSCs due to increased proliferation, decreased differentiation, and decreased cell cycle exit. In the developing cortex, however, ablation of Smad4 alone did not have any effect on proliferation and differentiation of NSCs. In contrast, concomitant mutation of both Smad4 and Trim33 led to an increase in proliferative cells in the ventricular zone due to decreased cell cycle exit, revealing a functional redundancy of Smad4 and Trim33. Furthermore, in Smad4-Trim33 double mutant embryos, cortical NSCs generated an excess of deep layer neurons concurrent with a delayed and reduced production of upper layer neurons and, in addition, failed to undergo the neurogenic to gliogenic switch at the right developmental stage. Thus, our data disclose that in different regions of the developing CNS different aspects of the TGFß signaling pathway are required to ensure proper development.

Initial type and abundance of cyanobacteria determine morphotype development of phototrophic ecosystems.

Phototrophic aggregates containing filamentous cyanobacteria occur naturally, for example, as cryoconite on glaciers and microbialites in fresh or marine waters, but their formation is not fully understood. Laboratory models are now available to reproduce aggregation, that is, the formation of different morphotypes like hemispheroids, microbial mats or sphere-like aggregates we call photogranules. In the model, activated sludge as starting matrix is transformed into aggregates enclosed by a phototrophic layer of growing cyanobacteria. These cyanobacteria were either enriched from the matrix or we added them intentionally. We hypothesize that the resulting morphotype depends on the type and concentration of the added cyanobacteria. When cyanobacteria from mature photogranules were added to activated sludge, photogranulation was not observed, but microbial mats were formed. Photogranulation of sludge could be promoted when adding sufficient quantities of cyanobacterial strains that form clumps when grown as isolates. The cyanobacteria putatively responsible for photogranulation were undetectable or only present in low abundance in the final communities of photogranules, which were always dominated by mat-forming cyanobacteria. We suggest that, in a temporal succession, the ecosystem engineer initiating photogranulation eventually disappears, leaving behind its structural legacy. We conclude that understanding phototrophic aggregate formation requires considering the initial succession stages of the ecosystem development.

Simple Time-lapse Imaging for Quantifying the Hydrostatic Production of Oxygenic Photogranules.

Oxygenic photogranules (OPGs) are dense, three-dimensional aggregates containing a syntrophic, light-driven microbial community. Their temporal and spatial development interests microbial ecologists working at the bioprocess engineering interface, as this knowledge can be used to optimize biotechnological applications, such as wastewater treatment and biomass valorization. The method presented here enables the high-throughput quantification of photogranulation. OPGs are produced from a loose sludge-like microbial matrix in hydrostatic batch cultures exposed to light. This matrix transforms into a consolidated, roughly spherical aggregate over time. Photogranulation is quantified by time-lapse imaging coupled to automated image analysis. This allows studying the development of many OPGs simultaneously and in a fully automated way to systematically test what factors drive photogranulation. The protocol can also be used to quantify other types of (a)biotic aggregation.

Quantitative proteomics analysis identifies MUC1 as an effect sensor of EGFR inhibition.

Tumor responses to cancer therapeutics are generally monitored every 2-3 months based on changes in tumor size. Dynamic biomarkers that reflect effective engagement of targeted therapeutics to the targeted pathway, so-called "effect sensors", would fulfill a need for non-invasive, drug-specific indicators of early treatment effect. Using a proteomics approach to identify effect sensors, we demonstrated MUC1 upregulation in response to epidermal growth factor receptor (EGFR)-targeting treatments in breast and lung cancer models. To achieve this, using semi-quantitative mass spectrometry, we found MUC1 to be significantly and durably upregulated in response to erlotinib, an EGFR-targeting treatment. MUC1 upregulation was regulated transcriptionally, involving PI3K-signaling and STAT3. We validated these results in erlotinib-sensitive human breast and non-small lung cancer cell lines. Importantly, erlotinib treatment of mice bearing SUM149 xenografts resulted in increased MUC1 shedding into plasma. Analysis of MUC1 using serial blood sampling may therefore be a new, relatively non-invasive tool to monitor early and drug-specific effects of EGFR-targeting therapeutics.