Printing Green: Microalgae-Based Materials for 3D Printing with Light.

Microalgae have emerged as sustainable feedstocks due to their ability to fix CO2 during cultivation, rapid growth rates, and capability to produce a wide variety of metabolites. Several microalgae accumulate lipids in high concentrations, especially triglycerides, along with lipid-soluble, photoactive pigments such as chlorophylls and derivatives. Microalgae-derived triglycerides contain longer fatty acid chains with more double bonds on average than vegetable oils, allowing a higher degree of post-functionalization. Consequently, they are especially suitable as precursors for materials that can be used in 3D printing with light. This work presents the use of microalgae as "biofactories" to generate materials that can be further 3D printed in high resolution. Two taxonomically different strains -Odontella aurita (O. aurita, BEA0921B) and Tetraselmis striata (T. striata, BEA1102B)- are identified as suitable microalgae for this purpose. The extracts obtained from the microalgae (mainly triglycerides with chlorophyll derivatives) are functionalized with photopolymerizable groups and used directly as printable materials (inks) without the need for additional photoinitiators. The fabrication of complex 3D microstructures with sub-micron resolution is demonstrated. Notably, the 3D printed materials show biocompatibility. These findings open new possibilities for the next generation of sustainable, biobased, and biocompatible materials with great potential in life science applications.

Geometrically defined environments direct cell division rate and subcellular YAP localization in single mouse embryonic stem cells.

Mechanotransduction via yes-associated protein (YAP) is a central mechanism for decision-making in mouse embryonic stem cells (mESCs). Nuclear localization of YAP is tightly connected to pluripotency and increases the cell division rate (CDR). How the geometry of the extracellular environment influences mechanotransduction, thereby YAP localization, and decision-making of single isolated mESCs is largely unknown. To investigate this relation, we produced well-defined 2D and 2.5D microenvironments and monitored CDR and subcellular YAP localization in single mESCs hence excluding cell-cell interactions. By systematically varying size and shape of the 2D and 2.5D substrates we observed that the geometry of the growth environment affects the CDR. Whereas CDR increases with increasing adhesive area in 2D, CDR is highest in small 2.5D micro-wells. Here, mESCs attach to all four walls and exhibit a cross-shaped cell and nuclear morphology. This observation indicates that changes in cell shape are linked to a high CDR. Inhibition of actomyosin activity abrogate these effects. Correspondingly, nuclear YAP localization decreases in inhibitor treated cells, suggesting a relation between cell shape, intracellular forces, and cell division rate. The simplicity of our system guarantees high standardization and reproducibility for monitoring stem cell reactions and allows addressing a variety of fundamental biological questions on a single cell level.

Vector distribution and transmission risk of the Zika virus in South and Central America.

BACKGROUND: Zika is of great medical relevance due to its rapid geographical spread in 2015 and 2016 in South America and its serious implications, for example, certain birth defects. Recent epidemics urgently require a better understanding of geographic patterns of the Zika virus transmission risk. This study aims to map the Zika virus transmission risk in South and Central America. We applied the maximum entropy approach, which is common for species distribution modelling, but is now also widely in use for estimating the geographical distribution of infectious diseases. METHODS: As predictor variables we used a set of variables considered to be potential drivers of both direct and indirect effects on the emergence of Zika. Specifically, we considered (a) the modelled habitat suitability for the two main vector species Aedes aegypti and Ae. albopictus as a proxy of vector species distributions; (b) temperature, as it has a great influence on virus transmission; (c) commonly called evidence consensus maps (ECM) of human Zika virus infections on a regional scale as a proxy for virus distribution; (d) ECM of human dengue virus infections and, (e) as possibly relevant socio-economic factors, population density and the gross domestic product. RESULTS: The highest values for the Zika transmission risk were modelled for the eastern coast of Brazil as well as in Central America, moderate values for the Amazon basin and low values for southern parts of South America. The following countries were modelled to be particularly affected: Brazil, Colombia, Cuba, Dominican Republic, El Salvador, Guatemala, Haiti, Honduras, Jamaica, Mexico, Puerto Rico and Venezuela. While modelled vector habitat suitability as predictor variable showed the highest contribution to the transmission risk model, temperature of the warmest quarter contributed only comparatively little. Areas with optimal temperature conditions for virus transmission overlapped only little with areas of suitable habitat conditions for the two main vector species. Instead, areas with the highest transmission risk were characterised as areas with temperatures below the optimum of the virus, but high habitat suitability modelled for the two main vector species. CONCLUSION: Modelling approaches can help estimating the spatial and temporal dynamics of a disease. We focused on the key drivers relevant in the Zika transmission cycle (vector, pathogen, and hosts) and integrated each single component into the model. Despite the uncertainties generally associated with modelling, the approach applied in this study can be used as a tool and assist decision making and managing the spread of Zika.