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Babenko, I.

Publications and source records attributed to Babenko, I..

2 recordsLinked to original sources

Mechanism of branching morphogenesis inspired by diatom silica formation

The silica-based cell walls of diatoms are prime examples of genetically controlled, species-specific mineral architectures. The physical principles underlying morphogenesis of their hierarchically structured silica patterns are not understood, yet such insight could indicate novel routes towards synthesizing functional inorganic materials. Recent advances in imaging nascent diatom silica allow rationalizing possible mechanisms of their pattern formation. Here, we combine theory and experiments on the model diatom Thalassiosira pseudonana to put forward a minimal model of branched rib patterns - a fundamental feature of the silica cell wall. We quantitatively recapitulate the time-course of rib pattern morphogenesis by accounting for silica biochemistry with autocatalytic formation of diffusible silica precursors followed by conversion into solid silica. We propose that silica deposition releases an inhibitor that slows down up-stream precursor conversion, thereby implementing a self-replicating reaction-diffusion system featuring a non-classical Turing mechanism. The proposed mechanism highlights the role of geometrical cues for guided self-organization, rationalizing the instructive role for the single initial pattern seed known as primary silicification site. The mechanism of branching morphogenesis that we characterize here is possibly generic and may apply also in other biological systems. Significance statementThe formation of minerals by living organisms is a widespread biological phenomenon occurring throughout the evolutionary tree-of-life. The silica-based cell walls of diatom microalgae are impressive examples featuring intricate architectures and outstanding materials properties that still defy their reconstitution in vitro. Here, we developed a minimal mathematical model that explains the formation of branched patterns of silica ribs, providing unprecedented understanding of basic physico-chemical processes capable of guiding silica morphogenesis in diatoms. The generic mechanism of branching morphogenesis identified here could provide recipes for bottom-up synthesis of mineral-nanowire networks for technological applications. Moreover, similar mechanisms may apply in the biological morphogenesis of other branched structures, like corals, bacterial colonies, tracheal networks, fungal plexuses, or the vascular system.

biophysics↗

MultiomicsTracks96: A high throughput PIXUL-Matrix-based toolbox to profile frozen and FFPE tissues multiomes

BackgroundThe multiome is an integrated assembly of distinct classes of molecules and molecular properties, or "omes," measured in the same biospecimen. Freezing and formalin-fixed paraffin-embedding (FFPE) are two common ways to store tissues, and these practices have generated vast biospecimen repositories. However, these biospecimens have been underutilized for multi-omic analysis due to the low throughput of current analytical technologies that impede large-scale studies. MethodsTissue sampling, preparation, and downstream analysis were integrated into a 96-well format multi-omics workflow, MultiomicsTracks96. Frozen mouse organs were sampled using the CryoGrid system, and matched FFPE samples were processed using a microtome. The 96-well format sonicator, PIXUL, was adapted to extract DNA, RNA, chromatin, and protein from tissues. The 96-well format analytical platform, Matrix, was used for chromatin immunoprecipitation (ChIP), methylated DNA immunoprecipitation (MeDIP), methylated RNA immunoprecipitation (MeRIP), and RNA reverse transcription (RT) assays followed by qPCR and sequencing. LC-MS/MS was used for protein analysis. The Segway genome segmentation algorithm was used to identify functional genomic regions, and linear regressors based on the multi-omics data were trained to predict protein expression. ResultsMultiomicsTracks96 was used to generate 8-dimensional datasets including RNA-seq measurements of mRNA expression; MeRIP-seq measurements of m6A and m5C; ChIP-seq measurements of H3K27Ac, H3K4m3, and Pol II; MeDIP-seq measurements of 5mC; and LC-MS/MS measurements of proteins. We observed high correlation between data from matched frozen and FFPE organs. The Segway genome segmentation algorithm applied to epigenomic profiles (ChIP-seq: H3K27Ac, H3K4m3, Pol II; MeDIP-seq: 5mC) was able to recapitulate and predict organ-specific super-enhancers in both FFPE and frozen samples. Linear regression analysis showed that proteomic expression profiles can be more accurately predicted by the full suite of multi-omics data, compared to using epigenomic, transcriptomic, or epitranscriptomic measurements individually. ConclusionsThe MultiomicsTracks96 workflow is well suited for high dimensional multi-omics studies - for instance, multiorgan animal models of disease, drug toxicities, environmental exposure, and aging as well as large-scale clinical investigations involving the use of biospecimens from existing tissue repositories.

systems biology↗