Publications

The transition from an autotrophic to a heterotrophic lifestyle is associated with numerous genomic changes. These often involve large genomic alterations, potentially driven by repetitive DNAs. Despite their recognized role in shaping plant genomes, the contribution of repetitive DNAs to parasitic plant genome evolution remains largely unexplored. This study presents the first analysis of repetitive DNAs in Hydnoraceae genomes, a plant family whose members are holoparasitic. Repetitive DNAs were identified and annotated de novo. Abundant transposable elements and 35S ribosomal DNA in the Hydnora visseri genome were reconstructed in silico. Their patterns of abundance and presence-absence were individually and comparatively analyzed. Both Hydnoraceae genera, Hydnora and Prosopanche, exhibit distinct repeatome profiles which challenge our current understanding of repeatome and rDNA evolution. The Hydnora genomes are dominated by long terminal repeat retrotransposons, while the Prosopanche genomes vary greatly in their repeat composition: Prosopanche bonacinae with a highly abundant single DNA transposon and Prosopanche panguanensis with over 15% 5S rDNA, compared to ≤ 0.1% in the Hydnora genomes. The repeat profiles align with the phylogeny, geographical distribution, and host shifts of the Hydnoraceae, indicating a potential role of repetitive DNAs in shaping Hydnoraceae genomes to adapt to the parasitic lifestyle.

Alpha-synuclein (αSyn) is a major player in the pathophysiology of synucleinopathies, which include Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. To date, there is no disease-modifying therapy available for these synucleinopathies. Furthermore, the intracellular mechanisms by which αSyn confers toxicity are not yet fully understood. Therefore, it is of utmost importance to investigate the pathophysiology of αSyn-induced toxicity in order to identify novel molecular targets for the development of disease-modifying therapies.

Long-range morphogen gradients have been proposed to form by morphogen diffusion from a localized source to distributed sinks in the target tissue. The role of the complex tissue geometry in this process is, however, less well understood and has not been explicitly resolved in existing models. Here, we numerically reconstruct pore-scale 3D geometries of zebrafish epiboly from light-sheet microscopy volumes. In these high-resolution 3D geometries, we simulate Fgf8a gradient formation in the tortuous extracellular space. Our simulations show that when realistic embryo geometries are considered, a source-diffusion-degradation mechanism with additional binding to extracellular matrix polymers is sufficient to explain self-organized emergence and robust maintenance of Fgf8a gradients. The predicted normalized gradient is robust against changes in source and sink rates but sensitive to changes in the pore connectivity of the extracellular space, with lower connectivity leading to steeper and shorter gradients. This demonstrates the importance of considering realistic geometries when studying morphogen gradients.

The observation of Liquid-Liquid Phase Separation (LLPS) in biological cells has dramatically shifted the paradigm that soluble proteins are uniformly dispersed in the cytoplasm or nucleoplasm. The LLPS region is preceded by a one-phase solution, where recent experiments have identified clusters in an aqueous solution with 102-103 proteins. Here, we theoretically consider a core-shell model with mesoscale core, surface, and bending properties of the clusters' shell and contrast two experimental paradigms for the measured cluster size distributions of the Cytoplasmic Polyadenylation Element Binding-4 (CPEB4) and Fused in Sarcoma (FUS) proteins. The fits to the theoretical model and earlier electron paramagnetic resonance (EPR) experiments suggest that the same protein may exhibit hydrophilic, hydrophobic, and amphiphilic conformations, which act to stabilize the clusters. We find that CPEB4 clusters are much more stable compared to FUS clusters, which are less energetically favorable. This suggests that in CPEB4, LLPS consists of large-scale aggregates of clusters, while for FUS, clusters coalesce to form micron-scale LLPS domains.

It is shown that regularizing the signal gradient statistics during training of deep-learning models of super-resolution fluorescence microscopy improves the generated images. Specifically, regularizing the images in the training data set is proposed to have gradient and Laplacian statistics closer to those expected for natural-scene images. The BioSR data set of matched pairs of diffraction-limited and super-resolution images is used to evaluate the proposed regularization in a state-of-the-art generative deep-learning model of super-resolution microscopy, the Conditional Variational Diffusion Model (CVDM). Since the proposed regularization is applied as a preprocessing step to the training data, it can be used in conjunction with any supervised machine-learning model. However, its utility is limited to images for which the prior is appropriate, which in the BioSR data set are the images of filamentous structures. The quality and generalization power of CVDM trained with and without the proposed regularization are compared, showing that the new prior yields images with clearer visual detail and better small-scale structure.

Prolonged hospital waiting times are linked with increased patient mortality and cause additional financial burdens on institutions. Efficient point-of-care diagnosis would help alleviate this, but is hampered by a lack of cost-effective devices capable of rapid, in situ, wide ranging analyte detection. Lab-on-fiber technology provides an answer allowing for diagnosis, treatment, and monitoring in situ with real time feedback. Here, a device is demonstrated that harnesses motor-protein-driven-microtubule molecular detection assays to optical fibers. By developing a new method for microscope-free microtubule gliding speed determination, proof of concept is demonstrated in the detection of Monomeric Streptavidin and Neutravidin, which initiate a decrease in speed in biotinylated microtubules as well as bundling in the latter case. Utilizing antibody functionalizsed microtubules label-free and microscope-free detection of the heart attack marker Creatine Kinase-MB, as well secondary antibodies in nm concentration is demonstrated. This detector has the potential to be used in situ, providing rapid, low-cost, multiplex analyte screening and detection.

Motivated by the question of how biological systems maintain homeostasis in changing environments, Shinar and Feinberg introduced in 2010 the concept of absolute concentration robustness (ACR). A biochemical system exhibits ACR in some species if the steady-state value of that species does not depend on initial conditions. Thus, a system with ACR can maintain a constant level of one species even as the initial condition changes. Despite a great deal of interest in ACR in recent years, the following basic question remains open: How can we determine quickly whether a given biochemical system has ACR? Although various approaches to this problem have been proposed, we show that they are incomplete. Accordingly, we present new methods for deciding ACR, which harness computational algebra. We illustrate our results on several biochemical signaling networks.

Modelling liver disease requires in vitro systems that replicate disease progression1,2. Current tissue-derived organoids fail to reproduce the complex cellular composition and tissue architecture observed in vivo3. Here, we describe a multicellular organoid system composed of adult hepatocytes, cholangiocytes and mesenchymal cells that recapitulates the architecture of the liver periportal region and, when manipulated, models aspects of cholestatic injury and biliary fibrosis. We first generate reproducible hepatocyte organoids with functional bile canaliculi network that retain morphological features of in vivo tissue. By combining these with cholangiocytes and portal fibroblasts, we generate assembloids that mimic the cellular interactions of the periportal region. Assembloids are functional, consistently draining bile from bile canaliculi into the bile duct. Strikingly, manipulating the relative number of portal mesenchymal cells is sufficient to induce a fibrotic-like state, independently of an immune compartment. By generating chimeric assembloids of mutant and wild-type cells, or after gene knockdown, we show proof-of-concept that our system is amenable to investigating gene function and cell-autonomous mechanisms. Taken together, we demonstrate that liver assembloids represent a suitable in vitro system to study bile canaliculi formation, bile drainage, and how different cell types contribute to cholestatic disease and biliary fibrosis, in an all-in-one model.

Whereas alignment has been fundamental to sequence-based assessments of protein homology, it is ineffective for intrinsically disordered regions (IDRs) due to their lowered sequence conservation and unique sequence properties. Here, we present a web server implementation of SHARK (bio-shark.org), an alignment-free algorithm for homology classification that compares the overall amino acid composition and short regions (k-mers) shared between sequences (SHARK-scores). The output of such k-mer-based comparisons is used by SHARK-dive, a machine learning classifier to detect homology between unalignable, disordered sequences. SHARK-web provides sequence-versus-database assessment of protein sequence homology akin to conventional tools such as BLAST and HMMER. Additionally, we provide precomputed sets of IDR sequences from 16 model organism proteomes facilitating searches against species-specific IDR-omes. SHARK-dive offers superior overall homology detection performance to BLAST and HMMER, driven by a large increase in sensitivity to low sequence identity homologs, and can be used to facilitate the study of sequence-function relationships in disordered, difficult-to-align regions.