Biology subjects

Walther, A.

Publications and source records attributed to Walther, A..

12 recordsLinked to original sources

bioRxiv2026

RNA Organelles in DNA-based Artificial Cells Provide Spatial Aptamer Functions and Enhanced Signal Processing

Naturally occurring biomolecular condensates orchestrate key cellular processes by creating spatially distinct reaction environments, yet engineering synthetic condensates that combine structural programmability with spatioselectively encoded function remains challenging. Here we report multiphase DNA-RNA artificial cells (ACs) that embed functional RNA condensates as organelle-like compartments within programmable DNA core-shell ACs. A single thermal assembly protocol yields three-phase ACs comprising a glassy RNA core organelle embedded in a liquid-like DNA compartment, surrounded by a crosslinked DNA shell. The RNA organelles contain aptamer function, enabling selective protein recruitment and small-molecule activation, while the DNA scaffold provides independent addressability, regulates RNA-condensate size and enhances resistance to serum-mediated degradation. We further show that RNA chemistry can be used to adjust environmental responsiveness: unmodified RNA organelles undergo rapid degradation in serum and release captured protein cargo, whereas 2'-fluoro-modified RNA organelles remain stable for at least 24 h. Finally, by coupling transcriptional modules localized in the DNA core to cell-free protein translation in the surrounding medium, we establish sender-receiver communication between AC populations and self-actuating signal processing within individual DNA-RNA ACs. These results establish hybrid nucleic-acid ACs as programmable, spatially organized systems that couple compartment architecture, RNA molecular recognition and biochemical communication. TOC Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/733869v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@23ee9org.highwire.dtl.DTLVardef@1706336org.highwire.dtl.DTLVardef@181a241org.highwire.dtl.DTLVardef@1c374d8_HPS_FORMAT_FIGEXP M_FIG C_FIG Multiphase DNA-RNA artificial cells integrate a protective DNA shell, a transcriptionally active DNA core, and a functional RNA organelle. Spatial compartmentalization enables signal generation, external protein expression, and selective recapture via RNA aptamers.

Synthetic Biology↗

bioRxiv2026

DNA Protonuclei as Programmable Nuclear Mimics Reveal Environmental Context on Protein Phase Separation

Understanding protein phase separation in cellular environments remains a major challenge, as ex vivo assays often fail to capture the influence of environmental context - such as crowding, multimodal interactions, and the dynamic properties of the cytosol or nucleus. Here, we introduce programmable DNA-based protonuclei (PN) as nucleus-inspired compartments to probe phase separation of the neurodegeneration-linked protein FUS. We show that FUS partitioning and condensate formation are highly sensitive to nucleic acid sequence, spatial confinement, and viscoelastic properties of the PN core. Notably, classical test-tube affinity assays fail to predict protein behavior within the crowded and multivalent PN environment. By tuning DNA crosslinking, we modulate condensate dynamics and suppress liquid-to-solid transitions of FUS - a hallmark of disease. These findings demonstrate that multivalent, confined environments fundamentally reshape protein-nucleic acid interactions and phase behavior. The PN platform complements test-tube assays and complex cellular settings and enables to dissect nuclear condensates under controllable conditions.

synthetic biology↗

bioRxiv2025

Stable Synthetic Organelles from Aqueous Two-Phase Systemswith Access to the Cell Translation Machinery

Cells compartmentalize vital processes in membrane-less organelles to gain spatiotemporal control of metabolism, signaling, and for protection under stress. While such compartments can be manipulated or even de novo designed with genetic engineering of cells, the transfer and operation of exogenous synthetic compartments for intracellular engineering is challenged by developing pathways for implantation into the cell, stability issues, toxicity, ability to maintain compartmentalization, and access to cellular machinery. Here we introduce dextran-lipid droplets as versatile exogenous synthetic compartments, that are readily uptaken by model cancer and immune cells and are stable inside cells for days. Furthermore, the droplets can encapsulate nucleic acids with high efficiency, are non-toxic and endosomal escape occurs when formulated with ionizable lipids, as shown by reporter protein translation of an mRNA hosted inside the organelles. We propose that such synthetic microreactors implanted into the cells will become an important bioengineering tool to incorporate more complex and bioorthogonal molecular systems into cells.

synthetic biology↗

bioRxiv2025

ATP-Powered Signaling between Synthetic and Living Cells

ATP is the energy currency of life and overabundant in the tumor microenvironment, where it has been suggested as a target for cancer therapy. We introduce ATP-dissipative delivery of DNA signals from synthetic cells to living cells by exploiting an ATP-driven reaction network that transiently ejects DNA Signal strands from the shielded synthetic cell interior to the extracellular medium of living cells. We customize the Signal for intracellular uptake or for extracellular instruction using a cytokine-ssDNA chimera that can trigger efficient intracellular downstream signaling programs. Our study discusses details of system design on a timer circuit and synthetic cell level, system integration challenges, and how ATP concentrations regulate the transient delivery. The strategy can be extended to deliver therapeutic oligonucleotides for applications in gene therapy and gene silencing. For cancer therapy, it can use naturally enhanced ATP levels to induce selective delivery of therapeutic oligonucleotides.

synthetic biology↗

bioRxiv2025

Synthetic Aptamer Mechanoreceptors Enable Cell-SpecificForce Sensing and Temporal Control via DNA Circuits

Cells interpret mechanical cues from their microenvironment with spatiotemporal precision to guide adaptive behaviors. However, engineering synthetic mechanosensing systems with both cell-specificity and programmability remains challenging, especially when targeting ubiquitous classical mechanoreceptors. Here, we introduce an all-DNA mechanosensing platform based on aptamers that transmit force through noncanonical surface receptors. Aptamer-receptor recognition acts as a molecular gate for force transduction, enabling the design of mechanoprobes with cell-type selectivity. These probes interpret diverse mechanical inputs via distinct mechanisms, including actomyosin-driven contractility and membrane ruffling during macropinocytosis. By integrating aptamer mechanoprobes with upstream DNA reaction networks, we achieve reversible and temporally programmable mechanoresponses. This modular, all-nucleic-acid system offers a general framework for constructing tunable mechanotransduction circuits. It expands the design space for synthetic mechanobiology and provides new opportunities for autonomous, multi-layered mechanical-biochemical regulation in tissue engineering, morphogenesis, and dynamic cell programming.

synthetic biology↗

bioRxiv2025

DNA Flipping as Facile Mechanism for Transmembrane Signaling in Synthetic Cells

Transmembrane signaling is essential for cellular communication, yet reconstituting such mechanisms in synthetic systems remains challenging. Here, we report a simple and robust DNA-based mechanism for transmembrane signaling in synthetic cells using cholesterol-modified single-stranded DNA (Chol-ssDNA). We discovered that anchored Chol-ssDNA spontaneously flips across the membrane of giant unilamellar lipid vesicles (GUVs) in a nucleation-driven, defect-mediated process. This flipping enables internal signal processing through hybridization with encapsulated complementary DNA and activation of downstream processes such as RNA transcription. The phenomenon shows a high transduction efficiency, is generic across DNA sequences and lipid compositions, and can be enhanced by glycerol, which modulates membrane dynamics. Mechanistic insights using fluorescence microscopy, nuclease degradation assays, and membrane permeability assays reveal that flipping is dominated by transient membrane pores. Leveraging this facile translocation process, we demonstrate selective transcriptional activation inside synthetic cells, underscoring the potential of Chol-ssDNA flipping as a programmable tool for synthetic biology and bottom-up synthetic cell design.

synthetic biology↗

bioRxiv2024

Deep Learning Reaction Network: a machine learning framework for modeling time resolved data

Model-based analysis is essential for extracting information about chemical reaction kinetics in full detail from time-resolved data sets. Such analysis combines experimental hypotheses of the process with mathematical models related to the systems physical mechanisms. This combination can provide a concise description of complex system dynamics and extrapolate kinetic model parameters, such as kinetic pathways, time constants, and species amplitudes. However, the process leading to the final kinetic model requires several intermediate steps in which different assumptions and models are tested, even using different experimental data sets. This approach requires considerable experience in modeling and data comprehension, as poor decisions at any stage of time-resolved data analysis (such as time-resolved spectra and agarose gel electrophoresis) can lead to an incorrect or incomplete kinetic model, resulting in inaccurate model parameters and amplitudes. The Deep Learning Reaction Network (DLRN) can rapidly provide a kinetic reaction network, time constants, and amplitude for the system, with comparable performance and, in part, even better than a classical fitting analysis. Additionally, DLRN works in scenarios in which the initial state is a non-emitting dark state and for multiple timescales. The utility of DLRN is also shown for more than one 2D system, as it performed well for both spectral and time-resolved agarose gel electrophoresis data.

biophysics↗

bioRxiv2024

Nuclease-Resistant L-DNA Tension Probes Enable Long-TermForce Mapping of Single Cells and Cell Consortia

DNA-based tension probes with precisely programmable force response provide important insights into cellular mechanosensing. However, their degradability in cell culture limits their use for long-term imaging, for instance, when cells migrate, divide, and differentiate. This is a critical limitation for providing insights into mechanobiology for these longer-term processes. Here, we present DNA-based tension probes that are entirely designed based on the stereoisomer of biological D-DNA, i.e., L-DNA. We demonstrate that L-DNA tension probes are essentially indestructible by nucleases and provide days-long imaging without significant loss in image quality. We also show their superiority already for short imaging times commonly used for classical D-DNA tension probes. We showcase the potential of these resilient probes to image minute movements, and for generating long term force maps of single cells and for the first time, of collectively migrating cell populations.

biophysics↗

bioRxiv2020

Radiotherapy and chemotherapy alter migration of brain cancer cells before cell death

Although radiotherapy and most cancer drugs target the proliferation of cancer cells, it is metastasis, the complex process by which cancer cells spread from the primary tumor to other tissues and organs of the body where they form new tumors, that leads to over 90% of all cancer deaths. Thus, there is an urgent need for anti-metastasis strategies alongside chemotherapy and radiotherapy. An important step in the metastatic cascade is migration. It is the first step in metastasis via local invasion. Here we address the question whether ionizing radiation and/or chemotherapy might inadvertently promote metastasis and/or invasiveness by enhancing cell migration. We used a standard laboratory irradiator, Faxitron CellRad, to irradiate both non-cancer (HCN2 neurons) and cancer cells (T98G glioblastoma) with 2 Gy, 10 Gy and 20 Gy of X-rays. Paclitaxel (5 M) was used for chemotherapy. We then measured the attachment and migration of the cells using an electric cell substrate impedance sensing device. Both the irradiated HCN2 cells and T98G cells showed significantly (p < 0.01) enhanced migration compared to non-irradiated cells, within the first 20 to 40 hours following irradiation with 20 Gy. Our results suggest that cell migration should be a therapeutic target in anti-metastasis/anti-invasion strategies for improved radiotherapy and chemotherapy outcomes.

cancer biology↗

bioRxiv2020

Diverse deep neural networks all predict human IT well, after training and fitting

Deep neural networks (DNNs) trained on object recognition provide the best current models of high-level visual areas in the brain. What remains unclear is how strongly network design choices, such as architecture, task training, and subsequent fitting to brain data contribute to the observed similarities. Here we compare a diverse set of nine DNN architectures on their ability to explain the representational geometry of 62 isolated object images in human inferior temporal (hIT) cortex, as measured with functional magnetic resonance imaging. We compare untrained networks to their task-trained counterparts, and assess the effect of fitting them to hIT using a cross-validation procedure. To best explain hIT, we fit a weighted combination of the principal components of the features within each layer, and subsequently a weighted combination of layers. We test all models across all stages of training and fitting for their correlation with the hIT representational dissimilarity matrix (RDM) using an independent set of images and subjects. We find that trained models significantly outperform untrained models (accounting for 57% more of the explainable variance), suggesting that features representing natural images are important for explaining hIT. Model fitting further improves the alignment of DNN and hIT representations (by 124%), suggesting that the relative prevalence of different features in hIT does not readily emerge from the particular ImageNet object-recognition task used to train the networks. Finally, all DNN architectures tested achieved equivalent high performance once trained and fitted. Similar ability to explain hIT representations appears to be shared among deep feedforward hierarchies of nonlinear features with spatially restricted receptive fields.

neuroscience↗

bioRxiv2018

Evaluating causal associations between previously reported risk factors and epithelial ovarian cancer: a Mendelian randomization analysis

BackgroundPreviously reported observational associations between risk factors and epithelial ovarian cancer (EOC) could reflect residual confounding, reverse causation, or measurement error. Mendelian randomization (MR) uses genetic variants as proxies for modifiable risk factors to strengthen causal inference in observational studies.\n\nMethodsWe used MR to evaluate the causal role of 13 previously reported risk factors in overall and histotype-specific EOC in up to 25,509 case subjects and 40,941 controls in the Ovarian Cancer Association Consortium. Inverse-variance weighted models were employed to generate effect estimates and MR-Egger, weighted median, and weighted mode were performed to examine evidence of horizontal pleiotropy. A Bonferroni-corrected P-value threshold was used to establish \"strong evidence\" (P<0.0038) and \"suggestive evidence\" (0.0038<P<0.05) for associations.\n\nResultsThere was strong or suggestive evidence that 9 of 13 risk factors were causally associated with overall or histotype-specific EOC. Genetic liability to endometriosis was strongly associated with EOC (OR per log odds higher liability: 1.27,95%CI:1.16-1.40;P=6.94x10-7) and lifetime smoking exposure was suggestively associated with EOC (OR per unit increase in smoking score:1.36,95%CI:1.04-1.78;P=0.02). In histotype-stratified analyses, the strongest associations found were between: height and clear cell carcinoma (OR per SD increase:1.36,95%CI:1.15-1.61;P=0.0003); age at natural menopause and endometrioid carcinoma (OR per year later onset:1.09,95% CI:1.02-1.16;P=0.007); and genetic liability to polycystic ovary syndrome and endometrioid carcinoma (OR per log odds higher liability:0.74,95% CI:0.62-0.90;P=0.002). There was little evidence that genetic liability to type 2 diabetes, parity, or circulating levels of 25-hydroxyvitamin D and sex hormone-binding globulin were associated with ovarian cancer or its subtypes.\n\nConclusionsOur comprehensive examination of possible etiological drivers of ovarian carcinogenesis supports a causal role for few of these factors in epithelial ovarian cancer and suggests distinct etiologies across histotypes.

epidemiology↗

bioRxiv2017

Copy-number signatures and mutational processes in ovarian carcinoma

Tumours with profound copy-number aberration elude molecular stratification due to their genomic complexity. By representing this complexity as a mixture of copy-number signatures, we provide molecular explanations for differing clinical outcomes. Here we present a method for copy-number signature identification, deriving eight signatures in 117 shallow whole-genome sequenced high-grade serous ovarian cancers (HGSOC), which validated on independent cohorts of 95 deep whole-genome sequenced, and 402 SNP array-profiled cases. Three copy-number signatures predicted longer overall survival, while the others predicted poorer outcome. We found evidence for the mutational processes giving rise to copy-number change for six of the eight signatures via correlations with other genomic features. Our results provide insights into the pathogenesis of HGSOC by uncovering multiple mutational processes that shape genomes following TP53 mutation. Importantly, our work shows that most HGSOC have a mixture of mutational processes suggesting that targeting a single mutator phenotype may be therapeutically suboptimal.

cancer biology↗