Publications

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.

Microtubule gliding assays provide a unique mechanism for molecular detection in which binding of analytes to the microtubule lattice reduces the microtubule gliding speed. The reduction in the gliding speed correlates with the density of the bound analytes, enabling its quantification. Although promising, this technique is still in the proof-of-concept stage. Improving the sensitivity and limit of detection of the assay could make the technique comparable to that of advanced molecular detection methods. This study demonstrates that reducing the ionic strength of the buffer increases the sensitivity of the assay by enhancing the interactions between kinesin and microtubules. When using a low ionic strength buffer (BRB10) compared with a standard buffer (BRB80), we observed a more pronounced reduction in microtubule gliding speed in the presence of analytes, improving the detection limit. Therefore, this approach offers a simple and scalable way to improve the sensitivity of motor-based detection assays.

MEX-5 regulates the formation and dissolution of P granules in Caenorhabditis elegans embryos, yet the thermodynamic basis of its activity remains unclear. Here, using a time-resolved in vitro reconstitution system, we show that MEX-5 dissolves preassembled liquid-like PGL-3/RNA condensates by altering RNA availability and shifting the phase boundary. We develop a microfluidic assay to systematically analyze how MEX-5 influences phase separation. By measuring the contribution of PGL-3 to phase separation, we show that MEX-5 reduces the free energy of PGL-3, shifting the equilibrium toward dissolution. Our findings provide a quantitative framework for understanding how RNA-binding proteins modulate condensate stability and demonstrate the power of microfluidics in precisely mapping phase transitions.

Neurodegenerative diseases, such as amyotrophic lateral sclerosis, are often associated with mutations in stress granule proteins. Aberrant stress granule condensate formation is associated with disease, making it a potential target for pharmacological intervention. Here, we identified lipoamide, a small molecule that specifically prevents cytoplasmic condensation of stress granule proteins. Thermal proteome profiling showed that lipoamide stabilizes intrinsically disordered domain-containing proteins, including SRSF1 and SFPQ, which are stress granule proteins necessary for lipoamide activity. SFPQ has redox-state-specific condensate dissolving behavior, which is modulated by the redox-active lipoamide dithiolane ring. In animals, lipoamide ameliorates aging-associated aggregation of a stress granule reporter protein, improves neuronal morphology and recovers motor defects caused by amyotrophic lateral sclerosis-associated FUS and TDP-43 mutants. Thus, lipoamide is a well-tolerated small-molecule modulator of stress granule condensation, and dissection of its molecular mechanism identified a cellular pathway for redox regulation of stress granule formation.

Upregulation of insulin mRNA translation upon hyperglycemia in pancreatic islet β-cells involves several RNA-binding proteins. Here, we found that G3BP1, a stress granule marker downregulated in islets of subjects with type 2 diabetes, binds to insulin mRNA in glucose concentration-dependent manner. We show in mouse insulinoma MIN6-K8 cells exposed to fasting glucose levels that G3BP1 and its paralog G3BP2 colocalize to cytosolic condensates with eIF3b, phospho-AMPKαThr172 and Ins1/2 mRNA. Glucose stimulation dissolves G3BP1+/2+ condensates with cytosolic redistribution of their components. The aldolase inhibitor aldometanib prevents the glucose- and pyruvate-induced dissolution of G3BP1+/2+ condensates, increases phospho-AMPKαThr172 levels and reduces those of phospho-mTORSer2448. G3BP1 or G3BP2 depletion precludes condensate assembly. KO of G3BP1 decreases Ins1/2 mRNA abundance and translation as well as proinsulin levels, and impaires glucose-stimulated insulin secretion. Further, other insulin secretagogues such as exendin-4 and palmitate, but not high KCl, prompts the dissolution of G3BP1+/2+ condensates. G3BP1+/2+/Ins mRNA+ condensates are also found in primary mouse and human β-cells. Hence, G3BP1+/2+ condensates represent a conserved glycolysis/aldolase-regulated compartment for the physiological storage and protection of insulin mRNA in resting β-cells.

Cellular motion is a key feature of tissue morphogenesis and is often driven by migration. However, migration need not explain cell motion in contexts where there is little free space or no obvious substrate, such as those found during organogenesis of mesenchymal organs including the embryonic skull. Through ex vivo imaging, biophysical modeling, and perturbation experiments, we find that mechanical feedback between cell fate and stiffness drives bone expansion and controls bone size in vivo. This mechanical feedback system is sufficient to propagate a wave of differentiation that establishes a collagen gradient which we find sufficient to describe patterns of osteoblast motion. Our work provides a mechanism for coordinated motion that may not rely upon cell migration but on emergent properties of the mesenchymal collective. Identification of such alternative mechanisms of mechanochemical coupling between differentiation and morphogenesis will help in understanding how directed cellular motility arises in complex environments with inhomogeneous material properties.

Correct organogenesis depends on the timely coordination of developmental processes, such as cell proliferation, differentiation, and migration. This coordination is particularly critical in crowded tissues, such as pseudostratified epithelia (PSE) that are often found as organ precursors. They are composed of elongated epithelial cells with densely packed nuclei aligned along the apicobasal axis. While cell cycle-dependent nuclear movements in PSE are well studied, less is known about how nuclear packing influences tissue morphogenesis. To investigate this, we analyzed nuclear shapes, sizes, and neighborhood statistics in zebrafish neuroepithelia, focusing on the retinal PSE. We found that nuclei exhibit elongated shapes and biaxial nematic-like orientational order but remain positionally disordered. During retinal development, nuclear packing density increases, approaching theoretical limits. This occurs when the tissue transitions to a laminated structure and nuclear shapes are remodeled. Timely neurogenesis is critical as failure to initiate neurogenesis leads to tissue deformations. These findings highlight the influence of nuclear shape and positioning for organ morphogenesis.

Size and shape are critical discriminators between molecular species and states. We describe a microchip-based high-throughput imaging approach offering rapid and precise determination of molecular properties under native solution conditions. Our method detects differences in molecular weight across at least three orders of magnitude and down to two carbon atoms in small molecules. We quantify the strength of molecular interactions across more than six orders of magnitude in affinity constant and track reactions in real time. Highly parallel measurements on individual molecules serve to characterize sample-state heterogeneity at the highest resolution, offering predictive input to model three-dimensional structure. We further leverage the method's structural sensitivity for diagnostics, exploiting ligand-induced conformational changes in the insulin receptor to sense insulin concentration in serum at the subnanoliter and subzeptomole scale.

Considerable phenotypic variation under identical culture conditions limits the potential of stem-cell-based embryo models (SEMs) in basic and applied research. The biological processes causing this seemingly stochastic variation remain unclear. Here, we investigated the roots of phenotypic variation by parallel recording of transcriptomic states and morphological history in individual structures modeling embryonic trunk formation. Machine learning and integration of time-resolved single-cell RNA sequencing with imaging-based phenotypic profiling identified early features predictive of phenotypic end states. Leveraging this predictive power revealed that early imbalance of oxidative phosphorylation and glycolysis results in aberrant morphology and a neural lineage bias, which we confirmed by metabolic measurements. Accordingly, metabolic interventions improved phenotypic end states. Collectively, our work establishes divergent metabolic states as drivers of phenotypic variation and offers a broadly applicable framework to chart and predict phenotypic variation in organoids and SEMs. The strategy can be used to identify and control underlying biological processes, ultimately increasing reproducibility.