Biology subjects

Wood, N. E.

Publications and source records attributed to Wood, N. E..

6 recordsLinked to original sources

bioRxiv2025

Mechanistic modeling suggests stroma-targeting antibody-drug conjugates as an alternative to cancer-targeting in cases of heterogeneous target expression

Antibody-drug conjugates (ADCs) are gaining increasing traction in the treatment of oncological diseases; however, many clinical failures have also been observed. One key factor limiting ADC effectiveness is the heterogeneous expression of their target antigen. While the vast majority of ADCs in clinical development target antigens on cancer cells (cancer-targeting), they can also target antigens expressed on non-cancerous stromal cells in the tumor microenvironment (stroma-targeting). It remains unclear whether targeting cancer cells or stromal cells is more effective in suppressing tumor growth. Here, we present three related mathematical models to evaluate: (1) cancer-targeting ADCs with homogeneous target antigen expression, (2) cancer-targeting ADCs with heterogeneous target antigen expression, and (3) stroma-targeting ADCs. Our simulations suggest that cancer-targeting ADCs can achieve high efficacy when their target antigen is homogenously expressed. However, in cases of heterogeneous antigen expression, cancer-targeting ADCs may lead to an initial reduction in tumor size, followed by regrowth due to the elimination of antigen-positive cells and expansion of antigen-negative cells. This limitation could potentially be overcome by stroma-targeting ADCs, as antigen-positive stromal cells may continue to be recruited into the tumor by the oncogenic factors produced by the remaining cancer cells. Furthermore, we demonstrate that ADCs with more permeable payloads and less stable linkers may offer improved efficacy in the context of heterogeneous target expression.

pharmacology and toxicology↗

bioRxiv2021

When Yeast Cells Change their Mind: Cell Cycle 'Start' is Reversible under Starvation

Eukaryotic cells decide in late G1 whether to commit to another round of genome duplication and division. This point of irreversible cell cycle commitment is a molecular switch termed "Restriction Point" in mammals and "Start" in budding yeast. At Start, yeast cells integrate multiple signals such as pheromones, osmolarity, and nutrients. If sufficient nutrients are lacking, cells will not pass Start. However, how the cells respond to nutrient depletion after they have made the Start decision, remains poorly understood. Here, we analyze by live cell imaging how post-Start yeast cells respond to nutrient depletion. We monitor fluorescently labelled Whi5, the cell cycle inhibitor whose export from the nucleus determines Start. Surprisingly, we find that cells that have passed Start can re-import Whi5 back into the nucleus. This occurs when cells are faced with starvation up to 20 minutes after Start. In these cells, the positive feedback loop is interrupted, Whi5 re-binds DNA, and CDK activation occurs a second time once nutrients are replenished. Cells which re-import Whi5 also become sensitive to mating pheromone again, and thus behave like pre-Start cells. In summary, we show that upon starvation the commitment decision at Start can be reversed. We therefore propose that in yeast, as has been suggested for mammalian cells, cell cycle commitment is a multi-step process, where irreversibility in face of nutrient signaling is only reached approximately 20 minutes after CDK activation at Start.

cell biology↗

bioRxiv2020

Glucose restriction drives spatial re-organization of mevalonate metabolism and liquid-crystalline lipid droplet biogenesis

Eukaryotes compartmentalize metabolic pathways into sub-cellular domains, but the role of inter-organelle contacts in organizing metabolic reactions remains poorly understood. Here, we show that in response to acute glucose restriction (AGR) yeast undergo metabolic remodeling of their mevalonate pathway that is spatially coordinated at nucleus-vacuole junctions (NVJs). The NVJ serves as a metabolic platform by selectively retaining HMG-CoA Reductases (HMGCRs), driving mevalonate pathway flux in an Upc2-dependent manner. AGR-induced HMGCR compartmentalization enhances mevalonate metabolism and sterol-ester biosynthesis that generates lipid droplets (LDs) with liquid-crystalline sub-architecture. Loss of NVJ-dependent HMGCR partitioning affects yeast growth, but can be bypassed by artificially multimerizing HMGCRs, indicating NVJ compartmentalization enhances mevalonate pathway flux by promoting HMGCR inter-enzyme associations. We propose a non-canonical mechanism regulating mevalonate metabolism via the spatial compartmentalization of rate-limiting HMGCR enzymes, and reveal that AGR creates LDs with remarkable phase transition properties. One Sentence SummarySpatial compartmentalization of HMG-CoA Reductases at ER-lysosome contacts modulates mevalonate pathway flux

cell biology↗

bioRxiv2020

Nutrient signaling, stress response, and interorganelle communication are non-canonical determinants of cell fate

Isogenic cells can manifest distinct cellular fates for a single stress, however the nongenetic mechanisms driving such fates remain poorly understood. Here, we implement a robust multi-channel live-cell imaging approach to uncover noncanonical factors governing cell fate. We show that in response to acute glucose removal (AGR), budding yeast undergo distinct fates becoming either quiescent or senescent. Senescent cells fail to resume mitotic cycles following glucose replenishment but remain responsive to nutrient stimuli. Whereas quiescent cells manifest starvation-induced adaptation, senescent cells display perturbed endomembrane trafficking and defective nucleus-vacuole junction (NVJ) expansion. Surprisingly, we also show senescence occurs in the absence of lipid droplets. Importantly, we identify the nutrient-sensing linked kinase Rim15 as a key biomarker that predicts cell fates before AGR stress. We propose that isogenic yeast challenged with acute nutrient shortage contain determinants that influence their post-stress fate, and demonstrate that specific nutrient signaling, stress-response, endomembrane trafficking, and inter-organelle tether biomarkers are early indicators for long-term fate outcomes.

cell biology↗

bioRxiv2019

Transcription levels of a long noncoding RNA orchestrate opposing regulatory and cell fate outcomes in yeast

Many long noncoding RNAs (lncRNAs) act in cis through transcription-coupled chromatin alterations that drive changes in local gene expression. How some cis-acting lncRNAs promote and others repress gene expression remains poorly understood. Here we report that in S. cerevisiae transcription levels of the lncRNA IRT2, located upstream in the promoter of the inducer of meiosis gene, regulate opposing chromatin and transcription states. Low IRT2 transcription displays enhancer RNA-like features. At these levels, IRT2 promotes histone exchange delivering acetylated histone H3 lysine 56 to chromatin thereby facilitating recruitment of a transcription factor and consequently activating transcription. Conversely, increasing IRT2 transcription enhances chromatin assembly and transcriptional repression. The opposing functions of IRT2 direct a regulatory circuit, which ensures that cells expressing opposite, but not one of either, mating-type loci enter meiosis. Our data demonstrate that the transcription levels of an lncRNA are key to controlling gene expression and cell fate outcomes.

biochemistry↗

bioRxiv2018

A fully-automated, robust, and versatile algorithm for long-term budding yeast segmentation and tracking

Live cell time-lapse microscopy, a widely-used technique to study gene expression and protein dynamics in single cells, relies on segmentation and tracking of individual cells for data generation. The potential of the data that can be extracted from this technique is limited by the inability to accurately segment a large number of cells from such microscopy images and track them over long periods of time. Existing segmentation and tracking algorithms either require additional dyes or markers specific to segmentation or they are highly specific to one imaging condition and cell morphology and/or necessitate manual correction. Here we introduce a fully automated, fast and robust segmentation and tracking algorithm for budding yeast that overcomes these limitations. Full automatization is achieved through a novel automated seeding method, which first generates coarse seeds, then automatically fine-tunes cell boundaries using these seeds and automatically corrects segmentation mistakes. Our algorithm can accurately segment and track individual yeast cells without any specific dye or biomarker. Moreover, we show how existing channels devoted to a biological process of interest can be used to improve the segmentation. The algorithm is versatile in that it accurately segments not only cycling cells with smooth elliptical shapes, but also cells with arbitrary morphologies (e.g. sporulating and pheromone treated cells). In addition, the algorithm is largely independent of the specific imaging method (bright-field/phase) and objective used (40X/63X). We validate our algorithms performance on 9 cases each entailing a different imaging condition, objective magnification and/or cell morphology. Taken together, our algorithm presents a powerful segmentation and tracking tool that can be adapted to numerous budding yeast single-cell studies.

cell biology↗