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Burlingham, S.

Publications and source records attributed to Burlingham, S..

2 recordsLinked to original sources

Matrix Stiffness Dictates Doxorubicin-Induced Apoptosis by Modulating Cell-Cycle State in HeLa Cells

Drug resistance remains a major challenge in cancer treatment by contributing to recurrence and metastasis. Fractional killing, in which only a subset of cells undergo apoptosis after drug exposure, is a key contributor to this resistance and is influenced by genetic and nongenetic heterogeneity within the tumor microenvironment. Solid tumors display substantial variation in extracellular matrix stiffness, providing evidence that the mechanical context of cancer and stromal cells may play an important role in therapeutic response. Here, we investigated how substrate stiffness affects the dynamics of apoptosis and the mechanisms behind differences in the cell death response to doxorubicin (DOX). HeLa cells cultured on stiffer substrates exhibited enhanced caspase-3/7 activation and increased apoptotic cell death, whereas cells on soft substrates showed markedly reduced apoptotic signaling and improved survival. Although substrate stiffness altered cytoskeletal organization, pharmacological disruption of actin polymerization or actomyosin contractility did not influence nuclear DOX accumulation, indicating that cytoskeletal mechanics were not the primary factor in the stiffness-dependent sensitivity. Instead, flow cytometry revealed that substrate stiffness modulates cell-cycle distribution, with soft substrates enriched in the G1 population and a reduced fraction of cells in the DOX-sensitive S phase. Synchronizing cells at the G1/S phase boundary eliminated stiffness-dependent differences in apoptotic activation, demonstrating that cell-cycle state is a dominant driver of stiffness-mediated fractional killing. These findings highlight a mechanistic link between extracellular matrix mechanics and chemotherapeutic response by suggesting that microenvironment-regulated cell-cycle dynamics contribute to drug resistance in mechanically heterogeneous tumors.

biophysics↗

AI-assisted protein design to rapidly convert antibody sequences to intrabodies targeting diverse peptides and histone modifications

Intrabodies are engineered antibodies that function inside living cells, enabling therapeutic, diagnostic, and imaging applications. While powerful, their development has been hindered by challenges associated with their folding, solubility, and stability in the reduced intracellular environment. Here, we present an AI-driven pipeline integrating AlphaFold2, ProteinMPNN, and live-cell screening to optimize antibody framework regions while preserving epitope-binding complementarity-determining regions. Using this approach, we successfully converted 19 out of 26 antibody sequences into functional single-chain variable fragment (scFv) intrabodies, including a panel targeting diverse histone modifications for real-time imaging of chromatin dynamics and gene regulation. Notably, 18 of these 19 sequences had failed to convert using the standard approach, demonstrating the unique effectiveness of our method. As antibody sequence databases expand, our method will accelerate intrabody design, making their development easier, more cost-effective, and broadly accessible for biological research.

cell biology↗