Cancer treatment generally involves drugs used in combinations. Most prior work has focused on identifying and understanding synergistic drug-drug interactions; however, understanding antagonistic interactions remains an important and understudied issue. To enrich for antagonism and reveal common features of these combinations, we screened all pairwise combinations of drugs characterized as activators regulated cell death. This network is strongly enriched for antagonism, particularly a form of antagonism that we call “single agent dominance”. Single agent dominance refers to antagonisms in which a two-drug combination phenocopies one of the two agents. Dominance results from differences in death onset time, with dominant drugs acting earlier than their suppressed counterparts. We explored mechanisms by which parthanatotic agents dominate apoptotic agents, finding dominance in this scenario caused by mutually exclusive and conflicting use of PARP1. Taken together, our study reveals death kinetics as a predictive feature of antagonism, due to inhibitory crosstalk between death pathways.

Channelrhodopsin-2 (ChR2) is a light-activated channel that can conduct cations of multiple valencies down the electrochemical gradient. Under continuous light exposure, ChR2 transitions from a high-conducting open state (O1) to a low-conducting open state (O2) with differing ion selectivity. The molecular basis for the O1 → O2 transition and how ChR2 modulates selectivity between states is currently unresolved. To this end, we used steered molecular dynamics, electrophysiology, and kinetic modeling to identify residues that contribute to gating and selectivity in discrete open states. Analysis of steered molecular dynamics experiments identified three transmembrane residues (Val-86, Lys-93, and Asn-258) that form a putative barrier to ion translocation. Kinetic modeling of photocurrents generated from ChR2 proteins with conservative mutations at these positions demonstrated that these residues contribute to cation selectivity (Val-86 and Asn-258), the transition between the two open states (Val-86), open channel stability, and the hydrogen-bonding network (K93I and K93N). These results suggest that this approach can be used to identify residues that contribute to the open-state transitions and the discrete ion selectivity within these states. With the rise of ChR2 use in optogenetics, it will be critical to identify residues that contribute to O1 or O2 selectivity and gating to minimize undesirable effects.

Channelrhodopsin-2 (ChR2) is a microbial-type rhodopsin found in the green algae Chlamydomonas reinhardtii. Under physiological conditions, ChR2 is an inwardly rectifying cation channel that permeates a wide range of mono- and divalent cations. Although this protein shares a high sequence homology with other microbial-type rhodopsins, which are ion pumps, ChR2 is an ion channel. A sequence alignment of ChR2 with bacteriorhodopsin, a proton pump, reveals that ChR2 lacks specific motifs and residues, such as serine and threonine, known to contribute to non-covalent interactions within transmembrane domains. We hypothesized that reintroduction of the eight transmembrane serine residues present in bacteriorhodopsin, but not in ChR2, will restrict the conformational flexibility and reduce the pore diameter of ChR2. In this work, eight single serine mutations were created at homologous positions in ChR2. Additionally, an endogenous transmembrane serine was replaced with alanine. We measured kinetics, changes in reversal potential, and permeability ratios in different alkali metal solutions using two-electrode voltage clamp. Applying excluded volume theory, we calculated the minimum pore diameter of ChR2 constructs. An analysis of the results from our experiments show that reintroducing serine residues into the transmembrane domain of ChR2 can restrict the minimum pore diameter through inter- and intrahelical hydrogen bonds while the removal of a transmembrane serine results in a larger pore diameter. Therefore, multiple positions along the intracellular side of the transmembrane domains contribute to the cation permeability of ChR2.

Due to tumor heterogeneity, most believe that effective treatments should be tailored to the features of an individual tumor or tumor subclass. It is still unclear, however, what information should be considered for optimal disease stratification, and most prior work focuses on tumor genomics. Here, we focus on the tumor microenvironment. Using a large‐scale coculture assay optimized to measure drug‐induced cell death, we identify tumor–stroma interactions that modulate drug sensitivity. Our data show that the chemo‐insensitivity typically associated with aggressive subtypes of breast cancer is not observed if these cells are grown in 2D or 3D monoculture, but is manifested when these cells are cocultured with stromal cells, such as fibroblasts. Furthermore, we find that fibroblasts influence drug responses in two distinct and divergent manners, associated with the tissue from which the fibroblasts were harvested. These divergent phenotypes occur regardless of the drug tested and result from modulation of apoptotic priming within tumor cells. Our study highlights unexpected diversity in tumor–stroma interactions, and we reveal new principles that dictate how fibroblasts alter tumor drug responses.

Summary Triple-negative breast cancers (TNBCs) display great diversity in cisplatin sensitivity that cannot be explained solely by cancer-associated DNA repair defects. Differential activation of the DNA damage response (DDR) to cisplatin has been proposed to underlie the observed differential sensitivity, but it has not been investigated systematically. Systems-level analysis—using quantitative time-resolved signaling data and phenotypic responses, in combination with mathematical modeling—identifies that the activation status of cell-cycle checkpoints determines cisplatin sensitivity in TNBC cell lines. Specifically, inactivation of the cell-cycle checkpoint regulator MK2 or G3BP2 sensitizes cisplatin-resistant TNBC cell lines to cisplatin. Dynamic signaling data of five cell cycle-related signals predicts cisplatin sensitivity of TNBC cell lines. We provide a time-resolved map of cisplatin-induced signaling that uncovers determinants of chemo-sensitivity, underscores the impact of cell-cycle checkpoints on cisplatin sensitivity, and offers starting points to optimize treatment efficacy.