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Biology subjects

Bingham, E.

Publications and source records attributed to Bingham, E..

3 recordsLinked to original sources

Modulating AP-1 enables CAR-T cells to establish an intratumoral PD-1+Tcf1+ stem-like reservoir and overcomes resistance to PD-1 axis blockade

PD-1+Tcf1+ stem-like cells are critical mediators of endogenous T cell responses to PD-1/PD-L1 blockade and are maintained by MHC-dependent interactions with professional antigen-presenting cells (APCs). Unlike conventional T cells, CAR-T cells are activated by intact antigen expressed on tumors, not by peptide/MHC expressed on APCs, restricting their activation to the hostile tumor microenvironment (TME) that may impair preservation of this critical stem-like subset. Indeed, in an autochthonous model of ROR1+ lung cancer that we developed, CAR-T cells targeting the tumor-associated antigen ROR1 were uniformly Tcf1+ prior to infusion but rapidly downregulated Tcf1 in vivo and became terminally exhausted, similar to observations in patients, resulting in faster attrition and no enhancement in response to PD-L1 blockade. We hypothesized that overexpression of AP-1 family transcription factors, which can regulate T cell exhaustion, could enable CAR-Ts to maintain this critical PD-1+Tcf1+ subset within tumors independently of APCs and sensitize them to PD-1/PD-L1 blockade. Overexpression of the AP-1 TF c-Jun, but not BATF, improved preservation of PD-1+Tcf1+ CAR-T cells within tumors in a cell-intrinsic manner that correlated with increased persistence deeper within tumors. Notably, c-Jun overexpression alone was insufficient to prevent CAR-T exhaustion in the lung TME, in contrast to prior work in xenograft models, with progressive CAR-T dysfunction correlated with PD-1-dependent downregulation of c-Jun. However, c-Jun overexpression dramatically sensitized CAR-Ts to PD-L1 blockade, which restored c-Jun levels in CAR-Ts, drove log-fold expansion of CAR-Ts within tumors, and induced nearly complete eradication of ROR1+ tumor in highly aggressive models of lung cancer. Altogether, our data show that combination with PD-L1 blockade is necessary to unleash the full potential of c-Jun-overexpressing CAR-T cells in aggressive solid tumors like lung cancer and suggest that strategies to enhance formation of intratumoral PD-1+Tcf1+ reservoirs can overcome CAR-T resistance to PD-1 blockade.

immunology↗

Metabolically-driven flows enable exponential growth in macroscopic multicellular yeast

The ecological and evolutionary success of multicellular lineages is due in no small part to their increased size relative to unicellular ancestors. However, large size also poses biophysical challenges, especially regarding the transport of nutrients to all cells; these constraints are typically overcome through multicellular innovations (e.g., a circulatory system). Here we show that an emergent biophysical mechanism -- spontaneous fluid flows arising from metabolically-generated density gradients -- can alleviate constraints on nutrient transport, enabling exponential growth in nascent multicellular clusters of yeast lacking any multicellular adaptations for nutrient transport or fluid flow. Surprisingly, beyond a threshold size, the metabolic activity of experimentally-evolved snowflake yeast clusters drives large-scale fluid flows that transport nutrients throughout the cluster at speeds comparable to those generated by the cilia of extant multicellular organisms. These flows support exponential growth at macroscopic sizes that theory predicts should be diffusion limited. This work demonstrates how simple physical mechanisms can act as a biophysical scaffold to support the evolution of multicellularity by opening up phenotypic possibilities prior to genetically-encoded innovations. More broadly, our findings highlight how cooption of conserved physical processes is a crucial but underappreciated facet of evolutionary innovation across scales.

evolutionary biology↗

Pyro-Velocity: Probabilistic RNA Velocity inference from single-cell data

Single-cell RNA Velocity has dramatically advanced our ability to model cellular differentiation and cell fate decisions. However, current preprocessing choices and model assumptions often lead to errors in assigning developmental trajectories. Here, we develop, Pyro-Velocity, a Bayesian, generative, and multivariate RNA Velocity model to estimate the uncertainty of cell future states. This approach models raw sequencing counts with the synchronized cell time across all expressed genes to provide quantifiable and improved information on cell fate choices and developmental trajectory dynamics.

bioinformatics↗